System Analysis in Engineering and Control (Lecture Notes in Networks and Systems, 442) 3030988317, 9783030988319

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Lecture Notes in Networks and Systems 442

Yuriy S. Vasiliev Nataliya D. Pankratova Violetta N. Volkova Olga D. Shipunova Nikolay N. Lyabakh Editors

System Analysis in Engineering and Control

Lecture Notes in Networks and Systems Volume 442

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

More information about this series at https://link.springer.com/bookseries/15179

Yuriy S. Vasiliev Nataliya D. Pankratova Violetta N. Volkova Olga D. Shipunova Nikolay N. Lyabakh •

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Editors

System Analysis in Engineering and Control

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Editors Yuriy S. Vasiliev Peter the Great St. Petersburg Polytechnic University St. Petersburg, Russia Violetta N. Volkova Peter the Great St. Petersburg Polytechnic University St. Petersburg, Russia

Nataliya D. Pankratova Institute for Applied System Analysis National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kiev, Ukraine Olga D. Shipunova Peter the Great St. Petersburg Polytechnic University St. Petersburg, Russia

Nikolay N. Lyabakh Internationale Akademie für Management und Technologie (INTAMT) e.V. Düsseldorf, Germany

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-030-98831-9 ISBN 978-3-030-98832-6 (eBook) https://doi.org/10.1007/978-3-030-98832-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Foreword

Researches in systems analysis (SA) and systems science in the former USSR and modern Russia have a long (since the late 1960th) and glorious history. On the one hand, it was always natural to combine deep philosophical considerations with rigorous mathematical models, and on the other hand, there is a tendency to implement theoretical frameworks in mass practice. Saint Petersburg scientific school of SA has been (since the beginning of 1970th) and remains a leading one, created and supported by tremendous efforts of Professors Anatoly A. Denisov and Violetta N. Volkova, and Professor Vladimir N. Kozlov, who later joined them. Nowadays, it is well recognized not only nationally, but worldwide. Like any strong and growing scientific school, this one develops in several interconnected dimensions: – fundamental and applied scientific researches, reflected in journal and conference papers, monographs, etc.; – textbooks, reference books, and tutorials, which are a methodical basis for SA teaching in all universities of Russia and some universities in other countries; – regular scientific seminars and workshops (seminar “SA and its applications” continues since 1973); – scientific conferences (annual conference “SA in Engineering and Control” (SAEC) since 1998 remains a significant event in the “systemic world”). You are holding in your hands a set of selected papers of the 25th International Scientific, Educational and Practical Conference SAEC-2021, embracing many areas of SA, including its cognitive aspects, cyber-physical systems, digitalization, robotics, etc., with applications to control regions, enterprises, power systems, logistics, etc. This volume, appearing in the “Lecture Notes in Networks and Systems” Springer series, is a result of joint exertions of authors, program committee, and Professor Janusz Kacprzyk (Series Editor), who made regular and successful efforts for many years to strengthen the international network of scientists in SA and cybernetics. v

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Foreword

Twenty-five conferences are a rather long history. Meanwhile, one may be sure that this exciting team of our colleagues and friends, together with their future students and followers, will continue its diversified development for the benefit of SA and systems science. Dmitriy A. Novikov Corresponding Member of RAS Director of V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences, Russia

Preface

This book includes the scientific papers of the participants of two international conferences which are held at Peter the Great St. Petersburg Polytechnic University (SPbPU) in St. Petersburg, Russia, in 2021: • The 25th International Scientific, Educational and Practical Conference “System Analysis in Engineering and Control” (SAEC–2021, http://saec.spbstu.ru/en/), which was held from October 13–14, 2021. • The XIIth International Scientific and Theoretical Conference “Communicative Strategies of the Information Society” (CSIS–2021, https://csis.spbstu.ru/) which was held from October 22–23, 2021. In 2021, the International Conference “System Analysis in Engineering and Control” (SAEC) was held for the 25th time. The conference is held by the Scientific and Pedagogical School “System Analysis in Engineering and Control”, which unites scientists developing the theory of systems and system analysis in various universities and scientific organizations in Russia and other countries. The school considers itself a successor of the following scientist schools: • The school of the Moscow Power Engineering Institute (MPEI), where in 1970 for the first time in the USSR, a department in the field of systems theory and system research has been created. It was founded by Doctor of Technical Sciences, Professor Fedor E. Temnikov (1906–1993), and got the name “the Department of System Engineering” (in Russian “Kafedra Systemotechniki”); • The school of the M. I. Kalinin Leningrad Polytechnic Institute, where since 1973, at the Faculty of Technical Cybernetics, Doctor of Technical Sciences, Professor Anatoly A. Denisov (1934–2010) has been investigating the commonality of processes in systems of various physical nature. Professor Denisov proposed the theory of the information field and the information approach to the analysis of systems. The initiator of the SAEC scientific school formation was the Department of System Analysis and Control of St. Petersburg State Technical University (former name of SPbPU), created by Doctor of Technical Sciences, Professor Vladimir N. vii

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Preface

Kozlov in 1994 at the Faculty of Technical Cybernetics. On the basis of this department, new bachelor and master degree programs were opened, with specialization in system analysis and control. Currently, this field of activity at SPbPU is developing as a scientific direction of bachelors and masters degree programs at the Higher School of Cyber-Physical Systems and Control (CPS&C) of the Institute of Computer Science and Technology (ICST). Head of the CPS&C Higher School is Doctor of Technical Sciences, Professor Viacheslav P. Shkodyrev; and Head of the bachelor and master degree programs “Theory and mathematical methods of system analysis and control in technical, economic and social systems” is Candidate of Physical and Mathematical Sciences, Associate Professor Artem A. Efremov. The scientific supervisors of the Scientific and Pedagogical School “System Analysis in Engineering and Control” are Professor Vladimir N. Kozlov, Deputy Chairman of the St. Petersburg Branch of the International Higher Education Academy of Sciences (IHEAS), Doctor of Technical Sciences, Honored Worker of Higher School of the Russian Federation; and Professor Violetta N. Volkova, Member of IHEAS, Doctor of Economics, Honored Worker of Higher School of the Russian Federation, the Scientific Council on the problems of training and certification of scientific and pedagogical personnel of the IHEAS St. Petersburg Branch. An important goal of the school is to develop the methodological foundations and terminological apparatus of systems theory and system analysis based on a wide range of mathematical methods. The International Conference “Communicative Strategies of the Information Society” (CSIS) is held annually by the scientific school, which has been developing at SPbPU, on the basis of the Institute of Humanities. The school scientific supervisor is Doctor of Philosophy, Professor Olga D. Shipunova. The first CSIS conference took place in 2006; and in 2008, the conference was awarded an international status. The XIIth International Scientific and Theoretical Conference “Communicative strategies of the information society” goal is to form an interdisciplinary platform for discussing a wide range of problematic issues related to interactions in social and technical systems in the context of expanding digital culture. This book reflects the results of the work of the SAEC and CSIS scientific schools’ representatives over the past few years. It includes chapters prepared by the scientists, postgraduates, and students from Russia, Ukraine, Bosnia and Herzegovina, Canada, Estonia, Finland, Germany, Iraq, Kazakhstan, Mongolia, Poland, South Korea, and the USA. The works are grouped into the following parts: 1. general theoretical and philosophical-methodological problems of systems theory; 2. methods and models of system analysis; 3. innovation technologies in technical and socio-economic systems; 4. system analyses in the educational process and higher education management.

Preface

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The “System Analysis in Engineering and Control” and “Communicative Strategies of the Information Society” scientific schools have quite a long history of cooperation. The schools carry out an important mission of training highly qualified scientific manpower. The scientific results discussed at the SAEC and CSIS Conferences’ sessions are presented in annual conference proceedings, in textbooks and monographs prepared by the participants of these conferences, and on the System Analysis in Engineering and Control Web site (www.saenco.ru). Yuriy S. Vasiliev

Contents

General Theoretical and Philosophical-Methodological Problems of Systems Theory Origins and Prospects of Systems Theory . . . . . . . . . . . . . . . . . . . . . . . Yuriy S. Vasiliev, Violetta N. Volkova, and Vladimir N. Kozlov

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Complex, Adaptive, and Evolvable System Theory: Basis and Uses . . . . George Mobus

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Increasing Objectivity in the System Analysis of Socio-Economic Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nikolay N. Lyabakh Business Ecosystem Strategy: Design and Specifics . . . . . . . . . . . . . . . . George Kleiner and Alexander Kobylko Scientific Basis of Management and Cybernetics Methodologies Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Boris V. Sokolov and Rafael M. Yusupov System Analysis of the Russian Space Future . . . . . . . . . . . . . . . . . . . . Georgy G. Malinetskiy and Vladimir Smolin Application of Classification to Determine the Level of Awareness of the Foresight Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nataliya Pankratova and Volodymyr Savastiyanov Ontological Problems of System Analysis . . . . . . . . . . . . . . . . . . . . . . . . Michael S. Mokiy

29 43

52 60

74 89

System Analysis of the Intelligence Structures Evolution . . . . . . . . . . . . 100 Olga Shipunova System Analysis of Deep Trends in the Evolution of Science: From Fixed Concepts to Moving Artistic Images . . . . . . . . . . . . . . . . . . 109 Viacheslav E. Voitsekhovich, Ilia N. Volnov, and Georgy G. Malinetskiy xi

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System Analysis of Marginal States in the Development of Civilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Zhanna Bober The Ideal and the Material, the Subjective and the Objective in Systems Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Elena A. Tunda and Vladimir A. Tunda Similarity Principle and Bogdanov Tektology in Systems Analysis Evolution of Large Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Alexander I. Bogomolov and Victor P. Nevezhin Systems Analysis of the Digital Agent’s Role in Hybrid Social Interaction Forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Elena Pozdeeva, Olga Shipunova, Lidiya Evseeva, and Aktolkyn Kulsariyeva Conflict Misunderstanding in the Net Information Society . . . . . . . . . . . 166 Inna B. Romanenko, Stanislav Gapanovich, Yuriy Romanenko, and Stanislav Fedorin Methods and Models of System Analysis Stability Analysis of Dynamical Systems Based on Lyapunov Vector Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Artem A. Efremov, Vera V. Karakchieva, and Vladimir N. Kozlov Implementation of Control and Forecasting Problems of HumanMachine Complexes on the Basis of Logic-Reflexive Modeling . . . . . . . . 187 Igor B. Arefiev and Olga V. Afanaseva Adaptive Theory of Socio-economic Systems Management Based on Logical-Linguistic Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 Aleksandr E. Karlik, Boris L. Kukor, and Elena A. Iakovleva Cognitive Modeling of Complex Systems: State and Prospects . . . . . . . . 212 Galina V. Gorelova Participative Cognitive Mapping as a Multidisciplinary Approach for Managing Complex Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Aleksandr E. Karlik, Vladimir V. Platonov, and Elena A. Iakovleva Top Level Diagnostic Models of Complex Objects . . . . . . . . . . . . . . . . . 238 Stanislav Mikoni Energy and Power of Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 Ivan N. Drogobytskiy

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Hybrid Simulation as a Key Tool for Socio-economic Systems Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262 Aleksei M. Gintciak, Marina V. Bolsunovskaya, Zhanna V. Burlutskaya, and Alexandra A. Petryaeva Balancedness of Economic, Legal and Social Macrosystems Based on Decision Making Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Lidiya V. Zhukovskaya Numerical Implementation of an Adapted k-means Algorithm for Solving the Problem of Russian Industrial Regions Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 Olga M. Shatalova and Ekaterina V. Kasatkina Solving Fuzzy Equations Based on Fuzzy Interval Bisection Method for Intelligent Data Processing in Cyber-Physical Systems . . . . . . . . . . . 300 Konstantin Semenov and Anastasiia Tselishcheva The “Growing” of System Concept and Its Further Development . . . . . 311 Aleksandra V. Loginova, Alla E. Leonova, Svetlana V. Shirokova, and Yu. Yu. Chernyy Modeling the Effectiveness of an Investment Strategy in Conditions of Insufficient Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 Anatolii Smetankin, Sergei Efimenko, Dmitrii Garanin, Irina Malihina, Vladimir Shilkin, and Igor Chernorutsky Innovation Technologies in Technical and Socio-Economic Systems Digitalization as a Basis for Transformation of the Enterprise Organizational Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Galina P. Chudesova Analysis of Options for a Smart City Architecture Description . . . . . . . 347 Alexander N. Danchul Evaluating the Performance of the Electricity Sector in Iraq and its Relationship to Sustainable Development . . . . . . . . . . . . . . . . . . . . . . . . 358 Tatiana A. Makarenya and Ahmed Ibrahim Hussein Obaidi Development Challenges of Remote Rural Terrians: Network Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Olga Berestneva, Alexei Tikhomirov, Andrey Trufanov, Maria Kuklina, Vera Kuklina, Dmitriy Kobylkin, Natalia Krasnoshtanova, Victor Bogdanov, Elena Istomina, Eduard Batotsyrenov, Erdenebaatar Altangerel, and Zolzaya Dashdorj Prospects for Digital Transformation of Public Administration . . . . . . . 382 Galina S. Tibilova, Stanislav V. Kazarin, Anastasiya V. Potapova, Andrey V. Ovcharenko, and Natalia V. Diachenko

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Features of Developing the Concept of Digital Transformation Using Simulation Modeling Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 Alexey V. Boykov, Michail B. Uspensky, and Marina V. Bolsunovskaya Analysis of Innovative Technologies for the Formation of a Cyber-Physical System of an Enterprise . . . . . . . . . . . . . . . . . . . . . 401 Arina Kudriavtceva Expert Systems in Innovation Project Management: Architecture and Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 Nikita B. Kultin Experience in Design of Artificial Neural Network for Object Detection on Monochromatic Images . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 Anton V. Kvasnov, Nikolai A. Nikitin, and Vyacheslav P. Shkodyrev Deep Learning Applications in Industrial Grading System . . . . . . . . . . 431 Mikhail A. Miae, Galina F. Malykhina, and Dmirtii Manev Models of Cyber-Physical Control Systems for Pollution Minimization Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442 Gennady I. Korshunov, Remir I. Solnitsev, Natalia A. Zhilnikova, and Sergey L. Polyakov Robotics Systems Monitoring and Correction by Means of Automatic and Software Control . . . . . . . . . . . . . . . . . . . . . . 451 Tatyana S. Katermina and Maksim V. Sliva Using Loginom Low-Code Platform for the Modeling of LTV Site Subscriber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 Nikolay B. Paklin, Igor A. Katsko, and Elena V. Kremyanskaya Designing a Decision Support System for Capital Markets . . . . . . . . . . 473 Natalia S. Voronova, Andrei N. Vinogradov, Ermin E. Sharich, and Daria D. Iakovleva Combined Optimization Algorithm of Complex Technical Object Functioning and Its Information System Modernization . . . . . . . . . . . . 487 Zakharov Valerii Digital Interactive-Documentary Model in the Framework of Subject Ontology System Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498 Vasily V. Ponomarev and Vladimir E. Tumanov Application of a Non-invasive Interface “Brain-Computer” for Classification of Imaginary Movements . . . . . . . . . . . . . . . . . . . . . . 512 Anzelika Zuravska and Lev A. Stankevich

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System Analyses in the Educational Process and Higher Education Management Systemic Risks of Government Control Over Large-Scale Projects in the Development of the Russian Higher School . . . . . . . . . . 525 Vladimir G. Khalin, Galina V. Chernova, Alexander V. Yurkov, and Mikhail V. Zaboev A System Approach for Cognitive Learning in Digital Transformation of Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 538 Alexander V. Rechinskiy, Liudmila V. Chernenkaya, and Vladimir E. Mager Modeling of the Educational Process Based on Smart Technologies . . . 548 Sergey Yablochnikov, Mikhail Kuptsov, Kirill Bukhensky, and Ivan Kuptsov Quantitative Analysis of Informational Significance of SWEBOK Knowledge Areas in IEEE/ACM Curriculum Guidelines . . . . . . . . . . . . 561 Alain Abran, Alexander V. Yurkov, Vladimir G. Khalin, and Olga Shilova Contemporary Aspects of Online Teaching Mathematics in Technical Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574 Ekaterina A. Blagoveshchenskaya, Viktor V. Garbaruk, Nina V. Popova, and Lutz Strüngmann Flipped Learning and Education System: Key Activities and Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 Inna A. Seledtsova, Sergey G. Redko, and Iulia Shnai Clustering and Analysis of the Participants’ Results and Completed Test Tasks on Massive Open Online Course . . . . . . . . . . . . . . . . . . . . . 596 Sergey Nesterov, Victoria Sazhnova, and Elena Smolina Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607

Abbreviations

ACM ACS AI ANN AR CA CPS CPS&C CRM CTO CSIS DT PA ERP ES ES GR IASA ICST IHEAS IT LTV MA ML MPEI MS NN PATTERN PERT RF

Association for Computing Machinery Automation control system Artificial intelligence Artificial neural networks Augmented reality Cognitive analysis Cyber-physical system Cyber-physical systems and control Customer relationship management Complex technical object Communicative Strategies for the Information Society Digital Transformation of Public Administration Enterprise resource planning Emotional structures Expert system “Golden rule” The International Institute for Applied Systems Analysis Institute of Computer Science and Technology The International Higher Education Academy of Sciences Information technology Lifetime value Morphological analysis Machine learning Moscow Power Engineering Institute Mental structures Neural networks Planning assistance through technical evaluation of relevance numbers (technique) Program (project) evaluation and review technique Russian Federation

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xviii

PS SA SAEC SCPS SCSN SD SEO SPbPU SRM SWEBOK TAS UAV VR

Abbreviations

Physical structures System analysis System Analysis in Engineering and Control Socio-cyber-physical system Spaced combined stem network System dynamics Socioeconomic object Peter the Great St. Petersburg Polytechnic University Supplier relationship management Software Engineering Body of Knowledge Theory of active systems Unmanned aerial vehicles Virtual reality

General Theoretical and Philosophical-Methodological Problems of Systems Theory

Origins and Prospects of Systems Theory Yuriy S. Vasiliev , Violetta N. Volkova(&) and Vladimir N. Kozlov

,

Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya St., 29, 195251 St. Petersburg, Russia [email protected], [email protected]

Abstract. The article analyzes the formation of systems theory, its variants, applied directions. It is shown that systemic representations arose gradually, starting from the ancient Greek period, and began to develop from different sources – from philosophy, biology, mathematics, engineering, etc. Based on the analysis, it is concluded that in the current conditions of the introduction of emergent technologies and artificial intelligence, further development of systems theory is necessary. Therefore, it is necessary to rethink the law discovered by L. von Bertalanffy, which is opposite to the second law of thermodynamic; the concept of mobile equilibrium by A.A. Bogdanov, the fundamental disequilibrium of E. Bauer. Based on the analysis of the features and patterns of open systems with active elements and the state of mobile equilibrium, it is realized that such a system cannot be assembled from parts. Starting from a certain level of complexity, the system becomes more and more difficult to display with an adequate formal model, and it is easier to transform and change it with the help of control actions. Such systems need to be “grown”, developed through innovation (negentropic manifestations) and self-learning. Existing models, as a rule, are based on the binary logic of Aristotle, on the law of the excluded third. And for the study of mobile equilibrium, dialectical thinking is necessary, it is necessary to apply the laws of dialectical logic, the formalized representation of which is proposed by A.A. Denisov. Keywords: Movable equilibrium negentropic processes


Systems theory
Emergence
Entropy-

1 Introduction By this time, many special disciplines had emerged that often use similar methods, but refract them to the extent that they are tailored to the needs of specific applications that specialists working in different application areas cease to understand each other. There was a need to search for generalizing scientific directions. The role of the integration of sciences at all times was played by philosophy. However, philosophical terminology is not always easily refracted to practical activity. The concept of “system”, previously used in the ordinary sense, turned into a special general scientific category, generalizing scientific directions began to appear, which

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 3–15, 2022. https://doi.org/10.1007/978-3-030-98832-6_1

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Y. S. Vasiliev et al.

historically sometimes appeared in parallel on a different applied or theoretical basis and bore different names. Analysis of the available information shows that the formation of systemic knowledge has a long history. Systemic representations arose gradually, starting from the ancient Greek period, and subsequently began to develop from different sources. For this article, the origins of general disciplinary concepts using the notion “system” are the main ones. Proposes the prospects for the development of systems theory and systems analysis in the current state of the active introduction of emergent technologies are given.

2 The Origins of General Disciplinary Knowledge and Systems Theory 2.1

Prerequisites the Emergence of System Views

In the fourth century BC in Ancient Greece, Aristotle formulated the opinion, that the sum of the properties of parts is not the properties of the whole, which can be considered as the basis of the concept of integrity, “systemness”. During the 14th–16th centuries, aesthetic ideas contributed to the formation of the concept of integrity. Methods proposed in the 17th–18th centuries (inductions by F. Bacon and deductions by R. Descartes, integrodifferential calculus of Newton–Leibniz), made it possible to more deeply understand the interaction of the part and the whole. In the 17th century, thanks to B. Pascal, there was an awareness of the difference between the humanitarian and formal ways of thinking, which led to the division of scientific concepts into two groups: a) humanitarian disciplines; and b) natural science and physical and mathematical. In the 19th century, a deeper understanding of integrity arose. This is a study of the molecule’s structure (J. Berzelius), a discovery of the mutual influence and transformation of atoms (C. Gerard). This is also a creation of an integral cellular theory of the organism’s structure, proving the unity of the entire organic world (M. Schleiden and T. Schwann); and the development of the principles of differentiation and integration, later recognized as universal (G. Spencer). At the end of the 19th century interdisciplinary scientific directions, such as physical chemistry, mathematical physics, biogeological, biogeocosmic, biogeonocosmic approaches, have been created. These approaches are sometimes considered the first integral concepts, interdisciplinary directions, the beginning of the unification of ideas and methods that have arisen in different scientific directions. At the beginning of the 20th century, concepts began to emerge, in which the fundamental features of complicated processes and problems and the need for general disciplinary knowledge were comprehended. Currently, general disciplinary concepts that use the main concept of the system are usually combined by the term “General theory of systems” or “Theory of systems”.

Origins and Prospects of Systems Theory

2.2

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The First General Disciplinary Concepts

The Open Theory Science L. von Bertalanffy. Historically, the Austrian biologist L. von Bertalanffy, who in 1937 considered the beginning of the formation of systems theory as an independent scientific direction. L. von Bertalanffy proposed an organismic approach to biological and social objects and phenomena and the concept of an open system and discovered a law, which is opposite to the second law of thermodynamics. This new law ensures the appearance and development of living things. However, the first publications of L. von Bertalanffy appeared after the Second World War [1, 2, etc.]. An important role in the formation of General Systems Theory was played by the first interdisciplinary international society in the field of systems theory and systems sciences “The International Society for the Systems Sciences (ISSS)”, which began to form in 1954 and gradually transformed. This society united philosophers, psychologists, biologists, economists, sociologists, and other scientists who went to the ideas of systems theory in different ways. The post of President of the Society was held by well-known scientists: Ludwig von Bertalanffy (biologist), K. Boulding (1957–1958, economist) [3]. W.R. Ashby (1962– 1964, psychiatrist) [4], G. Clear (1981–1982, computer scientist, systems engineer), etc. The General Organizational Science — Tectology of A.A. Bogdanov. At the beginning of the twentieth century, the Russian scientist A.A. Bogdanov (Malinovsky) in his three-volume work “Tectology” [5], attempted to find and generalize organizational laws, the manifestations of which can be traced in the inorganic, organic, social, cultural, and other levels, and explain the processes of development of nature and society based on the principle of mobile balance, borrowed from natural science. A.A. Bogdanov explained “the movable equilibrium” by the presence of the activity of the elements in the system and the exchange of matter and energy with the environment, i.e. A.A. Bogdanov introduced the concept of an open system. But he called the proposed concept a general organizational science — “tectology”, and explained his ideas using specific terms, which still initiate discussions about their interpretation, which held back the understanding and spread of tectology. Thus, we can assume that A.A. Bogdanov introduced the concept of an open system earlier than L. von Bertalanffy. Therefore, some researchers consider that Bogdanov was first in open systems theory. However, L. von Bertalanffy not only introduced the concept of an open system, but the discovered a pattern explaining the development of systems, relying on the study of entropy-negentropy processes. E. Bauer’s Concept of the Fundamental Disequilibrium of Living Systems. Russian scientist E. Bauer, Hungarian by birth, in the early 1930s. proposed the important principle of the fundamental disequilibrium of living systems, i.e. the desire to maintain a stable disequilibrium and use energy not to ensure stability (which is characteristic of inanimate systems without active elements), but for maintaining oneself in a nonequilibrium state [6]. E. Bauer explains the fundamental disequilibrium by the fact that all the structures of living cells are pre-charged with “excess” energy in

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comparison with the same inanimate molecule, and the body uses the energy coming from the outside not immediately to work. This energy potential (or biopotential) is used when there is a goal, need, “desire” of the cell to do something. 2.3

The Systems Theory Concepts in the USSR

The Theory of Functional Systems by P.K. Anokhin. This theory was suggested by Russian physiologist P.K. Anokhin, who since 1935 has studied the systemic mechanisms of nervous activity, the integrative activity of the neuron, formulated the basic ideas about intraneuronal information processing, and in 1971 summarized the results of his research in the form general theory of functional systems [7]. Currently, this theory is being developed by the grandson of P.K. Anokhin neurobiologist, academician of the RAS K. Anokhin. The Parametric General Theory of Systems of A.I. Uyemov, who in the 1970s. substantiated the original ontological-methodological concept of the system, taking into account “things”, “properties”, “relations”; proposed a dual definition of the system, based on which one of the first methods of structuring the goals of control systems was developed [8, etc.]. Urmantsev’s General Theory of Systems (GTS) — the original version of the theory of systems, which was proposed in 1968 by the biologist and philosopher Yu.A. Urmantsev [9, etc.]. GTS of Urmantsev explains the processes of development of the phytosphere, and does not include the concept of purpose as unusual for this class of objects, and the concept of desirability, development reflects in the form of a special type of relationship — the laws of composition. The Concept of General Systems Theory by A.I. Kukhtenko. This theory was developed by the Soviet and Ukrainian scientist A.I. Kukhtenko at the Institute of Cybernetics of the Academy of Sciences of the Ukrainian SSR [10, etc.]. The ideological of interdisciplinary research, proposed by A.I. Kukhtenko, served as the basis for the creation of the Educational and Scientific Complex “Institute for Applied Systems Analysis” (IASA) in the system of the National Academy of Sciences of Ukraine and the Ministry of Education and Science of Ukraine. A school of systems analysis is developing at IASA [11, etc.]. The Theory of Highly Organized Systems F.Ye. Temnikov. Who proposed a classification of systems, a classification of methods for modeling systems, in which he identified generalized classes of methods and by defining their fundamental features and linking them with classes of systems [12]. An important role in the development of interdisciplinary areas of F.Ye. Temnikov assigned computer science, defining it in 1963 as the science of information elements, information processes, and information systems [13], saw the path of development of general disciplinary directions as follows “Informatics — Systematics — Intelletika” [14]. Theory of the Information Field and Information Analysis of Systems A.A. Denisov. This theory is based on mathematical field theory and a formalized representation of the laws of dialectical logic [15, 16, etc.]; it allows to describe from a

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unified position the processes in various systems — technical, organizational, social, etc. Following this theory, any models are based on the reflection of the situation in the mind of the researcher in the form of “information”, which is a paired category for the matter, and is the structure of matter. Based on the formalized representation of the laws of dialectical logic, models of kinematics and dynamics of the processes of functioning and development of systems are obtained. Deterministic and probabilistic estimates of the information of perception J and the potential H of the reflected components are introduced, based on which methods for organizing complicated expertise have been developed [16, 17]. Theory of Active Systems A.Ya. Lernerand V.N. Burkov. In the late 1970s, at the Institute of Automation and Telemechanics (currently the Institute of Control Problems of the Russian Academy of Sciences), A.Ya. Lerner began to study the role of man in the control system, formulated, together with V.N. Burkov, the principle of open control and the theory of active systems [18]. Control Theory for Systems of Interdisciplinary Nature D.A. Novikov. At present, Doctor of Technical Sciences, Professor D.A. Novikov is developing a more general theory — the theory of management of interdisciplinary systems that arise as a combination of organizational, ecological, social, economic systems, organizationaltechnical, socio-economic; ecological-economic, etc. [18]. 2.4

Applied General Disciplinary Directions

The System Engineering. The complication of the methods of organizing activities for the creation of engineering objects and the scientific, technical and managerial problems arising from this led to the emergence of a new applied system methodology called System Engineering, which began to actively develop after the publication in 1957 of the book by American system engineers G. Goode and R. Machol [19], in which systems engineering is defined as “a method of designing technical equipment, and the complicacy was defined as the main problem.” In 1965, the System Engineering Handbook was published, translated into the USSR, and in 1962, the book by Arthur D. Hall [20], which is considered very significant for understanding systems engineering. In 1969 at the Moscow Power Engineering Institute (MPEI) the first in the USSR Department of Systems Engineering was created (the head of the department was F.E. Temnikov). Military systems engineering was developed by V.V. Druzhinin and D.S. Kontorov. The Systemology. In 1965, the term “systemology” was used as a generalizing direction (from Old Greek rύrsηla — “whole, made up of parts”; kόco1 — “word”, “thought”, “meaning”, “concept”), which defined as a theory of complex systems; fundamental engineering science, which establishes the general laws of the potential efficiency of complex material systems of both technical and biological nature. This term was used by the mathematician B.S. Fleishman [21] and was independently proposed by the Ukrainian scientist V.T. Kulik. The term was also used in translations of foreign works ([22] and others).

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3 The Origins of Systems Analysis 3.1

PATTERN System

The PATTERN (Planning Assistance Through Technical Evaluation of Relevance Numbers) methodology, in which the first attempt was made to scientifically approach the process of formulating a goal, its structuring (building a “goal tree”), and the assessment of the priorities of the elements of a “goal tree”. The PATTERN technique was developed by the RAND Corporation, USA [23]. The main advantage of the PATTERN methodology is that it defines classes of criteria for assessing the relative importance, mutual usefulness, state, and timing of development (a couple “the state — the term”). But the logic of the formation of the structure, as noted by the authors themselves, was not worked out. Therefore, Russian scientists have focused on the development of principles and techniques for forming the structure of goals (the “goal tree”). This term became widespread after the publication in 1965 of S. Optner’s book, works by Yu.I. Chernyak [24], D. Cleland and W. King [25], publications edited by E. Quade of lectures read by employees of the RAND Corporation for government organizations [26]. Initially, the notion “system analysis” was associated with the formation and analysis of goal structures, with the development and analysis of the relationship of plans. However, there were other interpretations of the term. Subsequently, the ambiguous use of the term contributed to its interpretation in a broad sense as a direction based on a systems approach, using various methods, including both mathematical methods and qualitative methods. At now systems analysis is used to solve complex, poorly formalized problems. Often this term is also interpreted in a sense that summarizes all interdisciplinary areas associated with systems research. In this broad sense, the term is used in the names of institutes and many scientific schools. In 1972, in Laxenburg, Vienna, the International Institute for Applied Systems Analysis (IASA) was established, founded by the United States and the Soviet Union. Several other countries joined later). In the Soviet Union, in 1976, the Soviet branch of the IASA was organized — the All-Union Scientific Research Institute for System Research (headed and permanently 17 years by the Doctor of Philosophy Dj.M. Gvishiani). Many schools of systems analysis are developing in Russia (see in [27, 28]): the Tomsk school (created by F.I. Peregudov and F.P. Tarasenko); Peter the Great St. Petersburg Polytechnic University school “Systems Analysis in Engineering and Control” (founders — A.A. Denisov and the authors of this article [28]); schools of systems analysis in economics at the Financial University under the Government of the Russian Federation (G.B. Kleiner), at the Southern Federal University (V.E. Lankin), at the Rostov Economic University (G.N. Khubaev, V.A. Doliatovsky).

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Systems Analysis Methodologies for Management

To develop systems analysis methodologies the approaches that are focused on organizational management in the socio-economic sphere, the origins of which are cybernetics, operations research, and sociology, were used: • Viable Systems Methodology of Anthony St. Beer [29]. • World-renowned strategic assumptions methodology in operations research, systems analysis, and ethics of U. Churchman [30]. • The methodology of critical systems of the Swiss sociologist and practical philosopher W. Ulrich is focused on the development of the theory and practice of social planning [31]. • The methodology of interactive planning by the American scientist R. Ackoff, who had a great influence on the development of operations research, systems analysis, management [32]. • “Soft Systems” Methodology of P. Checkland [33].

4 General Disciplinary Directions Based on Methods of Systems Modeling 4.1

Mathematical Methods of Systems Theory

In 1965, Lotfi A. Zadeh proposed the term “fuzzy logic” and published in 1965 the fundamental work on the theory of fuzzy sets [34], and in 1973 he proposed the theory of fuzzy logic, later — the theory of soft computing. In 1969, on the initiative of N.N. Moiseev, the Laboratory of Theory and Design of Large Systems at the Computing Center of the USSR Academy of Sciences, was organized. The main tasks of this Laboratory included the development of mathematical methods of systems analysis and the theory of optimal systems [35]. At the Leningrad Polytechnic Institute (current name — Peter the Great St. Petersburg Polytechnic University) since 1979, Professor V.N. Kozlov began to develop a new direction in mathematical methods of systems theory and systems analysis [36]. A variant of the mathematical theory of systems was developed by the Serbian scientist Mihajlo D. Mesarović [66], who investigated the ideas of multilevel hierarchical structures, introduced the concepts of “strata”, “layers”, “echelons” [37]. The Analytic Hierarchy Process method was proposed by Thomas L. Saaty [38]. 4.2

General Disciplinary Directions Based on Special Methods

The development of these areas was based on ideas and methods of systems theory, the ones that had been formed by the 1980s. The most widespread are the following directions: System Dynamics Simulation Modeling (proposed in 1970 and applied in the development of models of global problems by Jay Forrester [30]); computer simulation (suggested by A.A. Emelyanov

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[39, 40]); situational modeling (suggested by D.A. Pospelov [41], developed by L.S. Bolotova-Zagadskaya [42], who applied the ideas of situational modeling in the field of researching artificial intelligence problems and developing decision support systems); lingua-combinatorial modeling (proposed and developed by M.B. Ignatiev [42]); logical–linguistic modeling (B.L. Kukor [43]); logical-reflexive modeling (I..B. Arefiev [44]); conceptual meta-modeling (V.V. Nechaev [45] and S.P. Nikanorov [46]); cognitive modeling (G.V. Gorelova [17, pp. 285–313]); systemic and structural synthesis (Yu.I. Lypar [17, pp. 267–285]); Foundations of the systemology of the phenomenal (B.F. Fomin [47]).

5 Prospects for the Further Development of Systems Theory An analysis of the origins of systems theory shows that the concepts and models created to date are models of an inanimate system (according to the figurative expression of M.B. Ignatiev, “models of a corpse”). On base analyzing the problems arising in the current state of the introduction of innovative technologies by Industria.4, it is predicted that the active development of these technologies will radically affect the conditions of human life, create a new environment of the intellectual space that is close to the living natural environment, which requires the development of new approaches, initiates new management problems not only of production processes but also of all social processes, including culture and education. With the invention of technologies that help in interaction with the natural environment, and especially with an artificially created environment, a person has always lived in conditions of mobile equilibrium. However, these changes did not occur so quickly, and after certain periods of adaptation, a person created for himself some formalized rules that ensure sufficient stability. In addition, until now, man has created technologies that helped him to perform certain functions. Existing concepts help to develop artificial products that enhance the ability to perform certain natural functions of living beings — acceleration of movement in space, the ability to carry out complex calculations, etc. At the same time, technologies did not surpass the intelligence of a person as a whole. And at present, the creation of artificial intelligence is predicted, which will not only behave independently but also surpass in its capabilities the natural intelligence of a person, which raises the question — we want to create systems whose behavior is close to living organisms and even surpasses natural human intelligence? Some studies make it possible to conclude that the basis of processes in such systems is the regularity discovered by L. von Bertalanffy, which is opposed to the fundamental law of physics — the “second law” of thermodynamics. As a result of the simultaneous manifestation of entropic and negentropic processes in open systems, a state, that A.A. Bogdanov name “movable equilibrium”, and E. Bauer —“principle noequilibrium” of systems. Non-entropic tendencies are manifested at all levels of the development of matter. At the same time, at the level of physicochemical processes, they manifest themselves for a short time (for example, A. Benard’s effect); to manifest themselves in a more

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explicit at the level of plants (a blade of grass asphalt breaks through) and are realized at the biological level. An analysis of the laws and problems that arise in open complicated systems allows us to conclude: negentropic tendencies are opposite to the second law of thermodynamic (the growth of entropy), and do not allow the system to die. Such a system cannot be assembled from parts (just as it is impossible to “assemble” a live organism). Now it is realized that starting from a certain level of complicity, the system is easier to transform and change, then to be described formally.

6 Results Our research has led to the conclusion that a complicated system should be transformed with the help of developing control actions. That is, such systems need to be “grown”, developed with the help of innovations (negentropic manifestations) and self-learning, controlling sustainability. An approach was applied proposed to the realization process of “growing” by gradually formalizing the decision-making model. The proposed approach is based on the idea of “switching” between formal methods and methods of activating the intuition and experience of specialists (persons, who form the model and make decisions) [48]. The method for assessing the sustainability of systems in a state of movable equilibrium, based on the use of the law of emergence and information theory is developed [49, 50]. The possibility of realizing the idea of a “living cell” by E. Bauer for the further development of an enterprise control system is being investigated [51]. A concept of an open system is used for the cyber-physical systems’ development [52, 53]. The concept of mobile equilibrium is being rethought [54], based on the further development of the theory of potential feasibility of B. Fleishman [21] and the use of a formalized representation of the laws of dialectical logic by A.A. Denisov [15].

7 Discussion The simultaneous manifestation of entropic and non-entropic tendencies in living organisms is regulated by natural laws that have formed over millennia and are characteristic of the corresponding species of living beings. And at the level of social systems, this becomes a special problem, which can be explained by the consequence of human intervention in the regulation of his relationship with nature with the help of science and technology. However, such conclusions exist only in the philosophical and methodological understanding of open systems with active elements. More in-depth studies of the manifestation of the basic law of L. von Bertalanffy, the ideas of A.A. Bogdanov and E. Bauer are needed now. For the study of mobile equilibrium dialectical thinking is necessary, i.e. it is necessary to apply the laws of dialectical logic.

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8 Conclusion The analysis of the origins of the concepts of systems theory shows that the concepts of systems theory were arousing from different origins and were focused mainly on the formation of a terminological apparatus and the development of methods for modeling systems for specific applications. But the features of the developing processes of open systems are little studied. An appeal to the initial model of an open system and the organismic concept of L. von Bertalanffy helps to understand the significance of the development of the theory of open systems, to think about how for artificial systems (such as control systems for socio-economic processes) to create models such as mobile equilibrium and metabolism, which were the basis of the original model open system of L. von Bertalanffy. It is necessary to rethink the original model and organismic approach of L. von Bertalanffy, the concept of mobile equilibrium by A.A. Bogdanov, E. Bauer’s ideas, and models based on the formalized dialectical logic of A.A. Denisov. It should become the basis for the further development of modern systems theory. It can be predicted that based on the development of these works shortly, a new theory of stability of developing systems will be created, and, possibly, new branches of mathematics based on axiomatics, taking into account the laws of dialectical logic.

References 1. von Bertalanffy, L.: General system theory. Foundations, Development, Applications. George Braziller, New York (1st Publ., FRG, 1945). (1968) 2. von Bertalanffy, L.: General System Theory — A Critical Review General System, vol. VII, pp. 1–20 (1962) 3. Bogdanov, A.A.: Vseobshchaya organizatsionnaya nauka: Tektologiya [General Organizational Science: Tectology. V 2-kh kn. Berlin–Sankt-Peterburg (1903–1922). Pereizdaniye: V 2-kh kn.] Ekonomika Publ., Moscow (1989). (in Russian) 4. Boulding, K.: General systems theory — the skeleton of science. Gen. Syst. 1, 11–17 (1956) 5. Ashby, W.: Ross: general systems theory as a new discipline. Gen. Syst. III, 1–6 (1958) 6. Bauer, E.S.: Teoreticheskaya biologiya. [Theoretical Biology] Izd, 206 pp. VIEM, MoscowLeningrad. (1935). (in Russian) 7. Anokhin, P.K.: Printsipial’nyye voprosy obshchey funktsional'nykh system [Fundamental questions of the general theory of functional systems]. Moscow (1971). (in Russian) 8. Uyemov, A.I.: Sistemnyy podkhod i obshchaya teoriya system [Systems approach and general systems theory.] Mysl’ Publ., Moscow (1978). (in Russian) 9. Urmantsev, Yu.A.: Opyt aksiomaticheskogo postroyeniya obshchey teorii sistem [Experience of axiomatic construction of the general theory of systems.] Sistemnyye issledovaniya 1971, pp. 128–152. Nauka Publ., Moscow (1972). (in Russian) 10. Kukhtenko, A.I.: Ob aksiomaticheskom postroyenii matematicheskoy teorii system [On the axiomatic construction of the mathematical theory of systems]. Kibernetika i vychislitel’naya tekhnika [Cybernetics and Computer Science], pp. 3–5. Naukova dumka, Kiyev (1976). (in Russian) 11. Zgurovsky, M.Z., Pankratova, N.D.: System Analysis: Theory and Applications. Springer (2007)

Origins and Prospects of Systems Theory

13

12. Temnikov, F.Ye.: Voprosy teorii i metodologii sistem [Questions of the theory and methodology of systems]. V sb. trudov Moskovskogo Energeticheskogo instituta. Vyp. 158. Sistemotekhnika [In: Proceedings of MEI. Issue 158. Sistemotechnika], pp. 3–9. Moscow (1873). (in Russian) 13. Volkova, V.N., Temnikov, F.Ye.: Podkhod k vyboru metoda formalizovannogo predstavleniya sistem [Approach to the choice of the method of formalized representation of systems]. V sb.: Modelirovaniye slozhnykh system, pp. 38─40. MDNTP, Moscow (1978). (in Russian) 14. Temnikov, F.E., Volkova, V.N., Makarova, I.V.: Systematik, Informatik und Intellektik als neue Verfahrender Datenverarbeitung. Rechentechnik Daten verardeitung, r.Iahrgang Beiheft, 1/2. Die Elektronisch Datenverer-beitung im Hochshulwe-senvert-Rage der wis senschaftlichen: Konferenz der DDR. Berlin, pp. 18─22 (1970) 15. Denisov, A.A.: Sovremennyye problemy sistemnogo analiza [Modern problems of system analysis]. 3rd ed. Izd-vo Politekhn. un-ta, St. Petersburg (2008). (in Russian) 16. Volkova, V.N., Denisov, A.A.: Teoriya sistem i sistemnyy analiz [Systems Theory and Systems Analysis]. Yurayt, Moscow (2014).(in Russian) 17. Volkova, V.N., Kozlov, V.N. (eds.): Modelirovanie sistem i protsessov [Modeling of systems and processes]. Yurayt, Moscow (2015). (in Russian) 18. Novikov, D.A.: Sostoyaniye i perspektivy teorii aktivnykh sistem [State and prospects of the theory of active systems]. Upravleniye bol’shimi sistemami. Issue 9, pp. 7–26. IPU RAN, Moscow (2004). (in Russian) 19. Good, H., Machol, R.: System Engineering: An Introduction to the Design of Large-Scale Systems. McGraw-Hill Book Company, Inc., Toronto, London, New York (1957) 20. Hall, A.D.: A Methodology for System Engineering. D. Van Nostrand Company Inc., Prinston, NJ (1962) 21. Fleishman, B.: Fundaments of Systemology. Lulu.com, New York (2007) 22. Klir, G.: Architecture of Systems Problem Solving, with D. Elias. Plenum Press, New York (1985) 23. Kushnerick, J.P.: Is Your Research Relevant? Aerospace Management, vol. 6 (Oct. 24–29, 1963). 24. Chernyak, Y.: Sistemnyy analiz v ekonomike [Systems analysis in economics]. Ekonomika, Moscow (1975).(in Russian) 25. Analysis for military decisions. In: Quade, E.S. (ed.). The RAND Corporation (1969) 26. Sistemnyy analiz i prinyatiye resheniy: Slovar’-spravochnik [System analysis and decision making: Dictionary-reference book]. In: Volkova, V.N., Kozlov, V.N. (eds.). Vysshaya shkola, Moscow (2004). (in Russian) 27. Volkova, V.N., Kozlov, V.N.: Sistemnyy analiz v proyektirovanii i upravlenii [Systems Analysis in Engineering and Control]. Izd-vo Politekhn. Universiteta, St. Petersburg (2018). (in Russian) 28. Beer, S.: Management science. Albus Books, London (1967) 29. Cherchman, C.W.: The Systems Approach and Its Enemies. Basic Books, New York, NY (1979) 30. Ulrich, W.: Critical Heuristics of Social Systems Design. Haupf, Berne (1983) 31. Ackoff, R.L.: The Democratic Corporation. Oxford Univ. Press, Oxford (1994) 32. Checkland, P.B., Scholes, I.: Soft Systems Methodology in Action. Wiley, Chichester (1990) 33. Moiseyev, N.N.: Matematicheskiye zadachi sistemnogo analiza [Mathematical Problems of System Analysis]. Nauka, Moscow (1981).(in Russian) 34. Kozlov, V.N.: Sistemnyy analiz, optimizatsiya i prinyatiye resheniy [System Analysis, Optimization and Decision Making]. Prospekt, Moscow (2010).(in Russian) 35. Zadeh, L.A.: Fuzzy sets. Inform. Control 8, 338–353 (1965)

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Y. S. Vasiliev et al.

36. Mesarović, M.: Mathematical Theory of General Systems With Y. Takahara. Academic Press, Cambridge, MA (1972) 37. Saaty, T.L.: The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York, NY (1980) 38. Forrester, J.: System Dynamics: The Foundation Under Systems Thinking (1999) 39. Komp’yuternaya imitatsiya ekonomicheskikh protsessov. In: Yemel’yanov, A.A. (ed.). Market DS Publ, Moscow (2010). (in Russian) 40. Pospelov, D.A.: Situatsionnoye upravleniye: Teoriya i praktika [Situational Management: Theory and Practice]. Nauka, Moscow (1986).(in Russian) 41. Bolotova, L.S.: Sistemy iskusstvennogo intellekta: modeli i tekhnologii, osnovan-nyye na znaniyakh [Artificial Intelligence Systems: Knowledge-based Models and Technologies]. Finansy i statistika, Moscow (2012).(in Russian) 42. Ignatyev, M.B.: Linguo-combinatorial simulation of complex systems. J. Math. Syst. Sci. 2 (1), 58–66 (2012) 43. Kukor, B.L.: Semiotika sistemnogo analiza i semanticheskaya sistema logikolingvisticheskoy modeli predmetnoy oblasti [Semiotics of system analysis and the semantic system of the logical-linguistic model of the subject area] Sistemnyy analiz v proyektirovanii i upravlenii: sb. nauch. trudov XIII Mezhdunar. nauchno-praktich. konf. Ch. 1, pp. 164– 169. Izd-vo Politekhn. un-ta, St. Petersburg (2009). (in Russian) 44. Aref'yev, I.B.: Logiko–refleksivnoye modelirovaniye tekhnologii izgotovleniya promyshlennykh detaley [Logical-reflective modeling of the technology of manufacturing industrial parts]. Iz-vo BFU im. I. Kanta, Kaliningrad (2012) 45. Nechayev, V.V.: Vvedeniye v teoriyu metamodelirovaniya system [Introduction to the theory of systems metamodeling]. Izd-vo “Informatsiologiya”, Moscow (1997). (in Russian) 46. Nikanorov, S.P.: Teoretiko-sistemnyye konstrukty dlya kontseptual’nogo analiza i proyektirovaniya [System-Theoretical Constructs for Conceptual Analysis and Design]. Kontsept, Moscow (2006).(in Russian) 47. Kachanova, T.L., Fomin, B.F.: Osnovaniya sistemologii fenomenal'nogo [Foundations of the systemology of the phenomenal]. Izd-vo SPbGETU, St. Petersburg (1999). (in Russian) 48. Volkova, V.N.: Postepennaya formalizatsiya modeley prinyatiya resheniy [Gradual formalization of decision-making models]. Izd-vo SPbGPU, St. Petersburg (2006). (in Russian) 49. Volkova, V.N., Loginova, A.V., Chernenkaja, L.V., Romanova, E.V., Chernyy, Yu.Yu., Lankin, V.E.: Problems of sustainable development of socio-economic systems in the implementation of innovations. In: Proceedings of the 3rd International Conference on Human Factors in Complex Technical Systems and Environments, Ergo 2018, pp. 53–56 (2018) 50. Volkova, V.N., Loginova, A.V., Leonova, A.E., Chernyy, Y.Y.: Development of the Theory of Sustainability Based on the Concept of an Open System. In: Proceedings of 2019 3rd International Conference on Control in Technical Systems, CTS 2019, pp. 15–18 (2019) 51. Volkova, V.N., Leonova, A.E., Romanova, E.V., Chernyy, Y.Y.: Engineering as a coordinating method for the development of the organization and society. In: Bylieva, D., Nordmann, A., Shipunova, O., Volkova, V. (eds.). “Knowledge in the Information Society”. Joint Conferences XII Communicative Strategies of the Information Society and XX Professional Culture of the Specialist of the Future. Lecture Notes in Networks and Systemsthis, vol. 184, pp. 12─21. Springer, Cham (2021). https://doi.org/10.1007/978-3030-65857-1 52. Vasiljev, Y.S., Volkova, V.N., Kozlov, V.N.: The concept of an open cyber-physical system. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds.) Cyber-Physical Systems and Control. Lecture Notes in Networks and Systems, vol. 95, pp. 146–158. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34983-7_15

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53. Volkova, V., Gorelova, G., Pankratova, N.: The development of the cyberphysical system concept on base of the interdisciplinary theories. In: 2020 IEEE 2nd International Conference on System Analysis and Intelligent Computing, SAIC-2020, 9239213 (2020). https://doi.org/10.1109/SAIC51296.2020.9239213 54. Volkova, V.N., Fleishman, B.S., Tarasenko, F.P., Loginova, A.V.: Further development of potential feasibility theory for complicated systems according to the unified general-system principle. In: Bylieva, D., Nordmann, A., Shipunova, O., Volkova, V. (eds.). “Knowledge in the Information Society”. Joint Conferences XII Communicative Strategies of the Information Society and XX Professional Culture of the Specialist of the Future. Lecture Notes in Networks and Systemsthis, vol. 184, pp. 446–453. Springer, Cham (2021). https:// doi.org/10.1007/978-3-030-65857-1

Complex, Adaptive, and Evolvable System Theory: Basis and Uses George Mobus(&) University of Washington Tacoma, Tacoma, WA 98402, USA [email protected]

Abstract. Complex, adaptive, and evolvable systems theory is an attempt to amalgamate and integrate several previously developed theories as well as bringing in newer theoretical understandings so as to cover all natural and artifactual such systems. A clear differentiation is made between merely adaptive systems and those that have mechanisms of evolvability. Examples are provided to help differentiate and distinguish these two related, but separate modes of system modification in light of changes in the system’s environment. The theory is used to produce an archetype model that can guide both analysis and design. The archetype describes three sub-models and their interoperations; an economy model, a governance model, and an agent model. The model tells us what subsystems and operations are needed to produce a viable whole system. The archetype and the three sub-models are shown to provide an efficient and effective way to guide the analysis of extremely complex systems. They specify the kinds of mechanisms that are needed in any viable system and provide guidance for how the various subsystems are to be integrated. Keywords: Complexity


Adaptivity
Evolvability
Archetype models

1 Introduction 1.1

Models of Complex Systems

For the last nearly fifty years numerous systems scientists and cyberneticians have developed ‘models’ of complex systems. A significant number of these models were based on analogic thinking about, metaphor for, or isomorphic relations with natural systems from single celled organism through multicellular animals through to and including social systems. The basis for these models has largely been based on argument rather than empirical evidence (though Miller’s [1], arguments are strongly based on empirically derived understanding). And they range in form from ad hoc, such as one finds in systems dynamics, Forrester [2], instantiating peculiarities of specific systems of interest, to abstract conceptual models such as Stafford Beer’s Viable Systems Model [3]. The latter is a good example of an analogic model in that Beer sought to generalize on the management/governance structures used in various enterprise organizations by relating the various functions one tends to find in such organizations to those of the human brain (and its relation to the human body). He parsed various management © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 16–28, 2022. https://doi.org/10.1007/978-3-030-98832-6_2

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levels and activities (below noted as decision types) into a formal set of subsystems with their interconnections and designated management functions and provided a rationale based on comparison with what was then known about the architecture and information processing functions of the human brain [3]. The VSM, like other such models, provided what I would call an archetype model. The purpose of such models can be seen as two-fold. On the one hand an archetype model gives references to look for in the analysis of complex systems. One knows in advance what should be found in an existing system. And if one does not find the component part or subsystem, then one has found a dysfunctional aspect of the system and has a basis for intervention. Alternatively, the archetype model provides a generic blueprint for the design and implementation of an artifactual system. Systems engineers know in advance what functions and their relations need to be accounted for and can attend to the detailed designs to implement them (c.f., [4].) The point of all models is to guide our acquisition of understanding of the systems we interact with. Some models are less comprehensive (compared with VSM or Living Systems). Some only cover a single aspect of a whole system, such as just the cybernetics or complexity or structural organization (e.g., hierarchies). All of these aspects are equally important to a holistic comprehension of all complex systems. All need to be taken into consideration simultaneously. For the last fifty years or so many of these models or modeling techniques have been the basis of systems practices, particularly with respect to systems analysis of natural systems and man-made (or to-be-made) systems. One has to ask, since these are somewhat disparate in their nature, have these models been sufficient, efficacious, or even correct for their intended purpose? Russell Ackoff [5] provided an early attempt to produce what he called a “System of Systems” approach, by which he meant a systemic framework for understanding systems concepts as a way to better understand how to work with systems. It has become increasingly clear that a general systems framework is needed to provide an integration of systems aspects. The archetype models presented here seek to provide that framework.

2 Method One of the concerns I have had as I studied and tried to apply a number of these models or approaches is that they are disparate and dated with respect to more current knowledge of systems science. Many of them overlap in one or more domain of interest, but they cannot be used directly to develop a truly holistic grasp of extremely complex systems. Moreover, many are based on knowledge that was derived over forty years ago, and I have not seen much in the way of updating (or correcting) them in light of more modern understandings of the sciences being used. For example, today so much more is known about how the brain works and much more about its structures at multiple scales (i.e., from the synapses to the whole brain). The analogic parts of VSM that relied on brain architecture and functions deserve to be reviewed in light of the newer understandings.

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Integration of a General Framework

To address these problems, I have been developing a much updated, more holistic theory of complex, adaptive, and evolvable systems. The method has been to examine aspects of several of the core models already being used, find the overlapping concepts to use as anchor points for integration, and then take remaining aspects and attempt to modify them as needed so that they are compatible. For example, both VSM and Living Systems employ homeostasis and error feedback mechanisms, so those are points of commonality. On the other hand, both models describe agents and agency in somewhat different ways and using different terms so I sought to reconcile the differences in a more holistic (and more abstract, see below) model of an agent possessing agency (ability to cause change in the world). In addition to merely combining models, I have identified a number of areas that needed refining or bolstering with concepts that were not covered in the original core models, as they were not as well developed at the time these models were being created. For example, contemporary network and complexity theories are more robust and have much more explanatory capabilities that were not available in the core models. These, among other more recent theoretical constructs, have been added or made explicit in the archetypes discussed here. For example, in Mobus [6] the structural aspects of Simonian [6] complexity and a mathematical model of what this means is elucidated. Finally, I have endeavored to compare the resulting model to a variety of natural complex, adaptive and complex, adaptive, and evolvable systems, from single celled organisms up to and including human societies, following Miller’s approach. The result of this work is what I have called complex, adaptive, and evolvable systems theory (CAEST) and the archetype model is a CAES [6, 8]. A merely complex, adaptive system (CAS) can be seen as a subset of a CAES in which the evolvability component is not present. As mentioned before, all living individual organisms up to human beings are CASs. Human beings, their social systems, and all of their societal organizations, clubs, churches, enterprises, etc. are CAESs, or at least can be in principle1. In this paper, after establishing some definitional positions regarding the name of such systems, I will describe the basic aspects of the archetype model and then explain how the model can be used in analysis and design. 2.2

Definitions

Complexity. There are many different definitions of complexity [9]. In the CAES theory we adopt the concept developed by Herbert Simon [7] of a hierarchy of systems and subsystems. That is, every system is composed of a set of subsystems, and they, in turn, are composed of yet lesser complex sub-subsystems. The recursion precedes downward until one comes to elemental components that require no further

1

Note that ecosystems are also CAESs but are organized and regulated differently. That is beyond the scope of this paper.

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decomposition. A system can then be represented as either a nested set of maps, or in the form of a tree rooted at the system level with n-ary branches reaching down to leaf nodes, the elementary components. In this scheme one may devise a measure, or index, of complexity based on a recurrent function of the complexity of each subsystem [6]. Though definable in principle, the index is only really valuable as a conceptual tool for thinking about the nature of complexity. In the prior reference I describe a methodology for constructing such an index in the process of doing a structural systems analysis. Adaptivity. Since Holland [10], the phrase complex, adaptive system (CAS) has won support in the systems community and is used frequently in different contexts. The concept of adaptivity has been associated with, for example, agent-based modeling, where subsystems сan adapt to changes in their environments, which often include other similar subsystems, leading to interesting non-linear dynamics. Another approach to modeling adaptivity has been in controls and cybernetic systems that display homeostatic behavior. While many fruitful insights into these various behaviors resulting from adaptivity have been gleaned from studying model systems there has been very little distinction between one form of responding to changes in an environment, adaptivity, and another form, evolvability. Many researchers have tended to conflate these, perhaps in part because evolutionary biologists often refer to “adaptations” of phenotypes that result from biological evolution in which, semantically, it sounds like animals adapt to a changing environment. But this is not the case. A species’ adaptation to an environment is the result of a slower process of more-or-less blind search in fitness space and it applies only to the population/species level, not to the individual level. At least until evolution produced a species wherein individuals have an extremely complex neocortex able to encode representations of abstract concepts, including symbols (words). Evolvability. Species and their populations are complex systems of complex individual adaptive agents. They do indeed display emergent behaviors (like birds flocking) replicated in many of the agent-based models [c.f., 11]. But the individual agents, though adaptive in some changing environments, do not evolve themselves. That is a function of the population and over time spans of many generations — not in one lifetime. Human beings are arguably the first species of animals that have a form of individual evolvability in their capacity to formulate wholly new concepts in their neocortices. These new concepts affect emergent behavior that imbues the individual with new or changed adaptivity. For example, the invention of clothing did not change an individual’s adaptivity to cold; take the clothes away and the individual will suffer hypothermia. But with clothing, individuals can survive better in colder temperatures. Individual human evolvability, in the sense that one can ‘invent’ a new concept and new behavior is the origin of cumulative culture in the social system.

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3 Results — Basic CAES Archetype In this section I will describe the basic archetype model. A fuller description, including an analysis of how many parts of the model were derived from the core models discussed above is the subject of a book project in press [6]. Here I only provide a basic description of the major parts of the model and provide arguments for their inclusion (see also [12] for an earlier explanation). The whole CAES is actually an integrated set of three types of sub-models which are abstractions of the three basic functional processes that all CAESs have. These are: an Economic System, a Governance System, and Agents that are distributed throughout the whole system. These will be explained below. Figure 1 depicts the main elements of a basic CAES embedded in an environment. Systems and subsystems (and sub-subsystems) are shown as oval shapes. The outer oval represents the whole CAES as a system of interest (SOI). Two of the main subsystems, the economy and the governance systems are similarly shown as open ovals. Agents, seen as pink ovals, are found within all of the other subsystems.

Fig. 1. This is a depiction of a CAES (outer blue oval) showing some aspects of the three subsystems, governance (dashed blue oval), the internal economy (inner blue oval), and agents (pink ovals within other processes). Arrows depict flows from sources to sinks. Thin black arrows depict a few of the message channels used to convey information among agents. The environment of the CAES is also depicted. See text for explanations.

The figure also depicts elements of an environment in which the CAES operates. Square brackets are used to depict sources of resources, particularly material and energy, and sinks to which outputs of the CAES flow. Arrows depict these flows.

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The lightning bolt, in the upper left corner, represents some stochastic disturbance, that which requires the system to be adaptive. And the cloud objects represent other entities in the environment that are not directly interacting with the CAES but may exert influences on those sources and sinks that do. As I will discuss below, a strategic management capability includes being able to monitor such entities and predict or anticipate what their behaviors might mean in regards to the behavior of the vital sources and sinks. 3.1

Basic Sub-model Archetypes

In the book mentioned above, I give a fuller explanation of how these three sub-models constitute the basic design of a CAES archetype model. In this section I provide a cursory, but hopefully sufficient, description of the three and how they interoperate to produce viability in the system. The Internal Economic Subsystem. The core process in a CAES is an economic subsystem. All examples of CAESs from single celled bacterium to whole human societies posses an economic subsystem [1]. An archetype economy is a system that extracts resources from its environment and performs physical or chemical work on those resources to generate low entropy objects and perform services in the autopoiesis of the CAES [13–15]. Additionally, the economy produces products for export. That is, it produces some material or service product that is useful to some other entities in the environment, here called a product sink. In Fig. 1, materials move from the input on the left, through stages of transformation by a series (and parallel) of “work processes.” These are subsystem modules that have specific transformation tasks that reduce the overall entropy of the material inputs, such as combining several materials to form new, more useful materials, or to shape a material so that it becomes usable in a downstream process. Examples of economies in CAS/CAESs are cellular metabolism, multicellular organism physiology, household maintenance, manufacturing enterprises, and, of course, national economies. The specification of an “internal” economy is to emphasize that a CAES has an internal set of work processes that produce products and services for the CAES itself. Economies in the listed examples form a nested structure. Metabolism in cells in multicellular organisms is made possible by the physiological milieu of the organism’s body. An organism, in turn, exists within an ecological system with its own energy and material flows. Humans, further, live in a social system with its own economy that supports individuals’ physiologies, and so on. Sustaining economies are far from equilibrium, generally steady-state, dissipative systems. Note in the figure that some form of high potential energy (power) is an input resource. This energy is distributed to work processes where it is used to do work and then waste heat is dissipated, ultimately to the environment (wavy red arrows.) I distinguish another category of processes that are not within the main work process network, but which are peripheral to it and only periodically are called to action. These include the adaptation, evolvability, repair & maintenance (autopoiesis), recycling, and waste exporting processes. The adaptivity processes are called into play when an existing work capacity needs to be enhanced due to possibly temporary

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increases in demand for a processes’ output. For example, a factory may need to hire additional workers, and increase its production facilities to meet an increase in demand for its products. In human physiology, muscles can increase their capacity to meet demand as when someone decides to take up weight lifting. No new functions are produced, as is the case with evolvability, only new capacities are built up. In other words, requisite variety in control is increased in response to demand. In evolvability, completely new resources and functions can be mustered to meet a completely new demand. An economy, consisting of many interoperating work processes in a supply chain or web, requires governing to maintain as steady a production process as possible. Work processes can be sources of variable dynamics so need individual, real-time management to assure the long-term viability of the whole system. The Governance Subsystem. The governance system is that which manages the activities and processes within the CAES. Meuleman [16] provides an argument for the notion of generic governance archetypes. He reviews what he takes to be three different generic architectural patterns of what he calls meta-governance structure/dynamics. While he focused primarily on human economies, his arguments can be extended to a fully general governance architecture. The architecture of a governance system is a hierarchy of decision types and time horizons. In Fig. 1, you see three agent ovals, labeled “Tactical Management”, “Logistical Management”, and “Strategic Management”. Tactical and logistical management functions are coordination agents. Tactical management is responsible for coordinating the activities of the CAES with the external entities in the environment. An example is the purchasing department in a corporation that coordinates the acquisition of materials. Logistical management is responsible for coordinating the activities of internal work processes as described above. Logistical decisions are often about optimization and synchronization across multiple work processes. Strategic management, which is only found in CAESs (not CASs), involves longterm processes of evolving a system so that it stays viable in a changing environment. Merely adaptive systems, such as individual organisms, do not have an ability to evolve themselves, so do not need a strategic management capability. Evolution decides what capacities the phenotype of a species needs to be viable. In humans, our brains have got a modicum of strategic thinking capacity. And, certainly, organizations, through collective thinking of, say, the board of directors, have the strategic management facility. Note that every process in the figure has within it a ‘pink’ oval representing local agents responsible for process management. In cases where the process itself is well designed in terms of its capacity to handle, for example, fluctuations in material or energy flows and the agent has requisite variety in affecting changes in the process the management is essentially real-time homeostasis (error feedback) keeping the process working at some near optimal level. Coordination comes into play across some clusters of closely coupled work processes in order to near optimize the aggregate output. Each process agent reports time-averaged data upward to the coordination level (black arrows from the work processes to the two coordinators). The coordinators operate over longer time scales and are interested in trends and/or integrals of error rather than

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real-time data. Using an appropriate decision model (see below) the coordinators select appropriate signals to communicate downward what changes the process agent should make in order to keep the whole cluster ‘in-sync’. This model relies heavily on cybernetic and control theories. The details of various principles in controls and quantitative models were reviewed in Mobus & Kalton [17]. Decision Agents. All agents in this model have the same basic architecture. Figure 2 depicts this generic architecture. The agent itself is comprised of a computational engine, an appropriate decision model used by the engine, and, at least in the case of CAESs, some experiential memory store (both explicit and implicit forms). When the agent is a human being, all three of these major functions take place in the neural tissues of the brain. In a computer-controller (embedded system) the three are separated but interoperable.

Fig. 2. This depicts the model architecture of decision agents. The three main components and the action/behavior of the agent is explained in the text.

The computing engine receives appropriately transduced messages about the environment in which the agent is embedded; messages carrying information relevant to the state of the system the agent is responsible for managing. Using the decision model and calling upon experiential memory for guidance, the engine then computes the choice of actions that are needed to affect the environment. The number of choices (or range in the case of continuous variable) and the power capacity to affect the environment are major characteristics of the agency of the agent. The agent is effectively a homeostasis mechanism [18, 19]. It detects changes in environmental variables that deviate from its model expectations (determines the error

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valence and magnitude) and directs, through sufficiently powered actuators, what changes need to be made. In doing so its inputs from the environment in the next time step should reflect the success (or failure) of the actions taken. All agents perform this basic cybernetic function. What differs between agents is the details of the model used, the power of the engine employed, and the extent of experiential memory and learning capacity. A computer-based PID controller need not have an extensive memory capacity, nor be able to learn from experience whereas a human process manager will need considerable experiential memory (expertise) to perform her functions. The decision models employed are based on where the agent sits in the hierarchical architecture. Operations managers can rely on simple error feedback and ‘fix’ things when the process is not functioning properly. In more complex situations, such as the management of a manufacturing process, the agent must generally employ information directly from other processes in the chain, feedforward. Similarly, the model must be subject to information coming from the cognizant coordinator, feeddown. Coordination managers employ more complex decision models because they are working with many more variables and across longer time scales than the real-time processes they govern. And, strategic management (in human social systems) employ models that rely extensively on experiential memory, learning causal relations, constructing ‘what-if’ models, and so on. At present it is doubtful that any mechanical agent could function at this level, but then even humans have great difficulty being strategic agents when the scale of the environment is extensive. These three archetype sub-models constitute the phenotype of a complete CAES. We can now use these archetypes to guide the analysis of existing CAESs of interest, or analyze the needs of a system yet to be built, and to guide the design of systems. 3.2

Integration and Interoperation

All of the processes depicted in Fig. 1 transform inputs into outputs. The work process network primarily transforms intermediate materials into lower entropy products in a supply chain fashion. The processes depicted in the Management Processes Network transform data into information to be used by decision agents. This particular network provides the global governance of the whole system. This governance includes governance of the economic system as well as the peripheral processes shown in the figure. Each process entails local real-time management, at the basic level through homeostatic mechanisms. But the agents in these processes are in communication with other agents. For example, a local management agent can receive messages from the near neighbor agents in upstream and/or downstream processes to implement cooperative actions. Agents also communicate with coordinators, supplying operational data upward, and receiving coordination directives downward. 3.3

Social Systems

Social systems are those where the agents have higher degrees of autonomy and fewer constraints on individual behavior. Such systems require a dual governance subsystem, one as described, for the economy processes and one for the participating agents. This

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is the case for all social animals, for example, where genetically conditioned behaviors act to constrain non-conforming behaviors. It is definitely the case for human social systems where a somewhat separate, special governance system, is required to maintain the social order outside of the economy management. Governments, evolving rules for behavior management to prevent disruptions to the social order and to sanction rouge agents when they emerge. In earlier human social systems, religion-based moral codes filled this function (governments were primarily concerned with managing food production and going to war!) Today in most societies the government has taken on the role of managing both production (or managing the markets) and civil life2. 3.4

Recursive (Nested) Architecture

Beer [3], famously noted how subsystems within the VSM model could, themselves, be VSMs at smaller scales. That is, systems can be comprised of subsystems that are viable systems in their own right. This reflects the nestedness of systems-subsystems, but puts an emphasis on the idea that extremely complex systems are often comprised of extremely complex systems. So, it is with the model of CAESs. The simplest example is that of a social system such as an organization. Humans are the decision agents and they are, as explained above, CAESs by virtue of their neocortex. But so are departments or committees (which are then comprised of human agents). See [6] and [8] for a fuller description of this recursive architecture.

4 Discussion 4.1

Analyzing a System Using the CAES Archetype

The CAES model is a high-level abstraction of the general category of complex systems, just as the general concept of a “house” is an abstraction of specific kind of building. When a real estate agent goes to look at a specific house, the general concept of what a house, the house archetype model entails, guides their exploration of that specific house. Houses are supposed to have at least one bathroom, a kitchen, a living area, etc. So, the agent can explore and assess the specific house having pre-knowledge of what rooms they should find. What varies from one specific house to another is the layout and number of the rooms, number of stories, kind of roof and exterior, etc. Some houses may have idiosyncratic layouts or even shapes of rooms, but a room’s function can generally be discerned in the context of the other rooms present. A modern western house is expected to be functional for living and the functions are a priori well understood. Thus, the job of the realtor in coming to understand a specific house is facilitated by having a generic archetype model of a house. They can simply check off the rooms, noting sizes and other specific attributes. The CAES archetype fills this purpose in a structured analysis of any complex existing system. It is based on the survey of what representative systems contain as

2

With recognition that some societies are secular while in others religion and government are combined.

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subsystems, how those generic subsystems are interrelated, and the sustainable dynamics of viable systems. The archetype, along with the sub-archetypes described above, serves as a guide to the analysis, specifying what should be found as the analysis proceeds. For example, having the basic understanding of what an internal economy is, provides the analyst with the pre-knowledge of what to look for in terms of the transformation of materials and messages from high- or intermediate-entropic states to low entropy states (products). The analyst has a basic pattern of what a work process looks like and can decompose the system looking for the elements of such a process. The fuller description of a CAES and its sub-models includes guides for identifying boundaries of subsystems and the flows of matter, energy, and information between them, as depicted in Fig. 1 in the Work Process Network. In the aforementioned book, a procedural method for conducting systemic decomposition is provided. That method coupled with the existence of an archetype model provide the means of coming to understand any CAES. 4.2

Designing a System with the CAES Archetype

As with the real estate agent (analyst) using an archetype model (of a generic house) to analyze a specific instance of a house (an existing system of interest), here we will be concerned with the role of a system designer (like an architect) and how they can use the CAES archetype model to guide the design process. First let me acknowledge the fact that in the future we will likely be designing extraordinarily complex systems in order to address the existential threats that face humanity today. The human niche is based on technology and culture. We construct our niche by invention of instruments, tools, and institutions. Those very artifacts are then subject to selection. As we survey the plethora of artifactual systems that humans have created in response to attempts to ‘solve’ problems, we can quickly see that our ‘solutions’ regularly fall short or often have unintended consequences that result in even more complex problems. The central reason for this dismal track record is that we humans have been pursuing technologies using an empirical approach (try it and see) and engineering based on linear, reductionist thinking. Our technologies are the result of an evolutionary process where the generation of novelty is the result of best guessing. And we let the ‘markets’ act to select for or against our inventions. The proposal here is that we humans will do a much better job of designing complex systems if we have a deeper understanding of a comprehensive model of a complex system. The CAES model provides a first approximation blueprint of such a system. It provides a set of design parameters and constraints on the requirements of the system. The job of the system architect is to translate the archetype model into the appropriate medium much as a building architect translates a picture of a wall into bricks and mortar specifications. The architect, given a set of stakeholder requirements and functional specifications can develop the overall design of a complex system by identifying each requisite component of a CAES into the technologies and organizations needed for implementation. For example, the architect knows a priori that a system will include an economy which is the network of work processes that produce the final product. Starting with the specifications of that product (or those products

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more generally) and working backward through the network to the acquisition of resources, the architect can designate the major work processes required. The CAES archetype provides a holistic and complete guide to what needs to be considered in the design of a specific system. A building architect knows in advance that a building design will include walls and roofs. The only thing they need do is design those functions in specific materials. The major challenges for model-based design for CAESs involve the design of adaptive processes and evolvable capabilities. Designing adaptivity into CAES subsystems should be a major area of research in the systems engineering community. The principle of adaptivity is embodied in the CAES model, but precisely how that is to be achieved is open to research. One approach that looks fruitful is the investigation of homeostatic mechanisms in processes. Designing into processes at all levels in the organizational hierarchy homeostatic mechanisms appear to be key to creating a highly resilient and easier to manage system. At present, in human social organizations, like enterprises, the homeostatic mechanisms in work stations, departments, and divisions (etc.) are embodied in human agents usually without any explicit specification of ranges of affect or requisite variety of actions. We tend to leave it up to the ‘expertise’ of individuals placed in management positions to “figure it out,” and take “corrective action.” In the future, as we continue the march toward more and more automation of processes and their management, we will need to pay particular attention to how to design adaptivity into everything. The same is true for autopoietic functions and their regulation. And the same is true, even more so, for evolvability. As with leaving many adaptivity functions to human managers, without providing any kind of real specification as to what that means, this area of CAES research is very challenging. A good starting place for better understanding evolvability would seem to be the strategic management of organizations. Currently, human upper management is tasked with making strategic decisions and performing strategic management (overseeing the implementation of a new product line, for example). Much has been written about strategic management based on, for example, case studies of successful and failed strategic decisions and implementation. But these case studies only at best describe aspects of the process and often are conflated with the study of the personalities involved. They are not, usually, analyses of a CAES and evolvability models. I suggest that a much better understanding of how to formalize strategic management will be gotten from a deeper study of the hierarchical cybernetic management architecture in a CAES.

5 Conclusion Progress in understanding is based upon reevaluating, revising based on new information, and integrating diverse and seemingly disparate models of systems and systemness itself. Within the systems sciences we have developed a number of ‘models’ of complex systems and methods for how to construct models of systems using a variety of approaches. Many of these were developed in the latter half of the 20th century. And many of those are still in use today. In the CAES archetype model I have attempted to amalgamate and integrate the best features of many of these historical models guided

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by examination of a wide range of example systems. Aside from this integration, the model makes explicit the features and mechanisms of adaptivity and evolvability and how these two are different. As we move forward in our attempts to design complex, adaptive, and evolvable systems we would be wise to proceed on the basis of a comprehensive standard model. The CAES theory and model, in their current manifestations, are by no means complete in details. There is much research to pursue here. But as described in the forthcoming book, it should become apparent that they do provide much more comprehensive guidance to our gaining and using knowledge of complexity, adaptivity, and evolvability through model-based analysis and design.

References 1. Miller, J.G.: Living Systems (1995 edition). University of Colorado Press, Niwot, CO (1978) 2. Forrester, J.: Industrial Dynamics. Pegasus Communications, Waltham, MA (1961) 3. Beer, S.: Brain of the Firm: A Development in Management Cybernetics. Herder and Herder, New York (1972) 4. Sillito, H.: Architecting Systems: Concepts, Principles, and Practice. College Publications, UK (2014) 5. Ackoff, R.L.: Towards a system of systems concepts. Manage. Sci. 17(11), 661–671 (1971) 6. Mobus, G.E.: Systems Science: Theory, Analysis, Modeling, and Design. Springer, New York (in press) 7. Simon, H.A.: The Sciences of the Artificial, 3rd edn. The MIT Press, Cambridge MA (1998) 8. Mobus, G.E.: A systems science framework for understanding the nature of governance. In: Governing the Anthropocene, Proceedings of the 59th Annual Meeting of the International Society for the Systems Sciences, vol. 1, no. 1, PKP Publishing Services Network (2015) 9. Mitchell, M.: Complexity: A Guided Tour. Oxford University Press, Oxford, UK (2009) 10. Holland, J.H.: Studying complex adaptive systems. J. Syst. Sci. Complexity 19(1), 1–8 (2006) 11. Politopoulos, I.: Review and analysis of agent-based models in biology. University of Liverpool Tech Report (Sept. 11, 2007) 12. Mobus, G.E.: A framework for understanding and achieving sustainability of complex systems. Systems Research and Behavioral Science, vol. 34, p. 5. John Wiley & Sons, Ltd (2017). https://doi.org/10.1002/sres.2482 13. Hall, C.A.S., Cleveland, C.J., Kaufmann, R.: Energy and Resource Quality: The Ecology of Economic Process. John Wiley & Sons, New York (1986) 14. Odum, H.T.: Environment, Power, and Society for the Twenty-First Century: The Hierarchy of Energy. Columbia University Press, New York (2007) 15. Volk, T.: Gaia’s Body: Toward a Physiology of Earth. Springer-Verlag, New York (1998) 16. Meuleman, L.: Public Management and the Metagovernance of Hierarchies, Networks and Markets: The Feasibility of Designing and Managing Governance Style Combinations. Springer, New York (2008) 17. Mobus, G.E., Kalton, M.C.: Principles of Systems Science. Springer, New York (2014) 18. Damasio, A.R.: Descartes’ Error: Emotion, Reason, and the Human Brain. G.P. Putnum’s Sons, New York (1994) 19. Mobus, G.E.: Foraging search: prototypical intelligence. In: Dubois, D.M. (ed.) The Third International Conference on Computing Anticipatory Systems, AIP Conference Proceedings, Liege, Belgium, August (1999)

Increasing Objectivity in the System Analysis of Socio-Economic Objects Nikolay N. Lyabakh(&) Akademie für Management und Technologie e.V. (INTAMT), Alt-Pempelfort 15, 40211 Düsseldorf, North Rhine-Westphalia, Germany [email protected]

Abstract. The article deals with the problem of subjectivity of system analysis and insufficient development of mechanisms for assessing its effectiveness. The role of intelligent systems for carrying out a system analysis of complex socioeconomic objects has been substantiated as a way to increase its objectivity and effectiveness. Emphasis is given on the research of the Russian scientific school of taxonomy, which contributes to the shift of systems analysis technologies towards the systems of “intelligent functioning”. The basic concept of the Systems Analysis Platform is presented, including theory, methodology and tools in a new paradigm. It presents artificial intelligence in three dimensions: “the translated natural intelligence of the subject”; “computer-generated machine intelligence”; “the collective intelligence of the community, formed within the framework of the Internet of things”. Private procedures for formalizing system analysis have been developed: SWOT analysis, morphological analysis, cenological analysis, reconciliation of conflicting interests of business entities of different levels of management. The proposed approach will increase the objectivity of the analysis of modern socioeconomic systems. Keywords: Socio-economic objects
Systems analysis
Artificial intelligence
Socio-economic systems
Intellectual functioning

1 Introduction The Urgency of the Problem. It is known that systems analysis (SA) is a powerful means of scientific knowledge in various spheres of human activity. The category of “systems analysis” has been repeatedly and thoroughly studied in numerous works of Russian and foreign researchers and does not need additional comments. Reviews are numerous; consideration of the Russian specifics of the study of the issue is presented, for example, in [1, 2]. It should be noted that all researchers and users expect from the system analysis of some socio-economic object (SEO) to obtain objective knowledge about it (about the object of research) and, as a result, the effective functioning of the investigated SEO. Efficiency in this case is understood in the broadest sense. That is, the functioning of the SEO should be sustainable, safe, give acceptable financial effects (maximize profitability, profit, income, minimize the cost of production), maximize production indicators (the number of customers served, passengers and cargo © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 29–42, 2022. https://doi.org/10.1007/978-3-030-98832-6_3

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transported, products sold, etc.), and ensure the specified level of quality of products (goods and services), comply with environmental requirements. This formulation of the problem raises questions that are the subject of this discussion: can SA be objective if it is carried out by a subject (person), and does SA exist at all with manual control of the economy and social sphere?

2 System Analysis as a Procedure for the Intellectual Activity of Experts: Possibilities and Problems Let us take as an example a comparison of the governance structure of two countries using open sources. So, according to the composition of the top management of Russia, the following distribution is: • Government — 32 people; • Head of Government — Prime minister, Deputy heads of Government — 10 people; • Ministers — 21 people; • Presidential Administration — 3,000 people; • Government apparatus — 1,500 people. This management corps serves the Russian population of 147 million people and the GDP of $ 1.5 trillion. Similar data for the United States is as follows: Government — 17 people, Head of government — president, Deputy head of government (aka vice president) — 1 person, ministers — 15 persons, Presidential Administration — 500 people. There is no separate government apparatus in the United States. In addition, there are no auxiliary administrative structures in the United States: no “federal districts” and plenipotentiaries. The power of the president is limited: he cannot remove governors “on loss of confidence”. The US has a population of 328 million and a GDP of $ 21 trillion. Considering the effectiveness of management, as a result of a systematic analysis of the socio-economic systems of the United States and the Russian Federation, we can conclude that the former are at a high level. Let us admit a slight inaccuracy of the given data, suppose that the difference with reality, if any, is not significant. It is obvious that the Russian Federation loses in the efficiency of state governance (both in terms of costs — in terms of the number of managers, and in terms of results — in terms of the state of the economy). What’s the matter? The main reason, in our opinion, is the underdevelopment in Russia of an objective and effective system analysis based on the economic mechanisms of self-organization, which requires frequent manual intervention in the management process, and, consequently, the necessary set of these hands. But it must be remembered that such interference carries not only the intelligence of the manager (this is a plus for this management scheme), but also his subjective opinion, his private interests, which can negatively affect the development of the economy. It also hides the reasons for the manifestation of corruption (in a hierarchical system of management, a higher-ranking

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leader always has the right to absolute use of power, but it can be bought and it becomes a subject of bargaining). In such conditions, when the causes of corruption are objective, generated by the governance structure, the fight against it is doomed to failure. It is not possible to oppose the hierarchy in management in general. If a person with the highest intellectual and organizational abilities stands at the top of the pyramid, then such a system works better than with collegial management. Indeed, the more experts participate in the discussion of the problem, the more difficult it is to make an agreed decision (and time is wasted) and, the more “gray” it is. But such a system is unstable and has a time-limited framework for effective functioning. It is generally accepted that SA is carried out through the implementation of the following methods and procedures: abstraction, induction and deduction, composition and decomposition of the research object, formalization of the problem, modeling processes and experimenting with data, expert assessment of situations and testing, algorithmization of activities and decision-making procedures, verification of the results obtained, etc. For example, when managing the development of an industrial enterprise, SWOT and PEST analysis tools are used, as well as cognitive analysis, which makes it possible to identify the properties of the environment of its immersion, strengths and weaknesses, opportunities and threats. Then classify the position of the object under study (for example, “industry leader”, “successful enterprise”, “sustainable enterprise”, “degrading enterprise”, etc.). In the future — to determine the ways of its optimal development, to calculate the parameters of management to achieve the goal (terms, resources, indicators) [3]. Obviously, all procedures are carried out by a person or a machine under his control. Consequently, it reflects his experience, knowledge, scientific position (belonging to a particular scientific school), private interests (status, material, etc.). Thus, SA is largely a subjective procedure. The second conclusion, following from the above reasoning, is that SA is a complex, multidimensional process carried out under conditions of high uncertainty and dimension of the research object, noisy and unclear data. SA is a procedure for the intellectual activity of experts from management. It should be noted that the intelligence of a group of experts is not simply the sum of the intelligences of its members. Their interaction during SA can have both positive and negative synergistic effects. And the success of the SA essentially depends on this collective intelligence. Consequently, it is necessary to introduce self-organization mechanisms into the joint work of experts, which form the collective intelligence of this group, complement their personal intellectual abilities, and provide a positive synergistic effect. Thus, the SA problem of a complex socio-economic object plunges into the sphere of interests of systems of intellectual functioning [4], which show themselves well when working with “big data”, simulate fuzziness and uncertainty (theory of fuzzy sets), reveal hidden patterns (cognitive analysis, neural networks, Method of group consideration of arguments), carry out the classification of inhomogeneities and data (pattern recognition theory), formalize contradictory relations of business entities

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(theory of active systems), implement augmented and virtual reality technologies and solve many other tasks of SA SEO. Since the natural intelligence of a developer is subjective, the emphasis in our study is on the creation of artificial intelligence (AI) systems that contribute to the improvement of AS. The format and parameters of these systems substantially depend on the type and properties of the studied SEO. For the formation of the author’s concept, the key parameters of the system analysis of SEO are further identified.

3 Description of the Model and the Author’s Hypothesis 3.1

Formalization of System Analysis Parameters Socio-Economic Objects

Their activity is manifested only in the functioning of the entire system. Examples of SES are: • teams of leaders, the time, intelligence and strength of the members of which are completely subordinated to the solution of the tasks of the first person of the organization; • enterprises and institutions of the micro level (for example, a university), the elements of which are their subdivisions (faculties, departments). SA of such SES is carried out by means of the theory of automatic control and regulation, using differential equations describing deterministic processes, Pontryagin’s maximum principle, mathematical programming methods, etc. Examples of SES are development clusters (regional and / or sectoral), selfregulatory organizations, transport and logistics chains for the production and transportation of products, etc. The main SA tools for this SEO are: SWOT-, PESTanalyzes, theory of active systems (TAS), morphological analysis (MA), cognitive analysis (CA), DEMATEL, etc. We emphasize another approach of taxonomy — cenoses. Socio-economic cenoses (SEC) are self-organizing multi-species communities of organizations of various branches of the allocated research space, characterized by connections of various strengths (strong, medium and mostly weak). They are united by the joint use of natural, technical, social resources and functioning in common economic niches of supply and demand for products (goods and services). In SEC, the actions of intraspecific and interspecific selection are decisive [5]. The basic method of SEC analysis is the cenological analysis, which is described in detail in [6]. This type of system analysis examines the nature of self-organization processes, which for SEO conditions can be illustrated by the following example from the classics of management. According to legend, one day Ford G. gathered the heads of the divisions of his company and sent them on a two-week cruise in the Caribbean. When they returned, some were to be promoted and others to be fired. Ford closely watched the work of the units left without leaders. If the performance indicators remained at a high level, and the subordinates acted harmoniously, which means that the manager has competently built an appropriate management system.

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If, in the absence of the chief, the work in the department stalled, then the built-up management system is not very effective and the manager should be changed. Thus, the management of an enterprise (project, territory, industry, state) should contain elements of self-organization, and collective intelligence should manifest itself in the management of production in the activities of employees. The members of the collective are not just elements controlled by tight ties with the leadership, but members of this living organism. The situations described above actualize the creation of SEO intelligently (intellectually) functioning in an unpredictable changing environment. Poor predictability of the environment excludes the possibility of building the economy exclusively within the framework of the planned concept of SEO development. In the United States, these systems are better developed (longer history of market development, higher level of economic development). 3.2

Sources and Mechanism for Improving SA

In the study of the Russian systemologist V. Volkova [7], the approaches and methods of formalized representation of systems (MFRS) are considered, among which there is a special class of models based on methods of activating the intuition of specialists (MAIS). This class of models includes models for developing collective decisions (for example, in the form of scenarios that can be considered verbal or verbal models), models of structuring, methods of complicated expertise organization, morphological models. MAIS and MFRS provide an increase in the degree of formalization of the SA procedure. Noting the merits of Western researchers in science and production, K.-L. Bertalanffy [8], St. Beer [9], N. Wiener [10], J. Schumpeter [11], R. Ashby [12] and other researchers in the field of SEO analysis and management, the authors propose to focus on Russian studies: • Volkova V.N. in the field of modern systems analysis of complex SEO [1, 7]. • Glushkov V.M. developed the project of the system of automated management of the economy of the USSR: the nationwide automated system of accounting and processing of information [13]. Glushkov V.M.’s ideas were ahead of their time: there was no computer technology, information technology (Big Data, Data Mining, the Internet of Things, blockchain, digital platforms, etc.) corresponding to the tasks set. • Ivakhnenko A., who created a toolkit for constructing models of complex processes based on self-organization of models [14]. • Kleiner G., who developed and systematized the theory of socio-economic ecosystems [15]. Interconnected processes in ecosystems with positive feedbacks can be unstable and require the development of regularization methods Lavrent’ev M., Tikhonov A. • Kudrin B.I., Fufaev V. [5, 6], who created technetics, and developed the theory of cenoses for technical and SEA. • Lavrentiev M. [16], Tikhonov A. [17] developed tools for solving ill-posed inverse problems (control problems).

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• Leontyev V. — the founder of the balance method [18]. • Novikov D. scientific supervisor of the scientific school of the Institute of Control Sciences on the theory of active systems [19]. • Pontryagin L., who created a rigorous toolkit for optimal control of complex objects [20], etc. All of the above studies can be combined within the framework of the paradigm of the Unified Platform for the Development and Conduct of SA of Complex SEO. Such a platform should contain theory, methodology, CA tools, taking into account the peculiarities of various SEO. The necessary prerequisites for the creation of such a single CA platform in the Russian Federation exist and include a number of government initiatives [21] and Presidential Decrees [22]. According to the authors, as part of such a CA platform, special software blocks should function that solve specific CA tasks. Including: • “Software system of cognitive modeling” (PS KM), which simulates the development of SEO with various options for control and force majeure [23]. PS KM allows in laboratory conditions to implement possible options for the development of SEO and select the most acceptable controls. • “Program for researching the effects of sectoral restructuring based on cenological tools” (Certificate No. 2018660744, 2018), as well as “Program for researching the structure of enterprise costs based on cenological tools” (Certificate No. 2018616600, 2018), performing price analysis (as part of CA) complex SEO [24, 25]. On the basis of similar software products, an intellectually functioning system is formed that provides an objective and effective SA SEO. It is further proposed to distinguish between three forms of organization of AI, ensuring the improvement of the CA: 1. Intelligence transmitted by a machine, but which is a cast of natural intelligence (NI) — human intelligence (a group of experts). It is implemented through the creation of specialized expert systems, software systems and decision support systems. 2. Machine intelligence proper (MI) — the intelligence formed as a result of the implementation of self-organizing computational procedures, examples of which are methods for solving ill-posed mathematical problems [4], neuro-fuzzy models, Method of group consideration of arguments A.G. Ivakhnenko [14]. 3. Collective intelligence (otherwise: ant, swarm, gregarious, herd), formed as a result of the interaction of agents of different nature (people, machines, organizations). The information technology basis of this intelligence is the Internet of Things. The development of this direction for improving the automation system is seen through the creation of special digital platforms using intelligent technologies, including virtual and augmented reality. Let’s consider specific examples of formalization of some CA procedures.

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4 Algorithm of Analysis in the Framework of the Integral Model 4.1

Formalization of SWOT and PEST Procedures

Currently, many procedures have been developed that are used for SA SEO: SWOT and PEST-analyzes, morphological analysis, cognitive analysis, various expert procedures, etc. But, unfortunately, they are currently used almost independently of each other. SA SEO should definitely start with SWOT and PEST analyzes. However, traditional qualitative reasoning and statements should be substantiated with statistical material of expert judgment. There are two ways for this purpose: – Organization of an expert community operating on a permanent basis. At the same time, each expert at each stage of assessment is characterized by a weight of significance, reflecting the degree of success of his previous expert activity. After the completion of the evaluation cycle, the expert weights are recalculated. The final judgment is formed on the basis of a weighted averaging of expert opinions. The following algorithmization scheme is proposed (using the example of SWOT analysis): 1. Formation of many factors (strengths, weaknesses, opportunities, threats) by interviewing experts without screening them out: F ¼ fF1 ;F2 :::Fk g: 2. Determination of the scale of assessments of factors ½aj ;bj . Each expert evaluates the factors according to a convenient scale that reflects the level of his competence. 3. Determination of factor estimates cjm ;aj  cjm  bj — the weight of the m-th factor determined by the j-th expert, j ¼ 1;l; m ¼ 1;k. 4. Expert estimates are scaled from 0 to 1: cjm ¼

cjm  aj : bj  aj

5. Calculation of integral estimates of factors Ck. Two modifications of the calculation of these estimates are proposed: • according to the arithmetic mean (if the expert uses the logical connection “or” to analyze the factors); • according to the geometric mean (if the expert uses the logical connection “and” for the analysis of factors). 6. In addition, one should take into account the unequal importance of experts. To do this, we use weighted expert assessments. 7. Selection of significant factors: in accordance with the chosen method (Pareto method, by values, etc.).

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Recalculation of expert weights (after the final result). If the expert’s weight falls outside the permissible limits, he is denied examination. This mechanism helps to improve professionalism and reduce the involvement of experts. If it is not possible to create a group of experts, then when using one expert, it is recommended to apply the method of paired comparisons, which simulates multiple evaluations of the set of considered alternatives. The mechanisms for organizing such expert assessment are presented in detail in [4]. 4.2

Modified Procedure for Constructing Cognitive Maps Using Morphological Analysis

Cognitive analysis usually starts with building a cognitive map. A cognitologist, based on knowledge and his own subjective experience, assigns concepts to the corresponding cognitive map. This subjective procedure can be formalized by means of morphological analysis (MA). The morphological model of SEO is given by a tuple: M = {Pi, Sk, T, J}, where Pi = {Pij} is a set of structural elements of the model; Sк is a set of connections between structural elements; T is a set of restrictions on the studied variables; J = (J1, J2,…, Jm) is a system of criteria that reflects the interests of all participants in the process. MA makes it possible to form a set of admissible alternatives Bi for the development of SEO. Optimization of the solution to the problem posed can be considered in two versions: complete data certainty (deterministic formulation) and statistical uncertainty. The information obtained as a result of MA is entered in the Table 1 of type 1 (illustrative example).Finding the guaranteed result is carried out according to the formula: Table 1. Decision making matrix. Bj B1 J1 0,8 J2 0,4 … … Jm 0,5

Ji

B2 0,6 0,7 … 0,5

… … … … …

BN 0,5 0,3 … 0,7

Bopt ¼ arg maxj mini Cij

ð1Þ

In our example, taking into account only options 1, 2 and N in the table, and criteria 1, 2 and m, we get Bopt = B2. Morphological analysis, structuring the problem area, provides objective information for cognitive analysis.

Increasing Objectivity in the System Analysis

4.3

37

The Procedure for Coenoses Analysis

Coenoses analysis is applied at the strategic level of the study of complex objects of any nature. It presents a technical, technological, socio-economic object (SEO) of research (regional economy, industry, large corporation, etc.) as a “living” organism with the properties of self-organization, self-improvement. If this state of SEO is achieved, then manual intervention in its work, as well as the negative role of the human factor, will be minimized. The essence of the target audience is as follows. All complex systems (SEO is no exception) under the influence of universal natural laws (conservation laws: energy, mass, matter, momentum; laws of cause-and-effect relationships, etc.) tend to form stable states. The stay of SEO in this state is called cenosis. The structure of cenoses can be described by different distributions. Species distribution is considered — the dependence of the number of species on the number of individuals in the species, the rank-species distribution (rank is the number in order when the species are arranged in decreasing order of abundance) and rank according to the parameter, when the species are arranged in the decreasing order of any parameter. To model the non-increasing function of all three distributions, a hyperbola of the form is used: NðrÞ ¼

A ; rG

ð2Þ

where, in particular, for the rank species distribution N(r) is the number of individuals in the form with rank r, pieces; coefficients A and G are constant distributions. In practice, model (2) is calculated as follows: 1. The research parameter is selected (types of enterprises, products, scientific and educational institutions, educational levels, etc.). 2. By means of passive and / or active experiments, a statistical database is formed that characterizes the selected parameters of the object under study. 3. Based on these statistical data, a histogram of the distribution of the parameter under study is constructed. 4. The histogram is approximated by a curve of the form (2). 5. The approximation error is estimated. 6. Procedure 1 – 4 is repeated for different points in time t1, t2, t3, …. We obtain a family of curves of the form (2) and, accordingly, the series of parameters Ai and Gi. The coenoses analysis model explains a number of performance indicators: – The existing disparities in the size of production and business, which are estimated by the value of the approximation error — point 5 of the algorithm. The smaller it (error), the more SEO is a socio-economic cenosis, since the points of the histogram in this case “more accurately” lie on the approximated curve. – The degree of development of the system (according to the change in the coefficients of dependence (2)). The optimal case is the value G = 1. If the value of G “leaves” from 1, then the cenosis “crumbles”.

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The completeness and high degree of development of the cenosis ensure its stability and efficiency. Cenosis optimization. Let us consider a few of the simplest optimization procedures that have already been well tested in practice. A) Determination of the direction of transformation of the rank species distribution. It is based on the concept of an ideal distribution (matches G = 1). B) Elimination of abnormal deviations in species distribution. As already noted, in the species distribution of the technocenosis, areas of maximum anomalous deviations can be identified. After identifying anomalies in the species distribution, according to the same tabulated distribution, the types of equipment “responsible” for the anomalies are determined, and priority measures are outlined to eliminate them. At the same time, upward deviations from the approximating curve indicate insufficient unification, and downward — on the contrary, excessive. It should be noted that the first and second procedures are interrelated, with the first showing the strategic direction of changing the species structure of the cenosis as a whole, and the second helping to locally identify the “most diseased” zones in the SEO nomenclature (list of species). C) Verification of the nomenclature optimization of the technocenosis. The stability of the cenosis is not always a positive quality, since the cenosis “resists” the necessary changes. Therefore, it is sometimes beneficial to have a quasi-cenosis that adapts to the current tasks of SEO development. That is, we are talking about controlled cenoses.

5 Results of the Interaction of Economic Entities at Different Levels of Management Analysis The theory of active systems [19] develops various mechanisms for managing SEO: complex assessment, active expertise, resource allocation, reverse priorities, financing and incentives, exchange, operational management, tenders, competitions, etc. Below, we comment on the mechanism of reconciliation of the interests of two economic entities within the framework of the formalisms of this theory [26]. It is known that it is possible to approximate the criterion of economic entities activity in the vicinity of the extremum point by a quadratic dependence: y ¼ a0 þ a1
x þ a2
x 2 :

ð3Þ

We transform (3), highlighting the full square, and we get: y ¼ m
ðx  x0 Þ2 þ b:

ð4Þ

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In Eq. (4) the parameters x0 > 0 and b > 0 of the model have a very definite economic meaning: x0 means the optimal value of the enterprise load, at which the maximum revenue is reached, equal to b. The dependence according to statistical data is constructed for the vicinity of the point (x0, b), therefore, a significant meaning of the points of intersection of this parabola with the coordinate axes cannot be derived due to the possible inadequacy of the model in the vicinity of these points. If the economic entities fulfills the plan established by the upper management level, then the reward is carried out according to the identified dependence (4), curve 1 in Fig. 1 (without penalties). For the loading section of the enterprise from x0 onwards, the enterprise tends to underestimate the amount of work performed. Then, if the economic entities does not fulfill the plan, sanctions are imposed on it by proportionally reducing the value of b with the coefficient 0 < k < 1. That is, if the plan is not fulfilled, the enterprise operates according to the model (5) (it corresponds to the curve 2 in Fig. 1): y ¼ m
ðx  x0 Þ2 þ k
b:

ð5Þ

y b kb

1 2

0

x0

xG

Fig. 1. Geometric illustration of the imposition of sanctions on the TP enterprise for failure to fulfill the plan.

Obviously, while the value of the plan is on the interval [x0; xГ] it is also beneficial for the enterprise to fulfill the plan. If the top management level assigns a plan exceeding the value of xГ, then it will be more profitable for the enterprise to sell products (goods, services) in the amount of x0 and receive for this revenue in the amount of kb (if the plan is overfulfilled, it is less). That is, segment ½xO ;xT  is the region of coordinated solutions of the considered economic entities.

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Let us find this area in terms of the known parameters a, k, b, m. To do this, we solve the equation (find the abscissa of the intersection of curve 1 with the horizontal line y = kb): k
b ¼ m
ðx  x0 Þ2 þ b:

ð6Þ

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi b
ð1  kÞ : xG ¼ x0 þ m

ð7Þ

From Eq. (6) it follows:

The parameters of the procedure and make it possible to regulate the relations between the economic entities of different levels of management. The possibility of distorting the data is excluded and acceptable indicators of joint activities of the economic entities are achieved.

6 Results On the basis of the analysis of the logic and technologies of the system analysis of complex socio-economic objects, its subjectivity is substantiated: dependence on the objectives of the study, scientific position, expert knowledge. At the same time, the lack of development of formalized mechanisms of system analysis and the lack of methods for assessing its effectiveness are shown. To solve these problems, it is proposed to use artificial intelligence systems. The analysis of research on the formulated topic is carried out, and the unrealized potential of the developments of Russian scientists, ensuring the creation of intelligent procedures for system analysis, is revealed. In order to deepen the system analysis, all socio-economic objects are classified into three types: socio-economic systems proper (in the narrow sense), socio-economic networks and socio-economic cenoses. This made it possible to clarify the goals of the system analysis, diversify and clarify the technologies for its implementation. The study also distinguishes three forms of manifestation of artificial intelligence: natural intelligence transmitted by a machine, enhanced by its capabilities (practically unlimited memory, speed); intelligence generated by a machine (e.g. neuro-fuzzy models); collective intelligence (people, machines, and other phantom agents can act as “members” of the collective). In terms of improving analytical methods of system analysis, it was proposed to formalize SWOT and PEST analyzes, improve the procedure for constructing cognitive maps using morphological analysis, expand the range of research procedures for cenological analysis, and introduce tools for analyzing the interaction of economic entities of various levels of management. It is proposed to implement the innovations discussed above on the basis of a specially developed system analysis platform.

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7 Discussion On the basis of the analysis of the logic and technologies of the system analysis of complex socio-economic objects, its subjectivity is substantiated: dependence on the objectives of the study, scientific position, expert knowledge. At the same time, the lack of development of formalized mechanisms of system analysis and the lack of methods for assessing its effectiveness are shown. To solve these problems, it is proposed to use artificial intelligence systems.

8 Conclusions The article was deals with the problem of subjectivity of system analysis and insufficient development of mechanisms for assessing its effectiveness. The role of intelligent systems for carrying out a system analysis of complex socio-economic objects has been substantiated as a way to increase its objectivity and effectiveness. The basic concept of the Systems Analysis Platform is presented, including theory, methodology and tools in a new paradigm of artificial intelligence. The proposed approach will increase the objectivity of the analysis of modern socio-economic systems.

References 1. Volkova, V.N., Denisov, A.A.: Teoriya sistem i sistemnyj analiz: uchebnik dlya akademicheskogo bakalavriata [System theory and system analysis: textbook for academic undergraduate studies], 2nd edn., 616 p. YURAYT, Moscow (2014). (in Russian) 2. Qi, L., Xu, Z.: Control theory for stochastic distributed parameter systems, an engineering perspective. Ann. Rev. Control 51, 268–330 (2021). https://doi.org/10.1016/j.arcontrol. 2021.04.002 3. Kuzminov, A.N., Yarovoy, N.A., Filippov, S.V.: Formation of mechanisms for sustainable development of industrial enterprises on the basis of informatization of production activities. Bull. Yuzh.-Ros. State Tech. Un-t (NPI) Series Social Econ. Sci. 1, 27–35 (2017) 4. Adadurov, S.Ye., Gapanovich, V.A., Lyabakh, N.N., Shabel’nikov, A.N.: Zheleznodorozhnyy transport: na puti k intellektual’nomu upravleniyu. Monografiya. [Rail Transport: Towards Intelligent Management. Monograph.] YUNTS RAN, Rostov-on-Don, 322 p. (2010). (In Russian) 5. Fufaev, V.V.: Economic Cenoses of Organizations, pp. 3–38. Center for System Research, Moscow-Abakan (2006) 6. Kudrin, B.I.: Tekhnetika: novaya paradigma v filosofii tekhnologii (tret’ya nauchnaya kartina mira). [Technetics: a new paradigm in the philosophy of technology (the third scientific picture of the world)]. TSU Publishing House, Tomsk, 40 p. (1998). (In Russian) 7. Volkova, V.N., Chernenkaya, L.V., Mager, V.E.: Classification of models in system analysis. Sci. Tech. Statements SPbSPU 3(174), 33–43 (2013). (In Russian) 8. von Bertalanffy, L.: [1933]: Modern Theories of Development: An Introduction to Theoretical Biology. Harper, New York, NY (1962) 9. Beer, S.: Diagnosing the System for Organizations. Wiley, Chichester (1990)

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10. Wiener, N.: Cybernetics or Control and Communication in the Animal and the Machine. Hermann & Cie Editeurs, Paris; The Technology Press, Cambridge, MA; John Wiley & Sons Inc., New York, NY (1948) 11. Schumpeter, J.A.: [1948]: There is still time to stop inflation. In: Clemence, Richard V. (ed.) Essays: on entrepreneurs, innovations, business cycles, and the evolution of capitalism. Nation’s business, vol. 1, pp. 241–252. Transaction Books, New Brunswick, NJ (2009) 12. Ashby, W.R.: Principles of the Self-Organizing System. In: von Foerster, H., Zopf, G.W., Jr. (eds.) Principles of Self-Organization, pp. 255–278. Pergarnon Press, New York, NY (1962) 13. Glushkov, V.M.: Makroekonomicheskie modeli i principy postroeniya OGAS [Macroeconomic models and principles of building OGAS], p. 160. Statistics, Moscow (1975).(In Russian) 14. Ivakhnenko, A.G.: On the problem of constructing an intelligent or thinking engineering computer. Control Systems Machines 2 (2003) 15. Kleiner, G.B.: Socio-economic ecosystems in the light of the systemic paradigm. In: The Proceedings of the Conference organized by the Department of System Analysis in Economy, vol. 6, pp. 5–14. The Financial University under the Government of the Russian Federation, Moscow (2020). (In Russian) 16. Lavrentiev, M.M., Saveliev, L.Ya.: Teoriya operatorov i nekorrektnye zadachi [Operator theory and ill-posed problems], 702 p. Publishing house of the Institute of Mathematics, Novosibirsk (1999). (In Russian) 17. Tikhonov, A.N., Arsenin, V.Ya.: Metody resheniya nekorrektnyh zadach [Methods for solving ill-posed problems], 2nd edn., 285 p. Nauka: Main edition of physical and mathematical literature, Moscow (1979). (In Russian) 18. Granberg, A.G.: Vasilij Leont’ev v mirovoj i otechestvennoj ekonomicheskoj nauke [Vasily Leontiev in world and domestic economic science]. Econ. J. High. School Econ. 3, 471–491 (2006). (In Russian) 19. Novikov, D.A.: Teoriya upravleniya organizacionnymi sistemami [The theory of management of organizational systems], p. 584. Moscow Psychology and Social Institute, Moscow (2005).(in Russian) 20. Pontryagin, L.S.: The maximum principle in optimal control, p. 64. Nauka, Moscow (1989) 21. Resolution of the Government of the Russian Federation of March 2, 2019 No. 234 “Polozhenie o sisteme upravleniya realizaciej nacional’noj programmy ‘Cifrovaya ekonomika Rossijskoj Federacii’ ”. [“On the management system for the implementation of the national program ‘Digital Economy of the Russian Federation’ ”.] (2019). (In Russian) 22. Decree of the President of the Russian Federation of 10.10.2019, No. 490 “O razvitii iskusstvennogo intellekta v Rossijskoj Federacii” [“On the development of artificial intelligence in the Russian Federation”.] [Electronic resource] http://www.kremlin.ru/acts/ bank/44731/page/1 (2019). Accessed 21 Sep 2021. (In Russian) 23. Gorelova, G.V., Karelin, V.P.: Methods of graph theory in cognitive analysis and modeling of socio-economic systems. Bull. Taganrog Inst. Manag. Econ. 1, 74–78 (2005) 24. Kuzminov, A.N., Korostieva, N.G., Khazuev, A.I., Ternovsky, O.A.: Fuzzy-multiple modeling for the analysis and forecasting of economic cenosis. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M.B., Sadikoglu, F.M. (eds.) ICSCCW 2019. AISC, vol. 1095, pp. 749–757. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-35249-3_97 25. Kuzminov, A.N., et al.: Interdisciplinary foundations for the study of large-scale economic systems based on the theory of cenoses: monograph. In: Kuzminov, A.N. (ed.) Publishing and printing complex of the Russian State Economic University (RINH), 238 p. Rostov-onDon (2018) 26. Lyabakh, N.N., Kolesnikov, M.V., Bakalov, M.V.: Modeling the activity of transport enterprises. Bull. Rostov State Trans. Univ. (RSTU) 1(69), 72–77 (2018). (in Russian)

Business Ecosystem Strategy: Design and Specifics George Kleiner1,2

and Alexander Kobylko3,4(&)

1

Department of modeling of industrial objects and complexes, Central Economics and Mathematics Institute of the Russian Academy of Sciences, 47 Nakhimovsky Avenue, 117418 Moscow, Russia [email protected] 2 Department of System Analysis in Economics, Financial University Under the Government of the Russian Federation, 49 Leningradsky Prospekt, 125993 Moscow, Russia 3 Laboratory of Publishing and Marketing, Central Economics and Mathematics Institute of the Russian Academy of Sciences, 47 Nakhimovsky Avenue, 117418 Moscow, Russia [email protected] 4 Department of Management, State Academic University for the Humanities, Maronovsky Lane 26, 119049 Moscow, Russia

Abstract. The article explores the creation of a business ecosystem strategy. This is a special kind of strategy. The ecosystem has business organization features. This specificity reveals with the help of systemic economic theory. These are companies and products with different spatial and temporal characteristics. They are a common platform, business incubator, cluster, and network. Ecosystem management is the soft control and soft coordination of firms while respecting their independence. This cancels the hierarchy and activates equal interaction of all elements. It is a tool for combining and coordinating the actions of different firms. The ecosystem strategy is based on these features. This type of strategy describes trends and vectors of the development of companies and products that enter the ecosystem. This strategic design describes as theses without time limits. This forms the goals for the corporate strategies of firms. Keywords: Strategy
Ecosystem strategy
Business ecosystem design
Strategic planning
System economic theory


Strategic

1 Introduction Many global companies such as Google (as a part of Alphabet), Apple, Amazon, Alibaba, etc. are building ecosystems now. Large enterprises from different industries are also developing as business ecosystems. If to mention those in Russia: Sber (Sberbank) and Tinkoff — finance, MTS and Beeline (as a part of VEON) — telecom, Yandex and VK (former Mail.Ru group) — IT, etc. M. Rothschild [1] proposed the concept of bionomics: to consider the economics from the standpoint of a biological ecosystem. J. Moore used the concept of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 43–51, 2022. https://doi.org/10.1007/978-3-030-98832-6_4

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ecosystem in his work on enterprise management [2]. He considered consumers and producers as interrelated and complementary actors in this process. The business ecosystem phenomenon was studied from a common point of view. Ecosystem management has become a part of corporate governance by now. Ecosystem development functions are included in the top management functional areas. This type of management is considered as the next level of a holding or corporation. Often this functionality depends on the main or traditional product of the ecosystem. We believe that an ecosystem is an independent unit of management. Therefore, it is necessary to develop independent approaches to management. One of them is an approach to the form of strategic planning and directly ecosystem strategy. It is reliance on connections between its partners acting as the ecosystem elements. These issues are analyzed in terms of systems economic theory [3]. We consider a company as a system and as a part of a system [4]. It is a set of four types of systems, classified according to space-time characteristics.

2 Materials and Methods There are four types of companies classified according to space and time. These types have different approaches to the organization of strategic management. They may differ in the specifics of planning, the duration of the strategy, its details, financial indicators, etc. For example, a plant has a space limit as a certain unit of business. Its product is stored in a specific place. The telecom operator provides services far beyond the company. It belongs to the environmental type of the firm. A university is a process-type company. It has cycles of education as time limit but has no spatial limits of knowledge students (or customers) may gain. In addition, any project has a time limit, because the contract is to be completed by a pre-set date, e.g. Olympic game, building construction, etc. A system view reveals the strategy design elements. It is the duration of performance, structure, and granularity of strategy components. This is the basis for recommendations for the strategy type formation (Table 1). Table 1. Company type and strategy type recommendation [3]. Company type Object type company Environmental type company Process type company Project type company

Strategy type (description) Environmental type strategy (internal and external environment, and the actions of the company itself) Object type strategy (strategy created by a certain company) Process type strategy (annually revised plans with a limited period) Project type strategy (a plan for the contract duration)

Specification Non-deadline strategy with significant changes in conditions Outsourcing and strategic consulting Fixed-period rolling planning Non-strategic variety

The design is a combination of the strategic structure and its external attributes. The strategic structure shows the presence or absence of strategic goals, objectives, decisions,

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plans, and schedules, etc. The attributes include the degree of content secrecy; the level of detail of indicators; the person in charge of the strategy development and familiarity with it (top management only, all employees, external experts); planning horizon (limited or unlimited); the strategy execution control measures and their frequency. Object type of company applies a perpetual strategy that changes significantly in the external or internal enterprise environment. The medium type of company uses outsourcing and transfers this function to strategic consulting. The process type of company has cyclical activities and uses rolling planning. The activity of the project type of company lasts only for a fixed period pre-set by a contract, which has nothing to do with strategic planning. We will similarly define an ecosystem. It is a set of enterprises, products, etc., that have space-time characteristics. An ecosystem is a complex of all systemic types of companies. M. Jacobides identified three approaches to defining an ecosystem as a business organization and described them in a large study [5]: a cluster of companies, a technological platform, and a range of goods and services. The first group within this concept determines an ecosystem as a group of different companies. An ecosystem can be defined as an economic community of actors that interact through their activities [6]. Other authors insist that the effectiveness of ecosystem individual elements directly depends on the effectiveness of the interaction of the whole set [7]. Companies affect each other. The result is the formation of a new market or industry. The second group defines an ecosystem as an interaction between the platform owner, its customers, and product suppliers [8, 9]. Such interaction may result in technological products: services, technological solutions, and goods based on the platform. The content makes the platform valuable and useful both for suppliers and customers. Researchers consider such an ecosystem as a collection of four agents that create its structural framework [10]. Owners control intellectual property and manage the ecosystem. Providers operate the platform and communicate with counterparties. Suppliers create their products based on the platform. Customers consume these products. The third group considers an ecosystem as a cooperation mechanism between suppliers. They combine their products and services to create fundamentally new complex products [11–13]. The partnership forms a common strong and broad product that is more interesting to the consumer. To use all the services and goods within a package offered by a business ecosystem can be more convenient and cheap than to use every single one as a separate offer. These approaches can be combined. Systems economic theory defines a business ecosystem as a set of properties [14]: 1. It is a complex of enterprises localized in space. 2. There is no hierarchical control of enterprises, business processes, innovation projects, and infrastructure systems. 3. All elements interact with each other to create and distribute material and symbolic goods and values. 4. Such groups can function long and independently due to the circulation of goods and systems.

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This approach characterizes an ecosystem as a group of the following subsystems: • • • •

a cluster as an object subsystem; a platform as an environment subsystem; a network as a processing subsystem; an incubator as a project subsystem.

This complex is what makes the main difference between a business ecosystem and a large corporation. It is a complex of all four functional units. This includes all companies, products, technologies, etc., and meeting the needs not only of the customers but those of the ecosystem itself. Thus, a looped system is created. This complex can be measured by a peer review. Expertise can reveal if a business has become an ecosystem and to what degree. We must pay attention to an important point. Object, environment, and process subsystems create products for customers. The project subsystem creates a product for the ecosystem itself: forecasts, analysts, and internal control mechanisms (see Fig. 1). The management of the entire ecosystem and the communications between companies are carried out through it. One of these functions is to search the development plans.

Fig. 1. Functional vectors of subsystems: primary and secondary.

How are the management decision and strategies created? There is no hierarchy in this scheme. All ecosystem elements are identical. There is no superiority of management over other functions. It cannot make decisions unilaterally and broadcast them to other subsystems. They do it all together. Each company has the expertise of its market. Therefore, the solutions are made on a basis of two-way communication between the design subsystem and the other. Management decides to collect and analyze information, research and identify threats, and develop instructions. They ensure the reproduction of the entire ecosystem.

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It is a product for internal consumption. Independent expertise of the developed solutions can be carried out but within the ecosystem. An expert examines the problem of a set of companies and a complex product.

3 Results Many researchers doubt if ecosystem management is being conducted directly. This idea was confirmed [15; 16; 17, p. 220]; 18]. This does not mean that the owner or the ecosystem founder gives up the business management. This is a horizontal interaction as opposed to the vertical hierarchy in classical corporate management [19, p. 19]. A founder starts a partnership that interacts on equal terms. Therefore, an ecosystem strategy should carefully coordinate the activities of its elements. The companies of an ecosystem are only limited by a unique philosophy of a complex product. All other planning types are performed within the limits of trends and markets in which they operate. A part of the company’s strategy relates to the ecosystem targets; another part concerns the interests of a company in the market. For example, there are limitations in marketing, brand development, and the extent of the supply of services for the ecosystem. The company meets these limits but creates an independent competitive strategy. A business ecosystem is present in all sectors of a basic branch. Large horizontal, vertical and diagonal integration processes make it possible. However, the ecosystem goes beyond the basic branch. This process forms a new scale of business organization. This is a supra-branch. Ecosystems shape an industrial level. However, we can identify features common to any ecosystem. These are IT, telecommunications, financial, media, and entertainment services. The systems approach shows that a corporation transforms into an ecosystem when it meets these requirements. For example, it should have: • • • •

a set of complementary goods and services united by a single philosophy; a technological platform for distributing their products; developed communications between different elements; corporate university, business incubator, educational projects for training employees and clients; • horizontal control, etc. All these function elements are specific to management, strategic planning, marketing, etc. However, the design subsystem must take a unified approach to manage and coordinate a complex product and its strategy. The ecosystem is not a microeconomic phenomenon or even a mesoeconomic one. J. Moore [2] wrote that the concept of a branch was outdated. Because it does not include cross-branch communication companies. Ecosystem companies are an entity of that type [20]. Therefore, the ecosystem forms new rules for management and marketing [18]. Ecosystem strategy is also formulated differently than company strategy. The ecosystem strategy is consistent with the strategies of the firms included in it as follows. Let us present the firm’s strategy according to Thompson and Strickland [21].

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It is a set of corporate, business, functional and operational strategies. From this point of view, a strategy is a hierarchy of its constituent parts: from corporate strategy to lower levels. An ecosystem strategy is a tool that brings together and harmonizes the actions of different companies. It works for corporate strategies only (see Fig. 2).

Fig. 2. The Impact of Ecosystem Strategy on Firms’ Strategies.

This is how the ecosystem and firms agree on solutions internally. Ecosystem strategy is a set of development vectors. These vectors set the general direction for the entire ecosystem development. They coordinate the actions of companies, products, departments, and so on. Ecosystem strategic decisions are the targets of corporate strategies, etc. (see Fig. 3).

Fig. 3. Strategic targets transfer.

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The specific features of ecosystem management are: • an industrial level of problem research, • an ecosystem complex product expertise. The industrial level is another area of problem analysis. It has its characteristics and specific features. Therefore, the strategy will have features like management at the industrial level. These features are in its design. The ecosystem strategy contains the following indicators: • • • • • •

unifying approaches to planning of the activities of all companies, the concept for a complex product, determination of the vectors of development of companies, small document volume, unlimited execution time, gradual updating of individual targets.

4 Discussion Managers usually interpret the ecosystem as an addition to the main product of a company. Nowadays companies take different approaches to creating strategies [22, 23]. The ecosystem strategy is formed similarly. It is created according to the classical rules of forming a corporate strategy. However, a systematic approach to management is confirmed in business practice. Google was transformed into the Alphabet conglomerate in 2015. The reason for this restructuring was the desire of the owners. They wanted to develop all of the company’s services equally rather than go on with core products. An alphabet is more a coordinating unit than a decision-making center for all subsidiaries. Local companies have experts in the specifics of a particular market and region. Subsidiaries care about management. Google as a business ecosystem is a collection of objects, environment, processes, and project services. Services have various properties. They are limited or unlimited in time and space. Their base is information technology. The conglomerate has an ecosystem too. There are different types of companies and products in it, but not only IT. The company management forms the policy of behavior and coordination of these companies for the long run. Another example of a similar approach is the VEON holding that unites telecommunication assets in Europe and Asia. This is not only a telecom operator, within which product ecosystems operate. VEON includes several standalone financial and information technology services. These as a development concept are the holding’s strategy. They indicate priorities and vectors that are important for the entire holding. If we compare the strategy of VEON and the strategies of local companies, the difference will be obvious. Local companies have a large-scale strategy with clear numerical indicators. It is detailed, it has a fixed deadline, etc.

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The holding uses consultative mechanisms at the international level and broadcasts development priorities to local companies. This should lead to the implementation of common targets while preserving the interests of the holding companies in each region. They develop the strategies of companies independently. They base on their management infrastructure. The operators have experience and expertise in the specifics of a particular market. They must implement these priorities as part of their corporate strategies. Lower-level strategies follow the ecosystem targets and the targets of the local companies. These examples show the practice mechanism for realizing the general targets of an ecosystem and the interests of its elements.

5 Conclusions We can summarize that an ecosystem strategy is more a vision of business development than a strategy as a form of achieving goals. This is a new form of long-term management of a group of companies and their products. A smart balance is needed to maintain coordination and enable companies to grow in their areas and markets. Ecosystems operate at a higher level that includes sectors from different branches in which companies operate. Each of them is unique and shapes an industry unlike any other. Ecosystem management is not considered as an independent direction and is based on classical approaches to management now. However, specific features dictate a need to develop a theory of ecosystem management. Acknowledgment. The paper was funded by the Russian Science Foundation (RSF) within the framework of the scientific project No. 19–18-00335 “System optimization of the modern economy structure: Ecosystems, clusters, networks, business incubators, platforms”.

References 1. Rothschild, M.: Bionomics: Economy as Business Ecosystem. Beard Books, Washington, D. C. (1990) 2. Moore, J.F.: The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems. Harper Business, New York (1999) 3. Kleiner, G.B.: A new theory of economic systems and its applications. Her. Russ. Acad. Sci. 81(5), 516–532 (2011) 4. Rybachuk, M.A.: Strategic planning as a method for solving the problems of inter-level interaction of economic systems. In: System Analysis in Economics – 2018. V International research and practice conference-biennale, pp. 91–94. Prometheus publisher, Moscow (2018) 5. Jacobides, M.G., Cennamo, C., Gawer, A.: Towards a theory of ecosystems. Strateg. Manag. J. 39(8), 2255–2276 (2018). https://doi.org/10.1002/smj.2904 6. Teece, D.J.: Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strateg. Manag. J. 28(13), 1319–1350 (2007)

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7. Iansiti, M., Levien, R.: The keystone advantage: what the new dynamics of business ecosystems mean for strategy, innovation, and sustainability. Harvard Business School Press, Boston, MA (2004) 8. Ceccagnoli, M., Forman, C., Huang, P., Wu, D.J.: Co-creation of value in a platform ecosystem: the case of enterprise software. MIS Q. 36(1), 263–290 (2012) 9. Gawer, A.: Bridging differing perspectives on technological platforms: toward an integrative framework. Res. Policy 43(7), 1239–1249 (2014) 10. Alstyne, M., Parker, G., Choudary, S.: Pipelines, Platforms, and the New Rules of Strategy. Harvard Business Review. https://hbr.org/2016/04/pipelines-platforms-and-the-new-rulesof-strategy (2021). Accesed 14 Oct 2021 11. Adner, R.: Match your innovation strategy to your innovation ecosystem. Harv. Bus. Rev. 84 (4), 98–107 (2006) 12. Adner, R.: The Wide Lens: A new strategy for innovation. Penguin, London (2012) 13. Kapoor, R., Lee, J.M.: Coordinating and competing in ecosystems: how organizational forms shape new technology investments. Strateg. Manag. J. 34(3), 274–296 (2013) 14. Kleiner, G.B.: Ecosystem economy: step into the future. Econ. Revival Russia 1, 40–45 (2019) 15. Jacobides, M.G., Sundararajan A., Alstyne M.: Platforms and Ecosystems: Enabling the Digital Economy. http://www3.weforum.org/docs/WEF_Digital_Platforms_and_Ecosystem s_2019.pdf (2021). Accessed 16 Aug 2021 16. Zakharov, V.Ya., Trofimov, O.V., Frolov, V.G., Novikov A.V.: The management of the ecosystem: integration mechanisms of the companies in accordance with the concept of “industry 4.0”. Leadership and Management 6(4), 453–468. (2019). https://doi.org/10. 18334/lim.6.4.41197 17. Markova, V.D., Kuznetsova, S.A.: Digital economy and the evolution of strategic management. Tomsk state university. J. Econ. 48, 217–232 (2019). https://doi.org/10. 17223/19988648/48/15 18. Kobylko, A.A.: Management functions in business ecosystems. ECO 8, 127–150 (2021). https://doi.org/10.30680/ECO0131-7652-2021-8-127-150 19. Ramenskaya, L.: The concept of ecosystem in economic and management studies. Upravlenets 11(4), 16–28 (2020). https://doi.org/10.29141/2218-5003-2020-11-4-2 20. Kobylko, A.: Telecommunication ecosystems: Special features of management and interaction. Upravlenets 11(1), 15–23 (2020). https://doi.org/10.29141/2218-5003-202011-1-2 21. Thompson, A., Strickland, A.: Strategic Management: Concepts and Cases. McGrawHill/Irwin, New York, NY (2001) 22. Alstyne, M.: The opportunity and challenge of plarforms. In: Jacobides, M., Sundararajan, A., Alstyne, M. (eds.) Platforms and Ecosystems: Enabling the Digital Economy. http://www 3.weforum.org/docs/WEF_Digital_Platforms_and_Ecosystems_2019.pdf (2021). Accessed 17 Oct 2021 23. Karpinskaya, V.A., Rybachuk, M.A.: The genesis of the ecosystem form of production organization in a modern economy: Factors and results. J. Econ. Regul. 12(2), 85–99 (2021). https://doi.org/10.17835/2078-5429.2021.12.2.085-099

Scientific Basis of Management and Cybernetics Methodologies Integration Boris V. Sokolov(&)

and Rafael M. Yusupov

St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), 14th Line Vasilievsky Island, 39, 199178 St. Petersburg, Russia [email protected], [email protected]

Abstract. The problem of integrating management methodologies and cybernetics to improve the efficiency of proactive management of socio-cyberphysical systems (SCPS) is described in a meaningful and formal way. It is shown that this problem belongs to the class of problems of multi-criteria dynamic structural and functional synthesis of the SCPS appearance and the formation of appropriate programs for managing their functioning and development. At the same time, the constructive definition of the necessary control actions that ensure limited self-organization and controlled instability of the SCPS lies in the path of ensuring the dynamic correspondence of the variety of states of both the external environment and the corresponding control system (CS) of the SCPS. As a methodological basis for integrating management theory and cybernetics, the authors propose the scientific foundations of neocybernetics. As the basic concepts in the developed methodology, the concepts of complex modeling, proactive intellectual management and control are proposed. Neo-cybernetics methodological basis development required from the report’s authors to move to a fundamentally new (compared to classical approaches) level and technologies of management processes organization, specifically to the level of complexity control. In order to implement this control concept two directions are suggested — to narrow external environment variety impact on SCPS and to expand variety of the control impact on SCPS. Keywords: Cyber-physical system
Socio-cyber-physical system modeling
Proactive intelligent control


Integrated

1 Introduction The major object of studies in the report is represented by socio-cyber-physical systems (SCPS) — complex interlinked network structures — that include both the existing physical objects and relevant information-control (cybernetic) systems, as well as social (organizing) structures, that define purpose of the above mentioned systems and act as the major consumers of their performance results [1]. Traditionally, such objects as CPS are studied by conventional cybernetics, while social systems and their control organization are analyzed in management [2–6]. In contrast to CPS’s, their active social subsystems are beginning to play a crucial role in SCFS’s. SCFS naturally integrate © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 52–59, 2022. https://doi.org/10.1007/978-3-030-98832-6_5

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computer and info-communication resources with the subjects who use them in their daily activities, which makes these systems attractive in terms of organizing the harmonious interaction of natural and artificial intelligences. In the modern world, characterized by abundance of information, data and knowledge, cognitive abilities of a person, making decisions independently, became insufficient. A man can no longer take into account all necessary information when making decisions. There is too much data and one has to look at it as a “big picture” — compare elements, analysis and make predictions. A human can take days or even weeks to process data that a computer can handle in tens of minutes. There are two solutions to this problem: to transfer all human functions to computer and infocommunication resources or to enhance human intellectual capabilities with the help of these resources. The task of processing and analyzing big data is left to computer and info-communication resources, but the final decision on their use in SCFS is left to the actors (humans). The implemented analysis as well showed that at all stages of SCPS life cycle, their structural dynamics is observed, caused by objective, subjective, external, internal reasons and their combinations. In order to ensure task-oriented character of the above mentioned processes, it is required to make them operable, which means to ensure proactive management of the structural dynamics [5, 7–11]. Thus, the major subject of studies in the report is the methodological and methodical basis for SCPS structural dynamics proactive management. On the whole, development of scientific principles for integrating management and cybernetics methodologies is aimed at increasing the level of sustainability and quality of functioning and development of modern and prospective SCPS.

2 Neo-Cybernetics Methodological and Methodical Basis As studies revealed, three fundamental system-cybernetic concepts were used as basic concepts of neo-cybernetics — the concept of SCPS complex (systematic) modeling, that suggests development and implementation of new principles and approaches to conduct multiple-model logical-dynamic description of different variants to build and use SCPS, as well as development and combined usage of methods, algorithms and methodic of multi-criteria analysis, synthesis and selection of the most preferable proactive management solutions (including the ones oriented to their reconfiguration), related to creating, usage and development of the considered objects under various conditions of the dynamically changing external and internal environment. The concept on proactive management of SCPS structural dynamics under changing conditions, caused by influence of disturbing environment, was chosen as the second neocybernetics basic concept [5, 11, 19–21]. Unlike SCPS reaction control traditionally applied in practice, that is aimed at rapid response to the already occurred events and their further development prevention, proactive management assumes proactive prevention of the causes of accidents by creating (or implementing targeted search) new system-functional reserves in the relevant system for proactive monitoring and control, providing dynamic forming of completely innovative possibilities on dealing with possible expected and unexpected abnormal and emergency situations, applying

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methodology and technologies of system (complex) modeling, as well as multivariate situational-adaptive forecasting. One more concept used by the authors of the report is the concept on SCPS control intellectualization, which suggests that in order to effectively control CSPS it is required to apply intellectual management tools (new intellectual information technologies), that are of a bright innovative character and aimed at complex integration of natural and artificial intelligence. Along with developed methodology the report represents multiple-model logical-dynamic description of tasks on synthesis of technologies and programs for SCPS proactive management, as well as combined methods and algorithms for joint and split solutions for the described tasks, and tasks for estimating possibilities and ensuring the required (optimal) level of indicators on reliability, persistence, sustainability and general efficiency of the considered information management processes [18, 22, 23]. Neo-cybernetics should ensure integration and further development of knowledge, that has been received so far in the framework of modern management and cybernetics oriented to solving the existing and prospective problems on SCPS proactive management. The conducted studies revealed that, along with the listed concepts, the neocybernetics methodology should be completed with structural-mathematical and category-functional approaches, as well as systematic approach and its concepts and principles, that include: principles of nonterminal decisions, variety absorption, hierarchical compensation, complementarity, multiple-models and multi-criteria descriptions, self-similar recursive notation and modeling of the studied objects, homeostatic interaction balance; overcoming the distribution principle. Neo-cybernetics methodological basis development required from the report’s authors to move to a fundamentally new (compared to classical approaches) level and technologies of management processes organization, specifically to the level of complexity control. In order to implement this control concept two directions are suggested — to narrow external environment variety impact on SCPS and to expand variety of the control impact on SCPS.

3 Example of the Implementation of the Developed NeoCybernetics Scientific Basis In conclusion, we consider a small example on implementing the developed neocybernetics scientific basis [5, 11, 18, 21, 23]. Analysis of the modern development state in the field of practical implementation of the suggested information technology (IT) and system for SCPS proactive monitoring and control shows that currently there are at least three major areas for introducing the considered methodology in practice. Dynamic expert systems, that are currently quite widespread, are referred to the first area. Among them, we can outline G2 (Gensym, USA, represented in Russia by Dubna Technopark LTD http://www.ntpdubna.ru), product family IBM WebSphere ILOG JRules BRMS. The second area includes results, received within the so-called theory of undefined calculations (based on the method of constraint satisfaction — constraint programming) and theory of multi-agent intellectual systems. The integrated software product

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SPRUT, intellectual mathematical problems solver UniCalc can be mentioned as the most common representatives of programme complexes, that support these areas of research. The third area of research includes the so-called systems for data collecting and management — SCADA-systems (Supervisor Control And Data Acquisition — systems for data collecting and management, systems of operator’s interface etc.). Programme complexes Genesis, IsaGRAF, TraceMode are examples of this area implementation. In case application of the above described software is inappropriate (lack of certification, lack of support for the applied platform etc.), a decision is taken to develop unique programmes. The scheme for developing SCPS software in such case is as follows (see Fig. 1) [5].

Fig. 1. Conventional scheme of development process for SCPS application software.

Within this approach there appears a number of problems, including: communication at the stage “Expert in the subject matter area — Systems analyst” — is related to the specific character of the subject field; quality of the developed software — correct definition of the task set for the programmer depends on the systems analyst. Correctness (failure robustness) of implementing algorithms suggested by the specialist in the subject matter area and processed by the systems analyst to a great degree depends on a programmer’s qualifications; the created software support — at a certain stage of the program development and application the support of the developed code takes a large amount of time. This requires additional human and time resources. Support becomes more complicated if the above mentioned specialists are replaced.

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The well-known approaches lack efficiency or do not provide solutions for a whole number of major problems, that are set for the theory and practice of creating a system for SCPS proactive monitoring and control (lack of a unified conceptual basis in building information systems, as it is fundamentally impossible to implement formal description of all the possible SCPS kinds, as well there is a big amount of forms for data provision, and, thus, types of models for delivering knowledge (MDK) on SCPS, insufficient inclusion of the estimating regime in the real time (RT) etc.). For implementation the proposed methodology of neo-cybernetics, intelligent information technology was developed [5]. The main content of which is shown in Fig. 2.

Fig. 2. Proposed new intellectual information technology.

This technology has received its practical implementation in the form of a software package that solves a large complex of interrelated applied problems related to monitoring and control of the SCFS. In Fig. 3 shows the main functions performed by this software. In general, the developed software package can be simultaneously classified as intelligent SCADA and MES systems [5]. Its structure is shown in Fig. 4. The suggested intellectual IT, built on the developed neocybernetics methodology and oriented to usage of DK on the object of monitoring (OM), allowed to sufficiently reduce the time terms and expenses for creating and modifying the systems on monitoring and control over states of complex technological objects and processes.

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Fig. 3. The main functions performed by proposed software.

Fig. 4. General structure of software complex for monitoring and control of SCFS.

4 Results Within the conducted research an analysis was held to define the modern state of studies on problems of integrating management and neocybernetics methodologies, as well as developing and using new SCPS classes on its basis. It is suggested to develop conceptual and methodical foundations of neocybernetics as the fundamental scientific area, that provides solutions for the whole spectrum of existing and possible application tasks, related to SCPS functioning organization.

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5 Discussion The analysis held in the report showed that the tasks on proactive (preventive) management of SCPS structural dynamics, based on their contents, are referred to the class of tasks on multi-criterion dynamic structural-functional synthesis of SCPS design and forming relevant programmes for managing their functioning and development. The existing specific approaches to solving the listed tasks assume anchoring some SCPS structures’ classes (i. e., technical, topological) and other classes’ parameters and structures optimization (i. e., optimization of structures on SCPS management technologies). At the same time within these approaches analysis of the occurring discrepancies from such decompositions is not usually held. Thus, it is suggested to conduct constructive search for the required structures and control actions in the considered tasks, ensuring limited self-organization and controlled instability of SCPS, implemented on the basis of providing states dynamic compliance of both external environment and relevant SCPS control system (CS). Authors suggest implementing the theoretical basis of this approach in the frameworks of the developed neocybernetics.

6 Conclusion Within the suggested report, based on the analysis of modern tendencies, occurring in the system industry of scientific knowledge and caused by the new stage of modern management and cybernetics development and integration, there was formed a new relevant fundamental scientific problem of developing scientific basis for new theory — neocybernetics, aimed at solving tasks on proactive intellectual management of social-cyber-physical systems (SCPS) structural dynamics management, and ensuring methodological, methodical and technological basis of complex automatization and intellectualization of the existing and perspective management systems, oriented to managing socio-economic processes and systems, implementing control over complex technical objects, that have to function coherently and efficiently in the frameworks of SCPS. Acknowledgement. The research described in this paper is partially supported by the Russian Foundation for Basic Research (grants 20-08-01046), state research FFZF-2022-0004.

References 1. Mancilla, R.: Introduction to socio-cybernetics (Part 1). J. Socio-Cybernetics 42(9), 35–36 (2011) 2. Edward, A.: Lee cyber physical systems: design challenges. In: International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), May 6, pp. 245–257. Fl, Orlando, USA (2008) 3. Khitsenko, V.E.: Self-organization: Elements of Theory and Social Applications. KomKniga, Moscow (2005)

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4. Chernyak, L.: From adaptive infrastructure – to adaptive enterprise. Open systems 10, 32–39 (2003) 5. Okhtilev, M.Y., Sokolov, B.V., Yususpov, R.M.: Intelligent Information Technologies for Monitoring and Controlling the Structural Dynamics of Complex Technical Objects. Nauka, Moscow (2006) 6. Novukov, D.A.: Theory of Control in Organization Systems. PHISMATLIT Publishing house, Moscow (2012) 7. Beer, S.: Brain of the Firm. URSS, Moscow (2005) 8. Beer, S.: Cybernetics and Management. URSS, Moscow (2007) 9. Gerasimenko, V.A.: Informatics and integration in technology, science and knowledge. Foreign Radio Electronics 05, 22–42 (1993) 10. Hyotyniemi, H.: Neocybernetics in Biological Systems. Helsinki University of Technology, Control Engineering Laboratory, Espoo (2006) 11. Yusupov, R.M., Sokolov, B.V.: Problems of cybernetics and informatics development at the modern stage. In: Cybernetics and Informatics. SPbSTU Publishing house, St. Petersburg, pp. 6–21 (2006) 12. HP Utility Data Center. Technical White paper, October 2001 (2001) 13. HP virtualization. Computing without boundaries or constraints. Enabling an adaptive enterprise. Hewlett-Packard (2003) 14. Building an adaptive enterprise. Linking business and IT, October 2003, Hewlett-Packard (2003) 15. Wong, K. Sycara: A taxonomy of middle agents for the internet. In: Proc. 4th Int. Conf. Multiagent Systems, IEEE CS Press (2000) 16. Chernak, L.: From adaptive infrastructure to adaptive enterprise. Open Syst. 10, 32–39 (2003). (In Russian) 17. Volkov, D.: IT in the era of “democratization”. Open Syst. 19–25 (2003). (In Russian) 18. Ivanov, D., Sokolov, B., Potryasaev, S., Chen, W., Dolgui, A., Werner, F.: A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints. IISE Trans. 53(1), 21–38 (2021). https://doi.org/10.1080/24725854.2020.1739787 19. Pavlov, A.N., Kovtun, V.S.: Cognitive-synergetic approach to the design of automated spacecraft with onboard systems with variability properties. In: Proceedings of Models and Methods for Researching Information Systems in Transport 2020 (MMRIST 2020 St. Petersburg, Russian Federation, Dec. 11–12, 2020. – CEUR-WS 2021, vol. 2803, pp. 76–83 (2020). https://doi.org/10.24412/1613-0073-2803-76-83 20. Sokolov B., Murashov D., Kofnov O.: Domestic intelligent information and analytical platform and its use in transport. In: CEUR Workshop Proceedings, vol. 29 24, pp. 50–57 (2021). https://doi.org/10.24412/1613-0073-2924-50-57 21. Mikoni, S.: Model of Stakeholders of the Socio-Cyber Physical System Life Cycle. In: Smirnov, N., Golovkina, A. (eds.) Stability and Control Processes, Lecture Notes in Control and Information Sciences – Proceedings. Chapter 67, pp. 1–8 (2022). https://doi.org/10. 1007/978-3-030-87966-2-67 22. Zakharov, V., Mikoni, S., Salukhov, V., Zaytseva, A.: Corporate information system modernization during enterprise digital transformation. In: Dmitry, G.A., Nabil, A., Ludger, O. (eds.) Cyber-Physical Systems and Control II. CPS&C’2021. Lecture Notes in Networks and Systems. Springer, Cham (2022) 23. Musaev, A.A., Borovinskaya, E.: Evolutionary optimization of case-based forecasting algorithms in chaotic environments. Symmetry 13(2), 301–317 (2021). https://doi.org/10. 3390/sym13020301

System Analysis of the Russian Space Future Georgy G. Malinetskiy

and Vladimir Smolin(&)

Keldysh Institute of Applied Mathematics, Miusskaya Square 4, 125047 Moscow, Russia {GMalin,Smolin}@keldysh.ru Abstract. From 1972 to the present, the “space pause” continues — the piloted ships in it do not rise above the low earth orbit. At the same time, space has fundamentally changed the information field of mankind. Large-scale plans promoted since the 1960s for the space mastering, were gradually canceled. The place of Russia in the world space industry development has changed. The country that opened the way to space for the world is now playing a “space taxi” role to a large extent. It is necessary to shift priorities to space information and technological programs, to give preference to devices and systems using artificial intelligence for working in space. And the main thing is not to solve shortterm tasks, but to dream of great goals and implement plans to achieve them. The article purpose is to reveal the reasons for the “space pause” and try to outline the ways to bring out of crisis the Russian space industry. Different methods were used: system analysis, management theory, self-organization theory, artificial intelligence theory, international comparisons, the future designing methods. Keywords: Space project


Artificial intelligence
Space strategy of Russia

1 Introduction Space exploration has played a huge role in the development of the science and culture of mankind. The transition from the Middle Ages to the New Time was largely associated with the shift in emphasis from comprehending God to his creation study — the cosmos. The discovery of Kepler's laws and the understanding that the planets’ orbits are determined by ellipses, known to the Greeks, showed the most important role of the Universe quantitative understanding, convinced that “the great book of Nature is written in mathematical language”. For many centuries, space has been the realm of utopia and mankind’s great hopes. The history of the 20th century provided 2 key scientific and technical projects — Space and Atomic. They have been providing strategic stability in the world for more than 70 years. A huge role in these projects’ implementation was played by the Institute of Applied Mathematics of the Academy of Sciences of the USSR, the founding fathers of which were academicians M.V. Keldysh, I.V. Kurchatov, and S. P. Korolev. Academician M.V. Keldysh was often called “the chief theoretician of cosmonautics”.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 60–73, 2022. https://doi.org/10.1007/978-3-030-98832-6_6

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The following results are substantiated: 1 The world space industry is at a bifurcation point, which can be traversed in different ways — either in the post-capitalism direction, which is being put forward by the leaders of the Davos Economic Forum, or in the post-industrial direction [1]. 2 Conceptual changes in space strategy and the space transformation into a large project of the 21st century can be associated with the forced use of artificial intelligence systems in this area. 3 The Russian space industry transformation should be associated with a shift in emphasis from launching the construction of rockets and spaceports to the field of receiving and processing space information. 4 In space military use, a revolution is taking place associated with the tens of thousands of satellites launching in low orbits, opening up the prospects for “deserted wars”. 5 The most important scientific perspective is the existing space systems and promising devices modernization based on artificial intelligence systems using nextgeneration neural networks for the planets and their satellites study [2]. According to Elon Musk, historians in the future in the 15th century will remember the name of only one person — Christopher Columbus, who gave the West a huge new space, and in the 20th century — Yuri Gagarin, who opened the way for the world to space. Musk hopes to be remembered in the 21st century if he is the first to step onto the surface of Mars.

2 Problem Statement The pause in the space industry stemmed from poor governance and poor performance measurement criteria. For example, 44 years have passed without the delivery of lunar soil to Earth (from “Luna-24” on July 20, 1976, to “Chang’e-5” on December 16, 2020). 40% of the created American Shuttles crashed for technical reasons. Outstanding geochemist, academician E.M. Galimov called published in a scientific series the review of the new Russian space activities results “20 years of fruitless efforts”. In those years, NASA spent $ 18 billion a year, Russia — $ 3.5 billion, but this money by organizational confusion was used ineffectively. In our opinion, the most effective strategy in space exploration today is the activity of the navigator and public figure Amerigo Vespucci (1454–1512), who developed economic exploration of huge new lands. Path to the future: Sergey Pavlovich Korolev believed that in space exploration, designers should rely on the most rapidly developing technologies. Now, these are computer technologies — there are 6.2 billion computing systems in the world [3]. Their productivity has increased 1015 times during its existence. The AlphaZero program, which has learned to play Go from scratch at the level of world champions, has shown that now neural networks allow computers to “learn computers”. The main result of space activities is monitoring and information transfer. About 50% of the profits are from the receipt of space information, almost 50% — the operation of ground systems with it. And only 3% ($ 6 billion) is the launch market in which Russia competes.

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Imperative: our country's breakthrough into the world of space information. The future will be determined by whether we can turn from “taxi drivers” into “passengers”.

3 Military Threats Prevention Many tragic pages of Russian history: the Crimean War (1853–1856), the Battle of Tsushima (1905) were associated with the fact that the country’s leadership was preparing “for the wrong war.” Strategic stability does not mean the local conflicts absence and “deserted wars”. Now it is important to worry, what can happen in the cosmic forces area. The signal delay during transmission from a geostationary orbit (35786 km) is 0.119 s and from an orbit of 550 km — 0.0018 s, which already makes it possible to effectively control weaponry. In the Starlink network, about 1,700 communication satellites have already been launched into an orbit of 550 km with an inclination of 53 degrees (in the future, it is planned to launch from 12 to 40 thousand satellites). According to E. Snowden, there are already 1 billion people “under surveillance”. The active use of space systems makes it possible to make the “surveillance” even denser and to move to post-capitalism or intuitive capitalism with global control of what is happening by the world elite. It is necessary today either to conclude treaties that prevent the start of this round of arms and conflicts or to create a serious counterbalance to such systems.

4 The New Look at Space Science The most important results of space activities — the detection of exoplanets, the Hubble telescope achievements, the gravitational waves discovery, stations on Mars, and much more — were made using automatic machines. Artificial intelligence and a new generation of sensors are our continuations in space. The technological revolution (the creation of “intelligent” robots) may occur in the next three to five years. Now the volume of funding for work on artificial intelligence (AI) in Russia is 350 times less than in China [4, 5]. If Russia wants not only to develop AI [6], but to break out into the world leaders in artificial intelligence, which determines its future in space, then it is necessary. • to increase funding for scientific activities at least 3–5 times (up to 3–5% of GDP); • to increase the priorities of financing research and development in the field of AI within the science budget.

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5 Space Debris as a Systemic Problem Since the beginning of the space era, more than 5,000 rocket launches have been carried out, which have launched about 50,000 vehicles for various purposes into orbit. Of these, 5000 continue to fly and tens of thousands of new vehicles will appear in the coming years in connection with the deployment of low-orbit constellations OneWeb and Starlink. At all altitudes in the near-earth space, extending to a sphere with a radius of 380 thousand km, there are 130 million objects with a size of 0.1–1 cm. The hit of such an object in a spacecraft can cause serious damage to it. In addition, there are 900 thousand objects 1–10 cm in size in this space. There are about 500 thousand such objects in low-earth orbit (from 100 to 2000 km above the Earth’s surface). A collision with such debris can seriously damage or destroy a spacecraft [7]. There are examples of this — in March 2021, the Chinese satellite Yunhai 1–02 was destroyed as a result of a collision with the body of the Russian Zenit–2 rocket launched in 1996. Model calculations carried out at the Institute of Applied Mathematics show that in many scenarios the destruction of spacecraft leads to the fact that after a while debris appears moving at a speed of 7 km/s towards each other. Their collision can cause the appearance of new debris. If there are a lot of fragments in space, then a kind of “chain reaction” and massive destruction of space vehicles begins. This scenario is called the “Kessler effect”. Preliminary calculations show that in this case, space will be lost by us for about 200 years. At this time, due to the high risk, it will be necessary to abandon manned astronautics and many projects of the modern space industry. We can only hope for artificial intelligence and systems that can be controlled with its help. Proceeding from this, another important criterion is introduced in solving space problems — the amount of debris that a mission will lead to. Without this, we can all be left without space and the opportunities associated with it for a long time. And here one more problem arises, connected with self-organization [8] at the international level, with the exploration of outer space. It is now quite clear that space debris will have to be removed. But who, how and at whose expense will it do this? At the conference on space exploration (June 14–18, 2021 St. Petersburg Global Space Exploration Conference — GLEX-2021), two options for the “most littering” countries were presented: China — 40%, USA — 25.5%, Russia — 25, 5%; or Russia — 39.7%, USA — 28.9%, China — 22.8% [7]. Each of these countries has its own weighty arguments showing why it is necessary to involve not only them in the “space cleanup”. We are dealing with the “tragedy of the commons” formulated in 1833 by W.F. Lloyd. Let’s say a community has only one available pasture. It is beneficial for each member of the community to increase the grazing of their livestock, but at the same time, the fertility of the pasture decreases. If everyone follows the strategy that gives the greatest personal gain, then the pasture will disappear and there will be no place to graze the cattle. The personal contradicts the general. Obviously, here we also have a socio-technological problem, the solution of which requires mathematical modeling, artificial intelligence systems, and serious agreements.

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6 Biological Hazards of Space Projects Systems analysis shows that in our civilization the most vulnerable part is now biological space. On the First World War battlefields 7 million people died, while the Spanish flu pandemic victims number, which took place in the same years, turned out to be more than 50 million people. The problem is that in the course of evolution, humans are a “slow variable” (generation change time is 20 years), and bacteria are “fast” (generation change time is 2 h). Antibiotics have proven to be a miracle that has saved hundreds of millions of people. However, now the era of antibiotics is ending — there are practically no strains that are not resistant to penicillin, but there are strains that are resistant to all antibiotics created. From a systemic point of view, this is natural — it is difficult to manage a fast system using only slow variables. Moreover, the founder of the Davos Economic Forum K. Schwab, and his coauthor T. Malleret believe that in the future we will have to live in a regime of constant pandemics [9]. The creation of an analog of COVID-19 costs several million dollars and requires about a year of work of a dozen qualified specialists. This is another reality, different from the one in which we lived before. Microorganisms in space behave in strange, paradoxical ways. Therefore, by developing manned astronautics, carelessly treating descent spacecraft, we, in fact, are starting another branch in biological evolution. Should we do this if we are faced with serious problems in the course of our own biological evolution?

7 The Martian Challenge and Technical Limitations A manned mission to Mars is being widely discussed. This project is estimated at $ 600 billion, but the “Mars Direct” plan, which provides for the use of the resources of Mars in such an expedition, reduces this amount to $ 60 billion [10]. The flight to Mars is an imperative of the billionaire I. Musk. “On the way to Mars, a bunch of people will die,” he believes, and nevertheless considers it necessary to develop and populate this planet [11]. Almost ten years ago the outstanding planetary scientist M.Ya. Marov put it this way: “Well, most importantly, I love the questions that I often ask myself and my graduate students: “Why?” This is the main question, to which, I think, we do not have a convincing answer from the point of view of such a manned flight to Mars” [[12], p. 158]. This is the most important argument in favor of exploring Mars based on artificial intelligence systems. But there is another argument as well. Modern manned vehicles are the most complex technical systems. And today, the work of a cosmonaut-researcher is largely associated with the constant repair of failing units and the elimination of emergencies. Russian cosmonaut Yu.M. Baturin, who described the profession of astronauts, formulated several rules to support this view. Among them: “Instructions are written in blood”, “Treat technology as a partner. If you have an inanimate object in front of you, this does not mean that it has no opportunity to do something different from what is prescribed for it”, “A complex technique lives its own life. Do so that it is in harmony with your life”, “You cannot learn the technique at the table. Try to understand it not only with your mind but also with your

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hands”,“ Study the material part” [13]. Other people, who spent many years in space, think in the same way [14]. Modern technical systems do not allow us to cross the chasm separating us and perform active exploration of interplanetary space in one or two jumps. And it is artificial intelligence that enables us to build a bridge across this chasm.

8 The Result Paradoxes We saw that Western civilization — the kingdoms and republics of Western Europe ... after 1500 really destroyed or enslaved most other civilizations ... Africa submitted not only to the Maxim machine gun but also to the messianic schools, telegraph, and medical laboratories. Niall Ferguson

It is quite clear that the significant results in space exploration, in the transition to a new level of space exploration, will require tremendous efforts. This work will be carried out by individual civilizations or by humanity as a whole, if there are serious reasons for this, justifying strategic space projects. Let’s look back. The most important scientific and technological results of the twentieth century — the discovery of antibiotics and the synthesis of ammonia from the air (paving the way for the production of nitrogen fertilizers and explosives) were associated with efforts to significantly increase the life expectancy of people and significantly improve its quality [15]. The largest projects of the second half of the twentieth century — Atomic and Space — were the guarantors preventing a new world war. The Western civilization traditionally emphasizes their scientific, technological, and cultural achievements, downplaying or simply hushing up the peculiarities of the military and economic policies pursued by Western countries. It was colonialism, and then neo-colonialism, the “gunboat policy”, protectionism and the struggle for markets that allowed Western Europe and the United States to get rich and allocate enough funds for the advanced development of science and technology. Together with the created “social myths” about bourgeois freedom and democracy, this ensured, if not crisis-free, but outstripping development. The main problems of mankind for the last 500 years have been and remain inequality in international relations and the predatory policy of the leading powers in relation to the rest of the world. So England in the 16–17th centuries encouraged piracy. Sir Francis Drake’s exploits made him a hero to the British, but his privateering led the Spanish to label him a pirate known to them as “El Draque”. The free spirit of piracy (along with other reasons) gave impetus to the development of technology: if in Spain the quality of ships and artillery was assessed by crowned persons, then in England successful shipbuilders and gunsmiths were supported by financially wealthy pirates. This quickly led to the fact that the English ships began to go faster than the Spanish, and the guns — to shoot farther and more accurately. The same period (16–17th centuries) saw the transition from cast bronze cannons to cast iron (cast steel appeared only in the 19th century). Cast iron cannons were significantly cheaper but were considered (and they really were) less reliable. Therefore, the transition to them was difficult and, even in England, they preferred to use bronze

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guns on the flagships [16]. The best in Europe (and in the world…) cannons, both bronze and cast iron, were produced in England during this period, also for export. But they cost foreign consumers several times more than they cost English sailors. The bourgeois revolution that took place in England in the middle of the 17th century facilitated the transition to cast-iron cannons. And it’s not just about the development of handicraft industries. With the repeated changes of power during this period, more progressive politicians appeared, capable of supporting new technologies in the field of artillery and the navy. Whereas in Spain, politics, including technological, was determined by irreplaceable representatives of the royal house with conservative views. All this together led to the fact that Spain in the 17th century bought more expensive bronze (brass) English cannons at a high export price, which exceeded the domestic English cost of cast iron cannons by several dozen times. And this was one of the most important reasons (there were, of course, a number of others) of the decline of the Spanish Empire and the creation and prosperity of the English colonial empire, which was later transformed into the United Kingdom of Great Britain, the largest empire in the history of mankind. Carrots and sticks that are used together are much more effective than carrots and sticks separately. Ideological and technological leadership allows the West to continue to pursue offensive economic and military policies with significantly fewer losses to its image. And advanced positions in science and culture make it possible to raise issues of ecology, social minorities, personal freedoms, etc., distracting from the main world problems. This is the case now, and so it was in the past. Among the several “killer apps” that allowed the West to surpass the East after 1500 are. – Scientific revolution. All the major discoveries made in the 17th century in mathematics, astronomy, physics, chemistry, and biology were made in Western Europe; – Modern medicine. Almost all the major discoveries in medicine in the 19th and 20th centuries (including those important for the fight against tropical diseases) were made by Western Europeans and North Americans; – Consumer society. The industrial revolution took place where there were prerequisites: the availability of technology that increases productivity, and the growing demand for high-quality and cheap goods, starting with cotton clothing [[17], p. 404]. Is it possible to designate space exploration as such a strategic problem? Apparently, with the exception of the arms race problem transferring into space, no. World science itself is in a “crisis of overproduction”. For example, in 2016, the number of papers included in the Web of Science scientific database grew to 1.6 million articles, that is, approximately 4400 new articles daily [18]. However, the fate of most of these works is unenviable. From 52 million articles and conference proceedings indexed in the Web of Science since 1970, only 6.5 thousand (0.01% of the total) have been cited 2000 or more times. In essence, it means “from quality to quantity”. Efforts are being invested not in the solution of several fundamentally significant problems, as at one time in the solution of problems associated with the Atomic and Space projects, but in the activity itself, in the development of science at any direction. The specific tasks associated with a breakthrough into space (like many others) are simply lost against this

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background… We are faced with the “effect of the Tower of Babel”, where the Internet plays the role of this tower. Clubs, communities, groups appear that do not see each other and, moreover, do not see the whole. In fact, there is a transition from the 17th century associated with science, technology, and the understanding of the importance of experiments to the 16th century, where the successes were much more modest. And often the question is not about breaking into the future, but about preserving existing knowledge and technologies… This new paradoxical situation, in which society does not understand and is not interested in what scientists are doing, is illustrated by the methodology for evaluating researchers and institutes adopted by the Ministry of Science and Education of the Russian Federation. In their methodology, everything is determined by how many articles they managed to “push” into the journals that appear in the Scopus and Web of Science databases. From essence to form, from strategy to tactics, from meaning to quantity… The continuation of this crisis will lead to stagnation in fundamental science, in culture, and then in technology. Artificial intelligence will not help if we do not see our goals, limitations, and the desired image of the future. Following the main thesis of Lem’s philosophy: “Technology is an independent variable of civilization” [19], can we expect that the current crisis of “subjectlessness in science” will be overcome? What is In this case, the role of strategic space projects? The transition from the Ptolemaic to the Copernican model of the solar system made it possible to rethink the place of man in the universe and gave a huge impetus to science. Another life form discovery in space or its traces on Earth can also have great importance. Therefore, this task is a strategic problem for all science. We, relying today on artificial intelligence, need to return to the stars! However, there is also the next step — determination of its place according to the history of development in the universe. In 1950, the famous physicist Enrico Fermi posed a question known as the Great Silence Hypothesis. In accordance with today’s concepts, there should be civilizations in the universe that are millions and even billions of years older than ours. Why don’t we see traces of their activities — probes, spaceships, radio broadcasts, convincing evidence in favor of the existence of a different mind? To date, about 4200 exoplanets have been discovered, a number of which are similar to Earth. And that makes the long-standing question all the more pressing. Many stars are located much closer to us than it seems at first glance. Several teams are seriously considering sending interstellar probes with artificial intelligence systems. The intent of these projects is to create systems with “photon sails”, which will accelerate systems of ultra-high power lasers. The closest star system to us (which is also visible from Earth), Alpha Centauri, is 4.3 light-years away. In other words, if the probe is accelerated to a speed of 0.1c (where c is the speed of light), then the journey to Alpha Centauri will take about 40 years, if up to 0.2c, then only 20 years… At the same time, the output to a speed of 0.1c is close enough to the capabilities of modern technology. There are several answers to the question about the Fermi paradox. Let’s pay attention to two. The first is related to the humanitarian sphere. If the search result will concern only scientists, then this is not an easy problem. But other options must also be considered. The key question was posed in the novel Solaris by Stanislav Lem. In

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Snaut’s monologue: “We go into space prepared for everything, that is, loneliness, struggle, suffering and death. Out of modesty, we don’t say it out loud, but we think to ourselves that we are great. But in reality, not everything, and our readiness turns out to be only a pose… We are humane, we are noble, we do not want to conquer other races, we strive to pass on our values to them and in return to accept their heritage. We consider ourselves the Knights of Holy Contact. This is the second lie. We are not looking for anyone but people. We want to find our own idealized image, these should be the worlds of civilizations more perfect than ours. In others, we hope to find an image of our primitive past. Meanwhile, on the other side, there is something that we do not accept, from which we defend ourselves” [20, p. 107]. This is a very important question about the meaning, the result of interaction with another reality for our world. The above-mentioned story tells about the useless contact with another civilization — the thinking ocean Solaris. On the one hand, a variant of mutual indifference and misunderstanding is presented. On the other hand, an attempt at contact demonstrates the “reverse side” of the researchers’ psyche, Kevin and his colleagues kill the creatures represented by Solaris with their own hands, even if they repeat in the image and psyche the people closest to the researchers… On the third hand, a person is a complex, contradictory entity about which we know clearly not enough. Researchers are well aware that it is difficult to work with a complex device, the properties of which we do not know. Therefore, it is quite possible that artificial intelligence systems in cognizing a new reality will have significant advantages in mastering and understanding new things than people focused on the perception of our world in all its complexity… A neutral version of the “interaction of civilizations” is considered in Lem’s novel. But history teaches other examples — we live in a technological world. In world history, the societies that occupied the leading positions in technologies have won again and again. Their opponents were destroyed or moved to the periphery of social evolution. Therefore, it is natural to look at the results of interaction from the standpoint of risk management theory. This view was developed by the Chinese science fiction writer Liu Qi Xin, who proposed the concept of a “dark forest” to explain the Fermi paradox [21]. Transferring our earthly experience into space, we can say that development implies a war for survival with an underdeveloped civilization. Therefore, they in every possible way hide the traces of their stay in space from competitors. Young civilizations, launching probes, and striving to tell everyone about themselves, are like an inexperienced traveler walking through a dark forest and believing that there is no one around. And so he will count until a deadly predator attacks him… From what has been said follows that, from a systemic point of view, an important element that hinders the development of outer space is the strategic uncertainty of the space project, lack of understanding of its possible results, and fundamental limitations. Let’s hope it is growing pains. Apparently, this is one of those difficulties with which the path to the stars is associated. There is one more worth making a systemic point of view. For many centuries, scholars have been discussing the role of personality in history. Large scientific and technical projects of the twentieth century showed that the role of individuals and small

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groups at bifurcation points is decisive. Moreover, in today's complex world, these people must be at several systemic levels. One level is dreamers, science fiction writers, idea-generators. In domestic space, this is the Russian cosmist N.F. Fedorov, who connected the science of the future with the revival of all dead people; and K.E. Tsiolkovsky, who took care of that to settle these people in the universe. Note that he wrote a cycle of science fiction novels, in which he outlined the contours of the future. Despite the complete Fedorov’s ideas isolation from the modern physical picture of the world, the level of dreamers was and remains very important. Many of Stanislav Lem’s forecasts anticipated the changes in society, the technosphere, and defense technologies that have taken place in recent decades [20]. Schoolchildren and students should be taught to dream! The second level — teams of creators, under whose leadership new things are created. It was the council of chief designers under the leadership of S.P. Korolev, which included 6 people, that played a huge role in the take-off of the space industry of the USSR. At the same time, the interdisciplinarity of this team is important — if something fundamental is not taken into account, the project may not take place. About 15 prominent scientists have played a key role in the Atomic Project. The third level is the elite leading persons who coordinate the programs being implemented. Soviet atomic scientists recognized the key role of the head of the Soviet Atomic Project — L.P. Beria and his scientific deputy I.V. Kurchatov. Note that earlier other people took up this business, but then they had to be changed. A telling illustration of this principle is the failure of the Atomic Project of Hitlerite Germany. It was started in 1939. An important role in it was played by the Reich Post Minister of the Third Reich K.V. Onezorge (also known for being one of the first to recognize the television’s key role in state propaganda). The scientific leader of the project was an outstanding physicist, one of the founders of quantum mechanics, W. Heisenberg. The leaders of the Reich were familiar with the progress of the work, but none of them considered it as their key business. This led to the fact that the Germans were late with the creation of their atomic and hydrogen bomb. At present, the accelerated development of systems with artificial intelligence is comparable in scientific, geopolitical, defense, and technological significance with the development of Atomic and Space projects. Therefore, the leading figures and the elite representatives’ role who will undertake the implementation of AI projects in our country and other countries can hardly be overestimated. In our opinion, now it is important to have time to realize it.

9 Keldysh Imperative Then a strange wind will blow A terrible light will pour from the sky, This Milky Way bloomed unexpectedly A garden of dazzling planets. N. Gumilyov

Outstanding mathematician, mechanic, organizer of science, President of the Academy of Sciences of the USSR Academician M.V. Keldysh was often called “the

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chief theoretician of cosmonautics.” At the Institute of Applied Mathematics (IPM), of which he was the first director and which now bears his name, a ballistic center was created in which the trajectories of Soviet spacecraft were calculated. According to M.V. Keldysh, the future of Soviet science should be associated with deep space exploration. On the one hand, these are tools, technologies, the knowledge that will open up a new scientific space, on the other hand, it is the space industry that can be a reference point for the country entire industry, in which new tools, strategies are created, new opportunities open up. The Ministry of General Machine Building, which coordinated work in the space industry, had 1,200 factories, which employed about 1.5 million people. Exploration of Mars, Venus, the planets’ atmosphere and geology, cosmology, problems of the origin of the universe occupied an important place both in the IPM topics and in the entire Soviet science. Realizing the importance of space problems, M. V. Keldysh has personally attended many of the most important launches. Until the 1960s, the imperative for the development of domestic and world science was the expansion of man into space. In the 1970s, this vector was radically changed — the focus of industry and many scientists were on the formation of a “third nature” — information, computer space. We will not discuss in these notes — consciously or not, this turn was made, which determined the development of world science in the last half-century. Although in those years the main ideas of systems analysis were formulated already, not all decisions were based on its results [22]. History teaches that in this case, it is extremely important to fix, preserve, be able to reproduce the achievements created during the space takeoff of mankind. Unfortunately, this is not being done, the space age is being carefully “deleted” from the history of mankind. The scientific imperatives of the space industry are being replaced by “economic” or “entertainment” ones. The launch into orbit of a teacher in the United States and an actress in Russia are symbols of this “sinking” of space. Naturally, these processes are exacerbated many times over by incompetent management. The eloquent title of the book of the famous geochemist, one of the leaders of the scientific space programs of the new Russia E.M. Galimova: “Twenty years of fruitless efforts” [23]. This attitude towards the technologies and knowledge preservation gained during the implementation of a space project is also typical for the United States. According to American experts, to restore the shuttle that participated in the Apollo project, now there is a lack of about one million sheets of drawings. To preserve technologies in this country, the firms that created them would have to receive certain funds. These funds were not available and these technologies were lost. This attitude is all the more surprising since the cost of the knowledge gained during the implementation of this program is very high — 40% of the shuttles created have suffered accidents. Huge opportunities were missed and, returning to the path of space exploration, they should have been borne in mind. The Minister of General Machine Building of the USSR O. Baklanov said: “But Energia–Buran was killed! I was its admissions committee chairman, so I know what I’m talking about. But thanks to Energia-Buran, a flight to Mars was already looming. Few people know about this, but there were these plans. We have created a new dimension! Today’s spacecraft has an inner diameter of no more than 3 m. A year to fly in space in such conditions — and you can go crazy. And “Energia–Buran” was inside (in length) more than 50 m! On the basis of

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technologies of a new dimension, a spacecraft could be created to enter a reference orbit of 200–300 km. There it was possible to assemble a space train from these modules with supplies of water, food, and so on; and start to Mars from there. After all, it takes a year to fly to Mars, and it takes also a year to get back. Humanity will come to this sooner or later. Moreover, a 740-ton engine was created for “Energia–Buran”. It made it possible to launch 105 tons into the reference orbit. And today’s Chalomey’s rocket is capable of delivering a maximum of 22 tons. It is then from these 22-ton modules that we build up a station in space for 200 tons, do you understand? By the way, it was planned to force the Energia-Buran engines, after which we would be able to carry 180 tons into orbit. Can you imagine what kind of dash into space it would be?” [[24], p. 9]. These opportunities have been missed. Moreover, the “weak link” at that turn of history turned out to be not only political leadership but also science — no space laboratories were created that would be worth putting into orbit. During the development of this system, about 1,500 new technologies were created, which should have become the basis of a new Soviet industry. It is also important for us that the automation coped well with the implementation of the flight of this most complex system. New industrialization and space planning could lead to remarkable results. In the 1990s, the RAMCON Corporation proposed organizing the production of scarce substances in zero gravity. Among them: tumor necrosis factor (world price in 1992 $ 31.5 million per gram), human growth factor ($ 115 million per gram), nerve growth factor ($ 9 million per gram), and many other important and expensive substances. Automation and artificial intelligence systems could successfully cope with this [25]. There is one more very important area. The world is worried about climate change. S. Manabe and K. Hasselman were awarded the Nobel Prize in Physics (2021) “for physical modeling of the Earth’s climate, quantitative expression of variability and reliable prediction of global warming.” In this context, processes in the atmosphere occurring at altitudes from 30 to 200 km, which could investigate aerospace systems, are extremely important. In 1966, the design bureau “MIG” began work on the creation of a light winged spacecraft “Spiral”, which was to be launched from a heavy supersonic aircraft. Yuri Gagarin in 1968 defended his diploma at the Air Force Academy. M.E. Zhukovsky on the topic of reusable aerospace systems. He designed a version of such a light shuttle “YUG”. A model of the Aerospace Forces “Bor” was created, which was launched from the “Proton” rocket. This system started in 1976, 1978, and 1979, was successful. In 1982, these vehicles splashed down near Australia, and in 1983 — near the Crimea. It is quite possible that this direction will be developed in the future. At one time, Academician T.M. Eneev, who worked at IPM, proposed organizing a space system for monitoring asteroid hazards. It is quite possible that the development of the technosphere will make another turn, and the cosmic vector, as predicted by M.V. Keldysh, takes on a special meaning. At the current turn, Russia needs to determine the meanings, directions, projects that will point the way into space. The closest historical analogy is the enormous effort that went into the development of America. After this world was discovered, it had to be mastered. Columbus did not know until his death that he had discovered a new continent. And the role of the

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Florentine traveler Amerigo Vespucci, after whom America is named, turned out to be huge. In 1501–1502, going to the shores of Brazil, he proved that open territories are a separate continent, and not the eastern outskirts of Asia, as previously was thought. The historical significance of his efforts can hardly be overestimated — the Spanish language today is the second in the world by the number of people who consider it their native language. Our country has opened the way to space for the world. It is very important not to turn off and not to lag behind, moving along the road leading to the stars. Russia has all the possibilities for this.

10 Conclusion A systematic analysis of Russia’s space future leads to the following conclusions: 1. The era of space exploration has come. The key point is the use of new computer technologies to work with information. 2. The era of unmanned wars has come. Innovation is needed to block the use of space for this purpose. It is time to conclude international treaties preventing this new arms race cycle from starting. 3. A breakthrough in space exploration will be associated with the use of artificial intelligence. The time of robots-astronauts is coming. We have a chance to be the first… Acknowledgment. The reported study was funded by RFBR, projects number 19–01-00602 and 20–511-00003.

References 1. Schwab, K.: The Fourth industrial revolution: what it means and how to respond. https:// www.foreignaffairs.com/articles/2015-12-12/fourth-industrial-revolution (2015). Accessed 18 Sep 2021 2. Bengio, Y., Lecun, Y., Hinton, G.: Deep learning for AI. Communications of the ACM 64 (7), 58–65 (2021). https://doi.org/10.1145/3448250 3. The number of computers in use worldwide will reach 6.2 billion in 2021. https://yandex.ru/ turbo/3dnews.ru/s/1036388/kolichestvo-ispolzeuemih-komputerov-vsyom-mire-dostignet62-mlrd-v-2021-gody (2020). Accessed 20 Sep 2021. (in Russian) 4. Lee, K.-F.: AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin, Boston (2018) 5. Almanac No. 8 “Artificial Intelligence – Index 2020”. https://aireport.ru/ai_index_2020 (April 2021). Accessed 20 Sep 2021. (In Russian) 6. Decree of the President of the Russian Federation of 10.10.2019 No. 490 “On the artificial intelligence development in the Russian Federation”. http://publication.pravo.gov.ru/ Document/View/0001201910110003 (November 2019), Accessed 20 Sept 2021. (In Russian)

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7. Dicky, Ch., Uvarov, V.: Novyj “Soyuz–Apollon” iz kosmicheskogo musora. [New “SoyuzApollo” from space debris.] Expert 40, 62–63. (2021). (In Russian) 8. Malinetskiy, G.G.: Prostranstvo sinergetiki: Vzglyad s vysoty (Sinergetika: ot proshlogo k budushchemu. № 60). [The space of synergetics: A view from above. (Synergetics: from the past to the future, No. 60).] 248 p. URSS, Moscow. (2017). (In Russian) 9. Schwab, K., Malleret, T.: COVID-19: The Great Reset. Cologny. Forum Publishing, 212 p. (2020) 10. Zubrin, R., Wagner, R.: The Case for Mars: The Plan to Settle the Red Planet and Why We Must. Touchstone, New York (1996, updated in 2011) 11. Lebedenko, S.: “Po doroge na Mars umret kucha lyudej” – 11 tezisov iz interv’yu Ilona Maska. [“A lot of people will die on the way to Mars” – 11 theses from an interview with Elon Musk.] https://trends.rbc.ru/trends/futurology/60927aae9a7947cead1c3b53 (2021). Accessed 20 Sep 2021. (In Russian) 12. Urmantseva, A.: Luna ili Mars. Mozgovoj shturm. Izbrannye diskussii. [Moon or Mars. Brainstorm. Selected Discussions.] CJSC SVR-MediaProjects, Moscow. pp. 122–158 (2013) (In Russian) 13. Baturin, Yu.M.: Vlasteliny beskonechnosti. Kosmonavt o professii i sud’be. [Lords of Infinity: An Astronaut on Profession and Destiny.] 696 p. Alpina Publisher, Moscow (2020). (In Russian) 14. Zhukov, S.A.: Stat’ kosmonavtom! Sub”ektivnaya istoriya s obratnoj svyaz’yu. [Become an astronaut! Subjective story with feedback.] 384 p. Publishing house “RTSoft”, Moscow (2011). (In Russian) 15. Malinetskiy, G.G.: CHtob skazku sdelat’ byl’yu… Vysokie tekhnologii – put’ Rossii v budushchee. [To make a fairy tale come true ... High technologies are Russia’s path to the future.] (Synergetics: From the past to the future. No. 58; Future Russia No. 17) 1024 p. URSS, Moscow (2021). (In Russian) 16. Lavery, B.: The Arming and Fitting of English Ships of War, 1600–1815. 316 p. ISBN 0870210092/9780870210099. US Naval Institute Press, Annapolis, MD (1989) 17. Ferguson, N.: Civilization: the West and the Rest. 432 p. (Oct 30, 2012), ISBN 9780143122067. Penguin Random House, New York, NY (2021) 18. Scientists published over 1.6 million scientific articles in 2018. This is a record figure in history. https://hightech.fm/2018/12/24/science-2. Accessed 20 Sep 2021 (in Russian) 19. Yaznevich, V.I.: Filosof budushchego Stanislav Lem. [Philosopher of the future Stanislav Lem.]. In: Nesterov, A.Yu. (ed.) Chetvertye Lemovskie chteniya [4th Readings of Lem], pp. 228–244. Samara Humanitarian Center, Samara (2018). (In Russian) 20. Lem, S.: Solaris. Translated by Kilmartin, J. Cox, S. 216 p. Walker and Company, New York (1970) 21. Liu, C.: The Three-Body Problem, No. 2: Dark Forest (Chinese Edition). Chongqing Publishing House-重庆出版社 (2008) 22. Bylieva, D., Nordmann, A., Shipunova, O., Volkova, V. (eds.): PCSF/CSIS -2020. LNNS, vol. 184. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-65857-1 23. Galimov, E.M.: Zamysly i proschety. Fundamental’nye kosmicheskie issledovaniya v Rossii poslednego dvadcatiletiya. Dvadcat’ let besplodnyh usilij. [Concepts and miscalculations: fundamental space research in Russia for the last twenty years. 20 years of fruitless efforts.] (Future Russia No. 25). 376 p. ORSS, Moscow (2021) (In Russian) 24. Belkin, S.: Vernis’, Mechta! [Come Back Dream!] Izborsk club, no. 1–2 (87–88), pp. 8–13 (2021). (In Russian) 25. Kalashnikov, M.: Vdohnut’ v dushi mechtu o zvyozdah… [Breathe into the souls the dream of the stars...] Izborsk club, no. 1–2 (87–88), pp. 14–27 (2021). (In Russian)

Application of Classification to Determine the Level of Awareness of the Foresight Process Nataliya Pankratova(&)

and Volodymyr Savastiyanov

Institute for Applied System Analysis, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Peremohy av. 37, Kiev 03057, Ukraine [email protected]

Abstract. The article is analyzed the modern process of foresight based on textual analytics to identify typical concepts, associations, and relationships to a large-scale phenomenon or problem from a non-monotonically increasing text array. Another case is a typical research task of changes in the functioning or structure of a complex object/system under foresight research. In the process of analysis of the studied object, subject or system is mirrored in the form of specific metadata recorded in a given period. The classifier has a simple appearance, which allows the expert to easily navigate the identified classes on one side, and is marked by different classes of the input body of weakly formalized data from the other. However, together, several classifiers and classifying ontologies can form a more powerful structure of knowledge navigation in the form of a faceted classifier. The analysis of the specifications of the foresight process methods made it possible to separate the entities that these methods operate on. These entities represent the metadata of the foresight process methods as classifiers that have been compiled/created to describe mentioned metadata. The final structure of the metadata classes was proposed depending on the industry or subject domain. To track the foresight process, to analyze the dynamics of quantitative and qualitative characteristics of knowledge acquisition, the following indicators of awareness are introduced: regarding the structure of acquired knowledge, regarding the media of the collected information, and Metadata awareness indicators. The practical calculation of awareness indicators was done. Keywords: System analysis analytics


Foresight
Indicators of awareness
Textual

1 Introduction In the modern process of foresight, there is a lot of semi-structured textual information in natural language. Bringing it into a more structured form is in itself a rather difficult task [1–4]. Additionally, the information can be fake [5], so analysis and perception of it can lead to erroneous decisions that cost financial or even loss of life, and lead to undesirable technical, economic, or social consequences, such as radicalization of society [6–9]. In other words, in-process and foresight it is necessary to identify typical concepts, associations, and relationships concerning a large-scale phenomenon or problem from a non-monotonically increasing text array. That is, there is the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 74–88, 2022. https://doi.org/10.1007/978-3-030-98832-6_7

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knowledge that mentions or refers to them and the volume of which is growing rapidly [9, 10]. At the same time quite often there is a lack of experts or examinations in the studied phenomenon. Another case is a typical research task of changes in the functioning or structure of a complex object/system, such as a metropolis. Addressing structural/functional change in the presence of many sources of centralized, decentralized, and self-management / regulation generates many phenomena, hopes, opinions, and speculations, as well as objective knowledge. All this information is covered in various sources and needs to be studied quickly to extract and identify factors of different nature to build a strategy of rational change in the functioning or structure of a complex object/system [11]. These issues are covered in [12], which uses textual analytics as a method that removes knowledge about the structure of an object/system and studies the relationships in this system. In this case, the method competes with the methods of qualitative analysis, namely the method of cross-influence to determine the mutual correlation of key concepts of the subject area. The advantage of the developed technique is the creation of an ontology of the subject area from the keywords of publications in a few years. The disadvantages of this method are that it does not take into account the emotional and semantic orientation and is context and body-dependent, i.e. it filters the links that were mentioned less because some studied problems were given fewer publications or collected fewer source texts. In [13] the contribution of text analytics to the foresight process is studied. The extreme relevance of the use of text analytics in the extraction of knowledge from the Internet, namely from social media, Twitter, media, and others. In this paper, the tools of text analytics are primarily a method of data extraction, data analysis, and scenario development based on the method of road maps. Techniques for data extraction, extraction, and weighing of terms, identification of trends using two points of division of historical data into 2 intervals, construction of associative pairs for studying contextual interrelations of the extracted terms are opened. The ideas of using road maps, forming a knowledge map of the subject area, and using associatively grouped terms to identify potential causal relationships were tested in [14] on the example of the methodology of predicting information and computer communication technologies. The disadvantages of these techniques are that text analytics replaces the methods of qualitative analysis, used to implement methods of road maps and scenarios based on the removal of associations of terms in context, the formation of reliability is an aspect of studying (experts) constructed roadmaps and scenarios in terms of completeness and integrity, which may increase uncertainty due to the subjectivity of the expert opinion. Other disadvantages include the following: the results obtained (keywords, associative links) depend on the capacity of the assembled body; there are no measures, whether enough information is collected, whether there is an information advantage (flood) of one subject area/object/indicator over others — that is, there is an information impact; there is no mechanism for reusing acquired knowledge. In the works [15–17] the role of building ontologies in decision support systems as an important method of presenting the subject area is noted. The importance of ontology lies in the fact that ontology defines commonly used, semantically significant “conceptual units of knowledge”, which are operated by

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researchers and developers of knowledge-oriented information systems. The advantage of ontology in contrast to the knowledge encoded in the algorithms is that the ontology provides their unified and multiple uses by different groups of researchers and on different computer platforms in solving different problems. As noted in [17], the behavioral description of entity processes in the form of ontologies is most often performed in the form of graphical diagrams and natural language descriptions. The development of the knowledge base is not the direct goal of known methods. Therefore, the methods of developing an ontology of processes are virtually unknown. This fact further emphasizes the main disadvantage of the process of anticipation without the support of automated means of processing and formalization of knowledge. The ideas of collecting concepts, forming an ontology of the subject area, the use of associatively grouped terms for associative relations were tested in [18, 19] on the example of using text analysis techniques to predict and model scenarios of different subject areas such as socio-economic systems, agro-complex, coronavirus, the underground and terrestrial infrastructure of cities, anti-corruption activities, crime and society, mitigation of social consequences of disasters and disasters.

2 The Problem of Knowledge Representation and Assessing the Level of Awareness In the process of analysis of the studied object, subject, or system mirrored in the form of specific metadata recorded in a given period of time. In formalized form, this mapping forms a subset of the information model [20], which establishes one type of function as one classifier / categorized, or several — which are linked together and form the ontology of the subject area. By definition in the works of A.V. Palagin [21], the ontology of objects of the subject area means four: O ¼ \X; R; F; AðD; RsÞ [ ; where X ¼ fx1 ; x2 ; :::; xi ; :::; xn g; i = 1,…, n; n = Card (X) is a finite set of concepts of a given subject areas; R ¼ fR1 ; R2 ; :::; Rk ; :::; Rm g; Rx1  x2  :::  xn ; where k = 1,…, m; m = Card (R) is a finite set of semantically significant relationships; F : X  R is a finite set of interpretation functions, data on concepts and/or relations. A is a finite set of axioms, which consists of a set of definitions and restrictions on the concept.

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The importance of ontology lies in the fact that ontology defines commonly used, semantically significant “conceptual units of knowledge”, which are operated by researchers and developers of knowledge-oriented information systems. The advantages of ontology in contrast to the knowledge encoded in the algorithms, ontology provides their unified and multiple uses by different groups of researchers on different computer platforms in solving different problems. As noted in [17], the behavioral description of entity processes in the form of ontologies is most often performed in the form of graphical diagrams and natural language descriptions. The development of the knowledge base is not the direct goal of known methods. Therefore, the methods of developing an ontology of processes are not clear. This fact further emphasizes the main disadvantage of the foresight process without the accompaniment of automated means of processing and formalization of knowledge. Another issue is the effectiveness of knowledge representation in the form of an ontology. The work of Gavrilova, Gorovoy, Bolotnikova [21] defines 10 metrics for comparing ontologies and calculating ergonometric characteristics of otology in terms of perception of knowledge by the human, but that does not define the optimal level of awareness. Depth, width, number of types of connections, and others determine the ease of perception of knowledge when navigating the ontology, and on the other hand limit the accumulation of knowledge in interdisciplinary tasks, such as foresight tasks.

3 Classifiers as a Tool for Assessing the Knowledge in Foresight Process To deal with the foresight tasks, it is expedient to use sets of classifiers — which implement a hierarchical tree-like structure with one relation-functional, for example, class-subclass, part-whole, etc. At the same time, in most tasks, it is advisable not to form an ontology and to allocate a classifier from it, but to use classifiers generally accepted in economics and industry, such as IPTC, NACE, etc. [22]. Here are some of them: • • • • • • • • • • • • • •

Nomenclature of fishery and aquaculture products (NPRA). Classification of Institutional Sectors of the Economy of Ukraine (KISE). Basic product range (SNR). Classification of economic activities (NACE). Statistical Product Classification (UPC). Nomenclature of industrial products (NNP). Main industrial groups (OPG). Nomenclature of construction products (NPB). State Classification of Buildings and Structures (SC BS). Nomenclature of agricultural products (NPSG). Classification of types of cargo (KVV). Classification of individual consumption by purpose (CISC). Nomenclature of domestic trade goods (NTVT). Ukrainian Classification of Goods for Foreign Economic Activity (UKTZED).

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Classification of Foreign Economic Services (CES). Statistical Classification of World Countries (GCC). Statistical Classification of Currencies (SCR). The classifier of objects of administrative-territorial organization of Ukraine (KOATUU). Classification of organizational and legal forms of management (KOPFG). Statistical Classifier of Public Administration Bodies (SKODU). Classification of scientific and technical activities (KVNTD). Classifier of professions (OC). Waste classifier (KVD).

The predominant use of standard classifiers together with the synthesized classifiers during the foresight process support ensures the compatibility of the stages and results of the foresight process (its data, metadata, results, scenario alternatives) with government and industry development and management processes. Figure 1 shows the general structure of the Classifier of branches of legislation [23], and in Fig. 2 a fragment of the branch “Economics, Business and Finance” of the IPTC classifier is presented [24].

Fig. 1. The general structure of the classifier of branches of legislation.

As illustrated in the figures, classifiers implement a hierarchical tree structure with a class-subclass-functional relationship.

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The classifier has a simple appearance, which allows the expert to easily navigate the identified classes on one side, and is marked by different classes of the input body of weakly formalized data from the other. However, together, several classifiers and classifying ontologies can form a more powerful structure of knowledge navigation in the form of a faceted classifier.

Fig. 2. Fragment of the branch “Economics, Business and Finance” of the IPTC classifier.

4 Foresight Metadata Classifiers The analysis of the specifications of the foresight process methods made it possible to separate the entities that these methods operate on. These entities represent the metadata of the foresight process methods. Classifiers have been compiled to describe this metadata. The final structure of the metadata classes that are proposed to be formed depending on the industry or subject domain is as follows: 1. Objects a. Indexes b. Properties 2. Technology a. Key b. Auxiliary The final structure of the metadata classes that are proposed to be categorized by purpose is as follows: 1. Purpose / Task 2. The reason

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3. The investigation 4. Time horizon 5. Forecast / Evaluation / Speculation (in the future). The final structure of the metadata classes that are proposed to be categorized by content is as follows: 1. The external environment 1.1. Macro-environment (factors based on STEEP [25]) 1.1.1. Social (S) 1.1.2. Technological (T) 1.1.3. Economic (E) 1.1.4. Environmental (E) 1.1.5. Political (P) 1.1.6. Personally significant (V) 1.1.7. Legislative (L). 1.2. Micro-environment (factors based on Porter’s Five Forces Framework [26]) 1.2.1. Market (M) 1.2.2. Consumer (Cons) 1.2.3. Regarding products (P) 1.2.4. Regarding suppliers (S) 1.2.5. Regarding competition (Cmp). 2. Internal environment 2.1. Organization Culture (CC) 2.2. Organization image (CI) 2.3. Organization structure (OS) 2.4. Key persons of the organization (KS) 2.5. Access to Natural Resources (ANR) 2.6. Position on the experience curve (PEC) 2.7. Operational efficiency (OE) 2.8. Operating capacity (OC) 2.9. Brand value (BA) 2.10. Market share (MS) 2.11. Financial resources (FR) 2.12. Exclusive contracts (EC) 2.13. Patents and trade secrets (PTS). The final structure of the metadata classes that are proposed to be categorized by content using emotional color analysis is as follows: 1. SWOT [27]. 1.1. Strength (due to high/low or potentially increasing/decreasing level of internal environment parameters). 1.2. Weakness (due to high/low or potentially increasing / decreasing level of internal environment parameters).

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1.3. Opportunity (due to high/low or potentially increasing/decreasing level of environmental parameters). 1.4. Threat (due to high/low or potentially increasing/decreasing level of environmental parameters). 2. Problem (due to the high/low or potentially increasing/decreasing level of frequently mentioned parameters and indicators around the objects of the subject domain/industry, which are in the fragments defined as the category of Cause or Effect). The final structure of the metadata classes that are proposed to be categorized by use, taking into account time parameters and the intensity of mention, is as follows: 1. Event (a sharp increase in the mention of the object and some of its properties). 2. Driving force (constant mention as the cause of an object, resulting in a change in other objects and some of their properties to emphasize the presence of high/low or potentially increasing / decreasing level of the property of objects). 3. Significant factors (constant mention of a specific object and some of its properties to emphasize the presence of high/low or potentially increasing / decreasing level of the property of the object or constant mention as the cause of the properties of a generalized object, resulting in change other objects and some of their properties to emphasize the presence of high/low or potentially increasing/declining levels of properties of objects).

5 Tracking Changes in Knowledge Through the Classifier. Integrated Awareness Indicators Depending on Time Chapter 2 raised the problem of the effectiveness of knowledge representation in the form of an ontology. In the case of using classifiers (categorizers) and classifying ontologies, optimization in terms of ergonomic representation is no longer appropriate. In the case of automated filling of the knowledge base with metadata, which marked the input texts containing at least classes of classifiers, and in the case of extracting complex metadata (mentioned above), there is a problem of evaluating quantitative and qualitative assessment of collected knowledge. The advantage of using classifiers with a tree is the ease of tracking not only changes in the structure of the classifier over time, but also the amount of classified knowledge and the dynamics of classification of input data (see Fig. 3). According to the conceptual model, in the period t 0 there is a set of indicators of awareness \Q1 ; Q2 ; :::; Qi [ , which record both the state of the structure of categorizers, and quantitatively marked by the classifier of knowledge and documents. At each subsequent time t1,… tN both the structure of categorizers and the structure of knowledge change.

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Fig. 3. Changing the structure of the system relative to the known branches of classifiers overtime.

To track the foresight process, to analyze the dynamics of quantitative and qualitative characteristics of knowledge acquisition, we introduce the following indicators of awareness: 1. Indicators of awareness regarding the structure of acquired knowledge: a. Number of identified subject areas (domains). b. The ratio of the number of identified subject areas to all subject areas (within each classifier or classifying ontology). c. The number of identified objects in each subject domain. d. Depth of coverage of the hierarchy of subject domain classes. e. Coverage width of each subject domain. f. Coverage density of the hierarchy of subject domain classes. g. The ratio of the number of objects of other domains concerning the densest domain. h. The coverage width of each subject domain to the densest domain. i. Depth of coverage of the subject domain to the densest domain. 2. Indicators of awareness of the media of the collected information: a. Some documents for the domain (subject area) and branches (subdomains). b. Some domains per document. c. Depth of coverage of domains per document. d. The number of documents that cover as widely as possible each subject domain. e. The number of documents that establish the maximum number of links between the dominant / densest domain and others (the core effects of key technologies).

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f. The ratio of the number of subdomains (“width”) of the domain to the power of “inter-domain” links. g. The number of repetitive inter-domain links at the levels of subdomains with others. h. Rarity / Novelty — the number of the rarest relationships to the average number of “popular”. i. Average macro-effect — the average number of STEEP spheres per document: (1) the average number of social facts; (2) the average number of technological facts; (3) the average number of economic facts; (4) the average number of environmental facts; (5) the average number of political facts; (6) the average number of personally significant facts; (7) the average number of legislative facts. j. Average micro-effect is the average number of facts under Porter’s Five Forces Framework per document: (1) the average number of market facts; (2) the average number of consumer facts; (3) the average number of facts about products; (4) the average number of facts about suppliers; (5) the average number of facts about competition. k. (Optional, in case of the importance of facts in the scale of the enterprise/company) Average effect on the internal environment on the document: (1) the average number of facts about the culture of the organisation; (2) the average number of facts about the image of the organisation; (3) the average number of facts regarding the structure of the organisation; (4) the average number of facts about key people in the organisation; (5) the average number of facts regarding access to natural resources; (6) the average number of facts about the position on the experience curve; (7) the average number of facts regarding operational efficiency; (8) the average number of facts regarding the operating capacity; (9) the average number of facts about the value of the brand; (10) the average number of facts about market share; (11) the average number of facts regarding financial resources; (12) the average number of facts regarding exclusive contracts; (13) the average number of facts regarding patents and trade secrets. 3. Metadata awareness indicators: a. Quantity and quality of facts about the time horizon: (14) mention of the past / present / future; (15) timeline and its scope. b. A number of past, present, and future effects (have been/are / will be achieved) by type: (16) number of achievements; (17) a number of problems.

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c. A number of past, present, and future effects (have been/are / will be achieved) by the detection means: (18) explicitly declared; (19) due to desirable/undesirable signs. d. Foresight macro-effect — the number of detected areas of STEEP. e. Foresight micro-effect — the number of detected areas of Porter's Five Forces Framework. f. A number of goals. g. A number of situations of qualitative change (upward or downward trend). h. A number of knowledge conflicts.

6 Results For the knowledge base of the energy resources domain, the following indicators of information were calculated (IPTC classifier/codes used). 1. According to the structure of the knowledge: 1. 2. 3. 4.

Number of identified subject areas in energy domain: 15. The depth of the hierarchy of classes in the subject domain (see Fig. 4). The degree of coverage of the subject domain (see Fig. 5). The ratio of the number of objects of other domains to the densest domain.

2. Apparently, in this corpus: 5. The number of documents per domain. 6. The number of domains per document. 3. For metadata transfer: 7. 25360 objects extracted. 8. 406 objects in the subject area of Energy. 9. 11191 objects that are participants of trends. 10. 2000 registered participants of problems. 11. 1862 objects in a special order. 12. 378 technologies. 13. 225 problems. 14. 1385 trends. 15. 112 goals.

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Fig. 4. The depth of the hierarchy of classes in the subject domain (IPTC codes).

Fig. 5. Coverage density of the hierarchy of subject domain classes (IPTC codes).

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7 Conclusion As the result of analysis of foresight process, an information model of the process was reviewed. The existing information model of the foresight process was analyzed, the basic information units — metadata were determined. The shortcomings of the existing information model of the foresight process were identified: the lack of a mechanism for metadata labelling of fragments of knowledge of input information with their subsequent storage, tracking, and reuse. The hierarchical representation of the studied system as a classifying ontology is given. The problem of presenting knowledge in the form of an ontology is considered and the expediency of using classifying ontologies that implement a hierarchical treelike structure with one relation-functional, for example, class-subclass, part-whole, etc. is determined. At the same time, in most problems, it is advisable not to form an ontology and select a classifier from it, but to use classifiers generally accepted in economics and industry, examples of which have been given. The conceptual model of knowledge quality is considered and the integrated indicators of awareness depending on the time in three dimensions are introduced: • regarding the structure of acquired knowledge; • about the carriers of the collected information; • relative to the metadata of the modified information model of the prediction process. In a practice, the given approach was used to determine key objects, goals, trends, etc. in the subject domain, build and track the indicators of awareness. Acknowledgement. This material is based upon work supported in part by the National Research Foundation of Ukraine under Grant 2020.01/0247.

References 1. Chakraborty, G., Pagolu, M., Garla, S.: Text Mining and Analysis; Practical Methods, Examples, and Case Studies Using SAS®. SAS Institute Inc., Cary, NC (2013) 2. Liu, B.: Sentiment Analysis and Opinion Mining, ISBN-10: 1608458849, ISBN-13: 978– 1608458844, Morgan & Claypool Publishers (2012) 3. Buneman, P.: Semistructured data. In: Proc. ACM Symposium on Principles of Database Systems, pp. 117–121, Tucson, AZ., Abstract of invited tutorial (1997) 4. Moldovan, D., Girju, R.: An interactive tool for the rapid development of knowledge bases. Int. J. Arti. Intell. Tools (IJAIT) 10(1–2), March (2001) 5. Feskov, I.V.: NU “OUA” basic methods of hybrid warfare in the modern information society. Current Policy Issues 58, 66–76 (2016) 6. Pankratova, N.D., et al.: Foresight and Forecast for Prevention, Mitigation and Recovering after Social, Technical and Environmental Disasters. In: Teodorescu, H.-N., Kirschenbaum, A., Cojocaru, S., Bruderlein, C. (eds.) Improving Disaster Resilience and Mitigation - IT Means and Tools. NSPSSCES, pp. 119–134. Springer, Dordrecht (2014). https://doi.org/10. 1007/978-94-017-9136-6_8

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7. Pankratova, N.D., Savastiyanov, V.V.: Modeling of alternative scenarios of the process of technological foresight. In: Gorelova, G.V., Pankratova, N.D. (eds.) Innovative Development of Socio-Economic Systems Based on Methodologies of Foresight and Cognitive Modeling. Scientific opinion, Kiev, pp. 344–360 (2015) 8. Gladwell, M.: The Tipping Point. How Little Things Can Make A Big Difference. Little, Brown & Company, Boston (2001) 9. Webb, J., O’Brien, T. (eds.): Big Data Now: Current Perspectives from O’Reilly Media. ISBN: 978–1–449–37420–4. O’Reilly Media, Inc., Sebastopol, CA (2013) 10. Agrawal, D., Bernstein, P., Bertino, E., Davidson, S., Dayal, U.: Challenges and opportunities with Big Data. Cyber Center Technical Reports 2011–1 (January 2011). Purdue e-Pubs, Purdue University (2011), https://docs.lib.purdue.edu/cgi/viewcontent.cgi? referer=https://www.google.com/&httpsredir=1&article=1000&context=cctech, last accessed 2021/09/04 11. Lu, Y., Huizhong, D., Hongning, W., Cheng, X.Z.: Exploiting Structured Ontology to Organize Scattered Online Opinions. In: Proceedings of the 23rd International Conference on Computational Linguistics (COLING’10), Beijing, August 2010, pp. 734–742, ACM, ACM Digital Library (2010). https://doi.org/10.5555/1873781.1873864, last accessed 2021/09/04 12. Rezaeian, M., Montazeri, H., Loonen, R.C.G.M.: Science foresight using life-cycle analysis, text mining and clustering: A case study on natural ventilation. Technological Forecasting and Social Change 118, 270–280. Elsevier (2017). https://doi.org/10.1016/j.techfore.2017. 02.027 13. Kayser, V., Blind, K., Dreher, C.: Extending the Knowledge Base of Foresight: The Contribution of Text Mining. Technische Universität Berlin, Berlin (2016) 14. Savastiyanov, V.V.: Tehnolohycheskoe predvydenye ynformacyonno-komp’yuternыx texnolohyj svyazy. [Technological foresight of information and computer communication technologies.] System research and information technologies (2005). (in Russian) 15. Kudryavtsev, D., Gavrilova, T., Smirnova, M., Golovacheva, K.: Modelling Consumer Knowledge: the Role of Ontology. Procedia Computer Science (Part of special issue: Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020) 176, 500–507. Elsevier (2020). https://doi.org/10. 1016/j.procs.2020.08.052 16. Karpov, I., Burov, E.: Use of ontological networks in decision supporting systems in the conditions of ambiguity. Journal of Lviv Polytechnic National University “Information Systems and Networks” (SISN) 7, 8–15 (2020). https://doi.org/10.23939/sisn2020.07.008 17. Palagin, A.V., Petrenko, N.G.: System-ontological analysis of the subject area. USiM 4, 3– 14 (2009) 18. Savastiyanov, V.V.: Development of tools for analysis of texts of public and specialized sources in the tasks of prediction and system analysis. Sys. Res. Info. Technolo. 4, 10–23 (2020) 19. Pankratova, N., Savastiyanov, V.: Assessment of situations in the field of social disasters basing on the methodology of foresight and textual analytics. In: Proceedings of the 2019 IEEE Second International Conference IEEE UKRCON-2019, pp. 1207–1210, ISBN 9781728138831. IEEE (2019) 20. Pankratova, N.D., Savastiyanov, V.V.: Modeling of alternative scenarios of the process of technological foresight. Sys. Res. Info. Technol. 1, 22–35 (2009) 21. Gavrilova, T., Gorovoy, V., Bolotnikova, E.: New ergonomic metrics for educational ontology design and evaluation. SoMeT 2012, 361–378 (2012) 22. Classifiers, http://www.ukrstat.gov.ua/klasf/zm_kls.htm, last accessed 2021/01/01

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23. MINISTERSTVO YУSTYCIYI UKRAYINY. NAKAZ 02.06.2004 N 43/5 Pro zatverdzhennya Klasyfikatora haluzej zakonodavstva Ukrayiny [MINISTRY OF JUSTICE OF UKRAINE. ORDER 02.06.2004 N 43/5 About the statement of the Classifier of branches of the legislation of Ukraine.] https://zakon.rada.gov.ua/laws/show/v43_5323-04, last accessed 2021/08/11. (In Ukrainian) 24. IPTC7901: The text transmission format. International Press Telecommunications Council (IPTC). https://www.iptc.org/standards/iptc-7901/, last accessed 2021/08/11 25. Walden, J.: Comparison of the STEEPLE strategy methodology and the Department of Defense’s PMESII-PT methodology. Supply Chain Leadership Institute, 1–14 (2011) 26. Porter, M.: The Five Competitive Forces That Shape Strategy. Harvard Business Review, Boston, MA (2008) 27. Humphrey, A.: SWOT Analysis for Management Consulting (PDF). SRI Alumni Newsletter. SRI International (2005). Humphrey, A.S. (December 2005). SWOT Analysis for Management Consulting. SRI Alumni Newsletter. SRI International. pp. 7–8. Archived from the original on 2013–01–04 (December 2005)

Ontological Problems of System Analysis Michael S. Mokiy1,2(&) 1

Russian Presidential Academy of National Economy and Public Administration, Prospect Vernadskogo 82, 119571 Moscow, Russia [email protected] 2 State University of Management, Ryazansky Prospekt 99, 109542 Moscow, Russia

Abstract. The article substantiates the thesis that the creation of universal, transdisciplinary methods of system analysis is impossible without solving ontological problems of systems theory. Based on the consideration of the evolution of the system approach, the authors have concluded that the main problem of creating a universal approach is the absence of a system-forming factor in the definition of the system. The use of the synthesis of philosophical and methodological principles of holism and unicentrism made it possible to designate as a system-forming factor the order that determines the unity and integrity of the object of the system. The postulation of the uniqueness of the order made it possible to substantiate the system dualism and draw a conclusion about the constancy of system functions and the variability of the structure and parameters of the main function. The article illustrates that for the system analysis of objects it is necessary to identify the basic elements, to study the structure and functions of the object. Keywords: System analysis
Ontology of the system approach dualism
Structural and functional analysis of the system


System

1 Introduction Any analysis involves the separation (mental or real) of an object into its component parts, elements for understanding their interaction with each other. Since the analysis relates to synthesis, two types of analysis can be distinguished depending on the objectives of the study: 1. In the analysis of the first type, the researcher makes a “division into component parts” to understand what the object under study is. The purpose of such an analysis is to clarify the composition of the elements, to determine the place and role of each element and the relationships between them, as well as to determine the parameters of the relationships and the state of the elements. As a result, an understanding of the object as a whole is formed. 2. In the second type of analysis, it is assumed that the researcher knows the elements that make up the object, their place, role and connections between them, as well as the parameters of the state of the elements and connections. That is, there is a certain standard, an idealized image of an object as a whole. In this case, the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 89–99, 2022. https://doi.org/10.1007/978-3-030-98832-6_8

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purpose of the analysis is to identify deviations in the state of existing elements and the connections between them, from the position of some idealized object. When it comes to system analysis, the second type of analysis is most often implied. The assumption is that the researchers who conduct a system analysis know what a system is and what are the patterns of its existence and development. However, despite the successes achieved by system scientists, the question of what a system is remains debatable. To paraphrase Lionel Robbins’ famous expression about the economy [20], we can say that when talking about the system, “we are all talking about the same thing, but we have not yet decided what exactly.” As the analysis shows, despite the large number of national and international organizations dealing with various aspects of systems research and a huge amount of literature devoted to them, there are currently more than forty definitions of the term “system”. Consequently, there are more than forty “idealized images” of the system. The tasks of this article do not include a critical review of the definitions of the system. However, I would like to note one important feature, the authors of the definitions of the term “system” are biologists, geologists, engineers, mathematicians, etc., that is, scientists disciplines. Therefore, when forming the concept of “systems”, they proceeded from those essential features that they found in the study of objects that they called systems. Moreover, as P.K. Anokhin quite rightly noted, in this case, the task was not to develop a systematic “methodology in general”, but “the methodology of my case”. [2]. As a result, an extensive field of system-disciplinary approaches and corresponding systemdisciplinary methods of analysis and design has been formed [17]. On the other hand, the systematic approach has been positioned since its inception as a “new approach to the unity of science”. This was the name of an article written by L. Bertalanffy in 1951 [4]. It is obvious that the creation of unified principles of system analysis necessitates the clarification of ontological issues in the theory of systems. In this article, under the main ontological problem of the system approach, we will understand the answer to the question, “what is a system?” Such a definition should contain some universal features that allow using this understanding to build universal system models.

2 Materials and Methods Since its inception, a lot has been done in the field of systems research in the field of systems theory and system analysis. The fundamental work of Charles Francois “International Encyclopedia of Systems and Cybernetics” contains the most detailed list of theoretical and methodological aspects in the field of systems approach [9]. This work contains about four thousand articles that describe most of the ideas and works on the theory and practice of the systems approach and cybernetics. V.N. Volkova, in her works, describes the presentation of the systematized history of the development of the system approach and system analysis in our country in detail [24]. One of the first works that attempts to stage the development of systems research from the standpoint of their methodology and ontology is the work of Derek and Laura Cabrera and Gerald Midgley “Four Waves of Systems Thinking” [5]. When staging systems thinking, the

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authors use an analogy with waves that bring and carry various objects ashore. It is worth noting that the “waves” in this article and the “epochs” in the work of V.N. Volkova coincide in features and only slightly differ in years. The first wave (the epoch according to Volkova) from the 50s to the 70s of the last century is characterized by “rigid systemic thinking”. This is because in many industries there were objects that already bore the name of systems, for example, the nervous system, monetary system, power supply system, communication system, etc. The emergence of complex technical systems, computers and the need for software for them led to the emergence of a whole family of disciplines and scientific directions using the terminology of the systems approach and cybernetics. System engineers and operations research specialists have applied and continue to apply a systematic approach in order to optimize the functioning of these systems using mathematical methods and models. The second wave is known as “soft systems thinking” and covers the period from the late 70s to the late 80s. The emergence of “soft systems thinking” is associated with attempts to apply a systematic approach to the analysis of problems of the development of nature and society. This wave began with the works of P. Checkland [6], R. Akoff [1], Robert Mason [12] and others. Awareness of the relationship between the controlling and controlled system, the subject of management and the object of management led to the emergence of cybernetics of the second order. The third wave is called “critical systems thinking”. D. and L. Cabrera have designated its time limits from the late 80s to the beginning of 2000. The works of Michael Jackson and Robert Flood named this wave [7, 8]. The third wave is connected with attempts to extend system laws to the development of society, global problems. However, the world around us is so multifaceted that methodological pluralism, including in the systemic approach, is necessary and welcome. Notably, the common denominator for most researchers working within the framework of the third wave is the use of the methodological principle of holism. Awareness of the interconnection and interdependence of the existence of people and other elements of the planetary system led to the consideration of integrity (holism) as the main essential feature of the system in general. Another significant feature of systems characteristic of works using systemic rhetoric is the so-called “complexity”. Despite the extremely vague criteria for determining complexity and criticism of this concept, the term “complexity” has become widespread. For example, the work of Donella Meadows “Thinking in the system” [13] is currently one of the most cited. The already mentioned Michael Jackson in his recent works connects the methodology of the system approach with various aspects of complexity [10]. The first and second waves led to the development of mathematical modeling, analysis and design of technical and organizational complexes. With the advent of the third wave, the solution of global problems such as global traffic, financial markets, the Internet and social networks actualized the use of the holism principle and complexity theory. This led to an even greater interest in the mathematical apparatus of research. The active growth of this direction was facilitated by the emergence and spread of various cyberphysical systems, that is, objects in which the ability to compute, communicate and store information is integrated with the management of objects of the physical world, for example, “smart house” or “smart city”, etc. Cybernetics of the third order has been designated.

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The unresolved ontological problems in the general theory of systems has led to the fact that the focus of systems research has shifted to the practical plane. The analysis of the topics of research works of organizations dealing with the problems of system analysis indicates that the majority of national organizations continue the mathematical direction and work on the creation and improvement of information technologies. Thus, this topic is the subject of research in most national societies and institutions dealing with systemic topics. Interest in systems research has led to the emergence of international organizations that unite the efforts of system scientists around the world. A list of these organizations is provided in the appendix. Studying the topics of conferences and research conducted in these organizations allows us to conclude that ontological issues of systems theory are not a particularly popular theme. Essentially, the necessity of solving ontological issues when creating a General theory of systems is noted [21]. After a long break in research devoted directly to system theory, the work of George Mobus and Michael Kalton “Principles of System Science” published in 2015 [14] appeared. This work reflects all the methodological approaches characteristic of the third wave – the “soft” system approach, holism, complexity, mathematical models. As it was indicated at the beginning of the section, the interest in the system approach worldwide is because the system approach is an approach to the unity of science. Therefore, Derek and Laura Cabrera rightly called the fourth wave “versatility and diversity”. While agreeing with this name of the wave, we cannot agree that the search for universal properties characteristic of all systems began with the works of L. Bertalanffy. Theories that have a universal character, common to all branches of theory and practice, appeared at the beginning of the twentieth century. Therefore, A.A. Bogdanov published three parts of the Universal Organizational Science from 1913 to 1922, that is, long before L. Bertalanffy. It is impossible not to mention the works of P. K. Anokhin on the creation of the theory of functional systems. Despite the fact that this theory was published in its most complete form in the 70s of the last century, P.K. Anokhin began to develop it in the 30s. As the analysis shows, the main ideas about the ontology of the general theory of systems were published in the well-known yearbook “System Studies” in the period from 1969 to 1981, which was published under the auspices of the USSR Academy of Sciences [23]. Such authors as L. Bertalanffy, A.I. Uemov, V.N. Sadovsky, E.G. Yudin, M. Mesarovich, A. Rappoport and many others, outlined ontological ideas that became the basis for subsequent generations of system engineers in our country. In the context of ontological representations of the system, most modern Russian works on system analysis and systems theory are a paraphrase of the ideas expressed in these works. Then, during the period of perestroika, scientists discovered A. Bogdanov’s “Tectology”, but A. Bogdanov’s ontological ideas about the system do not allow them to become universal, transdisciplinary. In foreign literature, the ontological issues of the system approach are considered in the works of scientists engaged in system philosophy, starting with the work of Erwin Laszlo “Introduction to System Philosophy: towards a new Paradigm of modern Thinking” in 1972 [11]. In connection with the search for universal approaches to research, Ken Wilber’s Theory of Everything should be mentioned [25]. The concepts of “holon” and “holarchy” have significantly enriched the ontology of holism and the systemic approach.

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The systematic approach still seems to be one of the main contenders for the creation of such universal ontological and methodological ideas and the formation of systemic thinking. However, back in the 70s of the last century, P.K. Anokhin wrote, “1. A theory can gain the right to become general only if it reveals and unites such patterns of processes or mechanisms that are isomorphic for different classes of phenomena. 2. Isomorphism of phenomena of various classes reveals itself only if we find a sufficiently convincing criterion of isomorphism. The more significant this criterion is for the phenomena under analysis, the more pronounced their isomorphism is. 3. For the adoption of a “general theory of systems” suitable for various classes of phenomena, the most important criterion of isomorphism, of course, is the isomorphism of the system-forming factor” [3]. Thus, this system theory should provide such ontological representations of the system that can be used for all objects studied as systems. In other words, such ideas should be transdisciplinary or (in the words of V.N.Volkova) – general scientific.

3 Results Systems theory as a branch of research presupposes the existence of some idealized image of the object of study. The idealized image of the object of study is a definition as a description of the essential properties of the object, that is, properties without which the object cannot exist and which are present in it under any conditions. Such idealized images of an object exist in every branch of science. The literature devoted to the system approach, identifies such signs of systems as elements, connections, orderliness, emergence, integrity and unity. Apparently, these signs can be attributed to some ideal image of the “system”. However, when studying the so-called self-organizing systems, an intractable problem arises, which V.N. Sadovsky designated as a paradox of system research. It sounds like this: “in order to correctly identify a self-organizing system, we need to know the conditions and reasons for self-organization; in order to understand these conditions and causes; we must single out a self-organizing system as a necessary moment for their theoretical study” [22]. In other words, in order to make the understanding of the system universal, it is necessary to identify a system-forming feature. By the way, P.K. Anokhin wrote about it: “… the absence of a system-forming factor makes it impossible to establish isomorphism between phenomena of different classes, and, consequently, cannot make the theory general” [3]. Our research has allowed us to conclude that such an essential feature of the system is orderliness or order [16]. Order in this case signifies the correct state of something, a good organization of something. It is the order in the form of a certain standard that determines the necessary quantity and quality of elements and connections in the system, determines the value of each element, its place and role in this system. These arguments allow us to interpret the idealized object “system” as the order of occurrence and existence of elements and relations between them, which determines the unity and integrity of the object ontologically.

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However, as we pointed out earlier, in order for the above ontological concepts to have a universal, transdisciplinary character, it is necessary that the order of origin and existence be uniform. The synthesis of the philosophical and methodological principles holism of Y. Smuts and unicentrism of V.S. Mokiy [18] allowed substantiating the possibility of the existence of a single order in the system. The features of the manifestation of such a unified order in the system are as follows. The main function of the idealized object “system” is the transformation of matter and energy in time and space. The functions of the system are its properties, the ability to perform the necessary actions. However, the implementation of functions is possible only if there are appropriate “performers” or carriers of functions, that is, a certain mechanism or set of mechanisms that carry out these functions or structures of the system. In this sense, the structure of the system represents the elements and the relationships between them. Thus, the idealized object “system” represents the unity of functions and structure. In terms of dialectics, the functions and structure of the system relate to each other as content (functions) and form (structure). In order for the system to perform the functions of converting matter and energy, the existence of a certain ideal is necessary, based on which the system arises as a real object. That is, the existence of an idealized image of the “system” presupposes its existence in two hypostases - as a kind of standard of the system and as its real embodiment. The appearance of the system as a real object is impossible without the existence of a standard. In other words, when studying an object as a system, we must imply the presence of a standard. This is the so-called dualism of systems thinking. To manifest the system as a real object, the presence of so-called basic elements is necessary. The basic elements form the elements of the structure and connections between them, according to the standard. It is worth indicating that that P.K. Anokhin, when developing the theory of functional systems, pointed out another very important feature of the unified order. “Any component can enter the system only if it contributes its share of assistance in obtaining the programmed result” [2]. That is, the properties of all elements of the system are deterministic and make sense only if they contribute to achieving the result or parameters of the main function. In this regard, P.K. Anokhin introduces the term “mutual cooperation” instead of the terms “interaction” or “interconnection” used in the definitions of the system. Moiseev N.N. pointed out the same property of the codirected existence and development of elements and called it coevolution [15]. In other words, the coevolutionality of the order is a system-forming property of the system and ensures its unity. The need to act “to obtain a programmed result” requires a description of the quantitative and qualitative parameters of the main function. These parameters should be in the standard of the system. The presence of a system's standard provides the ability to monitor the state of the object at every moment of its existence. 3.1

System Functions

The fulfillment of the main function is possible when performing two functions - selfpreservation and system development. The implementation of the self-preservation function is expressed in the desire of the system to maintain qualitative certainty.

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Therefore, at each stage of the development of the system, the quantity and quality of the main elements in the structure of the system and the connections between them is constant. In physics, this regularity is referred to as the Le Chatelier-Brown principle, in biology - homeostasis, etc. The realization of the development function is the appearance of new structural elements in accordance with the standard and the actualization of functions. At the same time, the most effective method is selected. With fixed result parameters, the least expensive method of performing functions is selected. In physics, this principle is known as the Hamilton principle. The fulfillment of the functions of existence and development involves the process of transformation of matter and energy in accordance with the parameters of the main function. At each stage of this process, the following functions must be performed. - the function of constant analysis of the parameters of the external and internal environment; - the function of assessing and anticipating the consequences of changing parameters; - decision-making function; - the function of evaluating options for action; - the action function; - the function of evaluating the result of an action; - comparison function with parameters; - the function of neutralizing dysfunctions. The presented set of functions is an augmented classification of P.K. Anokhin's system functions. Universality, transdisciplinary of the order of existence and development of objects considered as systems is ensured by the isomorphism of system functions. That is, the set of functions is always the same. 3.2

System Structure

The structure of the system as a mechanism for performing functions consists of a set of elements-carriers of functions. All the necessary elements of the structure are formed from the basic elements of the system. According to the standard, the parameters of the main function change at each stage of system development. In accordance with this, optimization (complication) occurs structures. At the same time, deviations from the reference parameters are possible at each stage of the system development. In this case, the mechanism of neutralization of dysfunctions should ensure the return of the system state to the reference values. However, in both cases, the achievement of the reference parameters of the object occurs due to a change in the state of the system. A change in the state of the system occurs by changing the state of the basic elements and the connections between them. The direction of changes in the state of the basic elements is because the parameters of the objective function provide strict determinism with respect to the quantity and quality of matter and energy. Therefore, these parameters are mandatory for each element of the structure. Such changes are conditioned by the requirement of coevolutionary development. This can be illustrated

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by the fact that the parameters of the main elements and structural elements should contribute to achieving the parameters of the main function. The transition to the next stage of system development can occur only when the number of modified basic elements reaches a “critical mass”. As a result, the structure of the system changes, that is, new elements of the structure appear and (or) the connections between them change. From the point of view of the above ontological concepts, system analysis involves the analysis of an object in functional and structural aspects. At the same time, special attention should be paid to the analysis of the parameters of the objective function, since these parameters determine the development of the system. The unity of the system functions makes it possible to identify the elements of the structure that implement these functions, as well as to identify the basic elements of the system.

4 Discussion Of course, the creation of a full-fledged general theory of systems and methodological apparatus for system analysis, forecasting and planning requires further research. However, even in this form, the described system dualism and the universal structural and functional features of the idealized object “``system” allow. - Correctly identify the object under study as a system, as something unified, and look for manifestations of structure and functions in the selected object; - to identify systemic patterns at objects for which search and verification experiments are possible, and then extrapolate these patterns to objects and processes where conducting such experiments is impossible or very costly; - When solving multifactorial problems, it is possible to interpret the knowledge gained through disciplinary methods from a single point of view; - To identify those relationships between and elements that could not be identified with the help of disciplinary research methods. The ontological representation of the system as about an idealized object needs methodological tools that have a transdisciplinary character. In our case, models of units of order developed within the framework of transdisciplinarity-4 [18] were used for this purpose. The uniform order and universality of the above models, which follows the principle of unicentrism, allows them to be used both in the study of any objects considered as systems and in the creation of the methodology of system analysis itself. In accordance with the information model of the order, depending on the objectives of the analysis, it is necessary to allocate either two, four, or eight functions, the corresponding number of structural elements, and basic elements. Models of the temporal unit of the order allow us to consider the development of the system not as a continuous growth, but as a strictly defined sequence of stages, periods, and cycles. This is a very important point for determining the parameters of the main function at each stage of the system development. If the objective function of the system, in general, is formulated in this concept as the transformation of matter and

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energy, then in each industry it is necessary to specify the goal itself and the qualitative and quantitative parameters of the goal. Models of the spatial unit of order and the mechanisms of the influence of objects on each other described in them give a fundamentally new possibility for analyzing the spatial structure of an object.

5 Conclusion The potency of the system approach as a universal, transdisciplinary research method suggests that a researcher can use system analysis for any object of reality that is considered as a system. To do this, an intersubjective understanding of the system in general must be formed. The proposed ontological representation of the system as an order that determines the unity and integrity of the object allows us to describe such an order. The synthesis of philosophical and methodological principles of unicentrism and holism allowed us to substantiate the existence of a single order that manifests itself in each object theoretically. In addition, the order models developed within the framework of the methodology of transdisciplinarity-4 allow them to be used in the system analysis of complex objects. The described ontological representations are universal, transdisciplinary in nature, and therefore serve the idea of the fourth wave in systems thinking. The understanding of the system as an order, the unity of the structure and functions of the system, the standard of the system and its manifestation in time and space, the coevolutionality of the development of the elements of the structure include, in our opinion, all the system signs and laws discovered today. This means that the prepositional ontology will make it possible to give system analysis greater universality and transdisciplinarity than it is today.

Appendix

1. Bertalanffy Center for the Study of Systems Science (BCSSS). Homepage, http:// www.bcsss.org, last accessed: 2021/08/17. 2. European Union for Systemics, http://aes.ues-eus.eu, last accessed: 2021/08/17. 3. Institute of Applied Systems Analysis (Ukraine). Homepage,http://iasa.kpi.ua/?set_ language=ru, last accessed: 2021/08/17. 4. Institute of System Analysis (Russia). Homepage,http://www.isa.ru/index.php? option=com_content&view=article&id=253%3A2009-10-08-11-34-16&catid= 42%3A2009-06-11-08-49-38&Itemid=76&lang=ru, last accessed: 2021/08/17. 5. International Academy for Systems and Cybernetic Sciences (IASCYS). Homepage, http://iascys.org, last accessed: 2021/08/17.

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6. International Federation for Systems Research (IFSR). http://www.ifsr.org, last accessed: 2021/08/17. 7. International Institute for Applied Systems Analysis (IIASA). Homepage, https:// iiasa.ac.at/web/home/research/twi/TWI2050.html, last accessed: 2021/08/17. 8. International Society for the Systems Sciences/ Homepage, http://isss.org/world/ bulletins, 9. Systems Research Institute (India). Homepage, https://ru.abcdef.wiki/wiki/ Systems_Research_Institute_(India), last accessed: 2021/08/17. 10. Systems Research Institute, Polish Academy of Sciences (SRI PAS). Homepage, https://www.ibspan.waw.pl/en/history-and-research/, last accessed: 2021/08/17. 11. UK Systems Society. Homepage, www.ukss.org.uk, last accessed: 2021/08/17. 12. World Organisation of Systems and Cybernetics (WOSC). Homepage, http://wosc. com, last accessed: 2021/08/17.

References 1. Ackoff, R.L.: Creating the Corporate Future: Plan or be Planned For. Wiley, New York, NY (1981) 2. Anokhin, P.K.: Filosofskie aspekty teorii funkcional’noj sistemy: izbrannye tr. [Philosophical aspects of the theory of a functional system: selected tr.] In: Konstantinov, F.V., Lomov, B.F., Shvyrkov, V.B. (eds.); USSR Academy of Sciences, Institute of Psychology. Nauka, Moscow, p. 6. (1978). (In Russian) 3. Anokhin, P.K.: Fundamental'nye voprosy obshchej teorii funkcional'nyh sistem. [Fundamental questions of the general theory of functional systems]. p. 9. Nauka, Moscow, (1973). (In Russian). http://www.bio.bsu.by/phha/downloads/anohin_obscaia_teoria_ fs.pdf3, last accessed 2021/09/20 4. Bertalanffy, L.: General system theory: a new approach to unity of science. John Hopkins Press, Baltimore, MD (1951) 5. Cabrera, D., Midgley, G., Cabrera, L.: The Four Waves of Systems Thinking. Handbook of Systems Thinking. Routledge (2021). https://www.academia.edu/45434072/, last accessed 2021/09/20 6. Checkland, P.: Systems Thinking. Systems Practice. Wiley, New York (1981) 7. Flood, R.L., Jackson, M.C.: Critical systems thinking: directed readings. Chichester. J. Wiley, New York, NY (1991) 8. Flood, R.L., Romm, N.: Critical Systems Thinking: Current Research and Practice. Springer Science & Business Media (1996) 9. François, C.: International Encyclopedia of Systems and Cybernetics. Walter de Gruyter, Berlin (1997) 10. Jackson, M.C.: Critical Systems Thinking and the Management of Complexity 1st Edition: Wiley, New York, NY (2019) 11. Laszlo, E.: Introduction to Systems Philosophy: Toward a New Paradigm of Contemporary Thought. Gordon & Breach, New York and London (1972) 12. Mason, R.O., Mitroff, I.I.: Challenging Strategic Planning Assumptions: Theory, Cases, and Techniques. Wiley, New York, NY (1981) 13. Meadows, D.: Thinking in Systems. Chelsea Green Publishing, London (2008) 14. Mobus, G., Kalton, M.: Principles of Systems Science. Springer Verlag (2015)

Ontological Problems of System Analysis

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15. Moiseev, N.N.: Koevolyuciya prirody i obshchestva. Puti noosferogeneza. [Coevolution of nature and society. Ways of noospherogenesis.] Ecology and Life, no. 2–3 (1997). (In Russian) 16. Mokiy, M.S.: Transdisciplinarnaya metodologiya v ekonomicheskih issledovaniyah. [Transdisciplinary methodology in economic research.] Dissertation for the degree of Doctor of Economics. specialty 08.00.01 “Economic theory”. Plekhanov Russian University of Economics, Moscow (2010) 17. Mokiy, M.S., Mokiy, V.S.: Transdisciplinarnaya metodologiya v ekonomicheskih issledovaniyah. [The use of interdisciplinary synthesis of knowledge in solving problems of socio-economic development.] In: Bondarenko, V.M. (ed.) Collection of scientific papers of participants of the International conference “XXIV Kondratiev readings” (2017). (In Russian) 18. Mokiy, V.S., Lukyanova., T.A.: Metodologiya nauchnogo issledovaniya. Transdisciplinarnye podhody i metody. [Methodology of scientific research. Transdisciplinary approaches and methods : a textbook for undergraduate and graduate studies]. Yurayt Publishing House, Moscow, 160 p. ISBN 978-5-53405207-7 (2017). (In Russian). https:// urait.ru/bcode/409126, last accessed: 2021/08/17 19. Professional standard: 06.022 System Analyst. https://classinform.ru/profstandarty/06.022sistemnyi-analitik.html, last accessed: 2021/08/17 20. Robbins, L.: The Subject-Matter of Economics. In: Robbins, L. (ed.) An Essay on the Nature and Significance of Economic Science. 2nd ed., ch.1, p.1. Macmillan, London (1935) 21. Rousseau, D: Homepage. https://www.systemsphilosophy.org/david-rousseau.html, last accessed: 2021/08/17. 22. Sadovsky, V.N.: Osnovy obshchej teorii sistem. [Foundations of the general theory of systems]. p.194. Nauka, Moscow (1974) 23. Sistemnye issledovaniya. [System research.] Yearbook. Published House “Nauka”, Moscow (1969–1981). http://systems-analysis.ru/systems_analysis_book.html, last accessed: 2021/08/17 24. Volkova, V.N.: Iz istorii teorii sistem i sistemnogo analiza. [From the history of systems theory and system analysis.] Publishing House of SPbGPU, St. Petersburg (2001) 25. Wilber, K.: A Brief History of Everything. Shambhala, USA (2000)

System Analysis of the Intelligence Structures Evolution Olga Shipunova(&) Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya Street 29, 195251 St. Petersburg, Russia [email protected]

Abstract. The article is devoted to the system methodology and philosophical analytics integration in explaining the regularity of the evolution of the intellectual structure. The article's purpose is to analyze the factors of the genesis of intelligence structures as functional systems due to the dynamics of social selforganization. The conceptual basis for the analysis of the intelligence structure's evolution is the macro-determination principle of the complex organized system with internal dynamics. The boundaries of the whole system's existence are a necessary condition in the organization of adaptive behavior of its elements and their interrelation. It is emphasized that in the human psyche evolution, it is the social way of life and its complication that stimulate the development of intelligence structures, the functions of which ensure the social coherence in the natural forms of speech-thinking activity. In this vein, the systemic factors of the intelligence structures evolution in genetic models created based on psychology, sociobiology, and social anthropology are considered. It is shown that the structures of AI are functionally inscribed in a multi-level system of social memory and a communicative network. It is concluded that the socio-genetic installation in the system analysis for the artificial and natural intelligence ratio allows us to present in a new way the regularity of cognitive evolution in human history and the prospects for the smart technologies development of digital society. Keywords: Philosophy of intelligence
Cognitive evolution
Genetic models
Communicative factors
Cognitive hyper-net
Artificial intelligence

1 Introduction The characteristic features of modern civilization development are associated with the intensive introduction of smart technologies in various spheres of professional and social interactions. Achievements in the smart innovations field are represented, among others, by systems with spatial display in an augmented reality environment, which are created to control a full-size virtual industrial robot in a natural mode of operation without additional devices [1]. Robots with artificial intelligence are used on platforms in the digital world everywhere, as the authors of the development of interactions in private and public spaces emphasize using the example of Amazon.Com applications [2]. It is assumed that the automation of unique processes of solving problems, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 100–108, 2022. https://doi.org/10.1007/978-3-030-98832-6_9

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accompanied by artificial intelligence, will become the basis for the integration of technologies for Industry 4.0 [3]. Along with the use of AI in the analysis processes of complex situations in individual and social activities, the action autonomy of an intelligent system is emphasized, when information processes and decision-making proceed independently of direct communication with a person and his intellect. In parallel, there is a desire to clarify the essence of intelligence as a phenomenon that characterizes the social world and the human psychophysiological nature. Artificial intelligence technology offers a variety of structures and reasoning strategies for analyzing thinking processes, notes George F. Luger [4]. However, this is not enough to achieve a complete imitation of a person in the knowledge of the world. It is necessary to develop an interdisciplinary epistemology to study the actions of people. 1.1

Problem Formulation

The problem of the intelligence structures evolution lies in the interdisciplinary field of modern science and philosophy. The advent of the systems sciences has promoted the formation of a general scientific conceptual apparatus and cognitive strategies for systemic, functional, informational approaches. The task of this article is to present the genesis of intellectual structures within the framework of the system methodology, to show the regularity of the functions of intellectual structures, which is due to the communicative network of society. The author considers the norms of the social way of life as the fundamental basis of intelligence genesis. The methodology of system analysis in the context of socio-genetic installation allows us to show the regularity of the intelligence structures’ emergence in the communicative space of a complex organized society. This aspect is aimed to identify the conditions that stimulate the genesis of intelligence structures in the history of culture and modern society. 1.2

Literature Review

In the scientific and philosophical literature, the status of intelligence appears to be quite ambiguous [5]. In psychological theories, intelligence is a human mental process. The functional specificity of intelligence in the cognitive psychology view is shown through the organization of mental experience and mental activity [6]. In the philosophical tradition, intelligence is identified with reason and thinking. That is interpreted as the generic quality of humans. Biologists investigate the intelligence phenomenon using the neurons and neural networks’ actions [7]. As the general scientific concept, the intelligence nature is examined in the context of the information paradigm. In this context, its functions correlate with expanded ideas about cognitive processes taking in any systems operating with knowledge in their actions [8–11]. The methodology for creating artificial intelligence is represented by a computer metaphor. In this view, the thinking processes and information processing are identified based on the similarity of the operation with knowledge systems [12, 13]. The idea of the functional systems containing specific elements of information nature (codes, languages, operations, and strategies) is the conceptual basis for the development of presenting knowledge and creating Big Databases.

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In the interpretation of artificial intelligence (AI), two positions can be distinguished. The first point of view connects AI with the field of systems engineering aimed to imitate human actions. Artificial intelligence is represented by a set of technological solutions modeling human cognitive functions, including self-learning and the ability to solve tasks. In particular, G.F. Luger considers artificial intelligence as a special field of computer science designed to automate intelligent behavior [14]. On the other hand, AI is interpreted as a techno-social phenomenon based on the independent activity of information processes in complex computing systems. In this case, the autonomy of the smart-system actions is emphasized. The information processes and decision-making proceed independently of the direct connection with a person and his intellect. A comparative analysis for the functioning of a living organism and a machine remains the main cognitive strategy in the design of intelligent robots since the time of Leonardo da Vinci. He designed a mechanical knight capable of sitting, standing, moving his arms [15]. A. Turing in [16] marked the beginning of control programs for autonomously operating industrial and military robots. Manipulators with digital control have been used in the automotive industry already in the 50s of the 20th century. Social robots acting as artificial companions and assistants for people in their daily lives appeared in the 21st century. Their design specificity is determined by the achievement of maximum comfort in communication, imitation of human appearance, and natural behavior, including the expression of emotions [17]. Although machines can simulate human behavior, play chess, and perform quite reasonable actions, the content of AI does not go beyond the calculation algorithms within the framework of the mathematical theory of information transmission. Increasing the speed of information exchange in databases is focused on the quantitative assessment of digital technology. There is no priority of the human life value. A direct analogy with the ability of emotional interaction with people and rational motivation in the form of artificial awareness looks unlikely [18].

2 Methodology The methodological basis for the proposed analysis of the intelligence structures evolution is the principle of the complex system internal dynamics macrodetermination by environmental conditions. The environment pressure is expressed in the limitation of the potential functioning for the elements of complexly organized integrity. And this pressure acts as a systemic reason for changing the norm of their adaptation when the macro-characteristics of living space change. The living conditions affect elements’ behavior and interrelations without affecting their physical structures and processes. In such a functional view modern biology interprets the role of the communicative signal in the populations and the evolution of cognitive structures of the psyche. There is a lot of factual material about communicative gestures and sound features of such signals, understood in the population unequivocally in a stressful situation [19]. Genetically, communicative signals (sounds, gestures) are not tied to a specific physiological organ (for example, to the digestive organ), but an instinct as a functional system of self-preservation at the level of an adaptive norm.

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The socio-genetic attitude in this study integrates the methods of philosophy analytics and system analysis for the cognitive evolution explanation in human history, highlighting information and communication processes as a factor for the intelligence structures genesis. In human history, the dynamics of communication in the community generates a new level of mentality which is necessary for the society's self-organization and meets the requirements of information control in speech communication processes. The speech culture of communication has become an adaptively valuable form of protection and preservation of social community. It ensured the survival of the species Homo sapiens for thousands of years. The need to include each individual into the discursive practice of community selforganization stimulated the development of the psyche's new structures and functions of the higher nervous activity in the form of a second signaling system of the brain. In the modern world, the evolution of the brain as the most variable organ in the human structure does not stop. It is necessary to synchronize the speed of morphological differentiation of the brain in a rich environment of social interactions to form the functional foundations of human consciousness [20].

3 Results 3.1

Genetic Model of Intelligence by J. Piaget

J. Piaget put forward the idea that the development of intellectual structures (as logical operations) is generated by the connection between the subject and the object [21]. He determined a fundamental feature of intellectual activity as the constancy of three cognitive strategies: the analysis of properties, the study of relationships, the construction of a verbal (formal-logical) model. He owns the idea of the existence of certain parallelism in the formation of the cognitive structure in ontogenesis and the phylogenies of thinking, as well as in the development of scientific knowledge structures. Piaget investigated the following stages in the development of intelligence based on the interiorization principle of action into thought. – Sensorimotor actions as systems of reversible actions with material objects, – pecific operations that are performed by the mind and based on external facts, – Formal structures (or operations) underlying the ability to hypothetical-deductive reasoning. J. Piaget identified the stages of relatively stable equilibrium in the interaction of the individual with the environment, which has a socio-natural character. He emphasized the abrupt nature of the formation of the intellectual function in ontogenesis. The transition to the next stage of intelligence development means the restructuring of previous forms of thinking. These forms do not disappear but remain in the form of a relatively autonomous intellectual practice. Such transition is expressed in the difference between sensorimotor, situational-practical, discursive, cognitive, theoretical, creative action.

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According to J. Piaget, action invariance and reversibility is a factor generating mental structures in ontogenesis. Firstly, the physical and communicative action is fixed in memory as a mental structure or image. Then operations are performed with the imprinted images in memory. The transition to mathematical calculation and logical operations with abstract concepts and judgments corresponds to the formation of the structures for rational thinking. The socio-genetic meaning of an action invariant is a compressed information exchange. 3.2

Cognitive Evolution Model in Sociobiology

In sociobiology, the explanation of the regularity of the human psyche and consciousness evolution is based on a multi-level model of direct and reverse interactions between genes and culture, in which the cellular level of nervous tissue and the level of cognitive development are highlighted [22]. The bilateral interaction of genetic and cultural factors is considered as gene-cultural coevolution. Human culture is formed by cognitive mechanisms that fix innate “epigenetic rules” that guide behavior and thinking. In community development, epigenetic rules act as an information filter that carries natural selection among cultural alternatives. The genetic programs of a biological species hiddenly drive the mechanisms of inheritance of cultural and lineage experience. For example, in the learning process, culturgens corresponding to a certain type of behavior are fixed. Culturgen is a certain abstract information unit (not biological). This information unit of social memory acts as an element of mental epigenesis that is already genetically determined in the initial installation. The history of hominids as a biological species testifies to the adaptive stability of collective survival. This feature of the lifeway is recorded in the genome of all direct predecessors of modern humans [23]. 3.3

Socio-anthropological Model of the Intelligence Structures Evolution

In the socio-anthropological model of the evolution of the cognitive structures, personal being acts as a micro-level of society. Vectors of motivation in mental dynamics are determined by the requirements of social adaptation, goal-setting, self-identification. The coherence of society as anthropological integrity is provided by a formalized communication space. The relationships of individuals in society require proficiency in the language that is a natural communication tool. Free orientation in the semantic field of verbal communication is also associated with a certain level of intellectual development. The emotional and intellectual norm of mental activity is fixed as a necessary condition for the translation of the meanings in speech communication. The paleopsychological concept of human history by B.F. Porshnev [24] emphasizes the communicative basis for speech and emotions as supra-biological functions that determine the norm of adaptation in the human community. The norm of reasonable human behavior is associated, on the one hand, with the assimilation of speech and understanding the meaning of words and expressions, on the other hand, with the limitation of the emotional reaction in ethical and moral norms that

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fix the internal barriers of impulses in maxims and attitudes. The dynamics of the communicative environment of society require a person to comply with the norm of emotional and intellectual activity. In the network model by K.V. Anokhin [25], socio-anthropological integrity is connected by a cognitive hyper-net, the dynamics of which generate the intentions of consciousness. From this point of view, the regularity of the intelligence structure's development is a functional norm of the human psyche determined by the total connectivity of socio-anthropological integrity at the level of the cognitive hyper-net. The semantics of the cognitive systems of culture and the discursive communication technique determine the natural norm of intellectual activity for a person. In this logic, a hierarchically organized cognitive hyper-net connects multi-level communication systems. The inclusion of a person in the cognitive network of society is a prerequisite for the activation of the psyche's intellectual structures and the neural network of the human brain. Thus, the general consistency of anthropological integrity stimulates personal mental activity and carries out implicit information control of intelligence systems in the community.

4 Discussion Traditionally, intelligence is understood as a system of cognitive abilities of an individual. In the field of philosophy, the term ``intellect'' is most often used as a synonym for consciousness or reason. The term ``artificial'' refers to an inorganic (or abiogenic), non-natural (cultural) origin. In attempts to define AI concerning the intellectual functions of a person, a vicious circle arises, since in this case, one indefinite concept corresponds to another rather uncertain idea of intelligence. The existing complex of tools, machinery, artificial environments are associated with the term “technology”. However, the functions of the intellect are quite consistent with artificial suprabiological structures correlated with the socio-cultural environment and language as a tool of thinking. In abstraction from humans, the main characteristic of intelligence is determined by the ability of an agent to set goals and solve various problems in a changing environment. Depending on the material substrate of the agent, one can distinguish natural intelligence, as human actions, and artificial intelligence, if the agent is a machine [26, pp. 156–157; 27]. This point of view becomes the basis of the ideology of machine super-intelligence. It is assumed that the expedient behavior can be performed not only by a living organism and a person, thanks to his natural abilities, intuitively or intelligently but also by some agent or system of other artificial nature. The interpretation of expedient behavior in a changing information environment is based on the idea of reflexive systems and self-referential systems. Such systems can differentiate relations with the outside world and rebuild their internal connections. Reflexive abilities allow them to predict the reactions and evaluate attitudes towards themselves and their possibilities in choosing actions. The life dynamics of the reflexive system are determined by selforganization, self-conception, and self-reproduction. It is similar to the consciousness construction by a human [28].

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The principle of expediency, identified by Aristotle, emphasizes the internal cause of the reactions of a living organism in contrast to external causes acting in the physical world. In the ideas about reflexive systems, the principle of expediency is abstracted from a person as an individual subject of intellectual activity. The key point in the process of information transmitting and processing is the distinction between data and cognitive levels of complexity that correspond to the criterion of intellectuality of action. Traditionally, the criterion of intellectuality is operations with symbolic systems of knowledge. In the field of information technology, intellectual actions are reduced to calculus in the space of choice. As Pylyshin notes [29], the work of the computer is modeled similarly to the logic of calculation characteristic of rational human activity. Therefore, the computer is associated with artificial intelligence. The modern world is filled with hybrid participants of interaction in the social space. Digital applications of cyber reality and personality modes, functionally related to the dynamics of the interactive network, are considered to be smart systems within the framework of artificial intelligence technology [30]. The success of intelligent technologies is associated with modeling a person's ability to analyze and diagnose situations, learn and make decisions. The range of actions of AI is increasingly approaching the intellectual actions of a person to replace him. However, as K. Schwabb notes, AI and robotics in the future will only change the human's tasks but won't make humans unnecessary since only a small percentage of jobs is available for full automation [31, p.154]. In addition, in smart technologies, the question of trust in machine actions remains uncertain. The decision-making mechanisms used in machine learning algorithms and self-learning algorithms of neural networks remain unclear to the developers themselves since AI operates on the principle of a “black box”.

5 Conclusion The conceptual basis for the natural and artificial integration in the intelligence structures evolution is the postulate of cognitive science about the identity for the natural and artificial agents of intelligence actions since they both use frame knowledge structures for semantic orientation in information environments. The relationship between artificial and natural intelligence structures is an acute problem in assessing the prospects of smart technologies. In a general sense, artificial intelligence is understood as a direction in systems engineering, the purpose of which is to imitate human cognitive actions. In this case, smart technologies have an instrumental character and do not go beyond the social interactions system. Another interpretation of artificial intelligence correlates it with a relatively independent techno-social phenomenon. Artificial intelligence as an independent phenomenon is considered in abstraction from humans and society. The reason for this view is an intensive growth of information flows and the volume of their processing, which a person cannot cope with. In this case, the activity of complex computer networks and systems with big data looks self-sufficient. The creation of various media spaces in cyber reality is the result of this activity. The prospect of the development of

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artificial intelligent structures in this vision is an independent super-intelligence or super-computer. It integrates information flows with a high speed of data processing. A person becomes only an element of cyberspace in this case. The philosophical aspects of this problem are determined by questions that go beyond the ideology of computer metaphor, which identifies human thinking with computational processes and decision-making with calculus in the space of choice. Against this background, philosophy analytics emphasizes the specifics of human intellectual activity in the ability of purposeful detail and the production of new knowledge, following the criteria for the adequacy of understanding and evaluating situations. The presented analysis shows that globally any intellectual structures are stimulated by a communicative environment, which is rooted in the history of human culture, which translates the stereotypes of cognitive behavior. Smart technologies of AI are functionally involved in the human social life-world through the cognitive network. So, perspectively they are to be brought following the purposes and values of human society.

References 1. Schloer, N., Bauer, B., Wittemberg, C.: Human-Robot-Interaction via AR: First Steps of Building a Human-Robot Interface on a Microsoft HoloLens. In: Ahram, T., Taiar, R. (eds.) IHIET 2021. LNNS, vol. 319, pp. 822–829. Springer, Cham (2022). https://doi.org/10.1007/ 978-3-030-85540-6_104 2. Musleh Al-Sartawi, A.M.A., Razzaque, A., Kamal, M.M. (eds.): EAMMIS 2021. LNNS, vol. 239. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77246-8 3. Tyagi, A.K., Fernandez, T.F., Mishra, S., Kumari, S.: Intelligent Automation Systems at the Core of Industry 4.0. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds.) ISDA 2020. AISC, vol. 1351, pp. 1–18. Springer, Cham (2021). https:// doi.org/10.1007/978-3-030-71187-0_1 4. Luger, G.F.: Knowing our World: An Artificial Intelligence Perspective. Springer Nature Switzerland AG. Springer, Cham (2021).DOI: https://doi.org/10.1007/978-3-030-71873-2 5. Hunt, G.: O prirode soznaniya: S kognitivnoj, fenomenologicheskoj i transpersonal’noj tochek zreniya. [On the Nature of Consciousness: From cognitive, phenomenological and transpersonal points of view.] Moscow, AST (2004) 6. Kholodnaya, M.A.: Psihologiya intellekta: paradoksy issledovaniya. [Psychology of Intelligence: Paradoxes of Research.] Moscow, “YURAIT” Publishing (2019). (In Russian) 7. Redko, V.G.: Evolyuciya, nejronnye seti, intellekt. Modeli i koncepcii evolyucionnoj kibernetiki. [Evolution, neural networks, intelligence. Models and Concepts of Evolutionary Cybernetics.] No. 23, Izd.10. URSS, Moscow (2019). (In Russian) 8. Bechtel, W.: Mental Mechanisms. Philosophical Perspectives on Cognitive Neuroscience. 2008 by Taylor & Francis Group, LLC, New York, NY (2008) 9. Bechtel, W., Herschbach, M.: Philosophy of the Cognitive Sciences. In: Philosophy of the special sciences, pp. 237–261. SUNY Press, Albany, NY (2010) 10. Maksimov, L.V.: Cognitive science: new life of old paradigms. Philosophical thought 11, 11–24 (2017). https://nbpublish.com/library_read_article.php?id=24657, last accessed 2021/09/11

108

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11. Maturana, U.: Biologiya poznaniya. [Biology of knowledge.] In: Language and intellect, pp. 95–142. Progress, Moscow (1995). (In Russian) 12. Minsky, M.: Frejmy dlya predstavleniya znanij. [Frames for the presentation of knowledge.] Energiya, Moscow (1979). (In Russian) 13. Simon, G.A., Kaplan, C.A.: Foundations of Cognitive Science. In: Foundations of Cognitive Science, pp. 1–49 (1991) 14. Luger, J. F.: Artificial intelligence. Strategies and methods for solving complex problems, 4th ed. Publishing house “Williams”, Moscow (2003) 15. Rosheim, M.E.: Leonardo’s Lost Robots. Springer, Cham (2006) 16. Turing, A.M.: Computing Machinery and Intelligence. Mind, New Series, Vol. 59, No. 236 (Oct., 1950), pp. 433–460. Published by Oxford University Press on behalf of the Mind Association Stable, http://www.jstor.org/stable/2251299, last accessed 2021/09/11 17. Broadbent, E.: Interactions with robots: the truths we reveal about ourselves. Annu. Rev. Psychol. 68, 627–652 (2017). https://doi.org/10.1146/annurev-psych-010416-043958 18. Alansari, H., Gerwe, O., Razzaque, A.: Role of Artificial Intelligence During the Covid-19 Era. In: Musleh Al-Sartawi, A.M.A. (ed.) The Big Data-Driven Digital Economy: Artificial and Computational Intelligence. SCI, vol. 974, pp. 157–173. Springer, Cham (2021). https:// doi.org/10.1007/978-3-030-73057-4_13 19. Kliks, F.: Probuzhdayushcheesya myshlenie. U istokov chelovecheskogo intellekta. [Awakening thinking. At the origins of human intelligence.] Progress, Moscow (1983). (In Russian) 20. Savelyev, S.V.: Morfologiya soznaniya. [Morphology of consciousness.] vol. 1. Moscow, VEDI (2018). (In Russian) 21. Piaget, J.: Psikhologiya intellecta. [Psychology of intelligence.] Piter, St. Petersburg (2004). (In Russian) 22. Wilson, E.O.: Sociobiology: The New Synthesis. Cambridge (Mass.). Belknap press of Harvard univ. press, London (2002) 23. Beskova, I.A., Gerasimova, I.A., Merkulov, I.P.: Fenomen soznaniya. [Phenomenon of consciousness.] Progress-Tradition, Moscow (2010). (In Russian) 24. Porshnev, B.F.: O nachale chelovecheskoj istorii (Problemy paleopsihologii). [On the beginning of human history (Problems of paleopsychology).] Mysl, Moscow (1974). (In Russian) 25. Anokhin, K.V.: Connectome and Cognitiom. https://newtonew.com/science/connectomicsbrain-models, last accessed: 2021/09/27 26. Shnurenko, I.: CHelovek vzlomannyj. [Man hacked.] Publishing house “Nashe zavtra”, Moscow (2021) 27. Legg, Sh.: Machine Super Intelligence: Doctoral Dissertation. Copyright © Shane Legg (2008). This document is licensed under a Creative Commons Attribution-Share Alike 2.5 Switzerland License 28. Shipunova, O.D., Mureyko, L.V., Serkova, V.A., Romanenko, I.B., Romanenko, Y.M.: The time factor in consciousness construction. Indian J. Sci. Technol. 9(42) (2016). https://doi. org/10.17485/ijst/2016/v9i42/104226 29. Pylyshyn, Z.W.: Computing in Cognitive Science. In: Foundations of Cognitive Science, pp. 49–92 (1991) 30. Prokhorov, A., Lysachev, M.: Cifrovoj dvojnik. Analiz, trendy, mirovoj opyt. [Digital double. Analysis, trends, world experience.] LLC “AlliancePrint”, Moscow (2020). (In Russian) 31. Shvabb, K.: Tekhnologii CHetvertoj promyshlennoj revolyucii. [Technologies of the fourth industrial revolution.] Eksmo, Moscow (2019)

System Analysis of Deep Trends in the Evolution of Science: From Fixed Concepts to Moving Artistic Images Viacheslav E. Voitsekhovich1(&) , Ilia N. Volnov2 and Georgy G. Malinetskiy3

,

1

Tver State University, Zhelyabov Street, 33, 170100 Tver, Russia [email protected] 2 Moscow Polytechnic University, Bolshaya Semenovskaya Street, 38, 107023 Moscow, Russia 3 Applied Mathematics Institute of the Russian Academy of Sciences, Miusskaya sq., 4, 125047 Moscow, Russia [email protected] Science progresses from Parmenides to Heraclitus I. Prigogine

Abstract. A systematic analysis of modern science shows that it is currently in a stage of a deep crisis caused by a number of factors (loss of integrity of the scientific worldview, no link between tens of thousands of research disciplines, highly complex concepts, impossibility to verify many hypotheses, extremely long mathematical proofs, etc.). The purpose of the paper is to define the causes of the crisis and suggest methods of overcoming it. The study employs the following methods: abstraction, generalization, induction, deduction, analysis, synthesis, analogy, interpretation, system-structural method. The following findings have been substantiated: the main cause of the crisis is thinking in permanent concepts. This method of thinking was ingrained in philosophy and science through the paradigm of Parmenides (“being is essentially motionless”) and Aristotle’s Logic, in particular, the law of identity (A = A); 2) The paradigm of Heraclitus (“everything flows”) and Hegel’s ontology present a possibility to create a new rationalism based on “dynamic” concepts; 3) The suggested “generalized identity law” can form the foundation of rationalism as “motion-based thinking”; 4) The prerequisite of the new rationalism is the paradigm of evolution (the object is an evolving human-dimensional system), i. e. concepts and methods assume the “mobility” of images-concepts in the process of reasoning (in topology, intuitionism, fuzzy logic, and other nonclassical logics); 5) Arts is a cultural area where images, concepts, and forms evolve as an artwork unfolds. Contemporary science and art converge in terms of style, methods, and techniques of using variable concepts and images, 6) A currently reanimated ancient Greek understanding of time as the unity of Chronos, Cyclos, and Kairos (i.e. human, natural, and spiritual time) is a contributing condition for synthesis of science and art; 7) Science-Art is a promising area of development of cognition and culture. The significance of the suggested ideas lies in the fact that adoption of thinking in “dynamic” concepts and images by the scientific community will make it possible for science and civilization to ascend to new promising paradigms, methods, and paths of evolution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 109–120, 2022. https://doi.org/10.1007/978-3-030-98832-6_10

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System
Logic
Art
Permanent
Motion
Time
Complexity

1 Introduction The central problem of the paper: can the controversy and challenges of contemporary science be overcome? The subject matter of this paper is rationalism understood as thinking in permanent concepts, which is ingrained in science since Parmenides, Aristotle, and Descartes. It has been elaborated in our earlier publications [1–3]. These publications look into 1) evolution of rationalism as ascension from the classical (mechanistic) stage to the non-classical (relativistic quantum) and post-non-classical (fractal) stages, 2) convergence of contemporary science and art, 3) evolution of the concepts of beauty, harmony, and measure in the information age. These studies reveal the following important trends in rationalism over the four centuries: a) strengthening of the aspect of humanities as the subject component of the scientific research diagram, convergence of science and art, b) transition to concepts that express motion and development. In addition, underlying root causes of the crisis of science are shown, collision with the complexity wall caused by the requirement to follow the logical law of identity. The research demonstrates the need for science to transition from the paradigm of Parmenides to the paradigm of Heraclitus (and a more general philosophy of motion). The hypothesis is suggested about the need for science to transition to the new rationalism, that is, thinking in ‘dynamic concepts’. Related publications from 2006 can be divided into two groups. Some authors are focused on the need to revise the patterns of rational thinking in various specific fields: psychology, psychiatry, economics, environmental sciences [4, 5]. The authors of the other group are focused on rational thinking in the theory of creativity, methodology of science [6], history of philosophy [7], history and foundations of mathematics [8], mathematical foundations of consciousness [9]. Other authors attempt to look into rationalism and “philosophy of mind” more fundamentally. They research fundamentals of rational thinking and radical changes of rationalism caused by the latest scientific advances: the theory of self-organization, complexity, virtualisation [3]. A number of authors attempt to construct scientific thinking as a “theory of creative subject” (Lepskyi V.E., Voitsekhovich V.E.). Since the Ancient world, attempts have been made to find the path of “Universal Cognition” as a synthesis of science, philosophy, religion, art (i. e., Aristotle, Thomas Aquinas, G. Hegel, F. Schelling, V. S. Solovyov). Publications in the field of science-art can also be divided into two categories: 1) that concern specific matters in narrow fields where the terms “art” and “science” are interpreted absolutely metaphorically, and even arbitrarily; 2) that elaborate “theories” of the interaction of art and science in the broader field of aesthetics. The first category “looks” into the interaction between science, art, and management in the field of medicine (psychotherapy, pharmacology, embryology, dentistry, etc.), environmental science, economics, pedagogics [10–12]. The other category of publications approaches museology, social psychology, development of certain types of arts-based on the latest scientific advances (fractals, AI, neural networks, lasers, discoveries in optics,

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psychology for deeper perception of paintings, poetry, music, theater, etc.) [13, 14]. The reverse impact of arts on science is evident in creativity, in the works of prominent researchers; however, no profound publications for the past 15 years have been found (or they are too rare). The following materials were used for the study: history of science, philosophy, and art, in particular, the paradigms of Parmenides and Heraclitus, the law of identity in the Aristotelian logic, Hegel’s dialectical ontology, and contemporary trends in the artistic thought in association with science and technology. The study employed the following methods: 1) analysis and abstraction (isolation of a property in a concept), 2) generalization and synthesis of the isolated concepts or trends, in particular, generalization of the paradigms of Parmenides and Heraclitus to a more general “philosophy of motion”, 3) analogy as a definition of resemblance between remote concepts, images, or processes. In addition, induction, deduction, interpretation, and other methods were used.

2 Problem Statement The civilization and science are currently in a severe mental and social disaster. The old mechanistic ultra-technocentric society will come to an end, and a more viable civilization will begin. It will be based on spiritual pillars [3]. In the 20th century, science went into a stage of super-complexity. The scientific body of knowledge grows quantitatively, rather than qualitatively. A holistic worldview is lost. The tens of thousands of scientific disciplines have little or nothing to do with one another. Speculativeness of scientific knowledge has reached its limit. Tens of cosmological hypotheses are still to be tested. Important mathematical theorems sometimes wait for centuries to be proved. However, contemporary proofs are exceedingly long, at times, amounting to thousands of pages of extremely difficult text. Only a few highly qualified experts can verify their validity. In the future, it will not be possible at all [15]. 2.1

Cause of the Crisis

The main cause for the crisis of cognition and civilization is the obsolescence of rationalism as thinking in “rigid”, unambiguous, “motionless” forms (images, ideas). This method of thinking is mechanistic and primitive. It is unable to encompass the complexity and wholeness of processes in nature and psyche. Meanwhile, in the physical and mental reality, “everything flows” (Heraclitus). Aristotle postulated the first principle of logic. A = A, that is, an idea shall remain the same in the process of reasoning. Thinking in motionless concepts produced great discoveries, in particular, in mathematics, science, and theology. However, the academic community recognized its inadequacy in social studies and humanities a long time ago. And in the 20th century, it no longer expressed the characteristic aspects of the new scientific approaches (anthropic principle, self-organization, virtuality, complexity).

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In Hegel’s dialectics, any disaster is caused by a lack of balance (disharmony) between form and content, rest and motion. This lack of balance not only induced the world wars, but also the opposition between science (the realm of logic) and arts (the realm of imagination, change, and unpredictability). 2.2

From Permanence to Motion

What do permanence and motion mean? In line with Hegel's dialectics (a branch of philosophy of motion and development), the permanent means the quantitative (structural) constancy of a property of a thing in the period between leaps of this property. Accumulated quantitative changes trigger a qualitative leap. Quality is the totality of the intrinsic properties of a thing. A thing is understood as a process ongoing in the internal space and time. This is why permanence is an artificial construct imposed by man on nature and himself. This construct used to serve its purpose, but these days have passed. Preservation and priority of permanence distort reality. As science evolves, concepts, quantities, and factors that have been originally taken as constants became variables. For instance, it is now found that many of the fundamental physical ‘constants’ (gravitational constant, speed of light, Planck constant, Hubble constant, fine structure constant) vary. Concerning the fundamental mathematical constants (0, 1, p, e, i), How are these permanent? Only as assumptions or axioms. However, these have brought mathematics to a profound crisis [3]. The community of philosophy researchers criticizes the notion of ‘law’ (permanent approach) and attempts to revise it and replace it with “a set of rules”, algorithm, i. e. with a variable approach. Motion as a universal concept is well-elaborated in philosophy (the teachings of Heraclitus and Hegel), science uses motion in the self-organization theory (I. Prigogine, G. Haken, S.P. Kurdyumov, G.G. Malinetskiy), art uses it as an artistic method (music, poetry, theater). These teachings, theories, and approaches use such images and concepts as chaos, entropy, continuity, fluidity, infinity, beauty, life, aspiration, will, etc. This is why the civilization is essentially experiencing a crisis of “thinking in motionlessness” in spiritual life, thinking, and in material and corporeal life. We believe that “getting over Parmenides and the law of identity” and transition from the finite world of permanence to the infinite world of motion and development would mean getting over the crisis. According to Prigogine, a philosophy of motion, motion-based thinking (new rationalism), and science of motion need to be created.

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3 Literature Review The idea of “flexible” concepts was put forward as early as in ancient Greece (sophists, Gorgias, Heraclitus). The paradigms of dialectics and thinking in mobile images continued to develop in philosophy, but they did not go any further in science as rational cognition based on the law of identity. However, in the 20th century, nonclassical logics come along starting from N.A. Vasiliev, and L. Brouwer’s intuitionism appear as a new foundation of mathematics. L. Brouwer reinforced the creative aspect of mathematics and introduced the concepts of choice sequence, creating subject, etc. He also understood mathematics as an art and creative thinking. That was also the beginning of criticism of the “old” mathematics, which prioritized proof and axiomatics and underestimated creativity. Philosophers, methodologists, and mathematicians began to realize how obsolescent it was to understand mathematics just as “science of proofs”. In reality, mathematical cognition is a two-phase process consisting of the creative process and justification (or disproof). F. Klein already divides mathematicians into “intuitionists” and “logists”. Some outstanding examples of the former are P. Fermat, R. Descartes, B. Riemann, A. Poincaré, S. Ramanujan. They put forward deep problems, fundamental conjectures (theses, theorems) and strongly believed in their validity. However, the overwhelming majority are “logists”, among which is C. Gauss. They prove (or disprove, refine) intuitionisits’ theses. It was “logists” who reduced mathematics to proofs, which is 50% wrong. The creative aspect of cognition is most developed in arts. Philosophers and scientists made multiple attempts to bring science and art (rational/logical and intuitive/illogical thinking) closer together, but for a long time, these attempts had remained unsuccessful and unrecognized by the scientific community. In the 20th century, the advent of synergetics revealed the need to renew the foundations of cognition and to transition to using “floating” concepts and images, to bring mathematics closer to dialectics and arts. I. Prigogine, the founder of the self-organization theory, stated explicitly that “science progresses from Parmenides to Heraclitus”. Thinking in “floating” concepts, rather than static ones, will take the civilization to another level of evolution of homo sapiens.

4 Methodology The methods employed in the paper are approaches and respective methods of Aristotle’s doctrine of categories (“reality can be fully expressed through categories — universal concepts”), R. Descartes’ theory of cognition (“clear means true”), B. Spinoza’s ontology (“The order and connection of ideas is the same as the order and connection of things”), and Hegelian dialectic (“being is evolving Spirit; life is expressed through movement of categories “reflecting” in one another”).

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5 The New Rationalism Generalization of the law of identity, which covers both the old logic (motionless concepts) and new logic (dynamic concepts), may become the linchpin of the new rationalism. Rationalism originating from Science-Art, the fractal theory, complexity theory, and teaching of the subject is based on “thinking in dynamic concepts”. Similar ideas were expressed by B. Spinoza, G. Hegel, and others. Philosophy of motion suggests that the subject (the cognizing community) believes that processes vary in speed: there are fast, slow, and very slow processes. Scientists conventionally assume the latter as eternal, permanent things (God, the universe, space, time, etc.). Then anything (metaphorically “what”) shall be described as follows: • there is an internal motionless core (the essence) of a thing; • and there are external images of the core (phenomena) with varying properties and relations. Those are expressed through ontological categories: 1) being and not-being, 2) potentiality and actuality (realization of possibilities), 3) motion and rest, 4) space and time, 5) part and whole, and also an element, structure, and a system, 6) cause and effect, etc. For example, according to the Western (Christian) tradition, man is a unity of spirit, psyche (soul), and body. The core (essence) is spirit (Leibniz’s monad, Atman in Brahmanism). Its “shell” is composed of phenomena (psyche, body, social relations, development algorithm of man as a living creature).

6 Law of Generalized Identity In the process of reasoning, an idea of an object can a) remain the same if it is about the essence of the thing, and b) evolve if it is an idea about a phenomenon, i. e. properties or relations of the thing. At the same time, transition from essence to a phenomenon (from permanent to a variable) and back is a continuous process. The law of generalized identity is the starting point of the structural-dialectical logic, i. e. the logic of rest/motion. If the essence is countered with the infinite, and the phenomenon is countered with the finite, then the transition is mathematically expressed through functions that translate the infinite into the finite and back, as this was done in V. I. Moiseev’s calculation of R-functions [16]. Below we compare deduction between Aristotelian classical logic and structuraldialectical logic. Classical logic Premises. Socrates is human. All humans are mortal. ! Hence, Socrates is mortal. Structural-dialectical logic Premises.

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• Socrates is the essence (spirit) + its manifestations (properties and relations of the psyche and body). • Spirit is eternal. The properties and relations are temporary and transient. • Socrates has the property of being included in the category of people (be human). • All people exhibit the property of having a mortal body. ! Hence, Socrates is both mortal (as a phenomenon, body, psyche, totality of social relations) and immortal (as a spirit, as the essence of Socrates). From the perspective of classical logic, there is no contradiction here: Socrates is mortal in one respect (body), and eternal in another one (spirit). There is still a continuity of evolution of logic. Classical logic is generalized by structural-dialectical logic. The law of generalized identity produces profound changes in thinking. The truth in classical logic gives way to the truth of essence and truth of relation (structuraldialectical logic), higher and lower, postulated and empirically testable. At the same time, the transition between these is a continuous process, hence the logic with a ‘ float’value of validity. The shift towards “thinking in dynamic forms” began taking shape in mathematics and logic a long time ago. This includes the topology, intuitionism, fuzzy logic, and other kinds of non-classical logic. For example, G. Perelman, who proved the fundamental Poincaré’s theorem, used Ricci flow with surgery, that is, deformation of the manifold with excision of singular regions and their replacement with spheres. With this representation, the object is replaced with an equivalent (in an isolated relation). The motion of the object is such that the main property remains unchanged. Dynamic thought-forms are most expressly used in art, the realm of flashes of intuition, leaps in quality, and free creativity. Synthesis of science and art, which is currently taking shape in the science-art movement, is an example of motion-based thinking.

7 Science-Art Below we compare science and art (see Table 1), Look into their specific properties, characteristics of thinking, explore how art can help science overcome the contemporary rationalism crisis. This shows that the opposition of science and art is highly exaggerated. Over the past 4 centuries, rationalism has evolved from classical (S ! O) to non-classical form (S ! O(s)), in particular, in light of special relativity and quantum theory. At the end of the 20th century, rationalism became post-non-classical. The cognition formula was now extremely complex: S ! [S1 ! (S2 ! O)]. This was a consequence of the advent of the anthropic principle, synergetics, virtualisation, development of the theory of complexity [1]. Scientific rationalism advanced towards art.

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No Properties 1 Dominating forms 2 Part of brain

Science Concept

Art Image

The Left hemisphere (rational thinking) Objectivity S ! O and other variations (S ! O(s)) Physics is an exemplar of scientific thinking

The Right hemisphere (emotional and artistic experience) Subjectivity S ! O(s), S ! S, S1 $ S2 Social science is closer to art

3

Subject, object, relation

4

Areas of proximity of disciplines to art Purpose of External (nature) cognition (external/internal) Logic Aristotelian logic, basic law: A = A. Premise: true or false. True vs False. Truth rejects falsity

5

6

7

The Rigidity of metal form

Rigid thinking

Internal (soul)

Non-Aristotelian, soft, floating logic. Premise A moves around freely. The truth and falsity merge into one another. True $ False Floating, soft experience

Notably, the convergence covers not only the post-non-classical area but also the time category area. Contemporary science has a non-classical understanding of time; time is seen as a dimension (like space) in line with the theory of special relativity. However, there are other historical interpretations of time: Greco-Roman world [17], H. Bergson [18], etc. Time: Chronos — Cyclos — Kairos The ancient Greeks had three concepts of time (see Fig. 1). Chronos is the time of people. Cyclos is the time of nature. Kairos is the time of gods. Chronos lies in the foundation of the scientific worldview and entire rationalized culture.

Kairos

Cyclos

Chronos Fig. 1. An antique model of time.

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Cyclos is partially represented in culture, and Kairos is currently unknown to the public. Along with these three concepts of time (which can be called basic), of particular interest are complex concepts of times composed of pair combinations of the basic ones. A triangular balance of time concepts shown below can help visualize these combinations, the triangle apexes representing the basic times, and the sides being their pair combinations. The Chronos — Cyclos side defines the spiral time of nonequilibrium thermodynamics, biological time of the living systems. The Chronos — Kairos side represents the time for prayer and meditation described by Fr. P. Florensky [19] and the spontaneous time of V. V. Nalimov’s probabilistic model of consciousness [20]. The Cyclos — Kairos side defines liturgical time, time of worship; A. Lidov’s “hierotopy” (the theory of creating sacred spaces) can be included here [21]. It should be noted that all three forms of time are manifested in human life. In accordance with the Ancient Greek understanding of harmony as symmetry, the symmetry of the time triangle can be considered a criterion of harmony in any organizational form existing in time: from man and nature to a Higher Power and any system of knowledge. Unfortunately, there is no symmetry of times in the contemporary scientific worldview. Instead, the scientific worldview is strongly dominated by Chronos, which is another reason for the systemic crisis of the technogenic civilization. To overcome the crisis, harmony and symmetry in the triangle of times have to be restored. A method for this is described below. Let us draw an analogy between the balance of time and another triangular balance composed of science (Chronos), technology (Cyclos), and art (Kairos). The most obvious conclusion derived from this analogy is that art is a medium and expression of Kairos and represents spiritual time. Further, the sides of the latter triangle can define a variety of practices of the new synthesis called Science-Art-Technology. Science-Art: the art of research, where a research problem is formulated using scientific tools, but without its rigid methodical limitations. This includes a large number of branches such as NeuroArt, BioArt, etc. developed by Science-Art authors as a method of thinking [2]. C. Snow defined this well-known conflict as the problem of “the two cultures”. Art-Technology: engineering art (Art & Science), where an artist chooses technology as the expression of his artistic statement. Among other things, this includes, a large variety of practices at the intersection of robotics and art, artwork by artificial intelligence, etc. And finally, the intersection of science and technology is represented by promoting science for the general audience through technological advances, also known as pop science. To summarize, Science-Art is popular today for a number of reasons; relatively obvious of those include the need for systematic work with exceptions (which is impossible in science); development of emotional intelligence, i. e. taking into consideration the inherent emotional background factor in intellectual activity; development of dialectical thinking that constructively handles contradictions such as science vs. art, natural vs. artificial intelligence, etc.; development of creative industries, etc. There are deeper causes, perhaps the most fundamental of those is harnessing of the resource of time in its entirety, not only spatially, as it is understood in contemporary science, but also in its basic (Cyclos, Kairos) and composite forms. In the conditions of

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the contemporary culture, art has become an expression of Kairos. The practices of Science-Art and Art&Science have become methods to reconcile Kairos with other forms of time.

8 Results and Discussions The results of the study are essentially substantiation of the following theses: 1) science is in a fundamental crisis, which can be overcome through transition to thinking in “flexible” ideas that constitute a new logic and new rationalism; 2) in the process of search for the new rationalism, we will see convergence and then synthesis of science and art. Today this is expressed in the Science-Art movement. Such revolutionary ideas are still yet to be discussed. But it is already evident that the discussion will boil down to the struggle between innovation and conservatism. And innovation will win this struggle, as it always does.

9 Conclusions Resolution of the crisis of civilization and cognition is possible through the new rationalism based on harnessing of dynamic images/concepts by the intellectual community, and the new understanding of time. They are manifested clearly in the synthesis of science and art taking shape, in the Science-Art movement. These ideas can be further developed in the following fields: 1) “philosophy of motion” that generalizes the paradigm of Heraclitus and Hegel's ontology; 2) “mathematics of motion” that uses varying concepts and brings the intuitive creativity closer to reasoning, 3) “logic of motion” that generalizes logic and research diagrams adopted by the scientific community; 4) “rationalism of motion” based on thinking in “dynamic concepts”; 5) the Science-Art paradigm oriented to synthesis of science and art and further to the convergence of science, philosophy, and religion. Acknowledgement. This research received a special grant No. 20-511-00003 from the Russian Foundation for Basic Research.

References 1. Voitsekhovich, V.E.: K metodologii postneklassicheskoi nauki. [Methodology of postnonclassical science.] In: Proceedings of the 19th All-Russian Research to Practice Conference “Science Days – 2019”. Tribute to 150-year anniversary of D.I. Mendeleev’s law. 17– 20.04.2019, pp. 165–168. Ozyorsk (2019). (In Russian) 2. Volnov, I. N.: Science-art: edinstvo nauki i iskusstva [Science Art: The Unity of Science and Art]. Vestnik Rossiiskogo universiteta druzhby narodov. Seriya: Filosofiya [People’s Frienship University of Russia Review. Series: Philosophy], vol. 21, No. 4, pp. 557–564 (2017). (In Russian)

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3. Malinetskiy, G.G., Voitsekhovich, V.E., Volnov, I.N., Kolesnikov, A.V., Skiba, I.R., Soroko, E.M.: Krasota i garmoniya v tsifrovuyu epokhu: Matematika – iskusstvo – Iskusstvennyi intellekt. Budushchee i gumanitarno-tekhnologicheskaya revolyutsiya. [Beauty and Harmony in the Digital Age: Mathematics – Art – Artificial Intelligence. The future and the humanities technological revolution]. LENAND Publ., Moscow (2021). (In Russian) 4. Hofkirchner, W.: A paradigm shift for the Great Bifurcation. Biosystems (2020). https://doi. org/10.1016/j.biosystems.2020.104193.PublishedbyElsevierB.V.Reference:BIO104193, lastaccessed2021/22/08 5. Zafirovsky, M.: Classical and neoclassical conceptions of rationality — Findings of an exploratory survey. The Journal of Socio-Economics 37(2), April 2008, 789–820 (2008). https://doi.org/10.1016/j.socec.2006.12.053 6. Sagasti, F.: Rethinking development at the twilight of Bacon’s age. Futures, vol. 114, December 2019, 102470 (2019). https://doi.org/10.1016/j.futures.2019.102470 7. Egmond, N.D. van Vries, H.J.M.de.: Sustainability: The search for the integral worldview. Futures 43(8), October 2011, 853–867 (2019). https://doi.org/10.1016/j.futures.2011.05.027 8. Katz, B., Katz, M.G., Sanders, S.: A footnote to the crisis in contemporary mathematics. Hist. Math. 45(2), 176–181 (2018). https://doi.org/10.1016/j.hm.2018.03.002 9. Miranker, W.L., Zuckerman, G.J.: Mathematical foundations of consciousness. J. Appl. Log. 7(4), 421–440 (2009). https://doi.org/10.1016/j.jal.2008.05.002 10. Caniato, F., Harland, C., Johnsen, T., Moretto, F., Ronchi, S.: The Art and Science of Procurement: Revisiting Leonardo da Vinci: Editorial of the 2019 IPSERA Conference Special Issue. Journal of Purchasing and Supply Management 26(4), October 2020, 100650 (2020). DOI: https://doi.org/10.1016/j.pursup.2020.100650 11. Shan, B., Werger, M., Huang, W., Giddon, D.B.: Quantitating the art and science of esthetic clinical success. J. World Federation of Ortho. 10(2), 49–58 (2021). https://doi.org/10.1016/ j.ejwf.2021.03.004 12. Rezvani, S., Wang, Z.: Class imbalance learning using fuzzy ART and intuitionistic fuzzy twin support vector machines. Inf. Sci. 578, 659–682 (2021). https://doi.org/10.1016/j.ins. 2021.07.010 13. Setchi, R., Spasić, I., Morgan, J., Corken, R.: Artificial intelligence for patent prior art searching. World Patent Information 64, March 2021, 102021 (2021). DOI: https://doi.org/ 10.1016/j.wpi.2021.102021 14. Noh, S., Tolbert, P.S.: Organizational identities of U.S. art museums and audience reactions. Poetics 72, 94–107 (2019). DOI: https://doi.org/10.1016/j.poetic.2018.10.002 15. Gromov, M.: Kol’tso tain: vselennaya, matematika, mysl’. [Ring of Mystery: Universe, Mathematics, Thinking.] MTsNMO Publ. (electronic publication), Moscow (2017). (In Russian) 16. Moiseev, V. I.: Logika otkrytogo sinteza: V 2 tomakh. T.1 Struktura, Priroda i Dusha. [Logic of Open Synthesis, 2 volumes. Vol.1 Structure, Nature and Soul.] Book 1. “Mir” Publ., St. Petersburg (2010). (In Russian) 17. Freier, M.R.: Kairos (2021). http://web.archive.org/web/20110728095413/http://www. whatifenterprises.com/whatif/whatiskairos.pdf, last accessed 2021/22/08 18. Bergson, H.: L'Évolution créatrice. Édition électronique (ePub, PDF) v.: 1,0 : Les Échos du Maquis, Avril 2013 (2013), https://philosophie.cegeptr.qc.ca/wp-content/documents/ Lévolution-créatrice.pdf, last accessed 2021/22/08. (In French) 19. Florensky, P.A.: Ikonostas. [Iconostasis.] Azbuka-klassika Publ., St. Petersburg (2010). (In Russian)

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20. Nalimov, V.V.: Spontannost’ soznaniya. Veroyatnostnaya teoriya smyslov i smyslovaya arkhitektonika lichnosti. [Spontaneity of consciousness: Probabilistic theory of meanings and semantic architectonics of personality.] Academic Project, Moscow (2011). (In Russian) 21. Lidov, A.M.: Ierotopiya. Sozdanie sakral'nykh prostranstv kak vid khudozhestvennogo tvorchestva. [Hierotopy. Creation of sacral spaces as a kind of creativity and a subject of a historical research.] (electronic publication) (2007), http://www.polit.ru/article/2007/06/14/ ierotop, last accessed 2021/22/08. (In Russian)

System Analysis of Marginal States in the Development of Civilization Zhanna Bober1,2(&) MTÜ “EVRIKA”, Joala 11-87, 20103 Narva, Estonia [email protected] Pushkin Leningrad State University, Petersburg Highway, 10, Pushkin, 196605 St. Petersburg, Russia 1

2

Abstract. Modern scientific research allows us to understand the complexity of the relationship between different periods of the development system through a network of cycles, and to clarify the direction of development of both human and being in the aggregate through the periodicity of alternation of certain cycles. Consideration of the sociosystem within the framework of system analysis as a complex dynamic structure makes is possible to look at the system dynamics of society from a different point of view and to comprehend the relations of marginal directions in the universal development. Structural changes in the development of society are objective preconditions for the formation of marginality states in the social system. Moreover, system analysis of marginal spheres in its synthesis shows the obviousness of the basis of fundamental prerequisites for universal harmonious development. Therefore, revealing the regularities of formation of marginal manifestations and their functions in the socio-cultural structure is of particular importance. Keywords: Dialectical system of marginality
Technocultural structure Network of cyclic transitions
System analysis




1 Introduction If we consider the entire human community as a complex dynamic system, subject to universal world laws, then from this point of view the pattern of sustainability of the entire social system corresponds to a dialectical spiral with divergent and convergent directions. Analysis of the processes and results of rapid human activity leads to the conclusion about the cyclic nature of the entire social system. This period of development of society takes place in a rapid mode of time and rather dense space. Changes in space and time greatly transform reality. A significant transformation is taking place at all levels of the real and digital lifestyle of society [11, 20]. Due to the appearance of computer technology in the 1950s and 1960s and its rapid evolution, we now have the opportunity to solve complex problems. But according to the synergetic theory of dynamical chaos, there are fundamental limits to predictions. Such difficulties were solved by neural network approaches [14].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 121–132, 2022. https://doi.org/10.1007/978-3-030-98832-6_11

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2 Methods In the modern conditions of superfast global changes [22] of socio-cultural evolution of planetary civilization and social paradigm change [21], the concept of marginality fits perfectly. The special concept of marginality was introduced into the scientific field at the beginning of the 20th century by R.E. Park, one of the founders of the Chicago school of sociology, in his essay “Human Migration and the Marginal Man” [15]. The definition of marginality is still being refined to this day, even though the preconditions for studying marginality were present to some extent in the circle of philosophical thought in the natural philosophy concepts of antiquity and even have their roots further back, which can be traced in earlier interpretations of several problems in which marginal phenomena were seen as a side state, peripheral to the main subject of attention [4]. The inner essence of development [1] lies in the inner regularity of the driving force — it is a universal regular dynamic structure, which we are not interested in while we consider the outer movement of various interactions. The regular internal cycle of human development is structured based on V.A. Bosenko’s ideas [6]. In the united cycle, the three-level dialectics [8] of the six semantic stages of dynamic forms is notionally defined: 1) Practical (received experience) level of the movement of reality (the field of ontology): a. Practice of development is the most accessible form of life experience in the material plane (actions, individual and collective sensations). b. Development of practice — the more complex form of using one’s own (and shared) mistaken and successful experiences to refine and accelerate practical actions. 2) Theoretical level of the movement of cognition (the field of gnoseology) as an extension of the movement of reality: a. Development of cognition — cognition of the logic of the movement of the objective world (immaterial plane) — the transformation of practice into abstract logic (thought process based on individual and collective experience). b. Cognition of development — a method in the ultimate universal form, acting as a general principle of the logic of movement in the content of the cognition of the objective world and the logic of the movement of cognition itself (descriptive process). 3) Philosophical level (principles, laws, methodology — comprehension of the value of cognition) (the field of axiology): a. Development of awareness — the formation of a unified theory of cognition in the content structure (transformation) of its movement towards itself (self-knowledge), “…the method can only be the nature of the content moving in scientific cognition…” [13] (explanatory process). b. Awareness of all development as a self-constructing and self-moving way of cognition (self-development) — the classification of descriptions, explanations,

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predictions of the self-development of being (principles, laws). The ultimate general principle of development stands “above” the world from which it is derived. Thus, the essence of the dialectic of development consists of three levels.

3 Research Results But since in the process of the human activity there is a consciously directed behaviour towards the material-energetic and informational result, [16], the ultimate integration system of the universal development of humans includes one more structure: 1. Physical stage (material&objective) with marginal features at the intersections of the object-activity practice of physical space and time. The ultimate result (fixed dimensional marginality) of the process on this stage is physical maturity and physical health (of the individual, of society). 2. Informational stage with marginal features on the intersections of the mental space’s thought-emotional practice in the psyche’s structural reflection of information [14] of all three spaces available to the individual and their intersections — real, mental, virtual concerning time. The ultimate result (fixed dimensional marginality) is mental maturity and mental health. 3. Energetic stage (willpower energy as well as natural energy transformed by human activity) with marginal features at the intersections of tension-volitional selfdirection (freedom of will, purposefulness, responsibility) in self-movement of mental-virtual-physical space and time. The ultimate result (fixed dimensional marginality) is high-spiritual moral maturity and moral health (harmony of the noosphere and aesthetosphere plans). Here will be enormous difficulties in the analysis of the essence of dialectics of development especially in the study of complex biological and social structures including the exchange of substance, energy, and information as a necessary condition [7]. Nevertheless, being aware of the difficulties of research, the internal “marginal mechanism” of the process of absolute revolutionary transformation at the point [19] of the limit states and transitions/jumps as a universal principle that is present in the dialectics of being in the development of all things is very interesting. As we wrote in our previous studies of the marginal state, the category “marginality is (1) a limit state of elements and orders of interactions in changes of any structure, (2) with opposite and contradictory character concerning current directions of development in space and time, with (3) mandatory function of the destruction of old and simultaneously forming new possibilities, including (4) function of sharp change of development vector in case of unexpected changes of environmental parameters” [2–5]. If development is understood as a dynamic step-by-step transition on the path of improvement, then multiple levels of ascent from one limiting peak to another through cyclic regular marginal decline can be suggested for nature, man, and society. Each turn of the cycle has its specific level of upper limit and lower limit, which will be characterised by its own specifically marginal parameters (limit position, limit stress, limit speed).

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Our research reveals the marginal features of development in general in its ultimate universal form, its internal dialectical logic of the limit/transition “mechanism” and solves the problem of expressing development in the logic of these concepts [17, 18]. In this regard, it is realised that the category of marginal mechanism is a complex mobile structure of limit/transition of dialectical internal links of the self-development system, which is beyond the scope of natural science, being a universal principle in the general cycle of transformation of everything into everything. The structure of limit/transition is notionally divided into dynamic and static marginal forms, where static marginal forms are expressed at points having an extreme/limit state of elements and orders of connections before and after the “transcending” of something else new (relatively fixed dimensionality), while dynamic marginal forms are expressed in transitions/jumps (relative unfixed dimensionlessness) of changes determined by bilateral “frontierness” and “dialectical center within the transformation frontiers” — ‘before’ limit, ‘between’ transitivity/jump, ‘after’ limit. Static and dynamic marginal forms are notional, hence the fixed marginal states of the elements of any system as some notional statics in dynamics”. The development of being is all the time in absolute movement, but the individual human memory holds in relative immobility certain results of the movement of reality and the results of its activities, which is fixed as a certain achievement in a symbolic collective form, in culture. Exploring the theoretical forms of cultural models, we see that scientists often use concepts that they borrow from other fields of knowledge to describe culture. So, for example, we try to rethink the works of A.S. Karmin, a Russian cultural scientist [10]. He suggested using “as an auxiliary means to describe culture its threedimensional physical-geometric model, in which it is likened to ‘substance’ that fills ‘cultural space’ (or “space of culture”)”. By analogy with physical space, the model of cultural space identifies three dimensions of cultural worlds in the spatial structure of culture, corresponding to the main types of meanings contained in social information — knowledge (the result of cognitive activity), values (meaningful material embodiment of the ideal aspect), regulations (“blocks” with “should” prescriptions, “cannot” prohibitions, “can” permissions). They respectively define three mutually intersecting “coordinate axes” – cognitive (x), value (y), regulatory (z). Between the three dimensions of cultural worlds, the birth of marginalism happens, i.e., these are the spatial domains where the marginal phenomena of socio-culture are located. We have tried to schematically represent the domain of marginal formations in Fig. 1 “Structure of the types of the main semantic axes (knowledge — x, values —y, regulations — z), where marginal cultural forms are formed on the frontiers of spatial domains” (see Fig. 1) [4]. In the intermediate domains of larger leading cultural forms, smaller, marginal forms are formed in the process of development, which appear in certain frontier cultural sub-domains of the structure. Among the cultural forms, a special niche is occupied by “paradigmatic forms” (sample forms). “Paradigmatic forms of culture are typical ‘attitudinal’ structures, which determine the organization of the semantic content of cultural phenomena” [10].

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Fig. 1. Structure of types of the main semantic axes (knowledge – x, values – y, regulations – z), where marginal cultural forms are formed on the frontiers of spatial domains.

Each of the three types of paradigmatic forms (cognitive, value, and regulatory) is symbolically represented as a plane (“layer” or “brane”), passing through one of the coordinate axes (x, y, z) of the conventional cultural space. Paradigmatic forms that are on one of the coordinate axes (x, y, z) are called “axial forms” of culture. Each axis is crossed by several forms, which are not isolated from each other in real-life conditions. Being open to intersection and interaction, the axial forms in the process of development create between them marginal domains of culture with specific semantic content. We will try to look at the three domains of the spatial structure of culture separately based on the physical geometrical model. The most important part of the cultural world is the domain of spiritual culture. A characteristic of all forms of spiritual culture is the combination of knowledge and values, while regulations play an auxiliary role in creating spiritual values. In this regard, the domain of spiritual culture will represent a plane model between two axes “cognitive” (x) and “value” (y) — that is, the “cognitive-value” (x, y) plane is the domain of spiritual culture. In the same way, the totality of cultural forms, determined by people’s social relations, their interactions in society, i.e., the domain of social culture, will be distinguished. The third domain is characterized by a reference to the “cognitive-regulative” plane (x, z) and is the domain of technological culture [10]. Thus, the marginalities emerging at the edges of the cultural domains “turn on” the processes of self-organisation of the whole system. These complex processes can be classified, and appropriate models created [4]. Thus, the marginal domain in the structure of culture is an extremely marginal specific cultural form, which is formed at the edge of the main cultural forms and domains in the space of socio-cultural system development as the formation of a dialectical center within the transformation frontiers. Thus, marginality is an important aspect of system development as it has a networked, multi-layered structure that literally permeates the entire “body” of culture. From the concept of American philosopher Alvin Toffler [22], we now live in a transition period of cultural development. Toffler views social history through the Wave Model [4] of worldview.

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Based on the comparative analysis of forms of culture in different periods [4], the technological revolution at the turn of the twentieth and twenty-first centuries has the following characteristics from Toffler’s point of view: 1) Replacement of some activities by others: from industrialization to informatization and robotization. 2) Basis of the information society: information and electronic money, mass consumption. 3) Composition of society — Caste-Classes: Highly fragmented specialization of labor. Undifferentiated “infosocieties” of the global community, multiple selfconcepts, high dynamism of daily actions. Mass viewing of the same infostreams, mediated (by gadgets) daily constant use of global virtual social networks. 4) Ecology: planetary disruption of the natural balance, climate change technologies, large-scale landfills of disposable manmade objects and chemicals, new (previously unknown) mass diseases and infections, nervous and mental disorders. Natural disasters all over the planet. 5) Time: spiralling, superfast change. And millions of people are getting up at the same time, going to and from work (rush hour). 6) Space: the multiplicity of parallel event spaces, the super-dynamic movement, and change of planetary scale-spaces, the simultaneous play of multiple social roles in one space. 7) Knowledge and belief: multipolarity of perception of the world, the explosion of digital technology, information wars (fakes). According to another researcher, Professor Klaus Schwab, the German economist, we live in an amazing time when yesterday’s fiction becomes a reality today and tomorrow will already be a commonplace without which our everyday life is unthinkable [20]. By exploring the periodicity of change in the development of culture we have come to recognise the obligatory permanence of marginal states in individual and collective development. Based on a comprehensive view of how technology changes our lives and environments, Schwab identified an order of fundamental change — the agrarian revolution and four industrial revolutions, which we have conceptualised through a comparative analysis of fundamental change (see Table 1 — analysis based on Klaus Schwab’s presentation). Of the fourth revolution in which we now live, Schwab writes that “the development and adoption of the latest technologies are fraught with uncertainty and mean that we have no idea yet how the transformations resulting from this industrial revolution will develop in the future. The very fact of their complexity and interdependence in all sectors implies the responsibility of all participants in the global community” [21]. Schwab also draws attention to the enormous speed of change at an “exponential rate”. In connection with the above thoughts, the hypothesis of a certain dialectic nature of the flow of time in its movement-development arose. Let us try to notionally structure this modern phenomenon of fleetness and density of time.

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Table 1. Marginal states in social development and fundamental changes in technology. n Technological revolutions 1 Agrarian revolution

2

3

4

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Fundamental change analysis

Built on combining animal and human power to provide production, transportation, and communication Effect: stimulate population growth and make joint settlements viable The first industrial revolution – mechanical Built on a shift from the use of muscle power to production (1760–1840) mechanical energy. Launched by the construction of railways and the invention of the steam engine, which promoted mechanical production, transportation, and communication Effect: mechanization, urbanization, and the ascent of cities Built on the transition to the use of electricity. Second industrial revolution – electrical The invention of the electric motor for production (late 19th century-early 20th production, transport, and communication century) Effect: introduction of the assembly line in manufacturing and electrification of towns and small settlements Built on the transition to the use of computerThe third industrial revolution – digital production (from 1960 to the beginning of the controlled (digitally controlled) machinery. Triggered by the development of wiring and the 21st century) use of first large computers, then personal computers and internet networks Effect: robotization of production and domestic appliances Production by cognitive activity, creating the Fourth industrial revolution – systemic world. Virtual and physical systems of synthesis-production (since the beginning of operating models flexibly interact with each the 21st century) other on a global level. According to the classification of change, the development of innovative megaprograms for the control of external digital devices goes in 3 directions (or blocks): * The real level (in synergetic terms [9]) – physical (technical) – biology *Informational level (in synergetic terms) – digital Effect: ‘ubiquitous’ and mobile Internet, miniature manufacturing devices (which are constantly getting cheaper), artificial intelligence capability, and learning machines. Integral external and internal transformations of all systems across countries, sectors, and society as a whole

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To do this, let us return to the dialectics of development in the form of an unfolding spiral. Because everything is dual in our world, we must assume that the spiral of development also has dual characteristics in its essence. Taking into account dialectics of development and cyclic recurrence of periods (day&night; winter&summer; beginning&end), let us assume that the flow of time has a two-cone spiral repeating infinitely in the same cycles. Let us now try to schematically represent the dual essence of time in Fig. 2 on the example of the double Fibonacci spiral.

Fig. 2. Notional double Fibonacci spiral for fleetness and density of time.

The high density of the current period is determined by the three-dimensional reality of a saturated material world. In addition, in our time, the most important values for most people on the planet are material values. On this basis, we assume that the material world is in full swing at this point. This period should indeed be denser (material). And since this period, is more compressed in space, it is also more rapid in time (compression in time). We believe that this period must pass faster, but with incredible difficulty, through the ultimately “dense layers of reality (space & time)”. This marginal state of “strong reality density” according to the law of dialectics (“the negation of the negation”) should transform into its opposite when it reaches a critical mass and the extreme limit (dialectical Law of the Transition of Quantity into Quality with the Measure of reaching the “transition point”) is reached. The Fibonacci spiral will suit us best for the image since by its nature the double Fibonacci spiral has both convergent branches and divergent branches. Therefore, one can assume that there is both a marginal state with the limiting characteristic of “strong space density” with a fleeting time, “compressed density”, and a marginal state with the ultimate characteristic of “weak space density” with “scattered density”, where logically there must be a period of slow-flowing time (the spiritual world). This is shown schematically in Fig. 3. The alternative to intangible values is known to be spiritual values. Thus, in this development of convergent and divergent branches, “we see the dual essence of the matter. The first essence is physical; the second essence is energy-field. The physical essence is also based on energy (see the structure of the atom). Both of these essences: the nature of matter and the nature of its immaterial structures have energy essence, the

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basis of which is movement and energy. Naturally, these energetic essences interact with each other, nourish each other, and cannot exist without each other. This is the maternal and paternal phases of matter” [12]. This movement contains the universal law of the dialectics of the nature of things (the method of comprehending contradictions through the triad: thesis ! antithesis ! synthesis).

Fig. 3. Notional double Fibonacci spiral for fleetness and density of time.

At the beginning of the 21st century, there is a significant global change in climatic natural phenomena on planet Earth, not without the human factor, which requires adaptation to the changed conditions of life. “This incessant process of change represents a turning point in the modern world.” [11] The state of continuous flow is not just the thought that things will be different someday, but the ultimate real driving force of flow, which is more important today than the final result of fixation in the matter. The driving force of flow is not only creative but also destructive to cultural forms and norms, both individual and collective.

4 The Significance of the Research Results Almost all of us feel the fleetness of time at the moment. And nowadays, people have a dramatically increased need to master the immeasurably large number of kilobytes of information that we try to process every day to adapt to the constant changes of environments (real, virtual). Also, our current reality has been heavily densified by a multitude of everyday events, large and small. The results of our research offer an opportunity to take a new look at the regularities of development in terms of large-scale periods, using universal dialectical laws. Understanding universal development as a dynamic step transition/jump on the path of perfection for nature, man, and society, we have identified multiple cycles of space-time change from one state to another through cyclic regular marginal limits/transitions, which will be characterised by their specific marginal parameters (limit state, limiting properties, limiting speed and density, transition/jump to the opposite state).

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The ambiguity of development in dialectics is reflected in the external movement of the development of the social system in time and has a universal structure of transition/ jump in an infinite process of improvement. The category of development is a philosophical ultimate universal concept, so it is important to present what is the dialectical essence of development in a deep understanding and what underlying universal patterns of movement exist in general. Of course, it must be borne in mind that ‘Matter is certain through its systematicity. The recognition of the position that development is realized through certain holistic systems (summative ones are deprived of self-development) requires more clarity, the notion that development is the generation of a new holistic integrity’ [1]. And since we understand that “the regularity of integrity (emergence) leads to the appearance of new properties in the system that were not present in its elements” [9], the new elements are the opposite of the previous ones, characterized by contradictory states of limit/transition, described, for instance, in the cultural approach as the phenomena of marginality [5]. The study of marginality in a socio-cultural system [3] contributes to the appearance of new contemporary approaches [2–7, 17, 18].

5 Conclusion In our previous studies, we tried to understand the differences between human intelligence and highly intelligent AI (artificial intelligence). Unlike human intelligence, even the most highly intelligent AI does not have the ability in its relative focus of “awareness” of robotic devices to produce individual desire and individual idealization (the process of forming an individual ideal — a spiritual-value level containing generally meaningful socio-cultural ideals). The impossibility of such processes underlies the technical structural system of artificial intelligence, which has three main components: a logic core, autonomous control subsystems, and drivers. Our research shows the need to combine humanitarian and technical issues for a broader and deeper understanding of global contemporary processes of dialectical development and, in their unified synthesis, the aspiration for the next higher solar level of human development [17]. The ambiguity of development in dialectics is reflected in the marginal states both in the external movement of the development of things, nature, the directionality of the movement of history, and the movement internal in the development of thought about reality and the movement of social relations. The dynamic system of social development has a universal marginal structure of “limit/extreme” and “transition/jump” in an infinite process of its improvement. Our research makes it possible to identify three groups of different types of marginal state functions: 1) Deconstructive type of marginal functions is the one that by its actions destroys (eliminates) the usual course of things, which has ceased to correspond to the processes of general evolution.

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2) Conservation-type of marginal functions is the one that suspends and, perhaps, at the same time actions are aimed at long-term preservation of marginal objects in the system of general development. 3) Constructive type of marginal functions is the one that creates (gives birth to) unexpectedly new creation contributing to the growth of a new branch of diverse evolution. Based on this study, we assume that we have a material world in full swing for a given period, which in its limit will yield a transition or jump across a marginal conventional point according to the law of dialectics into the opposite state. But to identify and reveal the complex systemic marginal links in the culture of this factor of human existence, new approaches to the consideration of the complex problem of marginality must be sought. We believe that an interesting topic for future research will be “the intersection of three worlds — material, virtual, and mental – at the point of marginality”. According to the law of dialectics (or trialectics), there are three “worlds” of human existence: 1) Material world — a three-dimensional physical space. In the material world, we conventionally divide time into three parts – past, present, and future. 2) Virtual world — a multidimensional, infinite space. Possibility to control dimensionality. We divide time in the virtual world conventionally into three parts, and we also assume that there is “eternity in the form of a database”, and the possibility of turning back time. 3) Mental world — connection with all worlds through channels of perception; sensory-minded space, having multidimensionality, infinite space. Possibility to control dimensionality. Time in the mental world we divide conditionally into three parts, and also there is assumed “eternity as memory, dream (fantasy)” and the ability to turn back time by recalling events. We live in the age of information, constantly loading our brains and pouring huge amounts of knowledge into our minds. However, to learn how to benefit from them, an explanation and an appropriate mind map are needed. It must be accessible, comprehensible, and detailed, based on scientific views, take into account all human experience and knowledge and must be systematically updated.

References 1. Alekseev, P.V., Panin, A.V.: Philosophia: Uchebnik. [Philosophy: Textbook.] TC Velby, Prospect Publishing House, Moscow (2003). (In Russian) 2. Bober, Zh.: Znachenie marginalnosti v razvitii sociokultury. [Importance of marginality in socio-culture development.] J. Dev. Sci. Educ. (4), 157–163 (2014). (In Russian)

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3. Bober, Zh.: Kulturnaya marginalnostj i ejo mesto v razvitii cultury. [Cultural marginality and its place in the development of culture.] Pushkin Leningrad State Univ. J. Ser. Phil. 2(4), 136–143 (2010). (In Russian) 4. Bober, Zh.: Marginalnostj v socio-culturnoy sisteme. [Marginality in the socio-culture system.] Polytech-Press Publishing House, St. Petersburg (2019). (In Russian) 5. Bober, Zh.: Phenomen marginalnosti: synergeticheskiy podhod. [Phenomenon of marginality: synergetic approach.] J. Vestnik Leningradskogo gosudarstvennogo universiteta imeni A. S. Pushkina (Nauchnyj zhurnal) 3(14), Seriya Filosofiya, 99–106 (2008). (In Russian) 6. Bosenko, V.A.: Vseobschaj teoriya razvitiya. [The general theory of the development.] Kiev (2001). (In Ukrainian) 7. Branskij, V.P., Pozharskij, S.D.: Globalizaciya I sinergeticheskiy istorizm. [Globalization and synergetic historicism.] Polytechnic, St. Petersburg (2004). (In Russian) 8. Engels, F.: Dialectics of Nature. Marx, K., Engels F. Written Works, vol. 20 (1886) 9. Volkova, V.N.: Emergentnostj, sinergiya ili convergenciya? [Emergence, synergy or convergence?] In: Volkova, V.N., Loginova, A.V., Shirokova, S.V. (eds.) System Analysis in Engineering and Control. 21st International Scientific and Practical Conference, part 1, pp. 149–160. Polytech-Press Publishing House, St. Petersburg (2017). (In Russian) 10. Karmin, A.C.: Culturalogiya. [Culturology.] Lan’ Publishing House, St. Petersburg (2006). (In Russian) 11. Kelly, K.: Neizbezhnoe. Ponimanie 12 tekhnologicheskih sil, kotorye opredelyat nashe budushchee. [The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future.] Ivanov & Ferber, Moscow (2017). (In Russian) 12. Khaichenko, V.A.: Glubinnye znaniya ljudey — vehi na puti k istine I spravedlivosti. [Deep knowledge of people – milestones on the path to truth and justice.] In: Fundamental and Applied Problems of Sustainable DEVELOPMENT (2012). (In Russian). http://www.skibr. ru/ass_Dub.php?lang=ru&page=seminar&open=5. Last accessed 19 Oct. 2021 13. Lenin, V.I.: The philosophical notebooks. [Philosophskiye tetradi.] Complete Works, vol. 29. Political literature Publishing House, Moscow (1969). (In Russian) 14. Mikolov, T., Sutskever, I., Chen, K., Corrado G., Dean J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, arXiv:1310.4546 (2013) 15. Park, R.E.: Human migration and the marginal man. Am. J. Sociol. 33(6) (1928) 16. Pozharskij, S.D.: Acmeologiya – philosophiya uspeha. [Acmeology is a philosophy of success.] Ryazan (2010). (In Russian) 17. Pozharskij, S.D., Bober, Zh.: Problemy stanovleniya i razvitiya philosophskih issledovaniy categorii “Marginalnostj”. [Problems of formation and development of philosophical studies of the category of “Marginality”.] Sociology 1, 221–231 (2020). (In Russian) 18. Pozharskij, S.D., Bober, Zh.: Philosophskiye aspekty categoriy marginalnosti v dialektike razvitiya. [Philosophical aspects of the categories of marginality in the dialectic of development.] Sociology 6, 217–225 (2020). (In Russian) 19. Sagatovsky, V.N.: Tochnostj kak gnoseologicheskoye ponjatiye. [Accuracy as an epistemological concept.] Phil. Sci. (2) (1974). (In Russian) 20. Schmidt, E., Cohen, J.: The New Digital Age: Reshaping the Future of People. Nations and Business (2013) 21. Schwab, K.M.: The Fourth Industrial Revolution. World Economic Forum, Cologny, Geneva (2016) 22. Toffler, A., Toffler, H.: Revolutionary Wealth. Alfred A. Knopf (2006)

The Ideal and the Material, the Subjective and the Objective in Systems Research Elena A. Tunda1(&) 1

and Vladimir A. Tunda2

National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia [email protected] 2 Independent Researcher, Tomsk, Russia

Abstract. The article is devoted to the application of a new model of structuring of Matter, previously published by the authors, to some questions of systems research, which allows us to take a slightly different look at the fundamental categories of “ideal” and “material”, “subjective” and “objective” by way of realizing a more holistic model of the evolution of Matter than, for example, the Big Bang theory, and understanding the unlimited 5–10 billion years of the evolution of our macrocosm. The proposed model of structurization also allows one to establish deeper levels of abstraction in scientific research, relative to the levels of structurization of Matter. In addition, this model offers a new understanding of the prudence of nature in creating the mechanisms of what is called thinking, awareness, cognition up to the evolutionary destiny of man. Finally, the article offers system researchers the reasoning for modeling cognitive processes as an extension of processes in the environment. Keywords: Systems research
Ideal
Material
Subjective Universe
Evolution
Thinking
Abstractions levels


Objective


1 Introduction Considering what metaphysics does is a theory of what might underlie the phenomena studied by physics and all other natural sciences. The theory in which, among others, the essence of the categories “ideal” and “material”, “subjective” and “objective” are considered. The use of these categories is also necessary in the field of systems research. The authors took the liberty to offer their own views on the essence of these categories by understanding some metaphysical processes that underlie the universal mechanism of thinking that supports the universe evolution. Moreover, “By now, enough grounds have been accumulated to assert that over the past more than a century since the creation of the general theory of relativity (GR), the principles underlying its foundations have practically been exhausted. The time has come to pay special attention to the analysis of the prevailing ideas about classical space-time and physical reality. [2, p. 69]”. However, first things come first.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 133–145, 2022. https://doi.org/10.1007/978-3-030-98832-6_12

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2 Traditional Understanding of Ideal and Material It was the philosophers of antiquity who proceeded from the idea of the dichotomy of being into two main types: material and ideal. The problem of the ideal world as a whole and the individual spiritual world of a person has attracted man’s attention since ancient times – since he began to realize himself, to realize that he is thinking. “Material” is a philosophical category that reflects the materiality, perceptibility of real objects, i.e. substantiality of the material. However, Matter and substance should not be confused. The opposite of the category “material” is the category “ideal”. “Ideal” denotes an incorporeal, immaterial, and non-extended reality that exists in the form of ideas, ideals, prototypes given to a person in his consciousness as “intelligible essences”. This is the fundamental difference between the reality of consciousness and the reality of the material; mental — from the physical. There might be a question, what does all of the above have to do with system analysis? It turns out that a close look at systems that are very different in scale from the usual surrounding world given to us in sensations, such as large-scale astrophysical systems or small-scale systems of elementary particles, reveals a significant discrepancy between the proposed models and the facts. But even Josiah Willard Gibbs said that one of the main tasks of theory in any field of knowledge is to find a position from which the object is seen in the utmost simplicity. What is the simplicity of the Big Bang or quantum-wave dualism models?

3 Traditional Understanding of Subjective and Objective The meaning of the concepts used in the heading of this section will be revealed with a number of definitions. First, we give definitions from Wikipedia: “Subjectivity is the expression of a person’s (thinking subject) ideas about the world around him, his point of view, feelings, beliefs and desires” and “Objectivity is an attitude to an object (phenomenon) and its characteristics, processes, as to independent of person’s will and desires, – implies the presence of knowledge as such about an object (phenomenon)”. But how can we explain “objectivity” from the point of view of the observer’s influence on the results of quantum-mechanical experiments? And now let’s take a somewhat paraphrased definition given by Anatoly Alekseevich Denisov [3, p. 3], which corresponds to the topic of this article and will be developed further: “Objective reality is a philosophical category to designate that reality that is by no means given to us in sensations, but is a product of logical processing of sensory/measured/calculated data, while in sensations, measurements and computational experiments, we are given specific material properties inherent in the objects”. And, finally, we write out a number of clarifying definitions. The concept of “data” used in Denisov’s definition is associated with other concepts that clarify further reasoning. “Data” is the presentation of dedomains in a form suitable for communication, interpretation, or processing. “Dedomains” — heterogeneities of objective reality, what makes it possible to distinguish them — pure data before their interpretation or cognitive processing. “Information” is interpreted,

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cognitively processed data that has been given a new meaning and correct form: if we only have data, but we do not know their meaning, we don’t have information yet. “Knowledge” is information properly considered. “Facts” — fixed knowledge that can be verified, — a fact is opposed to a theory or hypothesis: a scientific theory describes and explains facts, and can also predict new ones. And again, we return to the topic of this article. “In the concept of ‘system’, as in any other category of the theory of knowledge, the objective and the subjective constitute a dialectical unity, and one should speak not about the materiality or immateriality of systems, but about the approach to the objects of research as to systems… Using these, as it were, different levels of display, a researcher can preliminarily represent an object or a process of solving a problem in the form of a system in which it has not yet been possible to distinguish elements, determine communications that are essential for achieving the goal, and then, moving to more formalized levels of representation of the system (engineering, design), refine the elements and communications, more and more approaching the achievement of the goal, to the creation of the desired system. At the first stages, it is important to be able to separate (demarcate) the system from the environment with which the system interacts, or to find some other way of representing the system, for example, to represent it as a block with an unknown structure and only known ‘inputs’ and ‘outputs’ (in cybernetics and systems theory, such a representation is often called a ‘black box’)” [4, p. 27]. Below, in a “new look at the subjective, objective” we will try to develop this classic approach to the study of systems.

4 Matter Structurization Model The author’s model of the structurization of Matter is based, first of all, on the notions of the structural organization of Matter by Ruger Osip Boshkovich [5], who based his view on Leibniz’s doctrine of continuity “Everything happens gradually”, the axiom of impenetrability of Boshkovich himself “no two material points can occupy one and the same spatial or local point at the same time” and the Law of Force, derived by himself. He assumed Matter to be composed of combinations of homogeneous, completely indivisible, without any extension and separated from each other “points”, each of which has the property of inertia, in addition, a mutual active force, depending on the distance. If the distance decreases infinitely, the repulsive force increases indefinitely, while if the distance increases, the repulsive force decreases, disappears and turns into an attractive force, which decreases in the inverse ratio of the squares of the distances, almost coinciding with Newton’s gravitational force. We, in the development of Boschkovich’s ideas, believe that the world around us arose on the path of endless (in time) self-improvement of Matter with the help of natural mechanisms of its self-organization. In our understanding, everything happened and is happening in the so-called Great Void, which is filled with an innumerable set of, let’s say, V-quanta, similar to Boschkovich’s points. First, from V-quanta, pramatter emerged in the form of an innumerable set of Platonic solids (tetrahedron, hexahedron, octahedron, dodecahedron, icosahedron) — the most durable structures formed from V-quanta, due to their volumetric symmetry, which (pramatter) became the substantial basis of our universe.

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The structuring of Matter began with the mutual combination of pramaterial structures and free V-quanta, leading to the emergence of more and more complicated structures and their reflections1 by each other from the simplest ways in collision to the most perfect way of reflecting reality — thinking. More detailed information on the structuring of Matter is given in [6]. In our opinion, after the birth of pramatter at the very first stage of the formation of Matter, the mental world arose. The mental structures (MS) that emerged as a result of incipient thinking themselves began to contribute to a more complex further structuring of Matter. Thoughts began to be accompanied by emotions, which contributed to the next level of structuring of Matter – the emotional world with its emotional structures (ES). Further, the physical world with the most diverse physical structures (PS) arose, the evolution of which gave rise to a person with an ineradicable natural desire “I want to know everything!” and objectified the creation of computer technology and artificial intelligence (AI), which greatly increased the mental capabilities of a person. So, on an enlarged scale, there are five levels of evolution in our model: The Great Void with its V-quanta, pramatter from the Platonic solids, the mental world, the emotional world, the physical world. Each level, starting with the deepest level of the Great Void, creates a structure or template for the “objects” of the next level. So, for example, so that individual particles of pramatter — Platonic solids — do not get lost among the innumerable set of V-quanta of the Great Void, in the depths of the latter a single stream of Platonic solids, directed along an elliptical orbit, arose — the dynamic structure of the Great Void, the disintegration of which is not allowed by the free Vquanta scurrying around it. The internal processes of this dynamic structure have led to the next level of structuring — mental level structures: memory stores, thinking patterns, etc., and the mental field became the carrier of the interactions of these MSs, which still carries thoughts throughout the mental world. In the process of its evolution, the mental world by its thinking contributed to the creation of structures of the emotional level — specific ES, the emotions of which are transferred throughout the emotional world by their own emotional field. In turn, the emotional world contributed to the creation of structures of the physical world, perceived by the senses given to us by nature. Finally, having learned to semanticize the surrounding reality, a person subdivided the physical world into inert and living matter, each of which was subjected to a thorough analysis, giving sense and meaning to their individual parts, etc., etc. Let’s recall the question and the assumptions on the answer to it by Vlail Petrovich Kaznacheev: “…there is an astrophysical horizon, what is beyond this horizon? There is not a vacuum, not an emptiness – there is something, there is also materiality. Something that exists outside of quantum particles of all categories known to physics. The presence of ethereal space is postulated, it is assumed… the presence of ‘great nothing’2 as a kind of Arche, which is then realized in a physical vacuum, in…

1

2

Reflection is a universal property of Matter, manifested in the ability of material forms to reproduce the certainty of other material forms in the form of a change in their own certainty in the process of interaction with them. This is a Great Void for us.

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quantum particles, then in all other gaseous, liquid, dense and other forms of matter” [7, p. 52]. Let’s pay attention to the enormous difference in the scales of all the mentioned five levels of structuring and to the energetic feeding by the fields of deeper levels of substances of the following higher levels of structuring. So free V-quanta by their motion feed the energy of the flow of pramatter — the flow of what in our world is called Ether. The ether feeds with its energy the fields of the mental level, which, in turn, are the fields of the emotional world, etc. To represent the enormous difference in the scales of all the five levels of evolution, let’s take as an example at least an electromagnetic field, the substance of which has not yet been registered — we see only the results of its interaction with conductive substances, not to mention the “subtler” substances of the emotional and mental worlds. In addition, the prudent nature could not afford to squander mental developments in the death of objects of the emotional and physical world – all the attributes, all the results of mental processes proceed and remain in the depths of the mental world.

5 Human Structural Model In the organism of a living substance, protein-nucleic space and field space are combined. V.P. Kaznacheev [7, p. 58]

In general, a person can be structurally represented as consisting of the skin, the osteo-muscular skeleton, the vascular system, the nervous system and the meridian structure. It is believed that a person thinks with his head, which contains the thinking apparatus — the brain. In our opinion, the brain is only a receiving-transmitting station, which, through the nervous system associated with it, removes signals from a wide variety of receptors and proprioceptors of the human body and transmits them in the form of oscillations to its meridian structure, which is connected with the human body at its inception. The meridian structure (in more detail in the next section) at its different levels can receive/transmit oscillations in the range of all three worlds of the evolution of Matter: mental, emotional and physical. The carriers of oscillations are the substances of the corresponding fields of these worlds. The main thing for the perception of oscillations from one world to others is the resonance effect, which occurs when the frequency multiplicity of the oscillations carried by the fields and the receiving-transmitting “station”. In other words, the meridian structure of a person perceives the entire range of vibration frequencies of all three worlds of the evolution of Matter. Thus, regardless of whether a person realizes it or not, his vital activity takes place simultaneously in all three worlds. If we switch over to religious terminology — in the spiritual (mental), soulful (emotional) and the world of actions (physical). The communicative center of interactions between all worlds is the brain with its nervous system and, of course, the meridian structure, which perceives the oscillations of all the above-mentioned fields-carriers of interactions.

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6 The Meridian Structure of the Human Organism Does the body have any sensory structure that is sensitive to the space-time Kozyrev fields? V.P. Kaznacheev [7, p. 179]

The meridian structure, as a product of the evolutionary structuring of the mental and emotional worlds, is able not only to perceive the entire spectrum of vibrations of the mental, emotional and physical ranges, but also to transform them to transfer the vibrations of one world to another. In addition, it serves as a limiting defense system that meets the evolutionary task of surviving what a particular meridian structure encompasses by maintaining the required internal homeostasis. The meridian structure of the (MSO) human organism has three levels, respectively, associated with his personal MS (PMS) the mind, personal ES (PES) — the psyche, and personal PS (PPS) — the body (and in the sum PMS + PES + PPS make up the human organism). The existence of the third level of the meridian structure can be judged by the fact that back in 1985 a group of Novosibirsk scientists led by Kaznacheev discovered the optical conductivity of the meridians. As a result of the research, it was possible to prove the conductivity of light, as well as the specificity of the conduction of light by acupuncture points lying on the same meridian. PES and PMS of each person are unique (like papillary patterns) and respond only to their own oscillation frequencies, like a radio receiver tuned to the wave of a certain radio station. So, the first level of the MSO is able to receive/transmit the oscillations of the PMS, the second — of the PES, and the third — the oscillations of the first and second levels of the MSO and the brain, for which the third level of the MSO is subdivided into two sublevels. The vibrations of the mental world are processed at the first level of the MSO and are transmitted to the first sublevel of the third level of the MSO. Fluctuations of the emotional world are processed at the second level of the MSO and are transmitted to the second sublevel of the third level of the MSO. Both sublevels of the third level of the MSO play the role of frequency transformers PMS and PES into frequencies that the brain is able to perceive. Directly the very processes of thinking and emotional experience occur in PMS and PES with all the attributes inherent in these processes. Communication with the brain is carried out indirectly through the levels and sublevels of the meridian structure of the organism. In other words, the receptors and proprioceptors of the body with their signals, transmitting to the brain, and then to the MSO and even further to the PES and PMS, form in the latter two a vision of our physical world. It is they (PES and PMS) who actually experience and think, “direct” the life of the body and human behavior. But the total results of the work of all PES and PMS of mankind serve the process of evolution of Matter as a whole.

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7 The Mechanism of Thinking The accepted definition of the concept of “thinking”: an indirect and generalized reflection of reality — a type of mental activity, which consists in cognizing the essence of things and phenomena, regular connections and relationships between them (see also below “Levels of abstraction”). We propose to consider a generalized model of the mechanism of reflection (mental activity, cognition). Enlargedly, the mechanism of thinking consists of five parts: the PMS, the mental field-carrier of thoughts, the meridian structure of the organism, the brain and the human nervous system with its receptors and proprioceptors. PMS is where thinking actually takes place. The mental field-carrier carries with its waves thoughts — the results of thinking. MSO, as a receiving-transmitting antenna, directly (through its third level) connected to the brain, receives mental vibrations from its PMS and transmits them to the brain. The brain receives decoded signals and transmits them through the nervous system to the corresponding organs or systems of the human body, guiding their life. Signals from receptors and proprioceptors of the body enter the brain, then to the third level of the MSO, where they are transformed into the frequency range corresponding to the mental world, then they are transmitted to the first level of the MSO, from where they are transferred by the mental field to the PMS, which thus “sees” what is happening in the physical the world. A person believes that he sees, feels the environment of the physical world, but in fact, despite all the complexity of his brain, he cannot perform any actions without “consulting” his PMS, except for skills innate or acquire by constant practice, mechanical movements. Sports skills gained in exhausting workouts, let us recall at least the legendary Bruce Lee, allow you not to waste time on “advice” with your PMS, i.e. to think, and perform the necessary movements reflexively. If the acquired skills are needed for evolution, nature supplies them to people of the next generations by creating the appropriate neural-genetic structures. MSO is able to reproduce any thought-vibrations formed by the PMS. The power of such vibrations is not great, but if they are long and multiple to the natural frequencies of an object or phenomenon in the universe, then in the resonance mode such vibrations can cause vibrations sufficient, say, to produce a change in this object or phenomenon. Thus, thinking, as a product of the evolution of Matter, can serve evolution itself. Kaznacheev uses the term “intelligence”, not “thinking”: “…the properties of intelligence are the properties of evolution, increasing improvement, accumulation of memory tools… They are concentrating more and more, and, apparently, the modern discovery, the decoding of the macromolecular genome, to some extent reflects a certain, not very large degree of accumulation of evolutionary information of human or animal intelligence” [7, p. 55]. Moreover, he, following Konstantin Eduardovich Tsiolkovsky and Vladimir Ivanovich Vernadsky, considers the transition “from heterotrophy to autotrophy — to field life”, to the noosphere as the main vector of the direction of human survival: “Spirituality and the world should be outlined as the movement of evolution towards cosmic planetary autotrophy — the formation of cosmoplanetary intelligence. In this we see the ways of survival and preservation of humanity” [7, p. 61].

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8 Abstraction Levels The notion of “level of abstraction” is associated with the concentration-tuning of the interlocutors’ thinking to one worldview, concepts, notions and terms that the answers are adequate to the questions asked in the process of discourse. Let’s briefly go over some of the attributes of thinking. Thinking is a cognitive activity of a person. It is an indirect and generalized way of reflecting reality. Thinking is opposed to “lower” methods of mastering the world in the form of sensations or perceptions. The result of thinking is thought (concept, meaning, idea). Concept – the emergence of a theoretical point of view on the situation about which the discourse is conducted [8]. Notion is a thought that, by pointing to a certain attribute, both selects and collects(generalizes) objects (that have this attribute) from the universe. Words and phrases denoting concepts are called terms. The universe is a set of objects and phenomena as a whole, that exists in our consciousness as an idea of the world around us. Abstract thinking — the formation of abstract concepts and the operation of them. With abstract thinking, a person concentrates exclusively on the delivery-perception of a thought or idea. In this type of thinking, images and symbols are often used, both generally known and those that get their meaning based only on the thought process or discussion itself. Abstracting is a method of phased production of concepts that form more and more general models — a hierarchy of abstractions, it is a distraction in the process of cognition from insignificant aspects, properties, connections of an object (object or phenomenon) in order to highlight their essential, natural features. Abstraction is a generalization of the result of abstracting. The level of abstraction is the degree of abstraction of the discussed concept from some attributes. Depending on the goals and objectives, you can talk about the same object at various levels of abstraction. For example, one can speak about a metal sample at a mechanical, chemical or physical level, using the terminology inherent in each of them. Without an explicit indication of the level of abstraction for a chemist’s question, a mechanic or a physicist, having understood the question at their own level of abstraction, may give an inadequate answer.

9 Recursiveness When Exploring Systems Let us introduce three very short definitions sufficient for reasoning at our very general level of abstraction: “Metaphysics is transcendental physics”, “The system is what is being investigated” and “Model is the result of studying the system”. In our opinion, the study of any system, subsystems and elements in a thought experiment should logically consistently and recursively scroll them down, down to the deepest structures of Matter, and vice versa, lift them back up from the deepest depths, reproducing what is being investigated. At the same time, depending on the purpose of the study, the constructed recursive model of the system should to stop at any mental level of recursion for a more detailed and comprehensive consideration. Anything explored at one level of recursion must belong to that particular level of abstraction. In addition, conceivable mutual transitions between the levels of recursion should be

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provided by acceptable consistent mental procedures for transforming objects from one level to another. Let’s consider an example. Any biological organism in our physical world has its own construction program — the genome. And what is the computer running this program? If in the case of mammals, it is somehow possible to imagine the mother’s organism as such a computer, then what about birds? Imagining an eggshell as a computer in the physical world is already somewhat more difficult. It is known that modern computer technologies make it possible to create real 3dimensional physical objects on 3D printers in accordance with the results of the execution of special programs on computers to which these 3D printers are connected. However, all artifacts, in our understanding, are the results of modeling something peeped from nature, so the design technology on 3D printers is most likely inspired by some natural processes. From our model of Matter structuring, the process of building biological organisms looks like this. The mental genome first develops, on its basis — the emotional genome, then — the physical genome, and appropriate “computers” are created to promote these genomes on the mental, emotional and physical levels. The prudent nature did not begin to carry out the processes of genetic construction of biological organisms (at least on Earth), each time repeating the entire way of structuring Matter, but uses the already existing “building materials” of the level for which the organism is according to the corresponding genome. Therefore, a pregnant mother needs enhanced nutrition for herself and her baby. The mental, emotional and physical genomes are tightly linked. The program of the physical genome is connected with the programs of the emotional and mental genomes. The theoretical and experimental substantiation by Petr Petrovich Gariaev of wave genetics as a direction in biology [9] to some extent confirms our reasoning. In addition, we will quote Kaznacheev: “… if we compare objective phenomena that are present in the surrounding world and are observed in humans and animals, then, undoubtedly, field forms will also be found… These field forms of cosmic intelligence are constantly present in the world and also evolve…. the more we delve into the social nature of animals, insects, certain groups of plants or mammals (this applies to freshwater, amphibians, reptiles), the more we are convinced that intracellular, intercellular interaction is replenished with field information flows”. [7, pp. 55–56]. Summarizing this section, we note that the above three programs of genomic construction are recursive. For example, a program for the construction of a particular protein recursively refers to itself until a special gene corresponding to a given protein signals the need to stop “recurring” of this construction program.

10 The Ideal and the Material: A New Look Let’s now consider the concepts of “ideal” and “material” in the light of the proposed above model of structuring, considering the levels of abstraction. From our point of view, the material macrocosm is “built” into the universe, at the base of which lies the pramatter or the flow of Ether [6]. The “ideal” in our model is what is at the lowest levels of structuring — emotional and mental. Thus, the specificity of the “ideal” is that

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its substantial particles and structures are many, many orders of magnitude smaller than the substantial structures of our physical world. At the same time, material reality is generative, primary in relation to the ideal. The “ideal”, consisting of ideas, ideals, prototypes accessible to man through what is commonly called consciousness, had arisen long before man realized it in our physical world. The ideal in our understanding is based on the material substance of our macrocosm. “Ideal” is a material kind of reality, which sharply differs from the physical only in its scale and internal structure. Indeed, Mr. Leibniz would not have been able to find any carriers of the ideal at the level of the physical world, but from this the “ideal” does not cease to belong to the deep levels of the material macrocosm. We support the opinion of Democritus that the human soul, like the entire surrounding world, consists of atoms, only lighter and more mobile, and completely disagree with the concept of idealism that the “ideal” is a priority substance that exists before and independently of the material world. Although we agree that the “ideal” (thoughts, ideas, images) promotes to the structuring of the material in the form of evolutionary self-organization of Matter. It is interesting that the meridian structure-antenna of some people is able to receive vibrations not only of their PMS, but also of deeper levels of the mental world (showing, as it were, farsightedness), which means that it is not so clear to recognize the vibrations of less deep levels, which makes them fight for the “ideal” as the basis of everything, including the material.

11 The Subjective and the Objective: A New Look When we spoke above about the traditional view of the subjective and the objective, we highlighted the phrase “… we should not talk about the materiality or immateriality of systems, but about the approach to research objects as systems…” Everything seems to be correct, but why not talk. Let’s recall the above “Structural model of a person” and “Mechanism of thinking”. The human body in our physical world is just a receptorproprioceptor system with a receiving-transmitting meridian antenna for two other main (experiencing and thinking) parts of his whole organism (PPS + PES + PMS). A prudent nature, while providing freedom of choice, at the same time uses templates for all life processes and all native structures. Let us recall at least the process of genetic reproduction of biological organisms, which is quite indicative in this sense. So, if the traditional vision of subjectivism consists in the ideas of a particular person, personality about the world around us, then we clarify it (this vision). Personality is a native structure, consisting of three main parts: PMS + PES + PPS. PMS thinks, PES colors thoughts emotionally, and PPS acts in the physical world, working out various ways and methods of further evolution of Matter, starting from the level of the created physical world with super-complex structures such as the human brain. So, for example, a person, in the usual understanding of him as a phenomenon, on the one hand, by his vital activity complicates his biological structures, the same brain, and, on the other hand, tries to penetrate into the deep structures of Matter itself, its internal structure, “helping” the native structuring with their nanostructures on 3D printers or genetic modification of certain natural biological structures, or creating digital technologies and self-learning AI distributed throughout the planet.

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At the same time, all human research activity is limited both by the possibilities of training his natural abilities, and by the level of achievements of modern technologies. Within the framework of this article, we are only talking about the limitations of the ranges of sensations or measurements, including the range of intuitive extraction of knowledge about the observed in systemic studies. From our point of view, a person as an integral system is formed by reuniting the PMS and PES with the PPS that begins after fertilization. PMS and PES consist, among other things, of mental and emotional patterns stored as on flash drives in their memory structures. These patterns after the birth of a person become his personal (his personality), i.e. subjective. It is them that a person extracts, so to speak, intuitively; it is him who is entrusted with developing and multiplying them by the Evolution itself. The totality of all PMS and PES, or rather their “average sum” of samples, represents what is called objective or simply generally accepted. Those people who can tune their mental vision to the perception of other subjects stored in the memory of PMS and PES, or at deeper levels of the most objective, are considered intellectually more developed, brilliant, wise. Now let’s recall the inseparable didactic trinity — “knowledge, ability, skill”. The point is that man is a social being. Knowledge, starting from birth, is given by the family, school, mentors from the surrounding society. More precisely, a person is taught to use objective knowledge. Then the person is taught the ability to use this knowledge. And, finally, if a person takes the trouble to work out the acquired skills to use the existing knowledge to the level of skills that justify certain evolutionarily significant aspects of life in the physical world, his knowledge by some native mechanisms is neatly built into the storage of his PMS and PES or even into the storage of the objective. The researcher, with his subjective thoughts-reasoning, introduces changes through mental and emotional fields into the system under study itself, and, moreover, into the resulting model and its constructive embodiment. It is another matter to what extent the scale of these changes is accessible to the modern level of development of sensations or measurements. We will end this section with a quote from Kaznacheev’s book [7, pp. 51–52]: “Objectivity is what exists, as indicated in the definition, in our sensations, reflections, devices, but there is also what exists outside of our perception, our sense organs and all measuring or other devices. This means that if we talk about objectivity, then it has a double meaning – the semantic content within itself. The first is what is given in the sensations, reflections and perceptions of a person and devices, and the second is what exists outside of us, outside of our perception.”

12 On the Question of the Very Study of Systems In our opinion, recently the issues of metaphysics (investigating essence), to put it mildly, have been bypassed. So, when studying systems, it is need to scroll through them on various levels of Matter structuring, considering that metaphysics is a zone free from levels of abstraction, where anyone can say anything without fear of ever being proved wrong, as long as the basic law of non-contradiction is respected. “Such

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an unconstrained game of ideas should be found dull and frustrating by anyone genuinely interested in the advancement of knowledge and understanding.” [1, p. 60; 10– 14] Especially now, when the information revolution we are observing, changing the physical reality and ourselves, causes a metaphysical drift – a change in our understanding of the ultimate reality together with a change in the information environment. This leads to a rethinking of our metaphysics (our understanding of the world around us) in informational terms. The information environment is being transformed from a way of designating the information space into a synonym for reality itself, which we begin to perceive as a kind of information metaphysics. And this is already so close to the invisible (hopefully only for now) deep levels of structuring of Matter.

13 Conclusion The considered model of structuring offers a single substantive basis for what has been opposed to each other and was called “ideal” and “material” until now. This model simplifies, but also deepens (ontologically) the study of systems by the fact that everything comes down to one substantial basis and the natural evolution of Matter, starting with its emergence from pramatter — an innumerable set of Platonic solids that make up the substance of the stream of Ether, swirling in the Great Void by the energy of its constituent parts — P-quanta. Where did the Great Void with its P-quanta come from, who replenishes the P-quanta themselves with energy, humanity will probably never be able to find out reliably. But, starting from the moment of the emergence of Matter from pramatter, then everything seems to be more or less clear and naturally evolving. At first, the increasingly complex structures of Matter reflect the environment purely mechanically, contributing to the growth of more and more complex structures. These increasingly complex structures begin to reflect each other in a more complex way, reproducing each other’s certainty in the form of a change in their own certainty, and so on until the emergence of that level of reflection, which is now called thinking, awareness, etc. The Evolution of Matter continues, but already with the mental participation of the emerging structures thinking up to their generalized manifestation in the form of psychical or soulful processes. And, finally, even greater evolutionary complications of the structures of the emotional and mental worlds in the general process of the evolution of Matter lead to the emergence of super-complex structures of inert and living matter up to the creation of such as the human brain. Thus, the entire evolution, from the moment the mental world emerged, takes place under the direct influence of the thinking mechanism, which sharply accelerates the evolution process itself (in a similar way, artificial intelligence created by man begins to develop itself, for example, with the help of so-called machine learning, multiplying the intellectual capabilities of man; in other words, artificial intelligence does the same for the development of man as thinking does for the evolution of Matter). The most evolutionarily interesting structures and mechanisms arise and remain in the memory of the mental world — the mental world teaches itself and advances along evolutionary steps. So, evolutionarily the best samples of thoughts, ideas, concepts, categories, paradigms, worldviews are stored in the memory of the mental world, and the evolution of all this “ideal” is influenced by humanity with its digital technologies.

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It remains for a person to realize all this deeper and expand the possibilities of his ineradicable desire for knowledge, including through the adoption of more developed models of the device of the entire universe with its native communication mechanisms. For the most fundamental levels of abstraction, it is probably necessary to cultivate and develop good explanatory dictionaries with clear formulations of all used fundamental terms, notions, concepts, paradigms and worldviews in general, with cross-references from dictionaries of one fundamental level to related terms and concepts of dictionaries of other fundamental levels.

References 1. Floridi, L.: The Philosophy of Information. Oxford University Press Inc., Oxford (2011) 2. Babenko, I.A., Vladimirov, Y.: Relyatsionnyy vzglyad na printsipy geometricheskoy paradigm. Metafizika 3(37), 69–81 (2020). (In Russian) 3. Denisov, A.A.: Vvedenie v informatsionnyi analiz sistem. Tekst lektsii, 52 p.. Izd. LPI, Leningrad (1988). (In Russian) 4. Volkova, V.N, et al.: Teoriia sistem i metody sistemnogo analiza v upravlenii i sviazi, 248 p. Radio i sviaz, Moscow (1983). (In Russian) 5. Boscovich, R.J.: A theory of natural philosophy. Latin–English edition from the text of the first venetian edition published under the personal superintendence of the author in 1763. Open Court Publishing Company, Chicago–London (1922) 6. Tunda, V.A., Tunda, E.A.: Propedevtika ili zachem vozvrashchatsia k voprosu pramaterii// Kommunikativnye strategii informatsionnogo obshchestva: trudy XII Mezhdunar. nauchnoteoreticheskoi konf, 23–24 oktiabria 2020, pp. 223–234. Politekh-Press, St. Petersbrug (2020). (In Russian). 7. Kaznacheev, V.P.: Dumy o budushchem. Rukopisi iz stola, 208 p. Izdatel, Novosibirsk (2004). (In Russian) 8. Pavlov, K.A.: O kontseptsiiakh logiki i smysle modelirovaniia “logicheskikh rassuzhdenii.” Filosofskii zhurnal [Philos. J.] 2(3), 93–117 (2009). (In Russian) 9. Gariaev, P.P.: Volnovoi genom. Monografiia. In: Entsiklopediia russkoi mysli v 23 tomakh (1993–2014 gg.), vol. 5, 279 p. Obshch. polza, Moscow (1994). (In Russian) 10. Floridi, L.: The logic of design as a conceptual logic of information. Mind. Mach. 27(3), 495–519 (2017). https://doi.org/10.1007/s11023-017-9438-1 11. Floridi, L.: Soft ethics and the governance of the digital. Philos. Technol. 31(1), 1–8 (2018). https://doi.org/10.1007/s13347-018-0303-9 12. Floridi, L.: Translating principles into practices of digital ethics: five risks of being unethical. Philos. Technol. 32(2), 185–193 (2019). https://doi.org/10.1007/s13347-019-00354-x 13. Floridi, L.: What the Near Future of Artificial Intelligence Could Be. In: Burr, C., Milano, S. (eds.) The 2019 Yearbook of the Digital Ethics Lab. DELY, pp. 127–142. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29145-7_9 14. Floridi, L.: AI and its new winter from myths to realities. Philos. Technol. (Feb. 2020). https://doi.org/10.1007/s13347-020-00396-6

Similarity Principle and Bogdanov Tektology in Systems Analysis Evolution of Large Systems Alexander I. Bogomolov

and Victor P. Nevezhin(&)

Financial University Under the Government of the Russian Federation, 49 Leningradsky Prospekt, 125993 Moscow, Russia {aibogomolov,vpnevezhin}@fa.ru

Abstract. It is proposed to use the principle of similarity in the system analysis of large systems to hypothesize about their structure, properties and evolution on the basis of analogy with similar, but more accessible to the study of other systems. Based on the principle of similarity and Bogdanov’s general theory of organization and disorganization of systems (Bogdanov’s tektology), we can judge the patterns of their evolution. The evolution of large systems towards sustainable development occurs with the formation of new types of their organization and restructuring of the former integrity under the influence of external. An example of a large system is our Universe, the origin, properties and evolution of which, despite the successes obtained in its study by astronomy and cosmology, still remain unclear. An analogy is drawn between the evolution of the Universe and Man, who can also be regarded as a “big system”. Based on the principle of similarity and the laws of development of systems discovered by Bogdanov, a number of hypotheses about the origin and evolution of our Universe are put forward. Keywords: Principle of similarity Universe
Noosphere


System analysis
Dark energy
Person


1 Introduction The principle or law of similarity was first mentioned in an ancient document entitled the “Emerald Tablet”, composed (according to legend) by Hermes Trismegistus. “That which is below corresponds to that which abides above; and that which abides above corresponds to that which is below, to accomplish the wonders of one thing. And so all things came forth from the One by means of the One: so all things came forth from this one Essence through adaptation” [1]. The Bible says that: “God created man in his own image and likeness”. In the original source “Let Us make man in Our image [and] in Our likeness…” [2, p. 26]. The study and application of similarities (analogies) are now widely used to obtain new knowledge and technical solutions. The history of the development of science and technology successfully confirms this. For example, drawing an analogy with the electric field, Faraday suggested the existence of magnetic lines similar to electric lines. His ideas to served as a program for further discoveries by Herschel, Lebedev, Popov © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 146–152, 2022. https://doi.org/10.1007/978-3-030-98832-6_13

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and Maxwell, the latter of whom, incidentally, in his research, often resorted to analogies, using analogy as a valuable independent method of research in physics. Modeling of ships in shipbuilding, research of airplanes in aerodynamics, of dams, hydroelectric plants and locks in hydraulic engineering, modeling of human thinking in artificial intelligence systems also use analogies and analogies, and the inference by analogy plays a special role in social–historical sciences, often becoming the only possible research method [3, 4]. Not having sufficient factual material, the historian often explains little-known facts, events and the situation by likening them to previously studied events and facts from the lives of other nations in the presence of similarities in the level of economic, cultural and political organization of society. In the system analysis of large systems, application of the similarity principle is especially effective if one of the systems is relatively well studied, for example, system #1, while the study of another, similar or similar to the first one, system #2, faces great difficulties. One of the founders of the theory of large systems is A. Bogdanov. His tektology (the term was first used by Ernst Haeckel [6, 7], the science of the principles of organization and development of systems, allows also to discover new regularities of systems development relying on the similarity principle. A. Bogdanov understood that the formation of general laws of evolution of large systems would be extremely difficult: “Generalization here must take into account facts of infinite variety, often belonging to the most distant from each other areas, to find the unity of organizational methods where it is masked by extreme differences in the elements to which they are applied. The force of habit inducing us to compare cognitively only those things which are similar in their very material, in the direct impressions we receive from them; and the deeply rooted prejudices of specialization, for which the comparison and comparison of dissimilar things seems to be either a logical leap or a fruitless play of imagination have to be overcome” [5, p. 87]. A. Bogdanov suggested that the structure and evolution of systems correspond to the realization of some goal of creating these systems. Thus, in his work he notes: “The idea of expediency encapsulates the idea of purpose. Organism, organization have a ‘goal’ and are arranged ‘according to it’” [5, p. 39]. Proceeding from the principle of similarity and Bogdanov’s assumptions about general regularities of structure, evolution and purpose of development of large systems, further we will consider them in comparison and similarity of such systems as the Universe and Man.

2 Statement of the Problem A number of scientists and philosophers regard Man as a kind of Universe which has a lot in common with the Big Universe [8, 9]. The main idea of tektology consists in the identity of natural and social phenomena from an organizational point of view. Each element of nature or society must be viewed as a system, for which both the relations between the parts and the relations of the whole with the external environment are important. Laws, regularities, and principles are uniform for any objects, and even the most heterogeneous phenomena are united among themselves.

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Let’s consider application of the principle of similarity and tektology of A. Bogdanov to make hypotheses about properties and evolution of such large systems as our Universe and Man. Although astronomy and cosmology have recently advanced considerably in the study of the Universe, many questions remain about its origin, structure, and evolution. In order to apply the principle of similarity, in order to propose new hypotheses concerning the Universe, it is necessary to choose another large system similar to it, whose properties have been studied to a greater extent. We will consider Man as such a system. Application of the similarity principle and tektology by A. Bogdanov’s principle of similarity in comparison of such large systems as Man (system 1) and the Universe (system 2) will make it possible to put forward a series of new hypotheses on the origin and evolution of the Universe.

3 Discussion A deep analogy can be found between the birth, life and death of man and between the birth, development and death of the Universe. The birth of the Universe occurred as a result of the Big Bang of some singularity (point), after which its evolution – development and expansion – began [10]. In modern theory of the Universe more and more entrenched idea is that the space–time continuum of the Universe also has informational component [11]. Development of the Universe takes place at increasing pace with increase of its mass, complication of its structure and increase of volume (mass?) of its information. Human birth also resembles the Big Bang of some singularity – the size of an egg cell is about 0.1–0.15 microns. Then man grows, his mass increases, and most importantly his complexity grows as a system with accumulation of information. There is no single definition of what information is [12]. The founder of cybernetics, Norbert Wiener [13, p. 34], gave several definitions of information: “Information is neither matter nor energy, information is information.” Information “speaks” of a change in the state of a system, whether micro (e.g., electron) or macro (e.g., man), and a change in the state of a system is related to a concept such as entropy. Further research led to the realization of the close connection between information and “negative entropy”, the concept of which was first introduced by Erwin Schrödinger [14]. Erwin Schrödinger: “A living organism continuously increases its entropy, or, in other words, produces positive entropy and thus approaches the dangerous state of maximum entropy, representing death. It can avoid this state, i.e., stay alive, only by constantly extracting negative entropy from its environment. Negative entropy is what the organism feeds on. Or, to express it less paradoxically, essential in metabolism is that the organism manages to free itself from all the entropy it is forced to produce while alive” [14, p. 41]. L. Brillouin introduced the concept of non-gentropy into information theory [15]. If an increase in entropy of a system indicates its destruction (simplification), then non-entropy indicates its complication, an increase in its information. Living systems,

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as they evolve, increase their non-heentropy, i.e., information. A similar process is going on with our Universe. Changes in a system’s state correspond to generate information. Thus, every material system is a source of information. Elementary particles and atoms, planets, stars and galaxies have their own information field, together creating the information field of the Universe. In ancient times there were profound ideas about the human being as a whole, its structure, its connections with the world. A system is a set of elements and connections between them, functioning as a unified whole and having a single goal of functioning. Man is a system with a hierarchical principle of construction (the so-called Maslow pyramid) [16]. Following the ancient Greeks, there are three levels: the lower, bodily (from Greek soma – body), the middle, mental (from Greek psyche – soul), and the top, the spiritual element (from Greek nous – spirit). The pyramid has its own laws of organization. This organization is hierarchical and the vertex is the defining, setting the mode of activity of the entire system. Interrelations between elements inside the pyramid are subject to the laws of harmony, these features of the system provide its dynamic stability and possibility of development. Man is part of the world; he is included in it as one of the subsystems. If the principle of similarity is indeed a law, then the Universe has a hierarchical structure, the highest element of which is the information field or noosphere. In the understanding of N. Vernadsky the noosphere is a natural, it is a strategy of human survival [17]. Man is not conceivable without a community of people, social organization. The law of similarity also implies a community of universes exchanging information and energy. The information field is inherent in both material objects and living systems, including man. But only man alone has coherent information or “thought-image” [18]. This information forms the information field of the Universe, which is commonly called the noosphere.

4 Results Information field of the Universe is the same attribute of the Universe as matter, space and time. If we consider the Universe as a system, there is a close relationship between these forms of its manifestation. Matter evolves, and its highest form is Man. Therefore, the information field of Man and the Universe must be interconnected. Developing V. Vernadsky’s concept of biosphere P. Teilhard de Chardin [17, 18] considered that the information field of Man and Mankind in general is a part of the Universe noosphere. The modern science assumes that any field has its carriers in the form of elementary or virtual particles having energy or mass. Attempts were made to measure the mass of the information field (soul) of a person, which is premature to call scientific [19]. However, the idea that information field of the Universe and Man has its carriers does not contradict modern views on the nature of the field.

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There are no candidates for the role of information carriers among currently known to science elementary particles due to uncertainty and ambiguity of the very concept of “information”. Before the discovery of Higgs boson, it was also difficult to define the concept of “mass”. Today it is assumed that the so-called Higgs field exists, interaction with which gives mass to elementary particles. The discovery of the Higgs boson required the efforts of scientists from over 100 countries, 10 years, and tens of billions of dollars. No less effort, apparently, is required if one considers that the information field of the Universe-Human has its own carrier as well. If there is an elementary particle, acting as a carrier of information, it must have, like the information field itself, amazing properties. Such mysterious substance as dark energy is suitable for the role of information field of the Universe satisfying the abovementioned requirements. The mass of ordinary matter in the Universe is no more than 5%, the rest of the mass falls on dark matter (about 27%) and dark energy (about 68%). Dark matter consists of undiscovered elementary particles and also as ordinary matter participates in gravitational interaction with star clusters and galaxies. Dark energy, the hypothesis of the existence of which is based, in particular, on observations of explosions of white dwarfs in binary systems, is also necessary to explain the expansion of the universe with increasing speed. That is, it has the property of anti-gravity. Perhaps it has other exotic properties. Dark energy is evenly distributed in the space of the Universe. Most scientists are of the opinion that sooner or later the evolution of the universe will end with its death of one kind or another. Either the expansion will stop and its contraction to some singularity and the next Big Bang will begin, or the entropy of the Universe will reach the maximum and it will turn into nothing. The development of Man and Mankind also ends with death and reaching the maximum of their entropy. But while they are developing at an accelerated rate, the amount of their non-gentropy (structured information) grows, which probably points to similarity (similarity) of these processes in Man and the Universe. Man is an open system and increase of the volume (mass?) of his structured information (knowledge) occurs due to interaction with external sources of knowledge – other people, books, Internet, etc. Our Universe is also an open system and interacts with other Universes. The birth of our Universe (by analogy with the human birth) took place in the depths of some parent M-Universe. And its development and growth, as well as the development of the born human, took place due to interaction, primarily informational, with other Universes and, possibly, with other, unknown to us, sources. This fits into the known to us concept of plurality of Worlds. Four-dimensional space–time continuum of our Universe is theoretically not the only possible. For example, the theory of supergravity admits the possibility of the existence of Worlds possessing eleven dimensions [20]. Other Universes, with which the informational interaction of our Universe is carried out, may be in dimensions inaccessible to us. According to another theory, namely the superstrings theory, the building blocks of matter are one-dimensional strings, located in the space of ten dimensions [21]. Attempts to reconcile supergravity theory and superstring theory led to the M-theory, which was developed by Edward Witten [22]. Informational interaction and the inflow of dark energy from other universes into our Universe ensure its accelerated expansion. This interaction of our Universe with

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other Universes is probably carried out through black holes. This was also the opinion of Stephen Hawking. In 1974, Stephen Hawking showed that black holes can radiate energy due to quantum effects, and M-theory exactly reproduced Hawking’s entropy formula. In 1998 Argentine physicist Juan Moldosena showed that all physical laws and events occurring inside the Universe can be described by those events that occur at its boundary, and we are just shadows on the boundary of the Universe of more dimensions [23]. Using the principle of similarity in drawing analogies between Man and the Universe leads to the possibility of drawing interesting or even paradoxical conclusions, which, however, do not contradict modern scientific views and can be confirmed in appropriate research. Bogdanov’s ideas on structural similarity and general principles of development and functioning of systems, their guiding and organizing goal, may also give a new direction in understanding the evolution of Man and the Universe. Some specific conclusions from the above approach resulting from analogies between Man and the Universe are given below.

5 Conclusions The following main theses were obtained in the work: 1. The birth of our Universe, by analogy with the birth of Man, occurred from some singularity inside the parent M Universe as a result of interaction with another Fsystem. 2. The evolution of our Universe and the increase of the dark energy mass in it, also considered as a carrier of information, is caused by its interaction with other Universes. 3. The dark energy, as a carrier of the Universe information field (noosphere), has a fractal structure and its elements are human-image thoughts. 4. According to the above-mentioned principle of similarity, the mass of both Man and the Universe grows in the process of development. 5. Black holes are gateways for energy and information from other Universes. 6. Structured information of an individual human and humanity as a whole is stored in the information field (noosphere) of the Universe and interacts with its elements and structures.

References 1. The Emerald Tablet of Hermes Trismegistus. http://www.sacred-texts.com. Accessed 11 Sep 2021 2. The Old Testament. http://bible.optina.ru/old:gen:01. Accessed 11 Sep 2021 3. Sazonov, D.O.: The Principle of Similarity in the Fractal Universe. http://sazonov-d.ru/?e=1. Accessed 11 Sep 2021 4. Novikov, N.B.: 1000 Analogies That Changed Science (A New Look at Genius). The Russian Academy of Sciences, Institute of Psychology, Moscow, 878 p. (2010)

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5. Heckel, E.: The Beauty of Forms in Nature. Werner Regen Publishers, St. Petersburg, 144 p. (2007) 6. Haeckel, E.: World Riddles. With an afterword, “Confessions of Pure Reason”. Transl. by S. G. Zaimovsky, Publishing House of the Partnership of Br. A. and I. Granat and Co., Moscow, 431 p. (1906) 7. Bogdanov, A.A.: The Universal Organizational Science (Tektology), 4., vol. 1, St. Petersburg, 255 p. (1913); vol. 2, St. Petersburg, 153 p. (1917) (in Russian) 8. Bendit, L.J.: Man and his Universe, The Theosophical Publishing House, Adyar, India. http://hpb.narod.ru/ManUniverse.htm. Accessed 12 Oct 2021 9. Egbai, U.: Origin of Man and the Universe. In: History and Philosophy of Science, Akwa Ibom State University Press, Ikot Akpaden (2016) 10. Hawking, S.: Theory of Everything, The Origin and Destiny of the Universe. In: Burba, G. (ed.), Amphora TID, St. Petersburg, 148 p. (2009) 11. Zimmermann, R.E.: Matter and information as attributes of substance. Eur. Phys. J. Spec. Top. 226(2), 177–180 (2017). https://doi.org/10.1140/epjst/e2016-60365-0 12. Everything is Information. Matter is Secondary. http://sensei.org.ua/znaniya/articles/naturesciences/28-sciences/86-i49. Accessed 24 Sep 2021 13. Wiener, N.: Nonlinear Problems in Random Theory. The Technology Press of M.I.T. and John Wiley & Sons, New York, NY (1958) 14. Schrödinger, E.: What is Life? The Physical Aspect of the Living Cell, Moscow-Izhevsk, 92 p. (2002) 15. Brillouin, L.: Nauka i teoriya informacii [Science and information theory]. Transl. from English by Harkevich, A.A. Publ. “Kniga po Trebovaniyu” [Book on Demand], Moscow, 390 p. (2012) (in Russian) 16. Taylor C., Lillis C., Lynn P.: Fundamentals of Nursing, North American ed., Eighth, 1816 p. (2014) 17. Vernadsky, V.I.: Perepiska s B.L. Lichkovym (1918–1939). [Correspondence with B.L. Lichkov (1918–1939)], Moscow, pp. 181–182 (1979) (in Russian) 18. Bogomolov, A.I.: Dark energy as the information field of the universe. J. Phys. Conf. Ser. 1703, 46–58 (2020) 19. Kaznacheev, V.P.: Uchenie V. I. Vernadskogo o biosfere i noosfere [Vernadsky’s V.I. doctrine on the biosphere and the noosphere], Nauka, Siberian Branch, Novosibirsk, 248 p. (1989) (in Russian) 20. Does Information Have Weight. https://pikabu.ru/story/est_li_ves_u_informatsii_2095708. Accessed 10 Oct 2021 21. Super-Gravity Theory. https://www.sites.google.com/site/nustebookstore/basic-sciences/ physics/relativity-gravitation. Accessed 10 Oct 2021 22. Berenstein, D., Maldacena, J., Nastase, H.: Strings in flat space and pp waves from N = 4 Super Yang Mills. In: AIP Conference Proceedings, vol. 646, pp. 3–14. Waterloo, Ontario (Canada) (2020). https://doi.org/10.1063/1.1524550 23. The Physics of Everything: Understanding Superstring Theory. https://futurism.com/branescience-complex-notions-of-superstring-theory. Accessed 10 Oct 2021

Systems Analysis of the Digital Agent’s Role in Hybrid Social Interaction Forms Elena Pozdeeva1(&) , Olga Shipunova1 , Lidiya Evseeva1 and Aktolkyn Kulsariyeva2

2

,

1 Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya Street 29, 195251 St. Petersburg, Russia [email protected] Abai Kazakh National Pedagogical University, Dostyk Avenue 13, 050010 Almaty, Republic of Kazakhstan [email protected]

Abstract. The article is devoted to the actual problems of human–computer interactions associated with the active inclusion of smart technologies and systems of artificial intelligence in the social spheres of activity. The authors turn to the study of the digital agents’ role in the organization of multi-level communication. They highlight the relevance of the problem of trust in innovations in the intelligent automation field, in particular to the functions of various assistants and consultants who are able to act autonomously in the social space, but are not endowed with responsibility, unlike a person. The conceptual basis of the system analysis of communication hybrid forms in the technosocial system is the idea of functional structures in combination with the installations of actor-network theory. The authors use methods of comparative analysis and generalization of sociological surveys that reveal the attitude of a person to the functional role of digital intermediaries in computer-mediated communication. As an empirical basis, statistical data of surveys conducted in various social groups of Russian society are used. The summarized results present a characteristic of the Russian citizens’ attitude to the prospects for the professional activity robotization and the spread of artificial intelligence systems. The positive assessment of innovations related to the automation of routine operations in solving intellectual and applied problems is emphasized. There is a tendency to strengthen the public negative attitude to the prospect of the dominance of AI, which is associated with threats to security and social order. The positive assessment of innovations related to the automation of routine operations in solving intellectual and applied problems is emphasized. There is a tendency to strengthen the public negative attitude to the prospect of the dominance of AI, which is associated with threats to security and social order. Keywords: Human–computer interaction Smart technology
Trust
Compatibility


Systems analysis
Digital agent


© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 153–165, 2022. https://doi.org/10.1007/978-3-030-98832-6_14

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1 Introduction New digital technologies and communication devices are becoming a constitutive factor of contemporary culture. By introducing new social roles, they influence the subjects’ identity and life practices, and adjust involvement into various types of professional and intellectual activity. The structure of society is changing under the influence of an increasingly active role of a new type of actor with a hybrid form of techno-subject [1]. There are serious shifts in business communications: the use of artificial intelligence is expanding, with an average of half of employees turning to electronic assistants. New AI-systems help people to perform various operations and manipulate digital artifacts [2]. The subject-object and service characteristics of “new actors” – “digital assistants” – are actively studied [3]. The social space perception has been significantly changed by the digital world. Business communications are progressively being conducted via new technologies based on machine learning and artificial intelligence, and the proliferation of digital assistants for business and service has caused a wave of both positive effects and serious concerns. The use of smart technology and robotics leads to erasing of the rigid boundary between the living and the inanimate, and a new fusion of interaction participants in hybrid entities represented by different associations, is being emphasised. Communications with human participants and their digital assistants (gadgets, robots) are more like flexible structural forms rather than systems aimed at long-term functioning within given functions and parameters. Computer-mediated interactions encourage all participants to constantly adapt and develop their communication skills. One of the main aspects of human–robot communication research is their compatibility, which is a problem area [4]. Despite the rapid inclusion of digital technologies into business and everyday life, there is an ambiguous attitude towards digital intermediaries. Digital transformation contributes to undermining trust in the messages received and in social groups [5]. There is a distrust of digital assistants in the communication space, and communicating with a chatbot often discourages people as well. This paper aims to identify the functional role of digital intermediaries in multilevel communication within modern computer-mediated social interaction, to investigate the compatibility of agents in the digital environment, and to analyze the problem of digital trust. 1.1

Literature Overview

The complicacy of interaction in social reality was analyzed by Whitehead, who distinguished both actual and conceptually subjective signs in the social space of interaction [6]. Considering society as an organism, he emphasizes the combination of two levels of the lifeworld reality: actual events and an entity of individual experience, which is manifested within social interactions. The focus is on a person’s involvement in the creation of new levels of mediated communication that generate complex functional structures in social space. A new level of analysis of subject-environment interactions in mutable configurations is offered by the assemblage theory [7]. According to DeLanda, assemblage

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implies the autonomy of the parts in relation to the whole. It is also impossible to reduce the assemblage properties to the ones of its components, but the connection between the part and the whole remains. Development occurs through complex evolvement, which is defined by the term “machine” rather than “mechanism”. This evolvement implies an assemblage of heterogeneous elements through communication and internal links. The whole emerges through dynamic sets of entity properties that have the infinite capacity to create interactions. The assemblage components functionally implement themselves along two lines – in physical space (physical capabilities) and in symbolic space (expressive function). The expressive function is related to the meaning transmission, it is not limited to the work of language and symbols, although is delivered through them. Newer literature pays much attention to the study of the role of digital assistants – smart systems based on data processing technology. However, it is emphasized that there is no theory linking user perceptions to this technology and there is a gap in research on trust to artificial intelligence attributes [8]. Despite the continuous development of AI-based digital assistant apps, there is no guarantee they can solve users’ problems. People use AT-assistants (such as Apple’s Siri, Amazon’s Alexa, Google Assistant, Microsoft’s Cortana) for personal tasks as well as for more advanced features and integration of connected devices. But a voice assistant’s functional role and its actual use vary from person to person. Research on human–machine interaction is conducted within theories related to self-service technologies [9]. It is noted that there is currently little empirical evidence of customer satisfaction with digital assistants [10]. The problem of compatibility between a person and their digital intermediary depends on how people perceive their robot companions and how they relate to them emotionally. Experimental studies of people’s feelings and behavior towards robots showed participants’ anxiety and negative attitude, causing a desire to avoid communication with a robot [11]. These results mark the relevance of studying the factors of comfortable human–robot communication, in which the robot’s appearance and behavior perception plays a significant role.

2 Materials and Methods The idea of functional systems containing specific elements of information nature (codes, languages, operations, strategies) is the basic concept of the interdisciplinary field of applied system analysis in the study of the role of digital intermediaries in communication. This paper focuses particularly on the dynamics of computer-mediated social space configuration. The system approach to understanding fuzzy communicative structures as independent objects is elaborated by the settings of actor-network theory and M. DeLanda’s assemblage theory. The role of interactive technologies and communicative practices as functional systems with flexible dynamics and complex composition is emphasized. The reference to the structure and dynamics of techno-social system within the actor-network theory is driven by the rethinking of new hybrid forms of communication in the context of “sociology of associations”, which can be regarded as

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assemblages. Such fuzzy structures include several elements: the person, the digital/electronic intermediary, the electronic platform of the communication, the software developers who manage the AI algorithms, the environment of digital communication. The assemblage theory helps to delineate these flexible structures, show their historical significance, and see the problem areas in communication that determine the future development. DeLanda refutes the organismic approach which sees the relationship between the parts and the whole as logically necessary [7]. His theory emphasizes the principle of co-evolution of parts in a unified whole, which allows to apply the notion of assemblage to the analysis of flexible systems in computer-mediated communication. The key concept in this theory is “agencement” (French), which refers to the process of connecting-assembling heterogeneous components, and the result of this process. The components of “agentcement” can participate in processes of stabilization and variability, drawing on and generating different sets of the participants’ capabilities, supporting and stimulating their flexibility. This approach offers a perspective for the analysis of techno-social systems with an unstable degree of agent participation, and makes it possible to see the multiplicity of vector paths in the development of communicative fields with various branches. The structural model of computer-mediated communication fixes the varying roles of components in a continuum of functions, which suggests creation of new virtual interaction forms. The main method of our study is the analysis of sociological surveys which reveal people’s attitudes towards the functional role of mediators in computer-mediated communication. The empirical base is formed by different social groups of Russian society. The statistical data of the surveys can be found on the following websites: – Media consumption and activity on the Internet. September 23, 2021 VTsIOM, https://wciom.ru/analytical-reviews/analiticheskii-obzor/mediapotreblenie-iaktivnost-v-internete – Professionalization Of Young People In Digital Environment, https://www.km.ru/ science-tech/2018/11/13/studencheskaya-zhizn/833442-professionalizatsiyamolodezhi-v-tsifrovoi-srede – Digital Assistants Age: Changes In Employee Experience, https://www. kommersant.ru/conference/633 – Study: About 85% Of Adults Trust Digital Public Services. April 12, https://tass.ru/ ekonomika/11121571 – Voice Assistants: Market Overview, Trends and Prospects, https://ict.moscow/ news/voice-assistants-2021/ – Digital Genies: Why Companies Need Virtual Assistants, https://trends.rbc.ru/ trends/futurology/5dfb8d889a7947340b313ed3. The study of affective factors that determine the immersion in network communication was based on the psychological testing method; that allows identifying priority behavioral attitudes among the representative groups of students and graduate students of St. Petersburg universities.

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3 Results To study Moscow students’ perception of the smart technology opportunities and the problems of human interaction with artificial intelligence, sociologists conducted a survey in April–May 2019 at Plekhanov Russian University of Economics (352 people) [12]. The survey (Table 1) shows that students are generally well aware of various digital technologies. 90% of respondents are aware of virtual digital assistants, and a high degree of awareness of AI technologies is shown by 35.9% of them. 89.3% of respondents study information about artificial intelligence, and awareness of intelligent search engines is indicated by 78.5%. Table 1. Student awareness of artificial intelligence (Plekhanov Russian University of Economics, Moscow, 2019) Awareness parameters Student awareness of virtual assistants: high/medium Studying information on artificial intelligence Awareness of intelligent search engines

Number of students, % 35,9/90 89,3 78,5

Four modes of human-AI interaction were highlighted in the perceptions of wellinformed students: routine activities, smart support of humans, applied tasks, and human prerogative. The latter reveals young people’s fear that in the future robots may replace humans in important decision-making and claim intellectual dominance, so humans should impose certain restrictions on machine intervention. The study of activity on the Internet was conducted on September 23, 2021 by the All-Russian Center for Public Opinion Research by interviewing a random sample of fixed and mobile phone numbers operating in the regions of the Russian Federation. The anonymous survey involved 1600 people over the age of 18 years. The results of the survey showed that every third resident of Russia (29%) daily included in social networks and messengers (72% among 18–24-year-olds). Preferences in the choice of a social network among Russian citizens are shown in Fig. 1. The distribution of daily immersion in these social networks (see Fig. 2) shows the preference of users for the WhatsApp service (63% of the total number of respondents). To identify the extent of computer addiction among young people and before switching to remote work due to the pandemic, we conducted a survey among representative groups of undergraduate and graduate students of St. Petersburg universities (Emperor Alexander I St. Petersburg State Transport University, Peter the Great St. Petersburg Polytechnic University, Herzen University). A total of 143 people were interviewed [14].

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90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Preferences among respondents, %

Fig. 1. Results of Russian citizens activity diagnostics in social networks in 2021 [13].

70% 60% 50% 40% 30% 20% 10% 0%

Largest daily audience ,%

Fig. 2. Distribution of daily immersion in social networks, typical for Russian youth aged 18– 24 years (according to [13]).

The study of computer addiction (see Fig. 3) shows that 60% of all interviewed young people are at the stage of “addiction” to computers and 40% are at risk of computer addiction. The empirical study of the motive for immersion in online communications and the development of Internet addiction show that for 40% of respondents the dominant motive is to use the Internet as a means of entertainment or to relieve the real life emotional pressure [15].

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40% 60%

40%

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Engagement in network interactions Computer addiction risk

Fig. 3. The diagnostics results of students computer dependence in St. Petersburg universities, 2019.

To get an overall picture of the spread of virtual assistants, a study of the voice assistants use in computer-mediated communication was undertaken [16]. The study shows that in 2020, the audience of voice assistants in Russia amounted to 52 million users. The most popular assistants are Alice (45 million users), Google Assistant (11 million), and Siri (6 million). At the same time a part of the audience uses several solutions at once (see Table 2).

Table 2. Use of voice assistants in Russia (2020) The most popular assistants Alice Google Assistant Siri Users total: 52 mln 45 mln 11 mln 6 mln

The ‘Just AI’ survey of smartphone users shows an increase in voice assistant consumers: in 2019, 71% of those surveyed have ever interacted with such services, and in 2020 this figure reached 77%. In 2020, 32% of Russians used voice assistants daily, compared to 29% in 2019 (see Table 3).

Table 3. Trends in voice assistant usage among Russian smartphone users, 2019–2020 Using voice assistant Daily, % Occasionally, % 2019 29 71 2020 32 77

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4 Discussion 4.1

Functions of Digital Intermediaries in the Techno-Social System

Active usage of virtual communications brings about a space of digital footprints. Communication traces left in cyberspace can be seen as dynamic assemblage structures that include various digital assistants and intermediaries. Services and smartphones employ voice assistants capable of responding adequately to human speech and executing various commands. Studies by ‘Accenture Interactive’ have shown that most consumers perceive voice assistants positively [17]. According to Voice Report research published by Microsoft in April 2019, 72% of residents in countries such as the US, UK, Canada, etc., have used virtual assistants at least once to do voice searches online, with 25% using them to make purchases. According to Nielsen, 24% of US households have a smart speaker (e.g. Amazon Alexa). Voice assistants help choose goods, give advice on money investing, and take care of the health and psychological state of their owner [18]. The spread of voice assistants changes the media environment of modern society, opens up new marketing opportunities, allowing a higher level of interactivity in engagement with the target audience. The use of voice input is invaluable for people with vision and movement coordination problems [19]. The undeniable comfort of voice assistants suggests that they will soon be answering household and business calls. Another trend that is rapidly gaining momentum is the active introduction of digital assistants in company management and socialization of an employee. The use of a digital assistant frees an employee from time-consuming tasks of searching for information and from routine operations that can be easily automated. Artificial intelligence systems built into the digital assistant functional structure allow it to speak to the user in a close to a human voice. Employees’ loyalty to smart assistants reveals the fact that they willingly communicate with digital assistants [20]. Digital assistants are in demand in education at different levels, including the school level. Digital assistants, built around chatbots, can be used to present teaching and learning materials and other documents not only to educational staff, but also to learners and parents. An important function of a chatbot is the speed and accessibility of information transfer. The chatbot allows for the statistics analysis, online surveys, direct contact with the audience, and research into the characteristics of requests. It is stored in the cloud space of a secure messenger, ensures smooth operation on a server, which is owned by an educational organization or third-party hosting (free or paid), does not require software, its constant updating or licensing [21]. 4.2

The Problem of Human and Digital Intermediary Compatibility

A study of companies’ communication with consumers via autoresponders reveals the problem of emerging customer dissatisfaction with communication with a digital intermediary. By examining the range of customer expectations when contacting a service company and communicating with an autoresponder, sociologists have identified the main customer demands, which boil down to waiting in a phone queue, the answers politeness, and the time it takes to solve the problem. Human contact with a

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virtual answering machine assumes the appropriateness of operator’s responses and the need for humans to adjust to the robot’s reactions. Research has shown that the robot interlocutor is good for a certain type of human–machine conversation where the robot’s tasks are regulated and involve the prompt delivery of accurate information. However, in free communication situations, a robot cannot match a human [22]. According to research by Mindshare UK, 63% of respondents do not mind communicating with a chatbot, with almost 80% wanting a live person to join the dialogue at any time [23]. Alongside doubts about positive communication with digital assistants, there is an opinion that digital assistants are already taking on increasingly complex social functions beyond the role of an advisor on uncomplicated issues. There are now chatbot psychotherapists (Woebot), and people are inclined to discuss their personal problems, traumas, and everyday conflicts [https://trends.rbc.ru/trends/futurology/ 5dfb8d889a7947340b313ed3]. 4.3

The Problem of Trust in Smart Technology

The development of trust in AI-powered systems is becoming one of the key trends of digitalization, contributing to new advances in business. The environmental and structural elements of digital trust that require attention include studying the transformation of social networks, new mobile technologies, data analytics technologies, the use of cloud technologies, digital identity issues, and measuring the pace of change [24]. Experts from the Higher School of Economics conducted a study, “Assessing Digital Readiness of Russia’s Population,” (an Internet survey with a sample of 2,180 people, March 2021). They found that 85% of the population between the ages of 18 and 75 trust the digital services by Gosuslugi (State Services) website, multifunctional centers, the Russian Federal Tax Service (FTS) and the State Traffic Safety Inspectorate. Researchers argue that the level of trust in a digital service varies according to the frequency of its use [25]. US communications agency ‘Edelman’ conducted a survey to benchmark developer and consumer perceptions of artificial intelligence. Approximately 80% of respondents show expectations of active resistance from those who perceive digital technology as a threat to the future. At the same time, almost 70% of respondents believe that further development of AI systems could lead to the loss of various human intellectual abilities, deepen social and economic inequalities and social exclusion due to people’s increasing dependence on artificial intelligence [26, 27]. This data matches the results of a VCIOM poll ‘Artificial intelligence: threat or opportunity? 27 January 2020’ [28] and a May 2019 study by the NAFI Research Center and ‘Digital Economy’[29] (see Table 4). The systematic approach to the AI definition allows us to distinguish 2 positions: 1) AI is a system that thinks like humans and is guided by rational decisions; and 2) AI is a system that acts like humans and is highly rational. Russians’ perception of the AI dangers in the long term (see Table 4) is mainly related to the possibility of technical failures, including the machines getting out of control. The negative attitude expressed by 21% of respondents is due to the threat of personal data security.

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Research into the determinants of openness to innovation assesses perception of new smart technologies. The methodology of these studies is based on the technology acceptance mode, which allows tracking the index of openness to innovation [30] while using computer technology for professional purposes. Pishnyak and Khalina found that the Russians’ openness to innovation positively correlates with the ease of use, usefulness, safety and reliability of technological innovations by respondents from different social groups [31].

Table 4. Attitude towards digital innovation, perception of artificial intelligence Perception of digital technologies and AI

Active resistance, Anxiety. deepening of threat to future % social inequality and dependency

Danger in the longterm

No danger in the long-term

In USA (according to Edelman agency, 2018) In Russia (according to NAFI Research Center, 2019) In Russia (according to VCIOM, 2019)

80

70

–

–

38

37

30

–

–

21

30

68

The problem of trust in digital intermediaries is analyzed in the context of perceived performance and usability [32]. The following identified features are important here: trust is influenced by such features of interactivity with digital intermediaries as: manageability, synchronicity and bidirectionality, as well as language differences. The exchange of personal data with digital intermediaries is seen by users as very vulnerable in terms of security and data protection. It is characteristic of Russian society that a new form of trust – digital trust – has become more prominent in the face of declining general trust. Research has shown that young people have the highest level of ‘digital’ trust, but this does not yet translate into increased trust in real life. In the long term, digital trust may affect the level of its generalized counterpart in society [33].

5 Conclusion The active introduction of new digital agents into the social communicative space requires new flexible roles of interaction participants, which would be aimed at minimizing threats to security and social order. The system approach to social phenomena allows us to focus not only on the structure, logical and genetic connection of features, but also on the dynamic properties of emerging interaction structures involving new agents. Given the rapid development of digital processes that permeate and transform social space, the theory of complex social objects must evolve by identifying historically relevant structural elements which characterize the digital communication space.

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Modern assemblage theory allows us to investigate properly the ability of complex techno-social systems to provide manageability and order in fuzzy changing structures that connect intelligent digital intermediaries, things, and people. Assemblage-based analysis of the digital intermediaries functions allows us to reconstruct the complex dynamics of communication involving hybrid agents, to take into account the history of interactions in the management of the techno-social system. At the same time, predicting the manageability of fuzzy functional structures depends on the extent to which these assemblages can obey and regulate their order by the institutions, law, norms and behavioral systems in place today.

References 1. Ignatyev, V. I.: I gryadet “drugoj” aktor… Stanovlenie tekhnosub’’ekta v kontekste dvizheniya k tekhnologicheskoj singulyarnosti [And the “Other” Actor is Coming… The Formation of Tehnosubject in the Context of the Movement to Technological Singularity]. J. Sociol. Sci. Technol. 10(1), 64–79 (2019) (in Russian). https://doi.org/10.24411/20790910-2019-10005 2. Bors, L., Samajdwer, A., van Oosterhout, M.: Introduction to oracle digital assistant. In: Bors, Luc, Samajdwer, Ardhendu, van Oosterhout, Mascha (eds.) Oracle Digital Assistant: A Guide to Enterprise-Grade Chatbots, pp. 3–14. Apress, Berkeley, CA (2020). https://doi.org/ 10.1007/978-1-4842-5422-6_1 3. Pankratov, S.A., Pankratova, L.S., Morozov, S.I., Gavrilov, S.D.: “Digital assistants” in the consumer society: global trends and vectors of Russia’s development. In: Popkova, E.G., Sergi, B.S. (eds.) “Smart Technologies” for Society, State and Economy. ISC 2020. Lecture Notes in Networks and Systems, vol. 155, pp. 499–506. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-59126-7_56 4. Pozdeeva, E., et al.: Assessment of online environment and digital footprint functions in higher education analytics. J. Educ. Sci. 11(6), 256 (2021). https://doi.org/10.3390/ educsci11060256 5. Osburg, T.: Changing relevance of trust in digital worlds. In: Osburg, T., Heinecke, S. (eds.) Media Trust in a Digital World: Communication at Crossroads, pp. 15–33. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-30774-5_2 6. Whitehead, A.N.: Process and Reality. Macmillan Company, New York, NY (1967) 7. DeLanda, M.: A New Philosophy of Society: Assemblage Theory And Social Complexity. Continuum, London (2006). ISBN: 978-0-826-49169-5 8. Balakrishnan, J., Dwivedi, Y.K.: Conversational commerce: entering the next stage of AIpowered digital assistants. Ann. Oper. Res. 2, 1–35 (2021). https://doi.org/10.1007/s10479021-04049-5 9. Balakrishnan, J., Dwivedi, Y.K.: Role of cognitive absorption in building user trust and experience. J. Psychol. Market. 38(4), 643–668 (2021). https://doi.org/10.1002/mar.21462 10. Brill, T.M., Munoz, L., Miller, R.J.: Siri, Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications. Journal of Marketing Management 35(15–16), 1401–1436 (2019). https://doi.org/10.1080/0267257X.2019. 1687571 11. Sayfudinova, N.Z., Pyrnova, O.A.: Psikhologiya v kommunikatsii mezhdu chelovekom i robotom [Psychology in communication between man and robot]. J. Educ. Law 4, 317–319 (2020) (in Russian). https://doi.org/10.24411/2076-1503-2020-10447

164

E. Pozdeeva et al.

12. Pinchuk, A.N., Tikhomirov, D.A.: O vzaimodeystvii cheloveka i iskusstvennogo intellekta: novaya sotsial’naya real’nost’ v predstavlenii moskovskikh studentov [On the interaction of man and artificial intelligence: a new social reality in the minds of Moscow students]. J. Knowl. Understand. Skill 3, 85–96 (2019) (in Russian). https://doi.org/10.17805/zpu. 2019.3.8 13. Mediapotreblenie i aktivnost’ v internete. 23 sentyabrya 2021 g. VCIOM [Media consumption and activity on the Internet. September 23, 2021]. https://wciom.ru/ analytical-reviews/analiticheskii-obzor/mediapotreblenie-i-aktivnost-v-internete. Accessed 22 Sep 2021 14. Berezovskaya, I.P., Shipunova, O.D., Kedich, S.I.: Internet addiction and youth coping strategies. In: CSIS’2019: Proceedings of the XI International Scientific Conference Communicative Strategies of the Information Society 23, pp. 1–6 (2019). https://doi.org/10. 1145/3373722.3373790. 15. Berezovskaya, I., Shipunova, O., Kedich, S., Popova, N.: Affective and cognitive factors of internet user behaviour. In: Bylieva, D., Nordmann, A., Shipunova, O., Volkova, V. (eds.) Knowledge in the Information Society: Joint Conferences XII Communicative Strategies of the Information Society and XX Professional Culture of the Specialist of the Future, pp. 38– 49. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-03065857-1_5 16. Umnye pomoshchniki: obzor rynka, tendencii i perspektivy [Smart Assistants: Market Overview, Trends and Prospects]. https://ict.moscow/news/voice-assistants-2021/. Accessed 24 Aug 2021 (in Russian) 17. Wander, E.: Infographic: here’s how much consumers will use voice technology in the near future. And how they feel about digital assistants. (adweek. com) (October 29, 2017). http:// tiny.cc/aczhlz. Accessed 29 Sep 2021 18. Cifrovye dzhinny: zachem kompaniyam virtual’nye assistenty [Digital Genies: Why Companies Need Virtual Assistants]. https://trends.rbc.ru/trends/futurology/ 5dfb8d889a7947340b313ed3. Accessed 21 Sep 2021 (in Russian) 19. Akhmaeva, L.G.: Pol’zovatel’skiy opyt i vozmozhnosti ispol’zovaniya golosovykh assistentov v interaktivnykh marketingovykh kommunikatsiyakh: Amazon Alexa, Google home, Apple Siri, Yandeks Alisa [User experience and possibilities of using voice assistants in interactive marketing communications: Amazon Alexa, Google home, Apple Siri, Yandex Alice]. J. Vestnik Universiteta 5, 49–57 (2020) (in Russian). https://doi.org/10.26425/18164277-2020-5-49-57 20. Kak menyaetsya opyt sotrudnika v epohu cifrovyh assistentov [How the employee experience is changing in the era of digital assistants]. https://www.kommersant.ru/ conference/633. Accessed 14 Aug 2021 (in Russian) 21. Personal digital assistant “Robot Boticom”. Management of corporate content of an educational organization through a chatbot system. http://detsad64pr.ru/wp-content/uploads/ 2017/04/booklet_final.pdf. Accessed 20 Jul 2021 (in Russian) 22. Zemnukhova, L.V.: Priklyucheniya tekhnologiy: bar’yery tsifrovizatsii v Rossii [Technology Adventures: Barriers to Digitalization in Russia]. FNISTs RAN, Moscow and St. Petersburg (2020). http://socinst.ru/wp-content/uploads/base/books/text/ wrongtechadventures.pdf. Accessed 22 Sep 2021 (in Russian) 23. Volkova, E.: Chetyre illyuzii o tsifrovykh pomoshchnikakh, kotoryye meshayut biznesu [Four illusions about digital assistants that hinder business]. https://rb.ru/opinion/4-illyuzii-ocifrovyh-pomoshnikah/. Accessed 22 Sep 2021 (in Russian) 24. Klough, T.: Ukrepleniye doveriya: kak rasti v epokhu tsifrovykh tekhnologiy [Building Trust: How to Grow in the Digital Age]. https://www.pwc.ru/ru/assets/director.pdf. Accessed 22 Sep 2021 (in Russian)

Systems Analysis of the Digital Agent’s Role

165

25. Research: About 85 % of the adult population trusts digital government services. https://tass. ru/ekonomika/11121571. Accessed 04 Aug 2021 (in Russian) 26. Edelman AI Survey Results Report. Edelman, Chicago, IL, 40 p. (2019) 27. The Impact of Artificial Intelligence on Work: An evidence review prepared for the Royal Society and the British Academy. Frontier Economics, 69 p. (2018). https://royalsociety.org/ -/media/policy/projects/ai-and-work/frontier-review-the-impact-of-AI-on-work.pdf. Accessed 04 Sep 2021 28. Iskusstvennyj intellekt: ugroza ili vozmozhnost’? [Artificial Intelligence: Threat or Opportunity?] (2020). https://wciom.ru/analytical-reviews/analiticheskii-obzor/ iskusstvennyj-intellekt-ugroza-ili-vozmozhnost. Accessed 08 Sep 2021 (in Russian) 29. Issledovanie: bolee treti rossiyan opasayutsya razvitiya sovremennyh tekhnologij [Research: more than a third of Russians fear the development of modern technologies]. (2019). https:// data-economy.ru/26082019. Accessed 24 Nov 2020 (in Russian) 30. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. J. MIS Q. 13(3), 319–340 (1989). https://doi.org/10.2307/249008 31. Pishnyak, A., Khalina, N.: Perception of new technologies: constructing an innovation openness index. Foresight STI Govern. 15(1), 39–54 (2021). https://doi.org/10.17323/25002597.2021.1.39.54 32. Purwanto, P., Kuswandi, K., Fatmah, F.: Interactive applications with artificial intelligence: the role of trust among digital assistant users. Foresight STI Govern. 14(2), 64–75 (2020). https://doi.org/10.17323/2500-2597.2020.2.64.75 33. Veselov, Y.V., Skvortsov, N.: Trust in the age of digital transformations: a sociological study. Sotsiologicheskie issledovaniya 6, 57–68 (2021). https://doi.org/10.31857/ S013216250012556-4

Conflict Misunderstanding in the Net Information Society Inna B. Romanenko(&) , Stanislav Gapanovich Yuriy Romanenko , and Stanislav Fedorin

,

Herzen State Pedagogical University of Russia, 48 Moika Embankment, 191186 St. Petersburg, Russia [email protected]

Abstract. The increased technological capabilities of communication digital means, the speed of information dissemination, and the disappearance of many barriers to its exchange lead to an increase in a mutual misunderstanding between conflicting groups. The indisputable authority of scientific rationality in the information society does not contribute to the improvement of mutual understanding between people. Socio-political conflicts are interpreted by people not only using scientific but also everyday, practical thinking. Practical thinking acts as a function of a living system, the main goal of which is selfpreservation. However, it is self-preservation not of an individual organism, but a population or a group. Practical thinking interprets the world as, first of all, a scene of actions of subjects who can independently make decisions and bear moral responsibility. Much less importance is attached to objective processes in this situation. As a result, the everyday thinking of people is permeated with anthropomorphism which was quite functional for previous historical eras. But in combination with telecommunications in the information society, it creates a kind of explosive mixture. Keywords: Information society
Digital telecommunications
Social conflicts
Mutual misunderstanding
Anthropomorphism
Sciencecentrism

1 Introduction The modern world is saturated with conflicts, moreover, in the most important sociopolitical sphere. F. Fukuyama more than a quarter of a century ago suggested that the collision of the main political directions of the last century concentrated in itself all possible main social collisions. The victory of liberalism indirectly testified to the fact that people finally agreed on the main principles of social structure. It can be argued that these views at that time reflected the mindset of the majority. But subsequently, Fukuyama became increasingly criticized because the picture he painted was less and less like what was happening in reality [1]. Nevertheless, Fukuyama’s speech deserves high praise, as it allows to highlights the extent of illusions and the depth of the crisis in the modern world.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 166–174, 2022. https://doi.org/10.1007/978-3-030-98832-6_15

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Indeed, instead of the supposed increase in consensus, unintended conflicts arise everywhere. It is noteworthy that they are often difficult to give a conceptual designation. If there was relative clarity about who was competing with whom (communists and liberals, etc.) before 1989, then later there was sufficient agreement on the basic principles: private property, human rights, etc. However, agreement on general points does not save one from a violent confrontation. Ideological worldview intolerance is still growing [2]. Here are just a few examples of conflicts: between Russia and the West, between Armenia and Azerbaijan, between supporters of Trump and Biden in the United States, between “maidan” and “anti-maidan” in Ukraine, between supporters and opponents of Navalny in Russia, between those who are speaking under the rainbow flag and their opponents, etc. The confrontation between countries, groups of countries, groups within countries, all this can hardly be considered something new in history, the struggle of interests is as old as the world. However, it seems not new, but something very strange and even paradoxical the lack of opportunity for the parties to the conflict to understand each other or just hear each other in the modern world [3]. After all, it would seem that the level of education of the population has become several times higher than several decades ago, it suggests an increase in the influence of rational approaches, detailed argumentation, and respect for opponents in the face of conflict situations. The possibilities of communication media have also increased incomparably: the speed of information dissemination and the disappearance of many barriers to its exchange in a society that likes to call itself informational or digital. Nevertheless, we have to admit that despite its technological advancement the so-called “information society” is seething with “cave passions” [4].

2 Literature Review The outstanding ethologist, the Nobel Prize Laureate K. Lorenz in his famous work “Aggression” suggested that the reduction of hostility between conflicting groups should be facilitated by strengthening personal contacts, increasing the awareness of these groups about the way of life, traditions of others [5], etc. But the experience of modernity destroys such hopes, as evidenced, for example, by civil conflicts on the territory of the former USSR, Yugoslavia, etc. The population of these territories is just perfectly aware of those “others” with whom violent clashes take place on these territories. From abstract humanistic positions, one could assume that the strengthening of the influence of the scientific approach could and should contribute to overcoming conflicting misunderstandings between people. One would hope that an objective scientific view of reality can help people to overcome subjective limited perspectives, getting rid of all kinds of superstitions, prejudices, etc. Science has undoubtedly gained immense authority in the modern world. As indirect but important evidence of this, we see a steady decline in the influence of religious institutions. Modern education presupposes familiarity with basic scientific theories. At the same time, the indisputable priority of the scientific understanding of reality hardly contributes to overcoming people’s misunderstanding of each other. Moreover, an important problem is seen in

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the fact that it is precisely the dominant positions of the scientific approach in education and official public opinion that rather contribute to the strengthening of this misunderstanding. Why is it happening? Scientific thinking has always aimed to develop a certain correct vision of what is happening and put it in the place of the wrong one. Correct thinking from this point of view is a scientific approach and an understanding of what is happening, worthy of a modern person. Unscientific views and approaches must be rejected, discarded, replaced by scientific ones. To denote such an attitude, a global strategy of modern European thinking, it is appropriate to use the already actively used term “science-centrism” [6]. Corresponding scientific theories on issues causing an ambiguous understanding of social processes simply do not exist. It is even problematic to formulate what would mean a “scientific” solution to the life-burning problems of democracy, the nation, the formation of various kinds of identities, etc. Of course, scientific research on these problems exists in abundance, but in essence, from the very beginning, they take a “descriptive” position, do not pretend to have an “explanatory” value that allows the theory to act as an arbiter, as is the case in the field of natural sciences. Therefore, science is simply de facto removed from attempts to make a definite judgment on issues of this kind. But since science in the modern world is the number one real authority, these questions, since they do not find a scientific solution, simply drop out of the competence of rational theoretical consideration altogether. From the point of view of scientific rationality, as it is noted by A.L. Nikiforov, “we have to admit that literature, art, everyday human behavior contain very little rational, and the only area that certainly claims to be rational is science” [7, p. 233]. People undoubtedly comprehend their conflicting interactions with the help of their means of thought, for example, in all kinds of the rhetoric of the mass media. But since the criteria of scientific character in such cases do not work and do not apply, then such an application of thinking drops out of scientific consideration. After all, the logic of sciencecentrism is not to correct a poorly functioning mechanism but to replace it with a well-functioning one. Not to understand why the medicine of the healer does not heal, but to offer an effective medicine. If an effective medicine has not yet been created, healers’ remedies for this do not cease to be assessed as unpromising and not worthy of attention. A man by his nature possesses the means of thinking about what is happening, although by no means always scientific. Relying on them, people find certain ways of solving their practical problems. It is appropriate to designate these extra-scientific means of thinking with the existing term “practical thinking” [8] or “life-practical thinking” [9], to emphasize its fundamental, ideological nature. It should be noted that the survival of human beings relying on exclusively mental means is associated with many “humanitarian” disadvantages, which were and are subject to criticism from the standpoint of the new European enlightenment humanism. Nevertheless, they are effective to the extent that they allow human populations to survive in the natural environment and have allowed the human race to take a confident position among other living things. And all this happened long before the emergence of scientific thinking. Socio-political conflicts, which were discussed above, are often comprehended by people precisely through life-practical thinking and not scientific. Scientific and theoretical thinking can be actively used or exploited for various purposes. So, for

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example, the data and theoretical approaches of anthropology were successfully used by the ideologists of racism many years ago [10]. But despite this the difference (abyss) between scientific and practical life thinking is not leveled, rather, on the contrary, there are some confusions, a kind of symbiosis of scientific and practical life. But it is the latter that has the real priority. And largely because it acts in the shadow of the scientific-theoretical, officially recognizing its predominant significance. It is important that scientific-theoretical thinking itself prefers not to notice its subordinate position. It is busy with itself, with its achievements where they are. As a result, the way people think, including in conflict, formulating irreconcilable positions, claims to each other, endlessly substantiating the superiority of their own and the insignificance of others’ points of view, all this is outside of scientific and theoretical interests. As some unfortunate but base reality that can be described, but which is practically useless to try to influence. As a result, life-practical thinking is left to itself, as well as to the influence of all kinds of rhetoric, manipulative techniques, which are arrogantly called “technologies” [11]. From its very inception, scientific-theoretical thinking took a position of sharp opposition concerning life-practical thinking. In early Greek philosophy, knowledge is opposed to opinion – “Doxa”, especially aggressively represented by the Eleans and Heraclitus. Similarly, further, the sophists flaunt their skill in front of the common people, similarly, such disdain appears in Socrates and his followers, Plato and Aristotle. At the beginning of the Modern times era, Descartes tried to maintain neutrality with the life-practical thinking of his era, assuring that he was not at all going to challenge the accepted behavioral norms and that he was only interested in scientific truth. However, such an attitude of the leaders of the new science did not last long, and the era of the Enlightenment violently fell under the banner of scientific reason (then identical to Newtonian mechanics) on everyday thinking, treating the latter as superstitions and prejudices that stand in the way of progress and happiness of mankind, and therefore deserve one thing – the death. Voltaire calls one of his central works “The Philosophy of Newton” to emphasize that he is acting precisely from the standpoint of scientific reason. Since the nineteenth century, there has been a rapid expansion of empirical science and the corresponding ideology of positivism (the next stage of science-centrism). The literary character Bazarov from Turgenev’s “Fathers and Sons”, who declares all cultural heritage worthless, can be considered a kind of illustration of this in the Russian context.

3 Research Methods The fundamental ideas and approaches formed within the framework of classical, nonclassical, and post-non-classical rationality (comparative-historical, paradigmatic, structural-typological analysis, methods of analysis and evaluation, formed within the framework of social constructivism, phenomenology, hermeneutics, etc.) was the theoretical and methodological basis of the study. The historical and philosophical analysis made it possible to work with extensive historical, scientific, and sociophilosophical material, during which it was possible to identify explicitly and transformed forms and types of conflicting misunderstandings, as well as to consider the

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causes that give rise to them. The formal-logical and epistemological analysis made it possible to structure and substantively systematize the concepts used in interdisciplinary research, but in demand within the framework of the study. Turning to the modeling method made it possible to determine promising approaches and directions for further research. The methodology of system analysis made it possible to analyze the phenomenon of conflicting mutual misunderstanding in the context of the information society as a complex system in the unity of its elements, levels, interconnections, and interdependencies. The philosophical and anthropological approach implemented in the course of the study as a whole made it possible to predict some of the consequences of the analyzed phenomenon in the life of a modern person as an active and free being, but not always responsible for the consequences of his actions.

4 Results and Discussion At the same time, the mentioned above expansion gives rise to a tendency of resistance. Its representatives on German soil are especially famous: Dilthey, neo-Kantians oppose the “sciences of nature” with projects of the sciences “about the spirit” and “about culture”; the phenomenological movement is also initially antiscience [12]. Throughout the twentieth century, significant attempts continued to master the problem of thinking “from the inside”, and not from the standpoint of external experimentation, the extreme manifestation of which was behaviorism. Undoubtedly, life-practical thinking could not fail to attract the attention of theorists. The prevailing project was the study of cultural artifacts, various kinds of texts, and the perception in them of certain regularities that would give the key to understanding a person. On the other hand, attempts to directly breakthrough to comprehending the essence of the matter through improved introspection, direct comprehension of the given of consciousness, played an important role. This is the way of the phenomenological description of human reality, the quintessence of which can be considered the presentation in existential analytics of “Being and Time” by M. Heidegger. Both culture-centric and existential-phenomenological lines have brought undeniably valuable achievements on the path of cognition. Their merit lies in the fact that they undoubtedly tried to understand life-practical thinking, which is being bullied by the positivist scientific program. But their opposition to the “natural sciences” led to the fact that man was presented as a non-natural being, comprehensible purely within the sphere of culture, language, sign reality, semantic intentional given. Their lack is seen in common with the lack of positivism – this is one-sidedness. In the same way, they postulate some way to comprehend the human essence, which should be stepped on, preferring it to others. Such sentiments, in our opinion, significantly contributed to the fact that one more barrier was raised on the way to understanding the real thinking of people. The consequence of this is, among other things, that human problems turn out not to be analyzed or difficult to analyze: problems to which the increasing “civilizational tension” belongs. The way out of the described difficulties seems to be in the need to interpret the development of society as a “natural-historical process” [10, 13], i.e. to take into

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account the relationship of cultural and natural components. From this point of view, “our cognitive apparatus is the result of biological evolution,” as Vollmer puts it [14, p. 191]. Life-practical thinking acts as a function of a living system, the main goal of which is survival. However, it is not the survival of an individual organism that comes to the fore, but of a population, i.e. quite a self-sufficient group. The natural program that underpins the thinking of the individual does not serve his self-preservation and improvement, and even less the goal of gaining theoretical truth. It provides first of all homeostasis of the social whole. The general features of this idea are differently traced in different concepts in the history of thought. In particular, Nietzsche expressed it in a vivid paradoxical form, arguing that truth is a lie. Indeed, many important principles and views that are presented and perceived as “true” actually function as a means to induce individuals to perform actions that are expedient for the system, but often to the detriment of their preservation. It follows from what has been said that much that goes beyond the limits of certain forms of reason can be an insurmountable difficulty for individual understanding. “The success of evolution does not guarantee and does not require that our cognitive structures are in full agreement with reality” [14, p. 192]. It seems possible that the program of thought tools acting in individuals by nature does not provide for the possibility of individuals understanding those social processes, the components of which are the actions of these individuals themselves. The latter can understand only what is happening only from a certain angle of their participation. This narrowness of the worldview, especially the predilection of individual subjects of one side or another in a situation of social conflicts, seems to be quite expedient from the biological point of view, i.e. in terms of preserving the life of the clan. The complex processes of the development of social systems are not captured by the natural program, according to which life-practical thinking works. They are hidden from individuals and are not always clear to them. However, a specific quasiunderstanding of world events as occurring at the will of certain subjects acts as a kind of compensation. This quasi-understanding is theoretically inadequate, but it importantly orientates individuals and creates for them the possibility of meaningful actions that are functional from the point of view of the system as a whole. Life-practical thinking interprets the world as, first of all, the world of subjects, i.e. creatures capable of independently making decisions and bearing responsibility for their decisions – primarily moral, by the prevailing norms, and only then having some objective consequences. As a result, the thinking of people is permeated with anthropomorphism: in the primitive era in an undisguised form, but in many respects retained its influence throughout history, including modernity. Max Weber proposed the famous reasoning according to which, due to the influence of the positive sciences, the world was “disenchanted”, i.e. it is not perceived by modern man as dependent on the will of spirits. But, according to M. Weber, the world mustn’t have become more understandable for people from this point. Science has illuminated only some aspects of what is happening (including the science of “information civilization”). Let us venture to continue and say that at the basis of people’s thinking there is an anthropomorphic understanding of reality – that is, interpretation of what is happening as dependent on someone’s will, aspirations, or intentions. However, this is no longer

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the will of the ancient spirits. They were replaced by modernized anthropomorphic entities: nations, civilizations, races, cultural codes, identities, values, etc. All of them are involved in some sort of showdown, just as it was customary among the pagan gods, and modern people are enthusiastically involved in these proceedings. “Numerous observations and targeted research on human ethology testify to how much we, humans, are rooted in the Stone Age” [14, p. 194].

5 Conclusions Public conflicts have been permeated and fed by mass participation at all times, without which there could be no wars, popular demonstrations, etc. But the conflicting enthusiasm of the masses spread differently in different epochs. Initially, it was orally, then it was more and more influenced by the institutions of religious, political, bureaucratic, print, and telecommunication means [15]. In modern society, confrontation strategies are based on the advanced technological basis of the information society. Public conflicts receive wide coverage in a variety of mass media. The increasing interactive nature of the latter involves an increasing number of participants. Comments, likes, subscriptions, etc. – all this is the subject of the closest attention and creates previously unseen opportunities for feedback. Broad strata of society are involved in significant conflict processes by the very fact of their direct discussion (“the Internet exploded”, etc.). On the contrary, the sharply increased degree of participation of the masses in discussions does not lead to any significant increase in the level of theoretical significance, and indeed simply rationality. Passions flare up on topical issues that have nothing to do with serious consideration and are deliberately aimed at escalating excitement and creating an atmosphere of hatred. Competent judgments are often drowned in a mass of emotional, exalted statements that subdue the general mood [16]. Moreover, technological advancement itself is too capable of serving deception and self-deception: Internet messages are often received uncritically, all kinds of “stuffings” flourish in the social nets, and so on. What is happening is similar to what K. Lorenz spoke about with anxiety when he studied aggression in animals. Such an instinct was functional for the survival of human ancestors, who did not have in their arsenal the means of inflicting serious harm on the enemy. But modern weapons – especially nuclear ones – have radically changed the state of affairs with the means of destruction, but the instinct of aggression is not capable of fundamentally changing over the same period. Thus, as K. Lorenz warned, the situation changed dramatically and became radically dangerous. Similarly, at an incredible pace, the developing telecommunications of the information society, it seems to us, create an explosive mixture in combination with the ineradicable anthropomorphism of life-practical thinking, which was quite functional for previous historical eras [17]. The problem described is practical and awaits practical solutions. However, it is theoretical thinking that can and should seek a way out of a threatening situation. At the same time, the essence of the problem is that modern theoretical thinking (for the reasons discussed above) does not see the problem, since it is itself a part of it [18].

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And if the progress of scientific and theoretical reason has put the human race in front of unprecedented dangers, then still the hope of deliverance can hardly be associated with anything else than the progress of the same theoretical reason, which, hopefully, can learn something from its errors. From the fact that theoretical understanding will take place, if it will take place in an indefinite future, no immediate changes can be expected. Whether this will be the beginning of some kind of mass sobering up or the beginning of some organized measures – these are the following questions needing research. However, theoretical thinking is in any case not limited to the question of its further applicability and is important in itself.

References 1. Mansfield, H.: Responses to Fukuyama. Natl. Interest 56, 34–44 (1999). www.jstor.org/ stable/42897176. Accessed 18 Aug 2021 2. Kavanagh, C.: Digital Technologies and Civil Conflicts: Insights for Peacemakers. European Union Institute for Security Studies (EUISS), pp. 1–9 (2021). www.jstor.org/stable/ resrep30222. Accessed 18 Aug 2021 3. Schroeder, R.: Digital Media and the Rise of Right-Wing Populism. Social Theory After the Internet: Media, Technology, and Globalization. UCL Press, London, pp. 60–81 (2018). www.jstor.org/stable/j.ctt20krxdr.6. Accessed 18 Aug 2021 4. Waltman, M.S.: Teaching hate: the role of internet visual imagery in the radicalization of white ethno-terrorists in the United States. In: Winkler, C.K., Dauber, C.E. (eds.) Visual Propaganda and Extremism in the Online Environment, Strategic Studies Institute, US Army War College, pp. 83–104 (2014). www.jstor.org/stable/resrep12132.7. Accessed 18 Aug 2021 5. Lorenz, K.: Agressiya [Aggression]. Publishing Group “Progress”, “Univers”, Moscow (1994) (in Russian) 6. Lektorsky, V.S.: Klassicheskaya i neklassicheskaya epistemologiya [Classical and NonClassical Epistemology]. Editorial URSS, Moscow (2001) (in Russian) 7. Nikiforov, A.L.: Filosofiya nauki: istoriya i teoriya [Philosophy of Science: History and Theory]. Idea-Press, Moscow (2006) (in Russian) 8. Popov, A.A.: Prakticheskoe myshlenie kak osnovanie sovremennogo yunosheskogo obrazovaniya [Practical Thinking as the Basis of Modern Youth Education]. Bulletin of Tomsk State University 308, pp. 49–52 (2008). https://cyberleninka.ru/article/n/ prakticheskoe-myshlenie-kak-osnovanie-sovremennogo-yunosheskogo-obrazovaniya. Accessed 18 Aug 2021 (in Russian) 9. Vvedenie v filosofiyu: Uchebnik dlya vuzov [Introduction to Philosophy: A Textbook for Universities]. Republic, Moscow (2003) (in Russian) 10. Klein, L.: Rasizm i “Psihologiya narodov” [Racism and “Psychology of Nations”]. Pers. Dev. 5, 56–90 (2009). https://cyberleninka.ru/article/n/rasizm-i-psihologiya-narodov. Accessed 20 Sep 2021 (in Russian) 11. Hansen, F.S., Fejerskov, A.M.: Political technologies threaten developing countries: Disinformation Goes South. Danish Institute for International Studies, pp. 1–4 (2019). www.jstor.org/stable/resrep21359. Accessed 20 Sep 2021

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12. Filosofskaya germenevtika i gumanitarnye nauki: Uchebnoe posobie [Philosophical Hermeneutics and Humanities: Textbook]. Publishing House of the St. Petersburg State University of Economics and Finance, St. Petersburg (2008) (in Russian) 13. Marx, K.: Capital, vol. 1. Marx, K., Engels, F.: Collected works, vol. 23. Publishing House of Political Literature, Moscow (1960) (in Russian) 14. Vollmer, G.: Evolyucionnaya teoriya poznaniya. K prirode chelovecheskogo poznaniya. Evolyucionnaya epistemologiya. Antologiya [Evolutionary Theory of Knowledge. To the Nature of Human Cognition. Evolutionary Epistemology. Anthology]. Center for Humanitarian Initiatives, Moscow (2012) (in Russian) 15. Bogatyrev, D., Romanenko, I.: Religioznye osnovaniya obrazovatel’nyh paradigm (ot antichnosti do postmoderna) [The Religious Foundations of Educational Paradigms (From Antiquity to Postmodernity)]. RXOKH. Ancient Philos. Classic. Tradit. 10(2), 495–511 (2016). https://classics.nsu.ru/schole/10/schole-10-2.pdf. Accessed 18 Aug 2021 (in Russian) 16. Greenberg, K.J.: Counter-radicalization via the Internet. Annals of the Am. Acad. Polit. Soc. Sci. 668, 165–179 (2016). www.jstor.org/stable/26361943. Accessed 20 Aug 2021 17. Romanenko, I.B., Puyu, Y.V., Romanenko, Y.M., Romanenko, L.Y.: Digitalization of education: conservatism and innovative development. In: Bylieva, D., Nordmann, A., Shipunova, O., Volkova, V. (eds.) Knowledge in the Information Society: Joint Conferences XII Communicative Strategies of the Information Society and XX Professional Culture of the Specialist of the Future, pp. 22–29. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-65857-1_3 18. Shipunova, O.D.: The role of relation to values principle in the social management practices. The existential-communicatory aspect. Middle-East J. Sci. Res. 19(4), 565–569 (2014). https://doi.org/10.5829/idosi.mejsr.2014.19.4.123684

Methods and Models of System Analysis

Stability Analysis of Dynamical Systems Based on Lyapunov Vector Functions Artem A. Efremov(&) , Vera V. Karakchieva and Vladimir N. Kozlov

,

Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya St. 29, 195251 St. Petersburg, Russia {artem.efremov,kozlov_vn}@spbstu.ru

Abstract. Method of vector functions A.M. Lyapunov, proposed by V.M. Matrosov, makes it possible to study the stability of decentralized systems based on the decomposition of the system into diagonal and off-diagonal subsystems. The paper uses estimates of the time derivatives of quadratic Lyapunov functions or Lyapunov functions in the form of D. Shilyak for the synthesis of comparison models (estimates) for the decomposition of decentralized systems considering subsystems. From the inequalities describing the comparison models, a transition is made to the differential equations of the comparison systems. The majorizing models of the Lyapunov vector function method are described based on estimates of the properties of Lyapunov functions in the form of N.N. Krasovsky, differential inequalities S.A. Chaplygin and T. Vazhevsky’s conditions for subsystems. A model of an electric power association has been synthesized in a form suitable for stability analysis by the method of Lapunov vector functions. A computational experiment has been performed for electric power association consisting of three electric power systems and two transmission lines. Keywords: Decentralization


System
Lyapunov vector functions

1 Decomposition of Dynamic Systems Models The equations of a complex system are transformed considering the requirements of the method of Lyapunov vector functions (MLVF) and decomposition — dividing the system into subsystems [1, 2]: x0i ¼ fi ðxi ;ui Þ ¼ Ai xi þ

r X

Aij xj þ Bi ui ; i ¼ 1;2;:::;s; xi 2 Rni :

ð1Þ

j¼1; i6¼j

P Diagonal matrices Ai 2 Rni ni define diagonal subsystems Si , and the terms Aij xj in (1) determine the influence of subsystems with coordinates xj on coordinates xi and x0i subsystems Si . Next, estimates of the Lyapunov function are calculated for the analysis of the stability of diagonal subsystems with zero constraints

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 177–186, 2022. https://doi.org/10.1007/978-3-030-98832-6_16

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x0i ¼ Ai xi þ Bi ui ; i ¼ 1;2;. . .;r:

ð2Þ

Such a decomposition can be ineffective if the elements of the diagonal blocks Ai do not dominate over the elements of the off-diagonal blocks Aij , i. e., if the connections are not weak enough. “Diagonal dominance” is also a condition for the stability of a system of piecewise linear difference equations based on the norm as a diagonal approximation of the Lyapunov function [3–5].

2 Comparison Principle and Lyapunov Vector Functions Lyapunov vector functions and decomposition of system (2) allow using Lyapunov functions of diagonal subsystems Vi ðxi Þ instead of a function V ð xÞ of a system with a complete vector of coordinates x 2 RN [1]. Diagonal functions Vi ðxi Þ form a vector function V ð xÞ ¼ fV1 ðx1 Þ;. . .;i ðxi Þ;. . .;Vr ðxr Þg;

ð3Þ

for which V.M. Matrosov obtained sufficient stability conditions. Estimates for the Lyapunov function were given by N.N. Krasovsky for exponentially stable subsystems of a decomposed system x0i ¼ fi ðxi Þ þ

r X

Pij xj ; i ¼ 1;2;. . .;r;

ð4Þ

j¼1;j6¼i

with linear connections of majorizing and minorizing estimates. From the inequalities describing the comparison models, one can go over to the differential equations of the comparison systems. The problem of formulating the conditions for the existence of the upper (majorizing model) and lower (minorizing model) estimates was formulated by S.A. Chaplygin. Conditions for the existence of a solution to the Chaplygin problem for a system of first-order differential inequalities were obtained by T. Vazhevskii in the theorem on the existence of upper and lower solutions to inequalities for a quasimonotone and nondecreasing function f ðt;xÞ in an equation x0i ¼ fi ðt;xÞ. The comparison principle was developed in the works of V.M. Matrosov, combining the method of N.N. Krasovskii [6] for estimates of Lyapunov functions, differential inequalities of S.A. Chaplygin, T. Vazhevsky’s theorem and the concept of several Lyapunov functions for comparison models [1]. The synthesis of comparison models of the system based on estimates of the derivatives of the Lyapunov functions of diagonal subsystems contains decomposition, the choice of a class of components of the Lyapunov functions of diagonal subsystems and estimates, and the solution of the comparison system for stability analysis.

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3 The Structure of Estimates for the Derivatives of Lyapunov Functions of Diagonal Subsystems Under the condition u ¼ 0 and Pij ¼ 0, from the general model (1) under the condition of zero actions, the equations of diagonal subsystems follow x0i ¼ Ai xi ; i ¼ 1;2;. . .;r:

ð5Þ

The Lyapunov function of diagonal subsystems is chosen in the form of a quadratic form Vi ðxi Þ ¼ xTi Hi xi ; i ¼ 1;2;. . .;r;

ð6Þ

where Hi ¼ HiT 2 Rni ni – is a positive definite matrix as a solution to the matrix Lyapunov equation ATi Hi þ Hi Ai ¼ Gi ; where Gi ¼ GTi 2 Rni ni is a positive definite matrix, often given as Gi ¼ En : From the theorem of N.N. Krasovsky it follows that if the equilibrium state of the system x ¼ 0 is exponentially stable, then there exists a Lyapunov function, interval bounded, with upper bounds [6] c21i kxi k2  Vi  c22i kxi k2 ; Vi0   c23i kxi k2 ; kgradxi Vi k  c24i kxi k2 ;

ð7Þ

where c2ki [ 0; k ¼ 1;2;3;4; kxi k ¼ ðxTi xi Þ1=2 is the Euclidean norm of the vector in Eq. (1). If the Lyapunov function is Vi ðxi Þ ¼ xTi Hi xi — quadratic form, then the constants 2 cki are functions of the eigenvalues of the matrices Hi and Gi : c21i ¼ km ðHi Þ; c22i ¼ kM ðHi Þ; c23i ¼ km ðGi Þ; c24i ¼ 2kM ðHi Þ; where km , kM are the smallest and largest eigenvalues of the matrix. Under external influences, the Lyapunov function of D. Shilak is introduced [2] vi ðxi Þ ¼ ðVi ðxi ÞÞ1=2 ¼ ðxTi Hi xi Þ1=2 :

ð8Þ

For this function, Krasovskii inequalities (7) yield the estimates c1i kxi k  vi  c2i kxi k; v0i   gi kxi k; kgradxi vi k  li ; where c1i ,c2i , gi and li are positive real numbers, and the parameters gi and li are related to cki and eigenvalues Hi and Gi : gi ¼

c23i km ð G i Þ c2 kM ðHi Þ ; li ¼ 4i ¼ 1=2 : ¼ 1=2 2c2i 2k ðHi Þ 2c1i km ðHi Þ M

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4 Comparison System Equation Calculation of the parameters cki , gi and li , determines the equations of comparison models for decentralized systems. a). For the diagonal subsystem under zero influences, the equation of the comparison system for the Lyapunov function in the form of the square of the norm kxi k2 , obtained from inequalities Eq. (7), has the form z0i ¼ c23i zi =c22i : Lyapunov quadratic functions Vi for zi0 ¼ Vi ð0Þ ¼ xTi0 Hi xi0 have the estimates c2

kxi ðt;t0 ;xi0 Þk2 

c2

 3i xTi0 Hi xi0 c3i2 ðtt0 Þ c2 2 ðtt0 Þ e 2i ; kxi ðt;t0 ;xi0 Þk2  2i ; i ¼ 1;2;. . .;r: kxi0 k2 e c2i 2 2 c1i c1i

b). The equation of the comparison system with respect to the first degree of the norm kxi k for the Lyapunov function vi has the form z0i ¼ c23i zi =2c22i : If we choose zi0 ¼ ðxTi0 Hi xi0 Þ1=2 , then the estimate of the norm of the solution takes the form  2  ðxTi0 Hi xi0 Þ1=2 c3i kxi0 k  exp  2 ðt  t0 Þ : kxi ðt;t0 ;xi0 Þk  c1i c2i 1=2 T or In this case, the gain of the comparison unit must be no less than c1 1i ðxi0 Hi xi0 Þ 1 c1i c2i . The comparison system equation for an isolated subsystem in the presence of action for the Lyapunov function vi for norms kxi k and kuk is equal to

z0i ¼ 

c23i c24i 1=2  T  z þ k Bi Bi kuk: i 2c22i 2c21i M

ð9Þ

If vi ¼ ðxTi Hi xi Þ1=2 , then the equation of the comparison system Eq. (9) takes the form z0i

 1=2  kM ðHi ÞkM BTi Bi km ðGi Þ zi þ ¼ kuk: 2kM ðHi Þ k1=2 m ðHi Þ

For this diagonal equation, the estimate of the Lyapunov function has the form vi ðt; t0 ;xi0 Þ  zi ðt; t0 ;zi0 Þjzi0 ¼vi0 ; kxi k  ðc1i Þ1 zi ; i ¼ 1;2;. . .;r:

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5 Conclusion of the Comparison Model Calculation of the time derivative of the Lyapunov function for the system, considering (4), determines the equalities: v0i ðxi Þ ¼ ½grad vi T x0i   ¼ ½grad vi T Ai xi þ Pi1 x1 þ . . . þ Pi;i1 xi1 þ Pi;i þ 1 xi þ 1 . . . þ Pin xn þ Bui ui ¼ ½grad vi T Ai xi þ ½grad vi T Pi1 x1 þ . . . þ ½grad vi T Pin xn þ ½grad vi T Bui ui : For diagonal subsystems, the required estimates are v0i ðxi Þ ¼ ½grad vi T Ai xi ; i ¼ 1;2;. . .;r: Estimates of the form v0i ðxi Þ follow from Krasovsky’s conditions for subsystems: v0i ðxi Þ   gi kxi k þ kgrad vi k  kPi1 x1 k þ . . . þ kgrad vi k  kPin xn k þ kgrad vi k  kBui ui k:    2 The estimates Pij xj  for the square of the norm are Pij xj  ¼ xTj PTij Pij xj : For symmetric square matrices PTij Pij the following estimates are valid      2 km PTij Pij  xTj PTij Pij xj xj   kM PTij Pij : Then the estimates hold           Pij xj 2  kM PT Pij xj 2 ; Pij xj   k1=2 PT Pij xj : ij

M

ij

ð10Þ

The required estimates for the norms are formed in a similar way. 1=2

kBi ui k2  kM ðBTi Bi Þkui k2 ; kBi ui k  kM ðBTi Bi Þkui k:

ð11Þ

From Eqs. (10), (11), the first and third Krasovskii inequalities, the estimates for the components v0i ðxi Þ; i ¼ 1; :::; n of the derivative of the Lyapunov function take the form  1 1=2  T v0i ðxi Þ   gi c1 2i vi þ li kM Pi1 Pi1 c11 v1 þ . . .  1=2  1=2  T  ::: þ li kM PTin Pin c1 1n vn þ li kM Bi Bi kui k:

ð12Þ

The set of inequalities (12) implies the comparison equation:  1 1=2  T z0i þ gi c1 2i zi ¼ li kM Pi1 Pi1 c11 z1 þ . . .  1=2  1=2  T  ::: þ li kM PTin Pin c1 1n zn þ li kM Bi Bi kui k:

ð13Þ

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The conditions under which the comparison (12) majorizes the Lyapunov vector vðtÞ function follow from the first Krasovsky inequality: kxi k  c1 1i vi ; i ¼ 1;2;. . .;r: and the comparison equations, majorizing kxi k take the form:  1 1 1=2  T z0i þ gi c1 2i zi ¼ li kM Pi1 Pi1 c11 c1i z1 þ . . .  1=2  1=2  T  1 1 ::: þ li kM PTin Pin c1 1n c1i zn þ li kM Bi Bi c1i kui k: For the Lyapunov function vi ðxi Þ, the comparison system has the form z0 ¼ Wz þ Ckuk;

ð14Þ

for which the coordinate form is determined by the system of equations z0i ¼ wii zi þ

r X

wij zj þ ci kuk; i ¼ 1;2;. . .;r;

j¼1; j6¼i

where z;C 2 Rr ;W is a constant r  r matrix, W and C are equal to wii ¼

c2  3i2 2c2i

1=2

; wij ¼

c24i kM

  PTij Pij

2c1i c1j

; ci ¼

 1=2  c24i kM BTi Bi : 2c1i

If vi ¼ ðxTi Hi xi Þ1=2 , then the parameters of the comparison system are    T 1=2  P P ij ij kM ðHi ÞkM BTi Bi km ðGi Þ ; wij ¼ 1=2 : wii ¼    ; ci ¼ 2kM ðHi Þ km ðHi Þk1=2 k1=2 Hj m m ðHi Þ 1=2

kM ðHi ÞkM

The conditions for the stability of the aggregate system Eq. (14) are sufficient, but not necessary, can be checked since the system Eq. (14) is stable if and only if the W— Hurwitz matrix. The parameters wij [ 0; W are M-matrix, the stability conditions of Sevastyanov–Kotelyansky have the form [1]: w11


w1k . .. .. ð1Þk .. . [ 0; k ¼ 1;2;. . .;r; . wk1


wkk or the conditions for the quasi-dominance of the diagonal in the matrix W, namely: the aggregate model is stable if and only if there are positive numbers dj ;j ¼ 1; 2; :::;r; such that any of the conditions are satisfied:

Stability Analysis of Dynamical Systems r X

di jwii j [

183

r X dj wij ; i ¼ 1;2;. . .;r; or dj wjj [ di wij ; j ¼ 1;2;. . .;r:

j¼1; j6¼i

j¼1; i6¼j

6 Application MLVF to Analyze the Sustainability of Electric Power Associations 6.1

Electric Power Association Model

The diagonal equations of the processes of the i-th electric power system, considering the subsystems of quasi-stationary stabilization, have the form X Tai2 x0i þ Tyi xi þ qij ðui  uj Þ ¼ pi  li ; TPi p0i þ pi ¼ kxi xi þ kPi ri ; j6¼i

Ti r0i

þ ri ¼ k i u i þ k 1 i u i þ k 2 i x i ; s i ¼

X

ð15Þ

qij ðui  uj Þ; i ¼ 1;2;3;

j6¼i

where ui ; xi ; li ; ri ; pi ; si ; ui — deviations of angles, frequencies, loads, signals of regulators, powers, overflows along lines and controls of the i-th electric power system; Tai2 ;Tyi ;qij ;TPi ;Ti ;kPi ;ki ;kxi — parameters of the electric power system. The asymptotic equations for super-large electric power systems of the type (11) at ui ¼ k1i ui þ k2i xi þ ui have the form Tai2 x0i þ Tyi xi þ

X

ðui  uj Þ ¼ pi  li ; i ¼ 1;2;3:

ð16Þ

j6¼i

Cauchy form for asymptotic Eqs. (12): u0i ¼xi ; x0i ¼Tai2 ½kxi xi þ kPi ðk1i ui þ k2i xi þ ki ui Þ X ðui  uj Þ; i ¼ 1; 2; 3; : li Tyi xi 

ð17Þ

i6¼j

The transformed second differential equation has the form. " x0i

¼

Tai2

kPi k1i ui 

X

# qij ðui  uj Þ  ðkxi  kPi k2i þ Tyi Þxi þ kPi ki ui  li ; i

j6¼i

¼ 1;2;3: The deviations of the sum of active power flows si along the lines connecting the diagonal electric power system with other electric power systems have the form

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X

qij ðui  uj Þ ¼ ui

j; j6¼i

X j;j6¼i

qij 

X

qij uj :

j; j6¼i

Then, the matrix form Eq. (1) of the equations of the electric power association has the form x0i ¼ ½ u0i

x0i T ¼ Ai x þ

n X

Pij xj þ Pi ui þ BMi li

j;j6¼i

"

#   0 1 ui     ð18Þ P ¼  T ai kPi k1i  j;i6¼j qij  T ai kxi  kPi k2i þ Tyi xi             0 0 0 0 0 0 u1 un þ þ...þ þ ui þ li :   qi1 0 T ai qin 0 x1 xn T ai kPi kCi For a vector xi ¼ ðui ; xi ÞT ; matrix Ai and Pin ; n ¼ 1;:::i  1;i þ 1;:::n are equal "

# 0 1     P Ai ¼ ; T ai kPi k1i  j;i6¼j qij T ai kxi  kPi k2i þ Tyi         0 0 0 0 0 0 ; Bi ¼ : Pi1 ¼ ;


Pin ¼ ; Bui ¼ qi1 0 qin 0 T ai T ai kPi ki The asymptotic model of an electric power association for stability analysis MLVF has the form x0i ¼ Ai xi þ Pi1 x1 þ . . . þ Pin xn þ Bui ui þ BMi li ; i ¼ 1:::n; i 6¼ j:

ð19Þ

As an example, let us investigate the stability of an electric power interconnection, consisting of three electric power systems and two transmission lines. 6.2

Lyapunov Functions for the Approximating Equations of the Electric Power System

For each electric power system, the Lyapunov function is given in the form of Shilak. The matrices Hi are calculated from the Lyapunov equation, where Gi = E. Then  H1 ¼ H3 ¼

0; 6714 0; 5  1; 8812 0; 5

    0; 5 1 0 1; 7373 ; G1 ¼ ; H2 ¼ 0; 9151 0 1 0; 5    0; 5 1 0 ; G3 ¼ : 0; 7354 0 1

  0; 5 1 ; G2 ¼ 0; 3231 0

The Lyapunov function V ¼ ðv1 ; v2 ; v3 ÞT 2 R3 ; in the Shilak form is [2]

 0 ; 1

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 1=2  1=2 v1 ¼ 0; 6714u21  u1 x1 þ 0; 9151x21 ; v2 ¼ 1; 7373u22  u2 x2 þ 0; 3231x22 ;   1=2 : v3 ¼ 0; 8812u23  u3 x3 þ 0; 7354x23

6.3

Estimates for the Lyapunov Vector Function

The estimates MLVF components are calculated using ki ðHi Þ and ki ðGi Þ; which for the electric power system No. 1 are equal to: c11 ¼ 0; 5278; c21 ¼ 1; 1436; c31 ¼ 1; c41 ¼ 1; 6173; g1 ¼ 0; 4372; l1 ¼ 2; 4778: The estimates for the electric power system No. 2 are given by the equalities: c12 ¼ 0; 4052; c22 ¼ 1; 3770; c32 ¼ 1; c42 ¼ 1; 9474; g2 ¼ 0; 3631; l2 ¼ 4; 6799: Similarly, the estimates for the electric power system No. 3 are calculated, equal to c13 ¼ 0; 5504; c23 ¼ 1; 1461; c33 ¼ 1; c43 ¼ 1; 6208; g3 ¼ 0; 4363; l3 ¼ 2; 3864:

6.4

Conditions of Stability of the Electric Power Association

Based on the estimates, a comparison system was obtained in the form of three linear differential equations: z01 ¼  0; 3823z1 þ 0; 1223z2 þ 0; 1801z3 þ 0; 0494u; z02 ¼  0; 2637z2 þ 0; 1773z1 þ 0; 1850u; z03 ¼ 0; 3807z3 þ 0; 1808z1 þ 0; 0406u: The stability conditions for Sevastyanov-Kotelyansky have the form w11 det w21 w11 w12 ð1Þ det w21 w22 w31 w32

w11 ¼0; 3823 [ 0; 0; 3823 0; 1223 w12 ¼ 0; 0791 [ 0; ¼ det 0; 1773 0; 2637 w22 0; 3823 0; 1223 0; 1801 w13 0 w23 ¼ð1Þ det 0; 1773 0; 2637 ¼ 0; 0215 [ 0: 0; 1808 0 0; 3807 w33

It can be seen that the conditions of Sevastyanov-Kotelyansky are met, therefore, the electric power interconnection of three electric power systems with two lines is stable.

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References 1. Matrosov, V.M.: Metod Vektornykh Funktsiy Lyapunova: Analiz Dinamicheskikh Svoystv Nelineynykh Sistem. [Lyapunov Vector Function Method: Analysis of Dynamic Properties of Nonlinear Systems.] Fizmatlit, Moscow (2001). (In Russian) 2. Shil’yak, D.D.: Detsentralizovannoye Upravleniye Slozhnymi Sistemami. [Decentralized Management of Complex Systems.] Mir, Moscow (1994). (In Russian) 3. Kozlov, V.N.: Upravleniye Energeticheskimi Sistemami i Ob’’yedineniyami. [Management of Energy Systems and Associations.] Izd-vo SPb. politekhn. un-ta, St. Petersburg (2008). (In Russian) 4. Kozlov, V.N., Kupriyanov, V.Ye., Zaborovskij, V.S.: Vychislitel’nyye metody Sinteza Sistem Avtomaticheskogo Upravleniya. [Computational Methods of Synthesis of Automatic Control Systems.] 222 p. Izd-vo LGU im. A.A. Zhdanova, Leningrad (1989). (In Russian) 5. Kozlov, V.N., Yefremov, A.A.: Vvedeniye v Funktsional’nyy Analiz. [Introduction to Functional Analysis.] Izd-vo Sankt-Peterburgskogo politekhn. un-ta, St. Petersburg (2019). (In Russian) 6. Krasovskiy, N.N.: Teoriya Upravleniya Dvizheniyem. [Theory of Motion Control.] Nauka, Moscow (1968). (In Russian)

Implementation of Control and Forecasting Problems of Human-Machine Complexes on the Basis of Logic-Reflexive Modeling Igor B. Arefiev1 and Olga V. Afanaseva2(&) 1

2

Emperor Alexander I St. Petersburg State Transport University, Moskovsky pr. 9, 190031 St. Petersburg, Russia Saint Petersburg Mining University, 21st Line (Vasilievsky island) 2, 199106 St Petersburg, Russia [email protected]

Abstract. Progress in the creation of automata for making decisions in humanmachine complexes is still permissible only on the path of creating separate software and hardware tools that enable the decision-maker (DM) to effectively implement the tasks of management, planning, and forecasting. This is due to the fact that the decision-making procedures in computer systems and the psycho-physical capabilities of the decision-maker have different nature and organization of their formation. The resolution of such a contradiction is possible when using logical-reflexive modeling of decision-making on object management both at the operational level and when predicting the state of this object over a predetermined period of time. Modeling of system-technical complexes is quite acceptable by means of modern concepts of the General theory of systems, reflecting the cause-and-effect relationships between their elements in the process of achieving a given goal by an object. Traditional methods of logical analysis are quite acceptable here. The work shows that such solutions will be much more effective when implementing the principle of reflection. Thus, the basis of the theory of logical-reflexive modeling of humanmachine complexes is being formed in order to make adequate decisions on the management and forecasting of their behavior. The present work is devoted to the presentation of this approach foundations. Keywords: Logic


Reflection
Model
Design
Management
Forecasting

1 Introduction Further progress in the field of system-technical complexes management requires the creation of new human-machine interfaces [9, 31], providing an increase in situational awareness and possessing learning and self-learning capabilities [17, 30]. It is clear that systems for analyzing large amounts of data on the state of such complexes in real time are needed here [12, 29]. To develop procedures for making decisions on the adequate management of such complexes, they should be considered as objects of modeling [5, 11]. Then the possibilities open up to predict the state of these objects at specified time intervals [1, 8]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 187–197, 2022. https://doi.org/10.1007/978-3-030-98832-6_17

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Modeling the structures of system-technical complexes is achievable by means of modern representations of the General theory of systems [6], reflecting the cause-andeffect relationships between their elements in the process of achieving a given goal by the object [7, 25]. Traditional methods of logical analysis [2, 21] are quite acceptable here. The process of achieving a given goal by the object requires from the subject a set of adequate control procedures, which he learns on the basis of mental-linguistic (verbal) activity and changes the means of his knowledge about the object [5, 23]. In other words, it is necessary to implement the principle of reflection [10, 24]. Thus, the basis of the theory of logical-reflexive modeling of human-machine complexes is being formed in order to make adequate decisions on the management and forecasting of their behavior [5].

2 The Concept of Logical-Reflexive Modeling To date, in the modeling technologies of human-machine systems and complexes, focused on the preparation and adoption of decisions on their control, a two-level system has developed [6, 7]: 1. First level: logical-reflexive. At this level, the following are determined: tasks, goals and sub-goals, limitations and assumptions, opportunities for implementation [5, 15]. 2. The second level: information technology, when the designer or decision-maker iteratively implements the goals and objectives of the created (investigated) object by technical means: databases and knowledge bases, computers and computers, the Internet, sources of current information [21, 28]. A generalized diagram of a logical-reflexive process is shown in Fig. 1. As for the problems of the first level, the overwhelming majority of its tasks are of a creative nature [5, 19]. The processes of their formalization and representation apparatus are either little studied today, or they still do not have developed technologies of representation due to the main principle of General Systems Theory: it is impossible to design an object at the level of its own complexity [5, 18]. First of all, let us dwell on the substantiation of the concepts that make up the essence of the proposed approach.

3 Principles and Forms of Reflection Reflection is a complex of actions of the subject, which, on the basis of mentallinguistic (verbal) activity, cognizes and changes the means of its cognition about the object [7, 9]. This principle is the main technique for developing a schema of a conceptual model of the projected process, element, object [5].

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Fig. 1. Generalized diagram of a logical-reflexive process [compiled by the authors].

With regard to objects of the “design” and “control” type, it is proposed to consider reflection on three strata of the first level [4, 5]: • situational reflection (on this stratum, in accordance with the changing conditions, coordination and control of the elements of activity (goal-setting, setting goals) is carried out); • retrospective reflection (analysis of past activities and events (experience, sources of information, intuition); • prospective reflection (thoughts about future activities (planning, finding effective ways to achieve the goal, forecasting results) [5, 27]. In the context of a systematic analysis of an object, such a reflection is possible only through information about it. In this case, information is the content of the reflection, and the signal (or symbol) is the form of its implementation. This separation is very important due to the fact that in this case information is a reflection of the state of the object, it can be obtained by the subject (decision maker) both by technical and organizational means.

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Reflection performs certain functions [15, 16]: • allows a person to plan, regulate, control thinking; • evaluate the truth of thoughts and their logical correctness; • find answers to problems that cannot be solved without applying it. The construction of a conceptual model is iterative [5, 13]. It is associated with the acquisition of new knowledge in the process of analyzing the state of the object at the previous stages and identifying possible states in the future [20, 22]. For organizational and social systems, a conceptual model can be formed as a set of mental activities and formalized as an intuitive model of its elements connections and relationships, processes that are represented by the means of a particular chosen (adopted) language [17, 26]. At this stage, the language for describing the conceptual model can be of a different nature: verbal-linguistic representation, formal logic apparatus, mathematical or statistical (graph-matrix) apparatus, graph, drawing, etc. The subject (designer, decision-maker, researcher) learns and changes the means of his knowledge of the object in order to achieve the maximum adequacy of the derived model to the real projected (investigated) object in accordance with the newly obtained means of knowing its state or the goal of behavior [5, 6, 14]. It is here that the principle of isomorphism of various devices for describing the state and behavior of systems (elements) of various physical and socio-economic nature is most clearly manifested. Any form of thinking, including inference, underlying the process of reflection, is the subject of formal analysis [3, 31]. The main element of this form is the judgment [5, 6]. To determine the inference that completes the first level of the logical-reflexive approach, it is necessary to find formal connections between judgments that are subject to the laws of formal logic and form a decision-making model. If we take into account that there are three main directions in logic, each of which goes to some sign system, defined as the “language” of description or semantic representation of the designed, investigated or analyzed object, then we can assert about the transition to the second stage of technology modeling by logical-reflexive method. The ternary system of logic fully covers the problems of logical-linguistic modeling and can serve as the basis for the next stage of describing the state of the system or its element [5]: 1. The first direction is deductive logic or inference logic. It is based on the construction of true assumptions with given rules and fixed linguistic values [4, 6]. This category of elements of the logical-reflexive model includes specific concepts of elements, dimensions, fixed data, joints, connections, etc. 2. Dialectical logic implies the implementation of the designer's true mental reflections within the changing linguistic meanings. Such elements of the logicalreflexive design method include adaptive databases and knowledge bases, the researcher's choice of new forms and terms of the product (process) in related areas of knowledge, patent search [5]. 3. Inductive logic or logic of experiment is based on the search for realities that exist within the framework of non-fixed linguistic means of description, but also do not belong to reflexive linguistic means [9]. It is clear that in this case, a designer, a

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specialist in modeling objects and control processes, uses new concepts, interelement relations, introduces specific terms, characteristics, data obtained as a result of an experiment, a search or a theoretical way in the period between research. The truth, clarity, and adequacy of any representation are significantly influenced by the nature of one’s own linguistic means. However, the identification, reflection, and change of these means depend on the idea itself. This leads someone that language appears as something given and uncontrollable. The aforementioned unites phenomena when many representations turn out to be essentially inadequate to the described reality by the nature of their expression. Reflexive logic is based on the conscious exploitation of language, its difference from the representation of an object in language, description, and study of its internally semantic form. The inadequacy of language usually manifests itself in the presence of specific forms and in contradiction. For this reason, one of the most important stages is the identification and description of inadequate representations, as well as internally contradictory semantic structures [5, 6]. The method of finding contradictions can be different depending on the class and type of a particular system. The issues of the formation of such methods are solved by antinomic logic. Another important section of the reflexive process is withdrawal [7, 9]. The removal procedure is based on the fact that the presentation language has an internal semantic form and the removal of the properties of external presentation means in it determines its own adequacy, for example, the possibility of constructing an adequate mathematical model. If the removal procedure succeeds, then the language is rebuilt considering the new semantic form (isomorphism). To formalize the process of reflection, these stages are performed in a certain sequence, called a reflexive syllogism, which we will further use as a working form of canonization of the reflexive technique [7]. The last form of logical-reflexive modeling is language, which is understood as a sign system and has three fixed functions: 1. Essential, associated with the storage of the cognition results. 2. Cognitive, defining language as a means of obtaining new knowledge about an object or process. 3. Communication, which is a means of verbal exchange of knowledge or obtaining information through technical media (electronic, paper, visual). Thus, the purpose of the logical-reflexive method of modeling the objects’ behavior and processes can be formulated as a formal apparatus for PM based on a reflexive model of experience indicators, qualifications, knowledge of the developer (DM), when he iteratively implements the goals and objectives of the created (investigated) object are known technical means of achieving the set goal of designing system-technical complexes or their control systems [5, 6]. The task of the method is to comprehensively consider the components of the physical and technical capabilities logical process of the designed system, causal relationships between the events (elements) of the designed system, and reflexive assessment of constraints. Conventionally, this process can be illustrated by the already given diagram (see Fig. 1).

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The concept of modeling the state and behavior of a control object, which is based on fairly general philosophical categories, such as “element”, “properties”, “relation”, allows us to hope for its generality, at least for a certain class of systems. This concept provides for the consistent solution of the issues of constructing methods and languages for describing the state, behavior, storage, updating, and management on a semiotic model of the system, the development of libraries of software modules that form the software systems of the entire model. This is especially important in operational control systems when it turns out to be practically impossible to formally describe the system directly, but it is possible to build a special language and, through it, compose a semiotic model of the system. Thus, semiotic modeling is the first stage in the reflection of linguistic modeling tools. Assuming that any language consists of expressions and texts marked in the alphabet by combining letters, numbers, logical meanings, and limiters, it can be argued that in natural languages the set of marked expressions and texts has blurred boundaries and the more formalized the language, the more these boundaries are tougher [5]. Thus, when there is no possibility to semantically change the syntactic structure of a certain language, the latter acquires a rigid structure and in a number of cases is not able to adequately reflect the dynamics of the object’s behavior. Another important section of the reflexive process is withdrawal [6]. The removal procedure is based on the fact that the representation language has an internal semantic form and the removal of the properties of external representation means in it determines its own adequacy, for example, the possibility of constructing an adequate mathematical model. If the removal procedure succeeds, then the language is rebuilt considering the new semantic form. To formalize the process of reflection, these stages are performed in a certain sequence, called “a reflexive syllogism”, which we will further use as a working form of canonization of the reflexive technique.

4 Reflexive Syllogism The syllogism [7] is the basis of logical-reflexive modeling. In control problems, a reflexive syllogism is formed from seven positions: 1. 2. 3. 4. 5. 6. 7.

Implementation. Metalanguage. Subject in language. Sublanguage. Adequacy (antinomics). Withdrawal (outcome). Oversymbolization.

To distinguish language from specific representations (texts) recorded in a specific language, we will form a triad “metalanguage-language-sublanguage”. Traditionally, this procedure is called “implementation”.

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Metalanguage (mL) is a subjective position formed in a language that constructs a given thought-linguistic movement — a language of reflection, a language for describing a sublanguage. The choice of a metalanguage is a prerequisite for reflection. The metalanguage should not be formal. Language is the pre-reflective form of the thinking-linguistic activity basis, as well as the post-reflective realization of it. Speech activity is realized in a language, a verbal statement expressed in a sublanguage or in a metalanguage. Reflection is carried out both in the language and in the sublanguage (metalanguage). After reflection (established by the rules of terminology and concepts), only one language remains. The monad “language” is, in principle, equal to the entire derived triad. We need a triadic representation only to distinguish aspects of the reflective thinking-linguistic activity. Thus, the concept of an object in the language is formed. Sublanguage (L) is a language given to us in its reflexive definiteness, the language of concrete mental and linguistic activity, of revelations in the metalanguage, and which is removed in language. It should be noted that the set of descriptions in a certain sublanguage L of objects, tasks, and resolution procedures forms a model. Thus, the model is a set of objects, tasks, and resolution procedures L(ov), L(R), L(A) described in the sublanguage L. To distinguish the semantic means of the sublanguage from the external means of resolution, we will formulate a number of concepts. External permitting structures A — description in the sublanguage of the LA procedure, the LA(A) resolution procedure. External variants are structures, reflected in relation to LA, structures described in LA that lead tasks to resolution. An example of an external permissive structure is an algorithm. Usually, external permissive structures are syntactic in nature [20, 29]. Internal resolving structures (in A) — a description in the mL metalanguage of the structures Lov, LR, LA, which allow external resolving structures to bring the problem to the resolution mL (in A). Internal permissive structures are of a semantic and intensive nature, however, they lend themselves to the extensive description in management, as they are implemented at the level of internal formation of the sublanguages Lov, LR, LA. At the level of language in A, representations of the behavior of an object work outside of any kind of reflection. Moreover, unjustified reflection frees one from all meaning. An example of in A is the rectangularity of a Cartesian coordinate system. It greatly facilitates measurements, and its mechanism (the cosine theorem) can be hidden from the user before reflection, but it must be revealed in the process of reflection. An example is the PERT system [5]. The semantic form of a sublanguage is the unity of all internal permissive structures of the sublanguage [6]. The semantic form of a language can be described by its internal permissive structures, semantic valencies of sublanguage elements. The semantic valences of a concept are presented as potential relationships of a pleasant (given) concept with others. These relations are set by the meaning of the concept and are fixed in its structure. For example, analytical and graphical assignments of the same function have different semantic valences [5]. Here, the replacement of one concept by another gives rise to the concept of adequacy (antinomics).

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The description of the stages of reflexive syllogism and a number of reflexive procedures requires the formulation of the concepts of removal, expansion, minimization of structures. The removal (outcome) of external permitting structures into internal ones is a transformation of the semantic form of the sublanguage, when the semantic structure of the resolution procedures turns into the semantic form of the internal permitting structures of the sublanguage LA ðAÞ þ mLðin AÞ þ mLðin A0 Þ: At the same time, new internal and external permitting structures in A’ and A’ are formed, which become the basis for the formation of a new language L, more adequate to the described process. One of the withdrawal methods consists of expanding, minimizing, and collapsing. When expanding, a syntactic structure is searched for, which actually realizes all the potential capabilities of this structure and does not contain arbitrary random elements. In general, expansion is finding a more general object. The extension does not necessarily require an exact specification of the extended structure, since in the subsequent minimization the inaccuracies can be removed. Sometimes an intuitive or descriptive extension is enough, sometimes it can be carried out formally. It depends on the metalanguage and the type of technique [5, 7]. Minimization of the structure is the finding of such a substructure, which, being minimal, would adequately remove the external resolving structures and would be, in terms of resolution, not lower than the original structure. In other words, minimization is finding such a structure that would be adequate to the initial one. Expansion and minimization can be done by collapsing structures. The folding of structures is defined as the formation of a certain structure from a number of other structures, which would contain all the structural features of the initial data (re-resolution) of the real procedure for making decisions on management. Various subclasses of logical calculus are used as a basis for semiotic models. On their basis, one or another semantic code is built that allows one to describe systems of varying complexity. In situational control, the language of binary relations is used as such a code, which allows one to describe the structure and laws of the system’s functioning. To describe more complex systems, semantic codes of increased complexity are built. It is especially important to develop semantic codes for modeling various kinds of dialogue systems, linguistic translators, artificial intelligence, etc.

5 Conclusion The method of reflexive logic for formalizing decision-making processes for managing human-machine complexes and predicting their state opens up the possibility of rebuilding the model of knowledge about an object in an operational mode. In the context of global digitalization [4, 25], the method makes it possible to quickly and inexpensively form adaptive models for diagnosing the state of production and socioeconomic systems, to analyze the dynamics of their work in real-time.

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The proposed method has shown its advantages in organizing control processes in transport systems, CAD “Rotation bodies” and on other objects.

References 1. Abramovich, B.N., Bogdanov, I.A.: Improving the efficiency of autonomous electrical complexes of oil and gas enterprises. J. Min. Inst. 249, 408–416 (2021). https://doi.org/10. 31897/PMI.2021.3.10 2. Adeyemi, S.L., Aremu, M.A.: The impact of forecasting on strategic planning and decision making: an exploratory study of Nigerian stock exchange. An Int. Multi-Discip. J. 3(1), 179– 205 (2009). https://doi.org/10.4314/afrrev.v3i1.43564 3. Ankoudinov, I.G., Gerasimov, I.V.: Research of a fuzzy maximin preference framework. In: Proceedings of the 19th International Conference on Soft Computing and Measurements, SCM 2016, pp. 348–350. IEEE, St. Petersburg (2016) 4. Arkhipova, Z.: Koncepciya informacionnoj sistemy monitoringa urovnya razvitiya cifrovoj ekonomiki. [The Concept of Information System for Monitoring Digital Economy Development Level.] Baikal Res. J. 9(3) (2018). https://doi.org/10.17150/2411-6262.2018. 9(3).8. (In Russian) 5. Ariefjew, I.: Logiko-refleksivnoe modelirovanie processov prinyatiya i podderzhki resheniĭ po upravleniyu. [Logical and reflective modeling of the processes of making and supporting decisions to management.] In: Sb. nauch. trudov XIII Mezhdunar. nauch.-prakt. konf. Sistemnyi analiz v proektirovanii i upravlenii. [System Analysis in Engineering and Control: collection of scientific papers of the 23rd International Scientific and Practical Conference.], vol. 1, pp. 194–200. Publishing house Polytech-Press, St. Petersburg (2019). (In Russian) 6. Arefiev, I.B.: Metodologiya proektirovaniya integrirovannyh ASU na baze logikoinformacionnogo podhoda. [Methodology of design of integrated automated control systems based on the logic-information approach]. Abstract. Thesis submitted for a doctor’s of science degree in technical sciences (05.13.06), 42 p. St. Petersburg (1995). (In Russian) 7. Arefiev, I.B.: Elements of the method of logical-reflexive modeling, p. 68. LAMBERT Akademik Publishing, Riga (2019) 8. Cherepovitsyn, A., Tcvetkov, P., Evseeva, O.: Critical analysis of methodological approaches to assessing sustainability of arctic oil and gas projects. J. Min, Inst. 249, 463–479 (2021). https://doi.org/10.31897/PMI.2021.3.15 9. Denisov, A.A.: Sovremennyye problemy sistemnogo analiza. [Modern problems of system analysis.] 3rd edn. 304 p. Izd-vo Politekhn. un-ta, St. Petersburg (2008). (In Russian) 10. Dimova, Y., Kamarska, K.: Rediscovering John Dewey’s model of learning through reflective inquiry. Problems of Education in the 21stcentury 63, 29–39 (2015) 11. Fadeev, A., Komendantova, N., Cherepovitsyn, A., Tsvetkova, A., Paramonov, I.: Methods and priorities for human resource planning in oil and gas projects in Russia and OPEC. OPEC Energy Rev. 45(3), 365–389 (2021). https://doi.org/10.1111/opec.12213 12. Gerasimov, I.V., Ankoudinov, I.G.: Evaluation of the fuzzy cognitive model of professional competences. In: Proceedings of 2021 24th International Conference on Soft Computing and Measurements, SCM 2021, pp. 131–132. IEEE, St. Petersburg (2021) 13. Gorelova, G.V., Zakharova, E.N., Radchenko, S.A.: Issledovanie slabostrukturirovannykh problem sotsial’no-ekonomicheskikh sistem: kognitivnyi podkhod. [The study of poorly structured problems of socio-economic systems: a cognitive approach.] 332 p. Izd-vo RGU, Rostov-on-Don (2006). (In Russian)

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14. Glinka, E.V.: Obshchesistemnye kategorii simmetrii i asimmetrii v izuchenii yazyka. [Sistem-wide categories of symmetry and asymmetry in study of language.] J. Min. Inst. 193, 222–225 (2011). https://pmi.spmi.ru/index.php/pmi/article/view/6271. Accessed 13 Sep 2021 (In Russian) 15. Ignatyev, M.B.: Linguo-combinatorial simulation of complex systems. J. Math. Syst. Sci. 2 (1), 58–66 (2012) 16. Ivanova, I.V.: Klassifikaciya ponyatij konechnoj algebry dlya opisaniya elementov nejronnoj seti. [Classification of the finite algebra concepts for the description of neural network elements.] J. Min. Inst. 208, 222–231 (2014). https://pmi.spmi.ru/index.php/pmi/article/ view/5359. Accessed 13 Sep 2021. (In Russian) 17. Katuntsov, E.V., Kultan, J., Makhovikov, A.B.: Application of electronic learning tools for training of specialists in the field of information technologies for enterprises of mineral resources sector. J. Min. Inst. 226, 503–508 (2017). https://doi.org/10.25515/pmi.2017.4.503 18. Klimenkov, G.V., Kukor, B.L., Pytkin, A.N.: Ekspertnye sistemy i sistemy situacionnogo upravleniya na baze logiko-lingvisticheskih modelej. [Expert systems and situational management systems based on logical-linguistic models.] Bulletin the Perm Scientific center of the Ural branch of the Russian Academy of Sciences 2, 26–37 (2010). Perm Federal Research Center of the Ural Branch of the RAS, Perm 19. Kopeikin, M.V., Spiridonov, V.V., Shumova, E.O.: Vybor sposoba hraneniya otnoshenij v informacionnyh sistemah. [The choice of data relations storing in informational systems.] J. Min. Inst. 208, 249–255 (2014). (In Russian). https://pmi.spmi.ru/index.php/pmi/article/ view/5364. Accessed 13 Sep 2021 20. Kryltcov, S., Makhovikov, A., Korobitcyna, M.: Novel approach to collect and process power quality data in medium-voltage distribution grids. Symmetry 13(3), 460 (2021). https://doi.org/10.3390/sym13030460 21. Kukor, B.L.: Semiotika sistemnogo analiza i semanticheskaia sistema logikolingvisticheskoi modeli predmetnoi oblasti. [Semiotics of system analysis and semantic system of logical-linguistic domain model.] In: Sb. nauch. trudov XIII Mezhdunar. nauch.prakt. konf. Sistemnyi analiz v proektirovanii i upravlenii. [System Analysis in Engineering and Control: collection of scientific papers of the 13th International Scientific and Practical Conference.], vol. 1, pp. 164–169. Izdvo Politekhn. un-ta, St. Petersburg (2009). (In Russian) 22. Liveng, A., Myglegård, A.H., Lehn-Christiansen, S.: Health promotion in context: A reflective-analytical model. Scandinavian J. Public Health 46(20_suppl), 66–71 (2018). https://doi.org/10.1177/1403494817743899 23. Razmanova, S., Andrukhova, O.: Oilfield service companies as part of economy digitalization: assessment of the prospects for innovative development. J. Min. Inst. 244, 482–492 (2020). https://doi.org/10.31897/pmi.2020.4.11 24. Rogachev, A.F., Melikhova, E.V., Rudenko, A.Yu.: Ocenka i prognozirovanie sel’skohozyajstvennogo proizvodstva i prodovol’stvennoj bezopasnosti na osnove nechetkih kognitivnyh matematicheskih modelej. [Assessment and forecasting of agricultural production and food security based on fuzzy cognitive mathematical models.] Izvestiya Nizhnevolzhskogo agrouniversitetskogo kompleksa: nauka i vysshee professional’noe obrazovanie. [Izvestia Nizhnevolzhskiy agro-university complex: science and higher professional education.] 4(56), 246–255 (2019). (In Russian) 25. Ryder, A.J., Kimball, E.W.: Assessment as reflexive practice: a grounded model for making evidence-based decisions in student affairs. Res. Pract. Assess. 10, 30–45 (2015) 26. Thibeault, I.V., Prichina, O.S., Gorelova, G.V.: Cognitive Russian modeling in the system of corporate governmance. Mediterr. J. Soc. Sci. 6(2), 442–452 (2015)

Implementation of Control and Forecasting Problems

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27. Trofimets, E.N., Mazakov, E.B., Trofimets, V.Ya.: On the identification of bearings of active interference sources. In: III International Conference MIST: Aerospace 2020: Advanced Technologies in Aerospace, Mechanical and Automation Engineering (Aerospace 2020) 20– 21 November 2020, Krasnoyarsk, Russia. IOP Conference Series: Materials Science and Engineering, vol. 1047, Issue 1, p. 012146 (2021). https://doi.org/10.1088/1757-899X/1047/ 1/012146 28. Kulyk, M., et al.: Using of fuzzy cognitive modeling in information security systems constructing. In: Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2015 8, Technology and Applications, pp. 408–411 (2015) 29. Volkova, V.N., Fleishman, B.S., Tarasenko, F.P., Loginova, A.V.: Further development of potential feasibility theory for complicated systems according to the unified general-system principle. In: Bylieva, D., Nordmann, A., Shipunova, O., Volkova, V. (eds.) PCSF/CSIS 2020. LNNS, vol. 184, pp. 446–453. Springer, Cham (2021). https://doi.org/10.1007/978-3030-65857-1_37 30. Volkova, V., Gorelova, G., Pankratova, N.: The development of the cyberphysical system concept on base of the interdisciplinary theories. In: 2020 IEEE 2nd International Conference on System Analysis and Intelligent Computing, SAIC 2020, 9239213 (2020) 31. Sousa, W.H., Yu, A.S.O.: Decision making planning: the meta-decision approach. Eng. Manag. Res. 3(1), 41–55 (2014). https://doi.org/10.5539/emr.v3n1p41/

Adaptive Theory of Socio-economic Systems Management Based on Logical-Linguistic Modeling Aleksandr E. Karlik

, Boris L. Kukor, and Elena A. Iakovleva(&)

St. Petersburg State University of Economics, Sadovaya Street 21, 191023 St. Petersburg, Russia

Abstract. In the article authors propose a conceptual framework of complex economic system model for strategic management of the enterprise, including assessment of the concepts of problem situations and their classification, the concept of dynamic security; management functions, given their semantic integration; definition of frame representation of problem situation and alternative ways of its solution; development and implementation of a logicallinguistic model of problem situations in the form of discrete-situational network in the management system with the application of the expert system “Leader”); development of approaches to the building on the basis of adaptive methods of alternative network models (graphs) to resolve the problem situation associated with inflation; identification of planning methods, protection against risks and threats of elementary objects, implementing the reverse logical. It is essential to highlight that the main challenge of recognizing and identifying risks and threats to the economic security of the socio-economic system is the interpretation of the results of situational analysis of the data aggregated in order to identify and recognize risks and threats. Given cognitive models reveal the problem of inflation within vertically-integrated companies to the fullest extent. According to these models, digital twins of the subject and object are further developed. Keywords: Problem situations
Cognitive models
Frame
Intelligent dynamic systems
Logical and linguistic modeling
Cognitive linguistics Socio-economic system




1 Introduction Modern breakthroughs in the theory of systems allowed us to identify features that are common to systems of different nature [1, 18, 28, 32]. One of these features is the problem situation. It is a characteristic, first of all, for complex systems, and assumes that there are elements that aspire to reduce the efficiency of functioning and development of a system. Thus, it is essential to detect these elements on time and neutralize or prevent their impact [29]. Weak formalization and heterogeneity of the management system in conditions of high uncertainty leads to the need to develop methodological support of strategic management for logical and linguistic [3, 6, 24, 29] modeling of ways to solve problem situations of strategic nature in a continuous mode, and the specifics of technological innovations (innovations in science and technology, innovations in management and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 198–211, 2022. https://doi.org/10.1007/978-3-030-98832-6_18

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IT-sphere) [31], political and economic risks, human factor require close attention to anticipate the threats of problem situations [2, 9, 10, 34].

2 Literature Review The contribution of scientists economists and mathematicians in the development of scientific foundations of management of the planned economy, integrated scientific and technological forecasting, optimal economic development, economic-mathematical methods of management, program-targeted method of planning, balance, thematic, network, scenario methods is highly appreciated worldwide, and the most famous scientists are recognized A.I. Anchishkin, V.K. Dmitriev, L.V. Kantorovich, N.D. Kondratyev, V.S. Nemchinov, V.V. Yaremenko [25]. Studies concerning the development of issues in the theory and methodology of planning were conducted by the famous scientist I.M. Syroezhin in the late 1980s, so special attention in his work is given to such problems of economic systems as “systematicity, balance and proportionality”, “responsibility” dynamism [26]. Studies on strategy and strategic planning were conducted by Henry Mintzberg [14]. Thus, considering in his work the ideas of representatives of the school of strategic planning I. Ansoff, Charles W. Hofer, D. Schendel, P. Lorange, P. Wack, and others, H. Mintzberg gives them a critical assessment, noting that the approaches of strategic planning school have excessive formalization of the process of strategy development, besides, in his opinion, the school representatives overidentify the concepts of analysis and synthesis. Representatives of the school regard planning as a tool for developing a strategic plan that provides a strictly defined sequence of steps [14]. In the 1990s, the adaptive approach in economic management and planning by B.L. Kukor, G.V. Klimenkov [9, 11, 12], which is based on the significant cybernetic concept of D.A. Pospelov’s theory of situational management of complex social economic systems, gets its name and significance. The basis of the methodology of the theory of adaptive management by B.L. Kukor [11] lies on logical-linguistic modeling, which, unlike mathematical modeling, provides taking into consideration the nature of data (numeric and non-numeric) and move from quantitative to qualitative indicators, while the relationship between these indicators are determined semantically (expressed by natural language) [6]. The specified knowledge of the subject about an object of research is represented in the form of frames that “allows making generalization of natural and real parameters of zero level (subdivision, factory workshop) and financial parameters of the higher levels (holding, economic sector)” [31]. Thus, the supposed methodology of the adaptive approach will be the foundation for the strategic planning process digitalization by achieving digital representation of the subject and object of management [33].

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3 Materials and Methods The socio-economic system economy, as an open system, can only be in a state of dynamic stability, inherent in systems in a state of dynamic equilibrium [15]. Stability of such systems is achieved by balancing each emerging change by the opposite change, i. e. by the processes that disrupt the functioning of a system and increase the level of the system efficiency. These processes go in parallel and balance each other, otherwise, the system will collapse [5]. As applied to socio-economic systems, “stability is the ability of a system to function in states close to equilibrium under constant external and internal disturbing influences” [21]. At the same time, adaptive stability is inherent to systems, which can change the structure (organization) after external influences, and preventive stability is inherent to the systems which are able to perform their function if they change before the impact of internal and external destructive factors of the environment [27, 30, 31]. The theory of adaptive management uses a conceptual framework of a socioeconomic system with adjustment of all its elements and links as the subject area itself, and the current situations, events, development, and management problems [7]. One of the methods for studying systems and the properties of their elements are semiotic representations, which serve the purpose of gradual formalization of system solution representation [2]. Semiotic representations can also be expressed through mathematical linguistics (thesaurus, grammar, semantics (meaning)), as well as linguosemiotics and linguistic representations [22], cognitive modeling [8], which found application in the theory of situational management of D.A. Pospelov [19, 20]. Proposed by S. Optner [18], methodology of solving strategic problems with the help of systems analysis is a process of cognitive activity of decision-makers to form a development strategy that enables them to correctly identify chains of interconnected management problems and correctly select methods for their solution [2, 13, 16, 17]. This approach was in the studies of the Central Economic and Mathematical Institute of the Russian Academy of Sciences D.A. Pospelov [19], L.S. Zagadskaya (Bolotova) [34], Y.I. Klykov [10]. Further, the properties of systems were disclosed by scientists A.I. Uyomov [28], Y.I. Chernyak [4], V. N. Volkova et al. [32]. D. Pospelov’s theory of situational modeling and control included a new cybernetic concept of control of large systems, the essence of which should be declared by the concept of “situation” as the object of study; they introduced the features of the classification of situations according to the typical solutions to the problems of managing large systems and proposed the formation of a semiotic model of the object to make decisions based on a specially designed language situational management; developed semantic networks to represent some kind of knowledge about the object [20]. Situation modeling developed in the studies of L.S. Zagadskaya (Bolotova) [34] led to the emergence of intellectual dynamic expert systems of decision support of the second generation, which are semiotic models of a control object, natural language interface, integrated logic of mental processes. Based on the systemic methodology, B.L. Kukor for the first time applied and significantly developed the tools of management decision support in economics in the

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form of: the formed conceptual framework of a social economic system, including the concepts of problem situations and their classification; basic (planning, organization, coordination and control) and descriptive (purpose setting, accounting, analysis, forecast, and control actions) functions of management; frame representation of knowledge bases of the following types [12]; definition of objective responsibility and forward and reverse logical conclusions in the system of strategic management; derivation of dilemmas of equilibrium, cooperation, trust, threat. It should be noted the allocation of three classes of problem situations (PS): “1 class is a deviation of the actual mode of functioning of the system from the planned one. A bottleneck, disproportion of power of resources of adjacent animals in the system, violation of synchronization of interaction. 2nd class is a divergence of goals and interests of socio-economic system (SES) elements. 3rd class is a slowdown in the speed of recognition and resolution of a problem situation by a control subject. Mismatch of available and required knowledge about the problem and the conditions of its resolution; violation of subordination relations, distribution of responsibility and authority between structural elements of the system and staff” [9, 11]. So, logical-linguistic models can be subdivided into productive, frame, semantic networks, linguistic-combinatorial models, ontologies, term-systems, which together represent models of knowledge of the system and its structure in the investigated subject domain [30]. Generalizing classification of logical-linguistic models is based on the development of formal system representation and considers the change of both properties of the object and the subject, and properties of relations and elements (components) of the system depending on goals and tasks of management and actual (external and internal) environment [1].

4 Results In the theory of adaptive management approaches to logical and linguistic modeling of frame data representation and mechanism of adaptive management, which is realized by alternative network diagrams and frame representation based on inverse logical inference were developed. As a result of our research, we will present a discretesituational network of problem situations “Inflation”, which is very relevant for strategic management of the enterprise. Firstly, it is essential to formalize legends for the future model (see Fig. 1). Designations in Fig. 1: G1 — decision maker (DM) — management subject; G2 — elementary object (EO) — object; G3 — goal of the decision center (GDC) — subject’s goal tree; G4 — goal of elementary object (GEO) — object’s goal tree; G5 — action of the decision center (ADC) — alternative network graph of the subject; G6 — actions of the elementary object (AEO) — alternative network graph of the subject; G7 — discrete situation network (DSS); G8 — phases of the resource complex “Finance” (FRF) — financial resources; G9 — phases of the resource complex “Innovations” (FRI) — innovative resources; G10 — phases of the “Production” resource complex (PFR) — production resources; G11 — phases of the “Information Funds” resource

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complex (IFRC) — information resources; G12 — phases of the resource complex “Labor resources” (LRCF) — labor resources; G13 — phases of the resource complex “Processing” (RPCF) — processing resources; G14 — suppliers; G15 — state; G16 — financial institutions; G17 — customers.

Fig. 1. Semantic model of a complex economic system.

Knowledge management in the system is carried out in the frame representation. Frames in the theory of adaptive management are understood as necessary and sufficient knowledge (further described as standard knowledge units) for recognizing and solving problem situations, and slots are descriptive management functions — goal setting, accounting, analysis, prediction, and control action. This model is necessary to establish the pattern of the elementary objects, the analysis of relationships and interrelationships, which is convenient to present in the form of Table 1. To determine the problem situation in financial strategic planning it is necessary to build a model of the object, considering the external influence on the financial condition and activity of the company. Based on the model will identify the impact of factors on the financial condition and planning of the enterprise. Therefore it is essential to define the main characteristics.Further for the analysis of a set of problem situations in financial strategic planning, it is necessary to establish the following interrelations (see Fig. 2).

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Table 1. Key relations in the model of a complex economic system. № 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Model node connections G1-G2 G1-G3 G1-G4 G1-G5 G1-G6 G1-G7 G1-G8 G1-G9 G1-G10 G1-G11 G1-G12 G1-G13 G1-G14 G1-G15 G1-G16 G1-G17 G2-G8 G2-G9 G2-G10 G2-G11 G2-G12 G2-G13 G2-G14 G2-G15 G2-G16 G2-G17 G3-G5 G3-G6 G3-G7

Relationship of concepts DM-EO DM-GDC DM-GEO DM-ADC DM-AEO DM-DSS DM-FRF DM-FRI DM-PFR DM-IFRC DM-LRCF DM-RPCF DM-S DM-G DM-FI DM-C EO-FRF EO-FRI EO-PFR EO-IFRC EO-LRCF EO-RPCF EO-S EO-G EO-FI EO-C GDC-ADC GDC-AEO GDC-DSS

№ 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57

Model node connections G4-G2 G4-G3 G4-G5 G4-G6 G4-G7 G4-G8 G4-G9 G4-G10 G4-G11 G4-G12 G4-G13 G4-G14 G4-G15 G4-G16 G4-G17 G5-G6 G6-G2 G6-G7 G6-G8 G6-G9 G6-G10 G6-G11 G6-G12 G6-G13 G6-G14 G6-G15 G6-G16 G6-G17

Relationship of concepts GEO-EO GEO-GDC GEO-ADC GEO-AEO GEO-DSS GEO-FRF GEO-FRI GEO-PFR GEO-IFRC GEO-LRCF GEO-RPCF GEO-S GEO-G GEO-FI GEO-C ADC-AEO AEO-EO AEO-DSS AEO-FRF AEO-FRI AEO-PFR AEO-IFRC AEO-LRCF AEO-RPCF AEO-S AEO-G AEO-FI AEO-C

Designation of vertices: EO11 — resource market; EO21 — production; EO22 — finance, investment; EO23 — human resources; EO31 — suppliers of equipment and components; EO32 — educational institutions; EO33 — logistics operators; EO41 — budget; EO42 — ministries, state authorities; EO50 — central bank; EO51 — banks, financial institutions, insurance companies; EO60 — customers, clients; EO70 — foreign market. According to Fig. 2, it is necessary to distinguish analyzed flows (informational, material, non-material) and to analyze the needs and possibilities of the input and output of each element according to phases of their functioning. After constructing a

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Fig. 2. Fragment of the management object.

semantic model of the complex economic system. The algorithm of semantic model integration reflects its sequential introduction into use with further control of execution. The analysis of the model object reflects the main factors of influence on the root causes and its most important elements. It is possible to distinguish the main factor of influence is inflationary policy, and more concretely inflation itself. Such phenomenon as inflation is one of the biggest problems of economic systems, it has one of the strongest impacts on it. To manage the level of inflation requires, first of all, correct and reliable information, which consists of the consolidated data from all areas of companies in the country, region, or world as a whole. It is also important to note the social factor of inflation, which strongly affects the level of income of socially unprotected strata of the population (pensioners, the disabled, students, etc.), which directly depends on the amount of levied taxes, which in turn depend on the development of companies and tax policy. Next, let us demonstrate a discretely situational network of problem situations of managing inflation development (see Fig. 3). Designations in the figure by the vertices of the graph: 1 — inflation, 2 — pricing policy; 3 — goods market deficit; 4 — appearance of shadow market; 5 — inflationary expectations; 6 — enlarge in the speed of money turnover; 7 — enlarging the level of nominal money supply; 8 — reduction in production volume; 9 — reduction in labor productivity; 10 — reduction in production volumes; 11 — structural disproportions; 12 — capital inflow from production to circulation; 13 — growth of staff turnover; 14 — lowering of employees’ motivation; 15 — increased requirements to employees; 16 — higher load on employees; 17 — reduction of real income of population; 18 —

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Fig. 3. Discrete-situational network of problem situations associated with the growth of inflation.

increase in raw material costs; 19 — increase in equipment costs; 20 — increase in production costs; 21— increase in production costs; 22 — decrease in product demand; 23 — decrease in labor productivity; 24 — increase in non-stochastic uncertainty; 25 — increase in risks of trouble situations; 26 — reduction in investments in development projects; 27 — corrections in strategic planning; 28 — reduction in solvency; 29 — increasing influence of oligopolies and monopolies; 30 — increase in tariffs of natural monopolies; 31 — reduction in the ability to pay; 32 — increase in competition with the state; 32 — lower competitiveness of domestic companies; 33 — decline in living standards; 34 — growing unemployment; 35 — growing supply on the labor market; 36 — lower wages; 37 — devaluation of education; 38 — lower company revenues; 39 — reduction in budget spending units; 40 — decrease in spending on information products; 41 — decrease in reliability of cyber-protection of the company; 42 — increase in cyber-attacks; 43 — public procurements; 44 — tenders; 45 — lobbying for their interests; 46 — growth of corruption in the country; 47 — increase in tax burden; 48 — concealment of actual data on activities; 49 — offshorization of business; 50 — lack of strategic planning; 51 — absence of crosscutting management technologies; 52 — lack of digitalization; 53 — inertia of digital transformation; 54 — delays in organizational changes; 55 — gaps in the links of the product and production vertical, organizational and technological chains; 56 — absence of change agents; 57 — dollarization of the economy; 58 — depreciation of the national currency; 59 — decline in the Russian productions’ share in the global economy; 60 — economic war.

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This discrete-situational network defines the main influence factors and control factors of inflation growth, on the basis of these factors’ frames will be built, in other words, the elaborated scenarios of target values on the main flows, with a reflection of necessary resources and dependences, which forms on the basis of inverse logical output mechanism of adaptive management, realized as an alternative network schedule. Alternative Network Graph is also a way of cognitive modeling, reflecting the stages of the formation of measures and decision-making to eliminate certain problem situations. The use of an alternative network graph of the subject allows you to consider the problem itself and its solutions from another side, so the author builds an enlarged alternative network graph of the subject on the basis of a discrete situation network (see Fig. 4).

Fig. 4. Alternative network model of inflation suppression.

Designations in Fig. 4: 1 — inflation; 2 — introduce regulated tariffs of natural monopolies; 3 — slow down inflation; 4 — direct excessive foreign currency funds to purchase equipment for modernization; 5 — reduce the cost of goods and services in the domestic market; 6 — increase production volumes (GDP growth); 7 — increase investment in real production; 8 — increase investment in innovation; 9 —reduce employment; 10 — increase wages; 11 — increase integration processes; 12 — increase labor productivity; 13 — increase investments in real production; 14 — introduce a tax on excess profits; 15 — create tax incentives for investment projects;

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16 — develop mortgage lending; 17 — provide partial state guarantees for investors; 18 — insurance of investment risk; 19 — concentration of investment funds on priority projects; 20 -control over budget execution; 21 — dedicated use of budgetary funds; 22 — rejecting emission coverage of budget deficits; 23 — regulation of money emission; 24 — regulation of Velocity of Currency Circulation; 25 — developing the stock market; 26 — attraction of population’s savings to the stock market; 27 — reduction of dollarization of the economy; 28 — transformation of excessive money supply in the hands of the population; 29 — development of the mortgage securities market; 30 — decrease of interest rates on mortgages; 31 — partial redistribution of population’s expenditures in favor of real estate; 32 — housing construction growth; 33 — legislative amendments; 34 — preparation of digital infrastructure; 35 — development of strategic financial planning methods; 36 — promotion of vertical integration; 37 — deoffshorization; 38 — providing a legislative framework within cybersecurity; 39 — digitalization of economy; 40 — popularization of end-to-end technology tools in the domestic market; 41 — introduction of transfer prices within the natural product vertical; 42 — limitation of trade margins; 43 — expansion of cryptoassets.

5 Discussion The main topic is an interpretation of conclusions from the situational approach to the analysis of the identification of risks and threats to economic security. Nevertheless, the most important issues of substantiation of indicators and indicators of threats to economic security have not been sufficiently developed to date. Such a criterion should not just state the existence of a threat to the economic security of the SES, but also assess its level and possible consequences (damage). If the purpose of the criterion will be reduced only to a statement of the absence of threat to economic security, in this case, the delay in taking measures to counteract the suddenly identified dangers (the absence of economic security) is inevitable. In the economic literature, attempts have already been made to quantify the level of economic security of the SES, that factor led to the appearance of several approaches to assessing the level of economic security. There is a need to define the conceptual apparatus of the science of strategic management SES and the degree of its development. The most important stage in the process of strategic management is recognition and prevention of threats in the SES problematic situations. The most important moments in the process of identifying the SES are the technology of adopting the subject (the governing structure of the SES), the technology of managing the control object (the zero tier of the system), the technology of production processes, which are set by algorithms. The algorithm is a system of rules of production of control actions, allowing to achieve the set goal. Given cognitive models reveal the problem of inflation within vertically-integrated companies to the fullest extent. According to these models, digital twins of the subject and object are further developed.

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6 Conclusion For this purpose, it is crucial to implement instruments and methods of linguoconceptual analysis of the concept sphere of the strategic management system, taking into account factors of both cognitive and communicative nature. Based on the described in the study alternative-network diagram, a conclusion can be drawn that in economic management the fact of uncertainty influence is recognized when some essential circumstances of decision-making are not completely or randomly known. In this case, it is enough to reveal a direction of this influence, approximate force “E” and duration “s”. Applying the theory of risk management in strategic management of SES, it will be feasible to establish what are essential factors in the future, not only to survive, but moreover, to achieve success with a relative investment of effort in action. It is crucial to highlight that the complexity of risk classification in the SES is the presence of interrelations with other concepts because hundreds of factors act in different directions, which leads to the destruction of the process of functioning of the SES. In order to judge the significance of one or another risk factor, it is essential to have a strategic plan, containing a number of statements regarding the future development of the environment external to the SES. Acknowledgment. The study was supported by a grant of the Russian Foundation for Basic Research grant 19-010-00257: Methodology for the analysis of the industrial enterprises and intangible industries in the conditions of information society and digitalization.

References 1. Barinov, V.A., et al.: Teoriya sistem i sistemnyj analiz v upravlenii organizaciyami: spravochnik, uchebnoe posobie. [Theory of systems and system analysis in organizational management: handbook, textbook.] Volkova, V.N., Emel’yanov, A.A. 845 p. ISBN 5–279– 02933–5. Publishing House “Finance and Statistics”, Moscow (2006). (In Russian) 2. Bergman, J.-P., et al.: Strategic interpretation on sustainability issues: eliciting cognitive maps of boards of directors. Corp. Gover. (Bingley) 1(16), 162–186 (2016) 3. Bieleke, M., Keller, L., Gollwitzer, P.M.: If-then planning: monograph. Eur. Rev. Soc. Psychol. 2, 1–35 (2020) 4. Chernyak, Y.I.: Sistemnyj analiz v upravlenii ekonomikoj: monografiya. [System analysis in economics management: monograph.] 191 p. Economics, Moscow (1975). (In Russian) 5. Gorelova, G.V., Pankratova, N.D.: Scientific foresight and cognitive modeling of socioeconomic systems. In: Proceedings of the 18th IFAC Conference on Technology, Culture and International Stability (TECIS-2018). IFAC Papers OnLine, pp. 145–149. Elsevier, Amsterdam (2018) 6. Ignatiev, M.B., Katermina, T.S.: Sistemnyj analiz zadach prognozirovaniya i planirovaniya razvitiya slozhnyh struktur: lingvo-kombinatornyj podhod. [System analysis of forecasting and planning tasks for the development of complex structures: a linguistic-combinatorial approach.] In: Sb. nauch. trudov XXII Mezhdunar. nauch.-prakt. konf. Sistemnyi analiz v proektirovanii i upravlenii. [System Analysis in Engineering and Control: collection of scientific papers of the 22nd International Scientific and Practical Conference.] May 22–24,

Adaptive Theory of Socio-economic Systems Management

7.

8.

9.

10. 11.

12.

13. 14.

15. 16. 17. 18. 19.

209

2018, St. Petersburg, Russia, pp. 115–118. Publishing house of Peter the Great St. Petersburg Polytechnic University, St. Petersburg (2018). (In Russian) Katermina, T.S., Karlik, E., Sharich, E., Iakovleva, D.: Measuring of the growth capacity of a semantic model of a defence-oriented industrial enterprise using a cyber-physical system. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 87–97. Springer, Cham (2020). https://doi.org/10.1007/978-3-03034983-7_9 Khachaturyan, A.A., Ponomareva, S.V., Bokova, K.I.: Planning of key performance indicators using cognitive modeling at the industrial enterprises of the Russian Federation. Soft Measurements Calculations 8(21), 51–60 (2019) Klimenkov, G.V.: Postroenie sistemy adaptivnogo upravleniya na baze logikolingvisticheskogo modelirovaniya. [Building an adaptive management system based on logical and linguistic modeling.] Vestnik Ufimskogo gosudarstvennogo neftyanogo tekhnicheskogo universiteta (UGNTU). Nauka, obrazovanie, ekonomika. Seriya: Ekonomika. [Bulletin of the Ufa State Petroleum Technical University. Science, education, economy. Series: economics.] 2(16), 66–70 (2016). (In Russian) Klykov, Y.I.: Situacionnoe upravlenie bol’shimi sistemami. [Situational control of large systems.] 136 p. Energia, Moscow (1974). (In Russian) Kukor, B.L., Klimenkov, G.V.: Adaptivnoe upravlenie promyshlennym kompleksom regiona: teoriya, metodologiya, praktika. [Adaptive management of the industrial complex of the region: theory, methodology, practice]. In: Kukor, B.L. (ed.), Russian Academy of Sciences, Federal state budget educational institution of higher education, Ural branch of RAS, Institute of economics, Ministry of Education and Science of the Russian Federation, Federal state budget educational institution of higher education “Saint-Petersburg state economic university”, 305 p. ISBN 978-5-94646-576-2, Yekaterinburg, St. Petersburg (2017). (In Russian) Kukor, B.L., Kurshev, E.P., Vinogradov, A.N.: Razrabotka dinamicheskogo kognitivnogo scenariya funkcionirovaniya predpriyatiya i proizvodstvennyh kompleksov v processe upravleniya ekonomikoj. [Development of a dynamic cognitive scenario for the functioning of an enterprise and production complexes in the process of economic management.] In: Sbornik dokladov uchastnikov sekcionnyh zasedanij XXI Vserossijskogo simpoziuma. [Collection of reports of the participants of the sectional sessions of the 21st All-Russian Symposium.] November 10–11, 2020, Moscow, Russia, pp. 98–101. RAS, Moscow (2020). (In Russian) Kwan, K.-M., Tsang, E.W.K.: Realism and constructivism in strategy research: a critical realist response to Mir and Watson. Strat. Mgmt. J. 12(22), 1163–1168 (2001) Mintsberg, G., Alstrand, B., Lampel, J.: Strategicheskoe safari. Ekskursiya po debryam strategicheskogo menedzhmenta. [Strategic Safari: Excursion through the wilds of strategic management.] Series “Skolkovo”, 367 p. Alpina Publisher, Moscow (2013). (In Russian) Mir, R., Watson, A.: Strategic management and the philosophy of science: the case for a constructivist methodology. Strateg. Manag. J. 9(21), 941–953 (2000) Mir, R., Watson, A.: Critical realism and constructivism in strategy research: towards a synthesis. Strateg. Manag. J. 12(22), 1169–1173 (2001) Montazemi, A., Chan, L.: An analysis of the structure of expert knowledge. Eur. J. Oper. Res. 3(45), 275–292 (1990) Optner, L.S.: Systems analysis for business management: monograph, 276 p. Prentice-Hall, Englewood Cliffs, NJ (1960) Pospelov, D.A.: Situacionnoe upravlenie: teoriya i praktika. [Situational management: theory and practice.] 288 p. Nauka, Moscow (1986). (In Russian)

210

A. E. Karlik et al.

20. Pospelov, D.A.: Opyt analiza myslitel’nyh aktov: monografiya. [Experience of the analysis of thought acts: a monograph.] 184 p. Radio i svyaz’, Moscow (1989). (In Russian) 21. Sejahtera, F., Wang, W., Indulska, M., Sadiq, S.: Enablersand inhibitors of effective use of Big Data: insights from a case study. In: Tanabu, M., Senoo, D (eds.) Proceedings of PACIS 2018 22nd Pacific Asia Conference on Information Systems, Yokohama (2018) 22. Seredinsky, E.I.: Linguocombinatorial possibilities and restrictions of system thinking in the business world. In: International Scientific and Practical Congress of Economists and Lawyers, Zurich (Switzerland), 30–31 January 2015 / International Scientific Association of Economists “Consilium” (ISAE “Consilium”), Zurich (Switzerland), pp. 262–298 (2015) 23. Sharich, E.E., Iakovleva, D.D.: The role of diagnosing the growth potential of a defenseoriented industrial enterprise in comprehensive personnel training. In: Proceedings of 2018 XVII Russian Scientific and Practical Conference on Planning and Teaching Engineering Staff for the Industrial and Economic Complex of the Region (PTES 2018), IEEE, 14–15 Nov. 2018, St. Petersburg, Russia, pp. 61–64 (2018). https://doi.org/10.1109/PTES.2018. 8604260 24. Skrebtsova, T.G.: Amerikanskaya shkola kognitivnoj lingvistiki: monografiya. [American school of cognitive linguistics: monograph.] 202 p. “Anatolia”, St. Petersburg (2000). (In Russian) 25. Smirnova, O.O.: Osnovy strategicheskogo planirovaniya Rossijskoj Federacii: monografiya. [Fundamentals of strategic planning of the Russian Federation: monograph.] 302 p. Publishing House “Nauka”, Moscow (2013) 26. Syroezhin, I.M.: Planomernost’. Planirovanie. Plan. [Plannability. Planning. Plan.] 247 p. Ekonomika, Moscow (1986). (In Russian) 27. Turing, A.: Computing Machinery and Intelligence. Mind 236(510), 433–460 (1950) 28. Uyomov, A.I.: Sistemnyj podhod i obshchaya teoriya system: monografiya. [System approach and general systems theory: monograph.] 272 p. Publishing House “Mysl”, Moscow (1978). (In Russian) 29. Vinogradov, A.N., Kurshev, E.P.: Primenenie tekhnologii sozdaniya intellektual’nyh dinamicheskih system v zadachah strategicheskogo planirovaniya. [The use of technology of intelligent dynamic systems creation in the tasks of strategic planning.] In: G.V. Gorelova, A.V. Loginova (eds.) Sb. nauch. trudov XXIV Mezhdunar. nauch.-prakt. konf. Sistemnyi analiz v proektirovanii i upravlenii. [System Analysis in Engineering and Control: collection of scientific papers of the 24th International Scientific, Educational and Practical Conference.]. St. Petersburg, October 13–14, 2020, vol. 3, pp. 152–158. Polytech-Press Publishing House, St. Petersburg (2020). https://doi.org/10.18720/SPBPU/2/id20-208. (In Russian) 30. Vinogradov, A.N., Vlasova, N., Kurshev, E.P., Podobryaev, A.: Modern Approaches to the Language Data Analysis. Using Language Analysis Methods for Management and Planning Tasks. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 470–481. Springer, Cham (2020). https://doi.org/10.1007/978-3-03034983-7_46 31. Volkova, E.S.: Sushchnost’ predpriyatiya Industrii 4.0 v sisteme strategicheskogo planirovaniya. [The essence of the enterprise Industry 4.0 in the system of strategic planning.] Global’nyj nauchnyj potencial [Global Scientific Potential] 5(98), 195–197 (2019). (In Russian) 32. Volkova, V.N., Denisov, A.A.: Teoriya sistem i sistemnyy analiz. [Systems Theory and Systems Analysis.] Yurayt, Moscow (2014). (In Russian)

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33. Yakovleva, E.A., Tolochko, I.A., Kiseleva, M.M., Popov, I.V.: Semanticheskij podhod i logiko-lingvisticheskoe modelirovanie v processe upravleniya riskami na predpriyatii. [Semantic approach and logical-linguistic modeling in the enterprise risk management process.] Ekonomika, predprinimatel’stvo i pravo [Economics, Entrepreneurship and Law] 11(7), 1809–1826 (2021). https://doi.org/10.18334/epp.11.7.112352. (In Russian) 34. Zagadskaya, L.S., Klykov, Y.I.: Prakticheskoe primenenie situacionnoj modeli upravleniya. [Practical application of situational management model.] Izv. AN SSSR. Tekhnicheskaya kibernetika [Bulletin of the Academy of Sciences of the USSR. Technical Cybernetics], issue no. 6, pp. 47–54 (1970). (In Russian)

Cognitive Modeling of Complex Systems: State and Prospects Galina V. Gorelova(&) Institute of Management in Economic, Social and Ecological Systems, Southern Federal University, Chekhov St. 22, 347922 Taganrog, Russia [email protected]

Abstract. The author’s methodology of cognitive modeling of complex systems, which is intended for the study of socio-economic, ecological, cyberphysical and other complex systems, is briefly presented. Its features are considered, the basic sources and the current state of research in this direction are indicated. The purpose of the article was to outline the possibilities, prospects for further development and practical application of the apparatus for cognitive modeling of complex systems. Cognitive modeling of complex systems is multistage, cyclical, consists of a set of methods for solving problems: methods for analyzing the properties of models (structural and dynamic), methods for modeling scenarios and predicting possible future development of the system, decision support, and includes a software system for Cognitive Modeling Complex Systems (CMCS). The methodology of cognitive modeling of complex systems belongs to the direction of intelligent systems in the group of cognitive sciences; it can serve to support managerial decision-making. The possibilities of cognitive modeling of complex systems are illustrated by an example of several stages in the study of the regional socio-economic mechanism. Keywords: Complex systems systems


Cognitive modeling
Imitation
Intelligent

1 Introduction Social, economic, political, ecological, socio-technical systems, etc. systems belong to the class of complex systems. The concept of “system” firmly entered scientific use in the 20th century, often with the definition of “large” or “complex”. This is due to the fact that during this period social, biological, economic, organizational, political systems began to be actively studied, at the same time the most complex technical systems were created. Many attempts have been made to define what is a “complex system”? On an intuitive level, it seems clear. But in order to operate with such a concept, intuitive perception is not enough. It is considered that the main features of a system are: the presence of various elements, among which there are necessarily system-forming ones, the presence of connections and interactions of elements, the integrity of the sets of elements (in the internal and external environment), the combination and correspondence of the properties of the elements and their totality as a whole. Systems, elements, subsystems have signs or © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 212–224, 2022. https://doi.org/10.1007/978-3-030-98832-6_19

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properties (characteristics) that can be qualitative and quantitative. The feature can be a measure of the efficiency of the system’s functioning. We are interested in a certain class of organizational systems, namely, socioeconomic systems. This means that their fundamental element is an individual; human activity determines the characteristics of all processes of functioning and development of such a system. The connections, thanks to which these systems exist, characterize complex and contradictory relationships between people based on their interests, values, motives, attitudes. Thus, organizational systems, which include socio-economic systems, contain active elements. Understanding, managing or adapting to such systems requires serious research and organizational decisions. The undesirability or impossibility of a full-scale experiment on such systems requires preliminary simulation of their structure and behavior in order to prevent unreasonable management decisions. One of the directions of such modeling is cognitive modeling. Cognitive modeling of complex systems, in the direction and form in which it currently exists, began to develop from the end of the last century on the basis of the works of the Institute of Control Sciences of the Russian Academy of Sciences under the name “Cognitive Analysis and Situations Management” (Abramova O.N., Avdeeva Z.K., Kovriga S.V., Kulinich A.A., Kulba V.V., Kononov D.A., Kovalevsky S. S., Kosyachenko S.A., Kochkarov A.A., Kuznetsov O.P., Maksimov V.I., Makarenko D. I. Novikov D.A., Nizhegorodtsev R.M., Rabinovich V.M., Raikov A.N., Salpagarov M. B., Trakhtengerts E.A., Chernov I.V., etc. [1–7], and further by the works of the staff of the Taganrog Radio Engineering University (now a division of the SFedU) and other universities and organizations under the name: “Cognitive modeling of complex systems” (Gorelova G.V., Ginis L.A., Zakharova E.N., Katsko I.A., Kataeva T.S., Kolodenkova A.E., Kalinichenko A.I., Lesnichaya M.A., Maslennikova A.V., Makarova E., Melnik E.V., Pankratova N.D., Ryabtsev V.N., Rakitina M.S., and etc.) [9–19]. The “Complex Systems” scientific field has emerged and exists at the Institute for Management of Economic, Social and Ecological Systems of the SFedU. Since the beginning of the 2000s, a large number of scientific research and applied works have been carried out in this direction under the grants of the Russian Foundation for Basic Research, Russian Humanitarian Science Foundation, as well as contractual and initiative works. There is a large number of publications: monographs (15), articles in Russian and foreign journals (over 100), speeches at all-Russian and foreign conferences with personal participation (at least 10–12 annually). Based on the results of studies of various complex socio-economic systems, doctoral (9) and master’s theses (more than 45) have been defended since 2003. Research results are applied in relevant training courses, a number of textbooks have been published on cognitive modeling or containing sections on cognitive modeling of complex systems. Among the conducted and ongoing studies of complex systems, the following can be noted: regional socioeconomic systems [9, 11, 13, 16] (Southern Federal District of Russia: Rostov Region, Krasnodar Territory, Adygea, Dagestan, Karachay-Cherkessia, Republic of Kalmykia; Perm Territory, Chusovsky District); regional socio-economic subsystems (interregional economic exchange, education system, health care system, labor market, small and medium business, tourism, industrial enterprises, metal trade, underground urban planning, etc.) [14, 17]. The processes of adaptation of the peoples of the South of Russia to transformational changes were investigated [10]. Geopolitical systems (Black

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Sea-Caucasus-Caspian zone, South of Russia in the system of neighboring states) have been studied and are being investigated [12], ecological systems (the aquatic ecosystem of the Azov-Black Sea region, water management) and other natural systems. The use and development of cognitive modeling of complicated systems is largely due to their characteristics and properties. Complex systems of various types are characterized by size (“large-scale systems”), complex behavior, functioning under conditions of various kinds of uncertainty, insufficient information and its heterogeneity, subordination to the general laws of the existence and development of systems, that together give rise to the weak structure of their problems, as well as the need to take into account both quantitative and qualitative factors of the systems. That includes taking into consideration how the changes in internal and external environment of a complex system will affect the system’s “behavior”. In the study of complex systems, a systemic and interdisciplinary approach should be used. Cognitive models of varying complexity (mathematically, cognitive maps are the most “simple” models — this is a sign-oriented graph, more complex models are vector parametric functional graphs [5, 9, 11, 13]) allow taking into account all these features in the process of cognitive modeling. Note that cognitive modeling of complex systems, based on the principles of systems theory [8], belongs to the class of simulation, it allows you to model the structure (cognitive model) and behavior of an object (scenarios of possible development). It should be added that the representation of a mathematical model of a complex system, a model of a so-called “multicomponent system” in the form of a directed graph, has been used since the middle of the 20th century. A feature of the multicomponent system model is that with the help of directed graphs it is possible to combine various social, economic, environmental and other indicators in it. Some of these indicators may have a statistical base, some may be assessed quantitatively, some qualitatively. Consideration of such models from the standpoint of the theory of knowledge (cognitive science that began to develop since the 1950s) focuses on the ability of these models not only to use existing knowledge, but also to generate new knowledge about a complex (multicomponent) system, describing and explaining it. In the process of developing a methodology for cognitive modeling of complex systems, practical experience was accumulated, and an appropriate software system was designed. Currently, cognitive modeling of complex systems is understood as the direction “Intelligent systems” in the cognitive sciences [18] and “Artificial intelligence” in the class of modern NBIC (Nano-Bio-Information-Cognitive) technologies [15].

2 Materials and Methods: On the Content of Cognitive Modeling of Complex Systems In various monographs, articles, at conferences, cognitive modeling of complex systems has been presented in its development many times. In this section, we briefly outline the main provisions necessary to understand the content and capabilities of simulation of the properties and behavior of complex systems. Note that cognitive modeling of complex systems is a multi-stage cyclical process. The main stages are as follows:

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– the first stage is the development of a cognitive model based on theoretical knowledge, statistical and expert information, practical research; – the second stage is the study of the properties of the cognitive model (structure, paths and cycles, stability, sensitivity, complexity, connectedness, etc.); – the third stage is scenario modeling, as a scientific prediction of the processes of possible development of situations in the system; – the fourth stage is the development and adoption of the necessary management and organizational decisions, assessment of their consequences. In cognitive modeling of complex systems, models such as are presented in the Eqs. (1)–(3) are used. IG ¼ hGk ;Gk þ 1 ;Ek i; k ¼ 1;2;. . .m;

ð1Þ

where IG is a hierarchical cognitive map in which Gk ¼ hV;E ik ; V ¼ fVi g; i ¼ 1;2;. . .n; E ¼ fei;j g; i;j ¼ 1;2;. . .n

ð2Þ

where Gk is a cognitive map of the k-level, V and E, respectively, are the sets of vertices and arcs of the cognitive map. The arcs of the cognitive map display the causal relationships of the system. If necessary and possible to build a more complex model, a parametric vector functional graph is used D E UP ¼ G ¼ hV;Ei;X ðVÞ ;F;h

ð3Þ

where X(V) are the parameters of the vertices; F ¼ f ðxi ;xj ;eij Þ ¼ fij reflects functional dependence between the vertices Vi,Vj which can be determined only by the weight coefficient xij. In addition to clear models such as (1), (2), (3), cognitive modeling can be used fuzzy cognitive models [27]. We will take into account the fact that the cognitive map (model) of a complex system, being the creative searches’ result of a researcher (expert), or a group of researchers (experts), reflects his (their) structure of knowledge in the studied subject area at the time of research, refracted through their consciousness and reflecting their process of understanding the world of a complex system. Thus, any cognitive model is more or less subjective, no matter how much one wants to avoid this subjectivity. In this case, we will not plunge into the serious philosophical problem of the objective and the subjective; we will accept the inevitability of this fact for cognitive models, as well as for other types of imitation models of complex systems. But in order to make sure to a certain extent that the model does not contradict a real complex system, theoretical and practical ideas about it (the term “adequacy” in the strict sense, from our point of view, does not really fit cognitive models), the methodology of cognitive modeling of complex systems provides tools analysis of the properties of the model, on the basis of which the model can be accepted, rejected, corrected or modified in the desired direction.

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3 Results: An Example of a Regional Socio-economic System Cognitive Modeling Stage 1. Development of a Cognitive Model. The cognitive model of the regional socio-economic system (see Fig. 1) was based on the scheme of the regional economic mechanism of Academician Granberg [20], and a number of models of system dynamics [13]. In Fig. 1, solid lines indicate positive arcs (+1), that is, those when positive/negative changes in the vertex Vi lead, respectively, to positive/negative changes in the vertex Vj; dashed lines indicate negative arcs (−1), when positive/negative changes in the vertex Vi lead, respectively, to negative/positive changes in the vertex Vj. Thus, the cognitive map G = < V,E > is a directed sign graph.

Fig. 1. Cognitive map G “Regional socio-economic mechanism”.

The cognitive map was built using the CMCS software system [19], which is also used in further cognitive modeling. In a number of tasks of cognitive modeling of regional mechanisms, variants of such a model were used, adapted to certain conditions of the region.

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This model is a hierarchical functional cognitive map Eq. (1), in which the top level includes the vertices highlighted in Fig. 1 by a frame, and as an example, a number of functional dependencies on arcs (f2(3)2, f2(4)2, f2(5)2). Stage 2. Analysis of the cognitive map. Vertex analysis of the cognitive map G. The vertices V7 have the highest degree. Production (P = 11), V4.Wage (P = 11) and V6. GRP (P = 10). These vertices have the largest number of inputs and outputs (halfdegree of vertices), which can determine their importance for the behavior of the system as a whole. Analysis of the Cycles of the Cognitive Map, Determination of Structural Stability. An important feature is inherent in complex systems — the presence of feedback loops in them, which should be displayed in cognitive models. The selection of cycles (contours) of a cognitive map and their analysis makes it possible, considering and interpreting closed causal cycles, to check whether they and the cognitive model as a whole contradict the knowledge and ideas about the real system, allows us to draw a conclusion about the structural stability or instability of the model. For this, the cycles of positive and negative feedback and their quantitative ratio in the model are determined. Cycles that increase the tendency to deviate from the initial state are called positive feedback cycles (their sign is the absence or even number of negative arcs). Loops that suppress the tendency to deviate from the initial state are called negative feedback loops (their sign is the presence of an odd number of negative arcs). The presence in the system of many cycles that amplify the deviations suggests its instability, but at the same time, the presence of many cycles opposing the deviation can also lead to instability, but of a different type — in the form of oscillations with increasing amplitude. If the fluctuations in the indicators damp and the system comes to a certain state, then we can talk about the stability of the system. It is necessary to distinguish between the stability of the system to perturbations and by the initial value, as well as structural stability (stability theory) [9, 23, 27]. A structurally stable system is a system in which an odd number of negative cycles is observed [23]. Figure 2 shows the results of the analysis of the cycles of the cognitive map G. Cognitive map G is structurally stable because of its 276 cycles, there is an odd number (143) negative cycles.

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Fig. 2. Cognitive map cycle analysis G, highlighting one of 143 negative cycles.

Analysis of Resistance to Disturbances. To analyze the stability of the model to disturbances, it is necessary to compose and solve the characteristic equation of the adjacency matrix RG. The system is not stable against disturbances, according to the criterion [22, 27] | M | 1. Simplicial Analysis of System Connectivity. The very concept of “system” means the presence of some elements that are in a relationship with each other, their connectivity in a certain structure, with the disappearance of structural connectivity, the system disappears. The representation of the system in the form of a graph allows you to study situations when the i-th component (element, subsystem) affects the j-th and other components along the communication chains. The strength of the connection can be taken into account by assigning to each arc a number — the weight xij. In the theory of algebraic topology, a method of a-analysis (named after its author, Atkin R.H.,) of qconnectivity (chain links) of simplices, and the construction of simplicial complexes has been developed [22, 23]. The application of topological analysis of connectivity to the study of social, biological, economic and other complex systems has shown its effectiveness.

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Fig. 3. Simplicial analysis of q-connectivity of the cognitive map G.

Figure 3 illustrates the result of the simplicial analysis of the cognitive map G. In Fig. 3, one of the simplexes of the rows of the matrix (“inputs” of the system) of the adjacency RG is highlighted - a simplex with dimension ƿ = 3 (tetrahedron) formed by the vertex V2 (Fixed assets). In this case, the top V2 is the “reason” that the tops V0 (Quality of life), V4 (Salary), V6 (GRP), V7 (Production) are interconnected. In Fig. 3, one of the simplexes of the columns RG (“outputs” of the system), formed by the vertex V0 (Quality of life), is also highlighted. This vertex is the reason that the vertices V1 (Population), V2 (Fixed assets), V3 (Final consumption), V4 (wages), V5 (Prices) are connected, form a block (simplex).The q-connected chains form simplicial complexes Qx and Qy at the inputs and outputs of the system, respectively. Stage 3. Modeling Scenarios. The object of modeling — a complex system — can be considered as a set of interacting dynamic processes occurring in real time. In impulse processes that simulate the development of situations on the cognitive model, time should also be present, but when simulating by different types of graphs, this time may not make sense of time, but reflect only the sequence of state changes [5–9, 21–25].

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Scenario № 1. Suppose that production begins to develop in the system, the impulse q7 = +1 is introduced into the vertex V7; the vector of perturbations looks as follows Q = {q1 = 0; … Q7 = +1; … q15 = 0} (see Fig. 4). Graphs of impulse processes and a histogram of the numerical values of impulses at the 6th step of the simulation in the vertices indicated in Fig. 4 are constructed. According to Fig. 4, tendencies in the development of processes in the system under the conditions set by the scenario are visible. Development trends can be considered positive: the values of indicators at all vertices are growing, prices are falling.

Fig. 4. Graphs of impulse processes on the cognitive map G, Scenario № 1.

Scenario № 2. Suppose that production in the system is falling, inflation is growing, the population is decreasing, the Federal authorities are trying to counteract all this: the impulse q7 = −1 is introduced into the top of V7; an impulse q1 = −1 is introduced into the top of V1; the impulse q9 = +1 is introduced into the vertex V9; the impulse q11 = +1 is introduced into the vertex V11; the vector of perturbations looks as follows.

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Q = {q1 = −1; … Q7 = −1;… q9 = +1;… q11 = +1; … Q15 = 0} (Fig. 5). As can be seen from the simulation results of Scenario No. 2, at the beginning of the processes, an oscillatory process is observed, but then the trends in the development of situations improve, the indicators grow. Thus, the efforts of the federal authorities can begin to counteract the negative trends that have arisen in the system due to falling production, rising inflation, and decreasing population.

Fig. 5. Graphs of impulse processes on the cognitive map G, Scenario № 2.

Impulse modeling should include the development of an experiment plan that determines which vertices or their aggregate will be introduced to initiate the onset of action processes (impulses). The CMSS software system allows perturbing one, two or more vertices at the same time or at certain stages of modeling. The continuation of the study of the regional socio-economic mechanism can be the deployment and refinement of the tops of the upper level of the cognitive map at the lower levels, the transition to functional parametric graphs, etc.

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4 Discussion The cognitive modeling of complex systems (in the field of economics, ecology, society, human-machine systems, etc.) is presented in this work. It differs in the goals, content and methods from cognitive modeling adopted in psychology, linguistics and other cognitive sciences. But the common thing is that an active element in the system and in the research process is a person who makes decisions, who is influenced by the very process of extracting knowledge about the system, their structuring and the way of obtaining new knowledge. This is reflected both in the consciousness of the researcher and in the intellectual system built on the basis of cognitive modeling of complex systems.

5 Conclusion The article presented basic information about the cognitive modeling of complex systems. The cognitive modeling methodology, which has been developed and continues to develop, includes models and methods for sequentially solving interdependent systemic problems. These are tasks: a) building a cognitive model based on theoretical, experimental, expert data; b) analysis of the properties of the model (structural stability, resistance to disturbances, paths and cycles, complexity, q-connectivity, sensitivity, etc.); c) anticipation of the possible development of processes in the system (impulse modeling of scenarios); d) decision making. Combining the above tasks into the system and creating on this basis the CMCS software system allows you to consistently establish the adequacy of the model to the object under study, modify the model if necessary. All this distinguishes cognitive modeling of complex systems from cognitive modeling in cognitive sciences, and the CMCS software system from the existing software systems “Kanva”, “Situation”, “KOMPAS”, “Course”, developed earlier at the Institute of Control Sciences of the Russian Academy of Sciences. In the world practice, the toolkit of cognitive maps (mind mapping, concept mapping, cognitive mapping) is widely used in geoinformation technologies for the study of intelligence (more precisely, for the presentation and visualization of ideas-concept-concepts), but for the purpose of a comprehensive study of socio-economic, environmental, political systems are practically not used. The results of the work can be recommended for use as a decision support tool: – analytical centers that prepare conclusions on the state of a complex system, give forecasts for its development, develop programs and strategies for the development of regions, – individual researchers or groups of researchers of complex systems, – when performing research within the framework of various targeted programs, – in the educational process, – authorities at various levels.

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References 1. Abramova, N.A., Novikov, D.A.: Concept development of human factors in management science. Foreword. In: Abramova, O.N., Ginsberg, K.S., Novikov, D.A. (eds.) Human Factors in Management Science. Collection of articles, pp. 5–51. KomKniga, Moscow (2006) 2. Avdeeva, Z.K., Kovriga, S.V., Makarenko, D.I., Maksimov, V.I.: Cognitive approach to management. Manag. Problems 3, 2–8 (2007) 3. Abramova, N.A., Avdeeva, Z.K.: Cognitive analysis and management of the situations’ development: problems of methodology, theory and practice. Problems of management, vol. 3, pp. 85–87. IPU RAN, Moscow (2008) 4. Kulba, V.V., Kononov, D.A., Kovalevsky, S.S., Kosyachenko, S.A., Nizhegorodtsev, R.M., Chernov, I.V.: Scenario analysis of the dynamics of socio-economic systems’ behavior. IPU RAN, Moscow (2002) 5. Kulba, V.V., Nazaretov, V.M., Chukhrov, I.P.: Modified functional graphs as an apparatus for modeling complex dynamical systems. Institute of Control Sciences of the Russian Academy of Sciences, Moscow (1995) 6. Kuznetsov, O.P.: Cognitive modeling of poorly structured situations. Electronic version of the article. http://post.raai.org/data/post2005/Kuznetsov/kuznetsov.html (2005). Accessed 21 Oct 2021 7. Maksimov, V.I., Raikov, A.N.: Collective cognitive maps in decision-making systems. In: Proceedings of International Symposium “Reflexive Control”. pp. 86–88. Institute of Psychology RAS, Moscow (2000) 8. Volkova, V.N., Denisov, A.A.: Teoriya Sistem i Sistemnyi Analiz. [Theory of systems and systems analysis.] Yurait Publ., Moscow (2019). (In Russian) 9. Gorelova, G.V., Zakharova, E.N., Radchenko, S.A.: Study of semi-structured problems of socio-economic systems: a cognitive approach. Russian State University Pub. House, Rostov-on-Don (2006).(In Russian) 10. Gorelova, G.V., Rozin, M.D., Ryabtsev, V.N., Sushchiy, S.Y.: Kognitivnye issledovaniya problem YUga Rossii. [Cognitive studies of the problems of the South of Russia.] Izvestia SFedU. Technical science. Thematic issue “Perspective systems and management tasks”, vol. 3, pp. 78–93. TTI SFedU Pub. House, Taganrog (2011). (In Russian) 11. Gorelova, G.V., Maslennikova, A.V.: Proektirovanie strategij razvitiya social’noekonomicheskih system na osnove kompozicii kognitivnogo modelirovaniya i sistemnoj dinamiki. [Designing strategies for the development of socio-economic systems based on the composition of cognitive modeling and systemic modeling.] In: Pankratova, N.D. (ed.) System Analysis and Information Technologies: Proceedings of the 15-th International Conference SAIT 2013, May 27–31, 2013, Kyiv, Ukraine, pp. 20–21. ESC “IASA” NTUU “KPI”, Kiev (2013). (In Russian) 12. Gorelova, G.V., Ryabtsev, V.N.: Modelirovanie Arhitektury i Dinamiki Geopoliticheskih Regionov Sovremennogo Mira: Kognitivnyj Podhod (zona “CHernomor’e-Kavkaz-Kaspij”). [Modeling the Architecture and Dynamics of Geopolitical Regions of the Modern World: A Cognitive Approach (Black Sea-Caucasus-Caspian zone).] 374 p. SFedU Pub. House, Rostov-on-Don (2014). (In Russian) 13. Gorelova, G.V., Pankratova, N. D. (eds.): Innovacionnoe Razvitie Socioekonomicheskih Sistem na Osnove Metodologij Predvideniya i Kognitivnogo Modelirovaniya. [Innovative development of Socio-Economic Systems Based on Foresight and Cognitive Modeling Methodologies.] 464 p. Naukova Dumka, Kiev (2015). (In Russian)

224

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14. Saak, A.A., Gorelova, G.V., Kaurova, O.V.: Imitacionnoe kognitivnoe modelirovanie molodezhnogo rynka truda. [Imitation cognitive modeling of the youth labor market.] Nauchno-teoreticheskij zhurnal “Fundamental’nye i prikladnye issledovaniya kooperativnogo sektora ekonomiki” [Scientific-theoretical journal “Fundamental and applied research of the cooperative sector of economy”] 3, 164–176 (2020). (In Russian). https:// doi.org/10.37984/2076-9288-2020-3-164-176 15. Volkova, V.N., Gorelova, G.V., Pankratova, N.D.: The development of the cyberphysical system concept on base of the interdisciplinary theories. In: Proceedings of IEEE Second International Conference on System Analysis & Intelligent Computing conference (SAIC) 05–09 October, 2020, Kyiv, Ukraine, pp. 20–25. (2020) 16. Pankratova, N.D., Gorelova, G.V., Pankratov, V.A.: Strategy for simulation complex hierarchical systems based on the methodologies of foresight and cognitive modelling. In: Advanced Control Systems: Theory and Applications. River Publishers Series in Automation, Control and Robotics. Chapter 9, pp. 257–288 (2021) 17. Pankratova, N.D., Gorelova, G.V., Pankratov, V.A.: Study of the Plot Suitability for Underground Construction: Cognitive Modellin. In: Advances in Intelligent Systems and Computing, 2021, 1246 AISC, pp. 246–264 (2021) 18. Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.): ICR 2021. LNCS (LNAI), vol. 12998. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87725-5 19. Gorelova, G.V., Kalinichenko, A.I., Kuzminov. A.: Program for cognitive modeling and analysis of socio-economic systems at the regional level. Certificate of state registration of computer programs No. 2018661506 (2018). (In Russian) 20. Granberg, A.G.: Fundamentals of regional economics: a textbook for universities, 5th edn. State university higher school of economics Pub. House, Moscow (2006).(In Russian) 21. Axelrod, R.: The Structure of Decision: Cognitive Maps of Political Elites. University Press, Princeton (1976) 23. Atkin, R.H.: Combinatorial Connectivies in Social Systems. An Application of Simplicial Complex Structures to the Study of Large Organisations, Interdisciplinary Systems Research (1997) 24. Casti, J.: Connectivity, Complexity and Catastrophe in Large-Scale Systems, 216 p. Chichester–New York–Brisbane–Toronto (1979) 25. Eden, C.: Cognitive mapping. Eur. J. Oper. Res. 36, 1–13 (1998) 26. Langley, P.: Cognitive architectures: research issues and challenges. Cogn. Syst. Res. 10(2), 141–160 (2009) 27. Kosko, B.: Fuzzy thinking. Hyperion, New York, NY (1993) 28. Roberts, F.S.: Discrete Mathematical Models with Applications to Social, Biological, and Environmental Problems, p. 559. Prentice-Hall, Englewood Cliffs, NJ (1976)

Participative Cognitive Mapping as a Multidisciplinary Approach for Managing Complex Systems Aleksandr E. Karlik , Vladimir V. Platonov(&) and Elena A. Iakovleva

,

St. Petersburg State University of Economics, Sadovaya str. 21, 191023 St. Petersburg, Russia [email protected]

Abstract. The fundamental methodological challenge of dealing with a complex system is the need for multidisciplinarity. The paper develops the approach to combine epistemologically different methods of cognitive maps, influence diagrams, fuzzy logic, Bayesian networks, using the capabilities of humanmachine interaction and artificial intelligence in the process of strategic management of complexity. The study is designed to update the participative cognitive mapping to account for system dynamics that include both the human and machine procedures. For this purpose, the paper discusses the opportunities to integrate artificial intelligence and closely related big data analysis in the participative cognitive mapping algorithm. The proposed procedural framework based on the participative cognitive mapping with human-machine interaction for strategic decision-making in complex organizational systems extends capabilities of the strategic management procedure as a foundation for the development of programs and projects of change. It combines different methodologies such as hybrid and fuzzy cognitive maps, Bayesian networks, influence diagrams, it integrates procedures implemented by humans and computers in a sequential manner. For scientists, this multidisciplinary framework provides the way for doing active research through participation in management procedures, for practice managers to apply cognitive mapping for the strategy development and implementation. Keywords: Multidisciplinarity
Strategy
Cognitive maps networks
Influence diagrams
Digitalization


Bayesian

1 Introduction The rise of information society increases the complexity of organization entities that share similarities with a corporation. This complicates management and requires the development or refinement of management methods. An important aspect of the current transformation is the increasing heterogeneity of complex systems, and, consequently, the need for multidisciplinarity in management, which has always been an organizational challenge. A consequence of the increasing importance of intangible factors in the information society is the growth of qualitative data. The extraction and analyzing © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 225–237, 2022. https://doi.org/10.1007/978-3-030-98832-6_20

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quantitative information is another management challenge. Fortunately, the development of the information society and revolution in information and communication technologies generates not only challenges but as well new opportunities and resources for the development of management methods. Artificial intelligence and machine learning technologies provide opportunity, and big data provide a new resource for management. The challenge is to harness the power of artificial intelligence and to apply big data as a resource for the development of the methodology of management. This article is designed to contribute to meeting this challenge in the field of strategic management by updating the participative cognitive mapping procedure. From a research perspective, participatory cognitive mapping is a method of active research. From a practical management perspective, it is a method for extracting and analyzing qualitative information for application in a strategic procedure. We consider the participatory cognitive mapping as a basis for integrating the methods of various disciplines and involving artificial intelligence and big data in strategic analysis. The paper proposes a way to combine the cognitive mapping, fuzzy logic, Bayesian networks in a single framework.

2 Literature Review Research of a complex social system often involves the active research i. e. the involvement of the researchers in the processes of development and improvement of management systems as well as involvement of decision makers in the research process [9, 12, 14, 26]. The management researcher cannot be seen as a neutral independent observer and, similarly, turning the decision makers are active participants in the research boosts its quality [12, 13]. The methodological approach proposed in this paper is an implementation of this epistemological premise. The approach is multimethodological, but its foundation is cognitive research namely the cognitive mapping. In this study, cognitive mapping is understood as a tool for identifying and describing decision makers’ mental models, and especially for explicating implicit expert knowledge about ill-structured problems [15]. This approach involves the quantitative analysis of cognitive maps with application of the methodology of the network analysis, first of all, the calculation of centrality. In other words, the issue connected with more significant problems and factors gains a higher value by making its centrality value proportional to the average of the centralities of its network neighbors [1, 18]. For the researcher, cognitive mapping provides access to the subjective reflection in the mind of decision makers, to influences and cause-effect relationships of the real world [3]. The mental model revealed by the cognitive mapping is a reflection of a complex system of high abstraction that exists in reality. The founder of the systems dynamics, Jay Forrester, pointed out that no one keeps a complete picture of the world in their head, but only the most important concepts and the relationships between them [5]. The hybrid approach for data collection and cognitive mapping combines nomothetic and ideographic techniques of eliciting mental models and building cognitive maps [1]. The digitalization and the development of artificial intelligence open up wide prospects for cognitive modeling making it possible to analyze a large number of structures and

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options for the behavior of a complex system, much more than experts can process on their own, without the help of cognitive maps and computer programs [6]. The participative cognitive mapping procedure assumes the involvement of decision makers in the research process was proposed for initiating interdisciplinary research projects in the MegaScience network [8]. The premise of the critical realism states that “practice comes before theory” [12, p. 494]. The soft approach to research 4A (Appreciation, Analysis, Assessment, and Action) proposed by Mingers and Brocklesby is the operationalization of this premise to solve strategic management problems in complex systems [12]. Graph methods (in the field of artificial intelligence called “semantic networks”) are applied as the representation of knowledge [7]. The application of this technique makes it possible to carry out scenario analysis, determining the initial and final equilibrium states depending on the identified dynamic properties of the system [10, 19]. A Bayesian network-based influence diagram in the form of a directed acyclic graph can be constructed based on the shared mental model of the decision makers. This allows the influence of the uncertainty factor to be analyzed on the factors identified in the participative cognitive mapping procedure using the evidence propagation algorithm for sensitivity analysis [17].

3 Materials and Methods The approach implemented in this research integrates the cognitive mapping procedure, fuzzy logic and Bayesian data analysis and strategic planning tools within a soft approach to operations research 4A (Appreciation, Analysis, Assessment, and Action) in the procedure of strategic management of a complex organization entity that shares similarities with corporation [12]. It is based on the application of the hybrid cognitive mapping technique [1] developed for the exploratory scientific research to expert knowledge explication in the strategic management procedure from generating strategic vision up to the development and implementation of a program of change or a project. The essential elements of the approach have been applied in the strategic procedure of a large Russian logistics company, a Finnish high-tech firm, as well as in a network of the Russian organizations whose activities are focused on the basic research.

4 Results The block diagram presented in Fig. 1 describes a multidisciplinary organizational model for making strategic decisions on the management of complex systems based on participatory cognitive mapping. It combines different methodologies such as hybrid and fuzzy cognitive maps, Bayesian networks, influence diagrams, involves decision makers in the process of scientific research and scientists in the process of strategic planning, integrates procedures implemented by humans and computers in a sequential process of human-machine interaction, and, provides the opportunity to apply capabilities of artificial intelligence and big data a new key resource resulting from digitalization in the single framework.

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In Fig. 1, the procedures carried out by humans are denoted by ‘H’; the steps carried out by computers, including the use of artificial intelligence, are denoted by ‘M’. To solve the dilemma between rigor and practicality in the different contexts the approach provides for bypath routes that allow skipping steps requiring advanced machine procedures including the application of artificial intelligence. Note that, in the case of participatory cognitive mapping, the experts and the decision makers might be the same. As this framework refers to an analysis of complex systems, it does not identify individual factors but provides a systemic picture of the internal and external business environment by a management team with a high degree of abstraction — organizational Weltanschauung (world picture) that in this context represents the shared vision of the management team [1] or dominant logic of the company [22]. It is the outcome of the Assessment phase and foundation for the Analysis, Assessment, and Action. The proposed framework is based on the hybrid approach [1]. The starting point of the given procedure is the identification of broad themes. Defining problem situations is the most sensitive procedure, as it identifies the initial pool (set) of the most significant topical issues including problems or the strategic factors that cause them. The topical issues are constructs that are literally being constructed in the course of active research. This pool represents the foundation for eliciting the individual and then shared mental models represented by cognitive maps as well as for building influence diagrams, providing the foundation of subsequent procedures. Compelling this pool has been the “weak link” of participatory cognitive mapping, due to the largely arbitrary, selection of topical issues with the obvious researcher bias. The compelling of the initial list of topical issues has been and will continue to be of subjective nature. However, big data and artificial intelligence could strengthen their link with the real world. Typically researchers have developed the initial list using academic literature, industry publications, news reports, and so on [1]. Inevitably, the selection of topics, issues, and factors is heavily influenced by the theories and experiences of the researchers. Nevertheless, the application of big data and machine learning techniques enhances the objectivity of the hybrid (nomothetic and ideographic) causal mapping procedure. This is one of the points of the proposed organizational model, along with the application of Bayesian networks and fuzzy cognitive maps, where the link between the constructed reality and the objective reality is provided, alongside or in combination with the identification of the objective reality through the cognitive lens of the decision makers. The application of big data and artificial intelligence allows this connection to be strengthened. The introduction of big data implies transformation from models to direct extraction of information from data [24]. In contrast, to apply a some theory in the secondary research to select the significant issues or apply some model for sorting qualitative data from original texts the researcher use the information directly extracted by machine from a very large original dataset. Unlike model-based analysis of well-structured problems, data-driven approaches identify the list of significant issues independently of a researcher giving him/her the insight or support to the identification and sorting-out of the problems and influences (factors). Instead of applying the model constructed or chosen by the researcher ad hoc, a machine algorithm itself generates a model that is able to extract categories, concepts, problems, and influences from an original illstructured but comprehensive database.Then, it is the turn of the human: the machine is

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APPRECIATION Big Data

Individual Cognitive Maps

A Defining broad topics H

Identifying the Most Significant Problem Situations and Influences M

List of Broad Topics

Verified Shared Cognitive Map

Analysis of centrality M Building Influence Diagram H

Influence Diagram

Eliciting Individual Mental Models H

Eliciting Shared Mental Models H

Identifying Cognitive Diversity M B

The Most Significant Problems and Influences

Shared Cognitive Map

Verifying Shared Cognitive Map with Fuzzy Cognitive Mapping Procedure (FCM) M

ANALYSIS Building Bayesian Network M

Verified Influence Diagram

Target cognitive distance

Elicited Dominant Logic

D

ASSESSMENT Drawing Implications H

Recommendations

ACTION

C Implementation H

Program of Change or Project

Developing Program of Change or Project H

Fig. 1. Procedural framework based on the participative cognitive mapping with humanmachine interaction for strategic decision-making in complex organizational systems.

not an alternative but complements the human appreciation. The role of the researcher and the moderator remains important, as does the significance of the theoretical framework of cognitive research chosen by the researchers that underpins the whole procedure. Nevertheless, the use of artificial intelligence and big data reduces subjectivity and bias in justifying management decisions.

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From the list of the 50 most significant problem situations and influences, the experts/decision makers should select the 12–15 issues that he/she considers most important. They should then link them to each other, thereby identifying the most important cause-and-effect relationships. The next procedure is to construct a shared cognitive map by quantitative analysis using adjacency matrices, followed by cognitive diversity analysis identifying the cognitive distance (diversity) between individual mental models. A 100  100 adjacency matrix corresponding to a list of 50 factors and problems is generated by computer processing [1]. The construction and refinement of the shared cognitive maps is the final result of the Appreciation phase. A shared cognitive map appreciates the key influences (factors) and problem situations by experts and management team, as well as appreciates the interrelationships of these most significant topical issues. It can be considered as the elicited dominant logic of the company [1, 22] or more general as the shared vision of the management team. The procedure then continues in a series of iterations (feedback loop B – D) until the target level of cognitive distance set by the researchers is reached. Once this is reached, the Appreciation phase is complete i. e. the picture of the organization’s internal and external environment, as well as its place in it (Weltanschauung) shared by the decision maker team, is revealed. In terms of strategic analysis, this is the vision or explicit dominant logic — the management team’s collective understanding [3, 22]. It will be the basis for the development of influence diagrams in the Analysis phase, recommendations in the Evaluation phase, to be implemented by the programs of change (Action phase) and, if necessary (and likely), to be revised by re-running the procedure or its individual steps (feedback loop D – C). The value of the target cognitive distance is determined by Eq. (1) [10]. The numerator of the formula is the distance between two cognitive maps and the denominator is the maximum possible distance, taking into account all the critical components of cognitive diversity. For identical cognitive maps, the value of DR is 0, and the complete difference between the individual representations of the decision makers about the internal and external environment of a complex system corresponds to DR equal to one. p P p P

DR ¼

i¼1 j¼1 2 6pc þ 2pc ðpuA þ puB Þ þ p2uA

8 = ID represents diagnostic information used to detect and search for defects: D is a list (class) of typical deviations from the norm, and RDM are connections of deviations from the norm with the variables of the Mc model. The four of symbols < AD, FD, RD, RD/ > is a model of a carrier (hardware) designed for storing and processing diagnostic information: AD are elements of its carrier, RD  AD  AD is a structure of carrier, FD are the functions of elements from AD, RD/  AD  F is the element-to-function attribute relation. Diagnostic models of test and operational diagnostics are formed on the basis of the process model (2) and differ in the method of generating and using diagnostic information. The following   are used as diagnostic information during testing: an array of test   actions Xt ¼ xtij  of length L applied to m inputs of the OD, i ¼ 1; L, actions applied     to m inputs of OD, j ¼ 1; m, xTij 2 XT , and an array of test reactions YT ¼ yTij  of the same dimension, yTij 2 YT . Responses to test stimuli are compared with expected responses from Yт using the comparison predicate P=(Yt, Ytn). Hence, the diagnostic testing model has the form: DMi ¼ \Xt ; Yt ; O; F; Rof ; Rso ; P¼ [ :

ð8Þ

The predicate P = (Yt, Ytn) is true if all test reactions coincide with normal (corresponding to the norm). Comparison and conclusion procedures are performed on an additional carrier < AD, FD, RD, RD/ > from formula (7).

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During operational diagnostics, diagnostic information is generated in the process of OD functioning with the use of control operations Ok, which partially or completely implement the function of the object being diagnosed. 1. The first case corresponds to the convolution of information, for example, the calculation of the checksum fк: X ! Yк, Yк  Y before (Yк,before) and after (Yк,after) information transmission. 2. The second case corresponds to the duplication of the OD function (fк = f), namely fк: X ! Y, Yк = Y. Duplicating functions is more expensive than using code’s methods. But it allows you to detect any malfunction of the working module of the system. This method was applied in a machine with dynamic architecture (MDA), developed under the guidance of Professor V.A. Torgashev in the 1980s in the Soviet Union [1]. Methods of operational diagnostics were implemented by various combining such check operations as repetition and duplication of calculations on the MDA computational modules. The third module was used as an arbiter to determine the failed module. The diagnostic model of operational diagnostics, reflecting additional operations in relation to model (2), and comparison by the predicate P= (Yк,before,Yк,afrer) of the control information Yк,before and Yк,afrer has the form: DMm ¼ \X; Y; O; F; Ro/ ; Rco ; Ok ; Yk ; P¼ [ :

ð9Þ

Expressed in a general form, the diagnostic models of the upper level reflect the general patterns of diagnostic support for any objects, regardless of their nature and purpose.

6 A Systematic Approach to the Design of Diagnostic Support for a Complex Object The tasks solved in the design of diagnostic support for a complex system belong to the class of optimization problems of the following type: 1. Maximization of the system fault tolerance indicator under resource constraints. 2. Minimization of the resource indicator used to ensure a given level of fault tolerance. 3. Multi-criteria optimization of fault tolerance and system resources. The formulation of diagnostic models of heterogeneous objects in a single general scientific and mathematical language allows us to apply a systematic approach to the design of diagnostic support for a complex object. It consists of the choice of diagnostic tools for a complex object based on general diagnostic models, taking into account the reliability of each element. This approach allows performing complex optimization of hardware and time redundancy introduced into different parts of the system, taking into account their different levels of reliability. Of no small importance for the mutual understanding and interaction of the participants in the design of the diagnostic support of a complex object is the use of a common language for representing diagnostic models.

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7 Results Diversity expands the range of tasks to be solved. But at the same time, the general vision of the problem is lost. So, for example, to get a general impression of the artist’s painting, you need to move away from it. This departure from details is formalized in mathematical logic, as the most general section of mathematics. The first-order predicate language (FOPL) included in it was taken as the basis for the construction of generalized diagnostic models of complex objects. Three groups of set-theoretic models (ST-models) are considered on the basis of the classes of the carrier, functions, and relations of the FOPL: functional (F-models), structural-functional (SF-models), and structural-operational (SO-models). The F-model (black box model) reflects the functions implemented by the object, the SF-model details the device (structure) of the object, the SO-model represents the process of implementing functions in the structure of the object [23]. The inclusion of diagnostic information about deviations from the norm and the rules for finding them transforms the ST-model into a diagnostic STmodel of the object. The difference in diagnostic information inherent in the methods of test and operational diagnostics is reflected in the corresponding diagnostic SO-models. Diagnostic models of the upper level for describing a complex object are in demand at the initial stage of developing its diagnostic support.

8 Discussion The contradiction between the general and the particular is resolved in establishing a connection between them. The transition from the general to the particular is ambiguous. Diagnostic models of the upper level applied for describing a complex object are informative in the sense of a general understanding of the problem, but not constructive in the sense of obtaining specific results. Different ways of concretizing the general mean that one of them is the most effective. This task should be solved by the developers of diagnostic software. In this task, information on the statistics of failures of each module of the system plays an important role. In accordance with it and the requirements for the reliability of a complex object, one or another method of detecting and searching for deviations from the norm is selected for each module of the system. This choice is associated with the solution of optimization problems. Their solution should be facilitated by the scientific systematization of private diagnostic methods, which characterizes their capabilities and consumed resources.

9 Conclusion The systematization of diagnostic models of the upper level of SCPS is based on the generalized concept of deviation from the norm. Due to external and internal reasons, deviations arise in any objects of inanimate and living nature. They are varied and depend on the characteristics of the objects under consideration. Regardless of the different terminology used in medicine and technology, general principles are applied to detect and search for deviations in human health and in the

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operation of a technical device. In the work, they are referred to the socio-cyberphysical system that unites objects of artificial and natural nature. SCPS, as a complex system, is divided into the material, energy, information, management, and functional components. Deviations should be considered at any level of object presentation: material, energy, information, management, functional ones. The top-level diagnostic model of each of these components is a set-theoretic model that takes into account the specifics of this component and includes a description of diagnostic information. Diagnostic models of the upper level of a complex object define a common language for describing particular models, and allow one to implement a systematic approach to the design of diagnostic support for heterogeneous components of a complex system. Acknowledgments. The studies carried out on this subject were carried out with the financial support of the RFBR grants No. 19–08-00989-a within the framework of the budget theme FFZF-2022-0004. The author would like to thank Professor Violetta N. Volkova for the useful discussions of this article.

References 1. Mikoni, S.V.: Obschie diagnosticheskie bazi znanii vichislitelnih system. [General diagnostic knowledge base of computing systems.] SPIIRAS, St. Petersburg. (1992). (in Russian) 2. Alberto, A., Cavalcanti, A.L.C., Gaudel, M.-C., Simao, A.: Formal mutation testing for Circus. IST 81, 131–157 (2017) 3. Czech, M., Jakobs, M.-C., Wehrheim, H.: Just Test What You Cannot Verify! In: Egyed, A., Schaefer, I. (eds.) FASE 2015. LNCS, vol. 9033, pp. 100–114. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46675-9_7 4. Ma, T., Ali, S., Yue., T.: Fragility-oriented testing with model execution and reinforcement learning. Simula Research Lab Technical report 2017–05 (2017). http://www.simulano/ publications/fragility-oriented-testing-model-execution-and-reinforcement-learning. Accessed 11 Nov 2021 5. Christakis, M.: Narrowing the gap between verification and systematic testing. Ph.D. Thesis. National Technical University of Athens, Athens, Greece (2015) 6. Efanov, D.V., Khoroshev, V.V.: Ternary questionnaires with errors and uncertainties in the answers. J. Ins. Eng. 62(10), 875–885 (2019). (in Russian) 7. Efanov, D.V., Sapozhnikov, V.V, Sapozhnikov, Vl.V.: Sintez shem vstroennogo kontrolya dlya kombinacionnih cifrovih ustroistv po metodu samodvoistvennogo dopolneniya do koda Bergera. [Synthesis of built-in control circuits for combinational digital devices by the method of self-dual complement to the Berger code.] Izvestiya visshih uchebnih zavedenii. Priborostroenie 64(9), 697–708 (2021). (in Russian) 8. Efanov, D.V., Sapozhnikov, V.V., Sapozhnikov, Vl.V.: Using codes with summation of weighted bits to organize checking of combinational logical devices. Auto. Cont. Com. Sci. 53(1), 1–11 (2019) 9. Efanov, D.V., Sapozhnikov, V.V., Sapozhnikov, Vl.V.: Sum codes with fixed values of multiplicities for detectable unidirectional and asymmetrical errors for technical diagnostics of discrete systems. Automation and Remote Control 80(6), 1082–1097 (2019)

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10. Efanov, D.V., Sapozhnikov, V.V., Sapozhnikov, Vl.V.: Boolean-complement based faulttolerant electronic device architectures. Automation and Remote Control 82(8), 1403–1417 (2021) 11. Efanov, D.V., Sapozhnikov, V.V., Sapozhnikov, Vl.V.: Organization of testing of combinational devices based on boolean complement to constant-weight “1-out-of-4” code with signal compression. Auto. Con. Com. Sci. 55(2), 113–124 (2021) 12. Sagatovsky, V.N.: Osnovi sistematizacii vseobschih kategorii. [Fundamentals of systematization of general categories.] Tom. gos. med. in-t. Izd-vo Tom. Un-ta, Tomsk (1973). (in Russian) 13. GOST 19919–74. Kontrol avtomatizirovannii tehnicheskogo sostoyaniya izdelii aviacionnoi tehniki. Termini i opredeleniya [Automated control of the technical condition of aviation products. Terms and Definitions]. (1974). (in Russian) 14. Sitdikov, F.G., Ziyatdinova, F.M., Zefirov, T.L.: Fiziologicheskie osnovi diagnostiki funkcionalnogo sostoyaniya organizma. [Physiological basis for the diagnosis of the functional state of the body.] Kazan Federal University, Kazan (2019). (in Russian) 15. Levitov, N.D.: O psihicheskih sostoyaniyah cheloveka. [On the mental states of a person.] Prosveschenie, Moscow (1964). (in Russian) 16. GOST 20911–89. Tehnicheskaya diagnostika. Osnovnie termini i opredeleniya [Technical diagnostics. Basic terms and definitions] (1989). (in Russian) 17. Suryanarayanan, S., Roche, R., Hansen, T.M. (eds.): Cyber-physical-social systems and constructs in electric power engineering (energy engineering). 520 p. The Institution of Engineering and Technology, Stevanage (2016) 18. Lipaev, V.V.: Nadejnost programmnogo obespecheniya ASU. [Reliability of ACS software.] Energoizdat, Moscow (1981). (in Russian) 19. Lipaev, V.V.: Kachestvo programmnogo obespecheniya. [The quality of the software.] Finansi i statistika, Moscow (1983). (in Russian) 20. Lee, E.A., Seshia, S.A.: Introduction to embedded systems: A cyber-physical systems approach, 2nd edn. MIT Press, Cambridge, MA (2017) 21. Lee, E.A.: Cyber-physical systems: design challenges. In: 11th International Symposium on Object and Component-Oriented Real-Time Distributed Computing, pp. 363–369. IEEE (2008) 22. Bures, T., Weyns, D., Berger, C., et al.: Software engineering for smart cyber-physical system towards a research agenda: report on the first international workshop on software engineering for smart CPS. ACM SIGSORT Softw. Eng. Notes 40(6), 28–32 (2015) 23. Mikoni, S.V., Sokolov, B.V., Yusupov, R.M.: Kvalimetriya modelei i polimodelnih kompleksov. [Qualimetry of models and polymodel complexes.] RAN, Moscow (2018). (in Russian) 24. Mikoni, S.V., Burakov, D.P.: Obosnovanie i klassifikaciya ocenochnih funkcii_ primenyaemih v reitingovih metodah mnogokriterialnogo vibora. [Substantiation and classification of evaluation functions used in rating methods of multicriteria choice.] Informatika i avtomatizaciya 19 (6), 1131–1165 (2020). (in Russian)

Energy and Power of Management Ivan N. Drogobytskiy(&) Financial University under the Government of the Russian Federation, Leningradsky Prospekt, 49, 125993 Moscow, Russia [email protected]

Abstract. We may say that history of management as a particular discipline began in the 17th century. And transforming management to complex science can be dated by 1930s only. However, there is still no clear opinion about foundations and nature of management. In special literature there is conception of 4 basics for management, such as science, skill, art and mission. But there is still no clearance which one of them is the main. In order to broad the area of scientific researches provided with management here we offer the conception of its energetic imperatives. These are rights, will, forcing and charisma. In the article we analyze the experience of management and come to conclusion that all forms of it are divided in these 4 basics. Although the energy of management from the first point of view seems to be very abstract construction and we don’t have yet any instruments for its measuring, the presence of this phenomena is rather clear. And exploring this type of energy gives start for new perspective achievements in managerial science. Keywords: Management
Energetic imperatives
Energetic potential
Managerial team
Managerial authority
Engineering of management
System approach
Cybernetics

1 Introduction Managing every economical system can be defined as a situation where it is forced to produce goods and services, and satisfy the demands of the external environment. Besides, for wider re-producing the system should be constantly being improved, developed, and studied. Thus, management has an aim to harmonize the working and development of an economic system. And finally, it should provide the consumers of the system satisfying in the current moment and further perspective [7, 12, 18]. The cybernetical theory postulates that management has informational fundament [3, 21]. However, exploring this problem from the physical point of view, we may conclude that the energetic component also presents in management. Because manager’s ability to influence the active subject, a sub-system or a system as a whole can be defined only as a result of energetic power. Accepting this energetic power of an authorized person or a body, some managed element obeys him, and realizes his orders. We do not know yet the nature of this energy, are not able to find it, and, moreover, we cannot measure the managerial energetic phenomenon. In spite of that, we always feel this energy, especially working with charismatic individualities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 250–261, 2022. https://doi.org/10.1007/978-3-030-98832-6_22

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2 Materials and Methods In the present exploration, we use the set theory. From its positions we define every managed system as a set æ consisting of three acts: decision’s creating, making, and realizing [7, 16, 17]. All the previous experience of management proves that the most difficult act is the last one provided with realizing managerial decisions. It is associated with materializing managerial decision created and made earlier just in a managed object. And there is a fair hypothesis that this materialization demands some energy. It is associated with the energetic imperatives of management which are included in a managerial decision and accompany it on the stage of realizing. The sources of this energy are the individual members of a managerial team, who are involved in the processes of managerial decisions’ creating, making, and realizing. Modern economics defines four energetic imperatives of management: rights, will, forcing, and charisma [5, 7, 9]. Rights are given to each manager for making and realizing certain decisions. They don’t depend on the manager’s personal characteristics, his job partners, and previous achievements. Rights depend only on a manager’s status in the administrative hierarchy of a managed system and are connected only with his professional position. If use analogies with mechanics as the most developed physical branch, we may define rights as an analog of potential energy: a manager has right to make decision, but there is no certainty about its being realized. The possibility of practical realizing established right to make managerial decisions depends on many additional factors. Will is a clear wish of a manager to force the managed system and its environment in such a way that provides its best dynamic work and development in the present moment and in further perspective. In the other words, the presence of an imperative named “will” is provided with knowledge of a way to force the managed system in the present moment to conserve its wished trajectory of work and development. Like each other knowledge, a will has a cultural moral foundation. And it may give equally two kinds of power: positive (kindness) or negative (evilness) [8, 11]. Unfortunately, the first acquaintance never gives exact information about the energy of a certain manager’s will. It can be defined only after the moment where the energy of will is being realized. I. e., the energy of will may be defined only in real acts and certain results’ materialization. Using the mechanical analogs again, we conclude that will is also a part of potential energy. But differently from the rights, it is provided with manager’s individual characteristics instead of his hierarchical status. Forcing should be associated with the manager’s ability to make a whole system, sub-system, or its active element to realize a decision created earlier. Forcing is based on the manager’s ability to punish or stimulate the other participants of producing process, including other managers who can impress loyalty or opportunism to the created decisions. Forcing potential appears when a certain element of a system has some needs, which can be satisfied by some other element of a system or a subject of the external environment. Based on this thought, we conclude that forcing is the other kind of potential energy in management, but there should be special skills for using this energy.

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This kind of managerial energy should not be connected exactly with the manager’s individuality or his hierarchical status. It is a result of both of these factors’ functioning. This result depends on the manager’s ability to transform potential energy of his hierarchical status to effective instruments which provide wished behavior of a whole managed system, sub-system, or a particular element. Of course, the described instruments should have legal fundament, without destroying the limits of manager’s competitions. In the opposite way, it leads to corrupting attempts from a manager’s side. During the life cycle, each certain person, economic subject, region, or national state meets many problems. And overcoming them sometimes is impossible only with these systems’ resources. For this reason, each element needed by a system gets the power of forcing this system. Such kind of power is impressed in situations where needed element forces a system to a certain type of behavior. On one hand, forcing is associated with the possibility of cooperation with a managed system that needs external participating. The level of forcing depends on how strong is the necessity of cooperation for a system which needs it [9, 14]. There should be an exact border between rights and forcing. On the higher levels of administrative hierarchy rights are used much oftener than forcing. Abilities of forcing concentrated on these levels are always official and have exact regulations. The real energy of forcing belongs to those who are needed by a managed system first of all in the processes of realizing the system’s mission. As a rule, these are persons provided with operational fundament of a system: e. g., actors in a theatre, lecturers at the university, doctors in a hospital, farmers in a village, etc. If such persons leave a system, it loses ability to realize its mission; and it leads to catastrophic losses for a system as well as for its external environment. Charisma is the ability of a manager to give a certain type of behavior to the managed system, sub-system, or its active element without using rights of forcing. It is easy to understand that manager’s charisma is based on his high authority and special trust in him. It’s a well-known fact that each person obeys absolutely to other person’s will only in the cases of resection and trust to him. Here we may define trust as understanding sureness that manager or team who made the decision are kind-hearted and honest. The respect we define as certain relation to a manager or a team, this relation is based on accepting the idea of their dignity, professionalism, previous achievements, and exclusive characteristics [4, 20]. Nowadays, the objects being influenced by charisma with certain aims, in most of the cases are situated behind the borders of a managed system. So, these objects have no connections with rights and forcing energy of this system’s managers. In relation to the managed system, these objects form so-called transactional environment [1, 2]. It includes consumers, partners, investors, owners, share-holders, state authorities, and other stakeholders. For this reason, in modern circumstances managing every economic system is highly provided with the manager’s charisma in the system’s translational environment. Charisma’s success strongly depends on culture. It is associated with laws, which form the base of our collective memory. These laws unite the elements of a managed system and external environment with causal connections to some completed structure which can be accepted as a social analog of DNA in biology. When a structure is controlled by itself only, its internal cultural codes develop like organizational

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principles. They reproduce the current order without any fluctuations. For this reason, culture is accepted as a constant part of organizational management. The following analyzed energetic imperatives: rights, will, forcing and charisma, are to be accumulated in a managerial decision in order to form its energetic potential. During the third final act of managerial influencing provided with decision’s realization this potential is being transformed to managerial work (direct managerial influencing), which’s mission is to give the needed trend of development to a managed system as a whole, its subsystem or a particular element. Finally, the results of the managerial decisions depend on their energetic potential. This energy is divided in different proportions between the team members; for this reason, an aim of a leader is to be able to accumulate enough quantity of managerial energy’s certain type for making a managerial decision.

3 Results Here we use the analyzed energetic imperatives as pure kinds of managerial energy. We explore the processes where they are mixed with each other. As a result of this mixing, they strengthen each other, and create hybrid kinds of managerial energy. This mixing may take place both in cases of individual managers and a whole managerial team. Mixing all kinds of managerial energy owned by an individual manager gives his energetic potential. In the same way, mixing all kinds of managerial energy owned by a team gives its energetic potential. Energetic potential defines the volume of managerial work which can be overcome by a team or a manager. So, an energetic potential may be associated with power owned by them. And the specialists use this definition in the same way. Managerial activity is based on information and knowledge. For this reason, an energetic potential of a team or a manager is not lost in these activities, but it is increased by them. The reason for this is a cyclic transformation of formalized and informalized knowledge of the each manager’s team during a process of management [10, 13]. The last one provides a constant increasing of information and knowledge owned by a team and every team’s member. Inside every active team, there are special circumstances where all members study and develop themselves. The experience of a team passes through the stages of socialization, externalization, and combination [6, 19]. And then it is internationalized to an informalized experience of each member, and the levels of energetic imperatives are increased. Firstly, the imperatives of will and charisma are increased. Later it leads to increasing imperatives of rights (owing to new professional position) and forcing (this ability is given by new position). In Table 1 there are hybrid kinds of managerial power formed by double mixes of energy. Here we expect that all pure kinds of energy are equal, and in pairs there is no difference between their positions.

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Kinds of managerial energy Rights

Rights

Will

Forcing

Charisma

—

Will

Will full of rights Forcing full of rights Charismatic rights

Will full of rights —

Forcing full of rights Willful forcing

Willful forcing Willful charisma

—

Charismatic rights Willful charisma Charismatic forcing —

Forcing Charisma

Charismatic forcing

If will and rights are mixed, there is a will that has the full rights. It means that a wish to improve a managed system is impressed by a manager who has enough rights for this. Such a wish has good chances to be realized. Rights without a will cannot give a result because a manager with these characteristics doesn’t know what to do. And a will without rights is even much worse because a manager has no ability to realize ideas provided with improving a managed system. Mixing rights with forcing gives forcing full of rights. Such a manager has the legal ability to give premiums or punish all the participants of business processes including other managers. It is the simplest kind of managerial power, but at the same time, it is the most dangerous. On one hand, it allowed receiving needed results quickly owing to prizes or punishments. But on the other hand, using this power often and without reason destroys its bases. This practice makes the manager’s authority and will lower, and his influence on the situation becomes weaker. Finally, personal has doubts about such manager’s legacy. Managers with rights without forcing can give the orders but they cannot influence people who do not obey them. Contrarily, a manager who has a big potential of forcing without rights stops cooperating with managed system and makes harm to it. Mixing charisma with rights forms characteristic rights. Manager with such kind of hybrid energy strengthens his official rights by professional status and personal features (respectable relations to others, kindness, and empathy). Giving official orders to colleges, such manager is also able to explain their necessity. Charisma without rights is present when the participants of business processes obey the orders of “forcing agents” instead of official representatives sent by the managerial body. Rights without charisma lead to a situation where a manager gives orders relying on his administrative resources only. Mixing will with forcing leads to willful forcing. As a rule, such kind of managerial energy is present in a situation where a manager realizes his will on the basis of bonusing loyal colleges and punishing deviants. Forcing without a will is usually associated with leader’s ambitions of a manager who hasn’t base for them. He doesn’t know his own wishes but uses bonuses and punishments in every case. At the same time, will without forcing (like will without rights) has no potential for being realized. A manager knows what to do but he can’t make colleges follow his orders.

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Mixing will with charisma gives willful charisma. This hybrid managerial power means that manager has his own opinion on the ways of system’s development and at the same time he is a well-known specialist in this professional field. Owing to it he has great charisma. It increases the chances that the decision is to be realized. The situation of will without charisma means that manager’s decisions for increasing powerful ambitions has no influence on the colleges. And charisma without a will should not be used at all. If you do not know what to do for improving managed system it is better to stop activities. Here you should follow the medical rule “do not cause harm”. Mixing charisma with forcing gives charismatic forcing. This kind of managerial energy means that a manager who can force his colleges realizes decisions on the basis which also includes charisma and ability to persuade the others. Such material behavior is able to reduce the conflicts provided with rude realizing of decision. If charisma is absent, a manager can rely on such æ rude force only. If forcing is absent, a manager can only believe that colleges follow the orders owing to his charisma.

4 Discussion There can be objections that double combinations of energetic imperatives cannot describe exactly the whole area provided with managerial activities. For broader description triple combinations should be taken into account too. If the rights, will and forcing are mixed there is rude dictator’s power (see Fig. 1a). A manager with such kind of power is able to make and realize all kinds of decisions. Even if he hasn’t enough authority and ability to influence, all his decisions are legal and compulsory, they express the manager’s will. Disobeying directly leads to legal punishment. This unlimited dictator’s powers may be stopped only by bringing down a manager. I. e., such kind of power belongs to the President of Byelorussia today. If will, forcing and charisma are mixed there is illegal dictator’s power (see Fig. 1b). Such a kind of manager compensates for the absence of rights with strong authority and professionalism in a field where managed system acts. Such kind of power belongs to the manager who unexpectedly became a head of a protective team which creates an innovation. The other example is a criminal leader in an area controlled by his gang. Mixing rights, will and forcing gives soft a dictator’s power (see Fig. 1c). A manager who has this kind of power is able principally to realize each decision. However, in case of strong resistance from a managed system he has to ask for help form his colleges who own the energy of forcing. The example, here are the prison administrators who use the support of criminal leaders for keeping discipline. Mixing rights, charisma and forcing gives silly dictator’s power (see Fig. 1d). Manager with such kind of power has the right to give orders to personal provided with ways of work. He may also give bonuses and punish personal according to the results of work. He may also influence on interests and moral atmosphere in a team. But because of will’s absence, such a manager has to look for his own way in the professional area of a managed system.

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Fig. 1. Kinds of unlimited managerial power.

Mixing all four imperatives creates absolute managerial power (Fig. 1e). A manager who has this kind of power is able to do everything he wants. In this case, he controls all types of three stages of managerial decisions: creating, making, and realizing. Such kind of power was actively used in the Russian Empire, and in the Soviet Union. Unfortunately, governing apparatus of modern Russia also begins to use some elements of it.

5 Conclusion Combinations of energetic imperatives described here are the base of managerial engineering.

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It is clear, that every manager’s potential is limited. It is equal to the sum of current energetic imperatives. PM ¼ PðoÞ þ PðvÞ þ Pð f Þ þ PðaÞ:

ð1Þ

Here PM is the total managerial potential; PðoÞ is the current meaning of rights, or office-power; PðvÞ is the current meaning of will, or volition; Pð f Þ is the current meaning of forcing; PðaÞ is the current meaning of charisma, or authority. Simple sum of managerial potentials owned by the members of a team gives its minimal managerial potential, or a lower border. Pj ¼

I X i¼1

PM i :

ð2Þ

Here Pj is a lower border of a team’s managerial potential; I is a number of members. Logically we may conclude that the current managerial potential of a team is much higher than its lower border because of emergent effect. Pj ¼ FðPj ; CÞ:

ð3Þ

Here Pj is a current managerial potential of a team; C means achieved levels of cooperation and compromise between the members. Increasing communicable skills and unity of a team leads to its ability to solve the most difficult tasks. We may conclude that an ideal team can overcome all the problems in its work. Addition managerial energy is generated by a team owing to improvement of cooperation between the members. During his business processes, a manager should be able to use rightly managerial potential owned by himself and by the members of his team. For this aim, he should have the right orientation in job problems. A manager usually receives information about a problem from mail-massage, phone call, or a visitor. First of all, he should coordinate this problem with his officepower. If the problem is out of square provided with the manager’s professional duties (see Fig. 2a), a manager should forward this information to some other specialist, who has enough competencies for decision. Here a manager doesn’t ignore a problem, he only plays a role of navigator. In the other case, a problem is in the center of the manager’s responsibility square, and all his energetic imperatives are mixed here. A manager has full power for decision-making (see Fig. 2b), and he doesn’t need any support from the other members of a team. In this situation, full managerial energy Pn has the following mathematical description:

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PP  PM ðoÞ þ PM ðvÞ þ PM ðf Þ þ PM ðaÞ:

ð4Þ

If a problem is in the circle of the manager’s office power (see Fig. 2c) he should make a decision using all acceptable instruments. But he has only office-power without a will, forcing, and authority. In this situation, a manager should organize a meeting with other members of the managerial team in order to unite his office-power with energetic imperatives owned by them. Full managerial energy in this situation: Pn  PM ðoÞ þ Pj ðvÞ þ Pj ðf Þ þ Pj ðaÞ:

ð5Þ

where PM ðoÞ is office-power of a manager; Pj ðvÞ; Pj ðf Þ; Pj ðaÞ are the levels of volition, forcing, and authority owned by the other members of a team. The second kind of situation provided with a problem which is in the circle of manager’s volition (see Fig. 2d). This case is a little more difficult than the previous one. Because a manager with a high level of volition needs help from a manager who has office-power enough. This person organizes meetings which allow uniting all kinds of managerial energy: PP  PK ðoÞ þ PM ðvÞ þ Pj ðf Þ þ Pj ðaÞ:

ð6Þ

Here a manager with volition translates it to all the team through a person with officepower. If a problem is in the circle of the manager’s forcing power (see Fig. 2e), he may give bonuses to the other members of a team or punish them for disobeying. It is a very effective resource, however, its frequent use may destroy the legal bases of power. It is better if in such a situation the manager with forcing power acts in the same way as a manager with strong volition from the previous case. He should ask a manager with office-power to organize a meeting in such a way as to unite all kinds of managerial energy. Pn  PK ðoÞ þ PK ðvÞ þ PM ðf Þ þ Pj ðaÞ:

ð7Þ

The most difficult case is provided with a problem that is in the circle of the manager’s authority (see Fig. 2f). Here he is between two fires. On one hand, his acts are limited by managers from the upper levels of hierarchy. On the other hand, forcing power belongs to those who are on the lower levels. And a manager with authority is between these two levels with no ideas about the ways of decision-making. Here he should be ä skillful diplomat so as to pass through three stages. Firstly, he should find a person who knows a way of decision-making and has enough volition for this activity. Secondly, he should receive support from those who have forcing power on the lower levels. It is necessary to avoid sabotage from staff in the process of further business. And on the final stage, he should ask a manager with office-power to organize a meeting and unite all energetic imperatives.

Energy and Power of Management

Fig. 2. Engineering of management.

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PP  PK ðoÞ þ PK ðvÞ þ PK ðf Þ þ PM ðaÞ:

ð8Þ

Success in this process can be reached by a manager with authority only if he makes effective communications with all other types of managers. So, we see that manager is able to realize the decision if he is a skillful diplomat, good organizer, strong politician, and cynical rationalist at the same time. Manager’s activity may be successful if he can exactly identify the qualities of all kinds of managerial energy needed for the decision. And then his mission is to organize the cooperation of managers with all these kinds of energy. Scientific and professional societies provided with management should accept the idea of its energetic nature. The explorations in this area are to be continued and we are sure that many great discoveries wait for humanity here. Reforming the paradigms of organizational management begins with this work.

References 1. Adizes, I.: The Pursuit of Prime. Knowledge Exchange, Santa Monica (1996) 2. Adizes, I.: Razvitie liderov: Kak ponyat’ svoj stil’ upravleniya i effektivno obshchat’sya s nositelyami inyh stilej. [Leader’s development: How to understand your management style and communicate effectively with speakers of other styles.] Alpina Business Books, Moscow (2008). (In Russian) 3. Viner, N.: Cybernetics: or control and communications in the animal and the machine. Hermann & Cia, Paris (1948) 4. Garfinkel, G.: Conception and exploration of trust as a factor of global development. Sociological explorations 1, 3–25 (2009) 5. Gumerov, M.: Novye podhody i modeli organizacionnogo upravleniya v usloviyah sovremennoj ekonomiki. [New approaches and models in organizational management under the circumstances of modern economy.] 289 p. Professor, Moscow (2018). (In Russian) 6. Drogobetsky, A.I.: Korporativnoe upravlenie v znanievoj ekonomike. [Corporate management in economy of knowledge.] 149 p. Ekonomika, Moscow (2008). (In Russian) 7. Drogobytskiy, I.N.: Sistemnaya kibernetizaciya organizacionnogo upravleniya. [System cybernatization of organizational management.] 332 p. Vuzovskij uchebnik [University Book] : INFRA-M, Moscow (2016). (In Russian) 8. Ianenko, M.B., Badalov, L.A., Rovensky, Y.A., Bunich, G.A., Gerasimova, E.B.: Essence, risks and control of uncertainties in the process of making investment decisions. Espacios 39 (2018) 9. Kapustina, N.V.: Teoretiko-metodologicheskie podhody risk-menedzhmenta. [Theoretic and methodological approaches in risk-management.] 138 p. Infra-M, Moscow (2016). (In Russian) 10. Kleiner, G.B., Karpinskaya, V.A.: Transition of Firms from the Traditional to Ecosystem Form of Business: The Factor of Transaction Costs. In: Inshakova, A.O., Inshakova, E.I. (eds.) CRFMELD 2019. LNNS, vol. 110, pp. 3–14. Springer, Cham (2020). https://doi.org/ 10.1007/978-3-030-45913-0_1 11. Maergoiz, L.S., Khlebopros, R.G.: The indicator of “happiness” in the resource-based economy: an extreme approach. Journal of Siberian Federal University. Humanities and Social Sciences 9(8), 1739–1745 (2016). (In Russian)

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12. Mazur, I., Shapiro, V., Olderroge, N.: Total history of management. Elma, Moscow (2006). (In Russian) 13. Makarov, V.L., Kleiner, G.B.: Mikroekonomika znanij. [Microeconomics of knowledge.] 203 p. Ekonomika, Moscow (2007). (In Russian) 14. Mintzberg, H.: Struktura v kulake: sozdanie effektivnoj organizacii. [Structure in fives: designing effective organizations.] Transl. by Raevskajya, D. Piter, St. Petersburg (2001). (In Russian) 15. Nonaka, I., Takeuchi, H.: Kompaniya — sozdatel’ znaniya: Zarozhdenie i razvitie innovacij v yaponskih firmah. [Company as a creator of knowledge. The origin and development of innovations in Japanese firms.] Transl. from Eng. by Traktinskij, A. 361 p. Olimp-Biznes, Moscow (2003). (In Russian) 16. Priven, A.I., Kynin, A.T.: A phenomenological model of parameter growth in engineering systems. Inter. J. Syst. Innov. 2, 9–23 (2013) 17. Raizberg, B., Tulyakov, A.: Strategicheskoe planirovanie i upravlenie social’noekonomicheskimi ob’’ektami. [Strategic planning and management in social and economic objects.] 222 p. Ekonomika, Moscow (2016). (In Russian) 18. Semenychev, V.K., Kurkin, E.I., Danilova, A.A., Semenychev, E.V.: Multimodel forecasting of non-renewable resources production. Energy 130, 448–460 (2017). https://doi.org/10. 1016/j.energy.2017.04.098 19. Tyukin, I.Y., Gorban, A.N., Sofeykov, K.I., Romanenko, I.: Knowledge transfer between artificial intelligence systems. Frontiers in Neurorobotics, AUG (2018) 20. Fukuyama, F.: Doverie: social’nye dobrodeteli i put’ k procvetaniyu. [Trust: social characteristics and way to prosperity.] Transl. from Eng. 730 p. AST : Ermak, Moscow (2004). (In Russian) 21. Shennon, K.: Raboty po teorii informacii i kibernetike. Sbornik statej. [Theory of information and cybernetics. Collection of articles.] Transl. from Eng. 829 p. Izd-vo inostrannoj literatury [Foreign literature], Moscow (1963). (In Russian)

Hybrid Simulation as a Key Tool for Socio-economic Systems Modeling Aleksei M. Gintciak(&) , Marina V. Bolsunovskaya , Zhanna V. Burlutskaya , and Alexandra A. Petryaeva Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya st., 195251 St. Petersburg, Russia {aleksei.gintciak,marina.bolsunovskaia, zhanna.burlutskaya,alexandra.petryaeva}@spbpu.com

Abstract. The purpose of this work is to prepare a methodological base for modeling socio-technical and socio-economic systems. The development of digital modeling practices will improve the quality of management decisions, as well as create a synergetic effect of the use of digital models in various sectors of the real sector of the economy. The need to solve this problem is due to an increase in complexity and a decrease in the level of determinism of socioeconomic systems in comparison with technical systems. This paper presents an analytical review of the basic modeling paradigms as the basis of hybrid models capable of solving complex problems of socio-economic systems. This paper also provides an overview of the modeling tools of technical systems that are applicable in the modeling of socio-economic systems: dynamic forecasting, interval approach, sensitivity analysis, calibration, verification, validation. Using these tools will improve the accuracy and quality of the model. The presented research will serve as an analytical basis for modeling socio-technical and socioeconomic systems. Keywords: Hybrid modeling


Socio-economic systems
Digital models

1 Introduction This research is aimed at solving the scientific problem of making informed management decisions in the management of socio-technical and socio-economic systems, which has a high scientific and practical significance. The peculiarity of modeling socio-economic systems is an increase in complexity and a decrease in the level of determinism in comparison with the modeling of technical systems and even sociotechnical ones. The lack of methods of management of socio-technical and socioeconomic systems adequate to the tasks to be solved leads to the non-optimality of management decisions, which significantly reduces the quality of management and the efficiency of the functioning of the system as a whole. Existing methods widely used in decision-making in socio-technical and socio-economic systems have weak predictive power, are focused on a short-term period, are often based on expert opinion, and therefore are not applicable for supporting managerial decision-making. The designated scientific problem is proposed to be solved using digital modeling tools. It is also © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 262–272, 2022. https://doi.org/10.1007/978-3-030-98832-6_23

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worth considering that each system is unique in terms of a set of key indicators and the pace of development. Accordingly, it is necessary to consider possible approaches to hybridization of existing modeling paradigms, from the point of view of their applicability to the specific features of each unique modeling object. An equally important problem is the reduction of the level of determinism of the system. The possible integration of technical systems modeling tools in the modeling of socio-economic systems is also presented as a solution to the problem of a high level of uncertainty. It is necessary to select the most applicable tools, taking into account the justification of the importance of their use in modeling socio-economic systems. The development of the methodological base and the development of practices for digital modeling of sociotechnical and production systems will improve the quality of management decisions, as well as create a synergetic effect of the use of digital models in various sectors of the real sector of the economy.

2 Materials and Methods 2.1

Basic Modeling Paradigms

Simulation modeling is an important tool in the study of socio-economic systems. It allows you to simulate the behavior of a real system, conduct various experiments on it, and predict the further development of events. In addition, the modeling process itself allows researchers to better understand the system under study and provide the results of the study to other scientists in a formalized form. Simulation models are ideal for analyzing the risks and prospects of certain management decisions. There are several basic paradigms of simulation modeling that radically differ from each other in goals, levels of detail and abstraction, and approach to the flow of time. The three main paradigms are system dynamics, discrete event modeling, and agent modeling. There are other modeling paradigms, for example, system dynamics or game modeling [1, 2], but their application is possible in too highly specialized industries to solve specific problems. Each of the three main approaches (system dynamics, discrete-event modeling, agent modeling) has its advantages and disadvantages, so it is very important to choose the approach that best meets the objectives of the study at the beginning of the model construction [3]. The discrete-event approach (DES) represents the simulated process, the dynamics of the system changes as a sequence of discrete events occurring at certain intervals. It operates with such concepts as entities (external elements entering the system), events (changing the status of entities), resources (objects that change the status of entities in the system) [4, 5]. Visualization of models in the discrete-event approach consists of statically located elements of the system (resources, queues, logical operators) and entities moving between them that are in a certain status at any given time. The discrete-event approach is usually used to model logistics [6] and production [7] systems, queuing systems [8].

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System dynamics (SD) uses a different approach based on the continuous change of some quantities over time. System dynamics, similar to the discrete-event approach, operates with three objects: stocks (accumulated integral quantities), flows (quantities that change the values of stocks), converters (auxiliary quantities without definite dynamics) [9, 10]. Visualization of models in system dynamics is a static combination of elements of all three types connected in a certain way. An important feature of models in system dynamics is the ability to qualitatively predict the behavior of the system without conducting a simulation experiment based on cause-and-effect relationships, which are presented and visualized in the model [11]. The simulation experiment consists of the numerical integration of a system of differential equations for different initial values of the indicators. Another advantage of the system dynamics models is the ability to consider the natural limitations of the system. This means that the classic approach can accurately predict the growth rate but does not consider the possible negative consequences of excessive or too rapid development of the system. System dynamics is used in the highly abstract modeling of sociotechnical systems [12, 13]. Agent-based modeling (ABS) presents the simulated systems as aggregates of autonomous elements (agents) that have their characteristics and behavioral models. In the process of modeling, they interact with each other and with the environment, while passing from one state to another [14]. Due to the comparative novelty of the approach, modeling visualization does not yet have standard solutions, therefore it differs significantly from the modeling software. At the same time, as it is correct, the tools for visualizing results in such software tools have an important significance [15]. Due to the universality of the representation of the simulated systems, agent modeling is used to solve a wide variety of problems of various degrees of abstraction and detail [16, 17].

3 Results 3.1

Hybrid Modeling

In the last decade, so-called hybrid modeling has been developed in operations research. This term refers to the combination of different paradigms to model a single system. The article [18] gives the following definition: “Hybrid modeling is a modeling that combines two or more modeling methods to achieve results that cannot be obtained using combined methods separately.” It should be noted that in this case, we are not talking about using different approaches to modeling within one model or even within one software tool. Hybrid modeling is an exceptionally methodological approach that allows studying the same modeled object from different sides or at different levels of abstraction [19]. This approach provides a comprehensive view of the processes implemented in the system, which means it will not be limited in terms of one of the three components: changes in indicators, changes in the behavior of agents, or events that have occurred. It is worth considering that the hybrid model does not imply the joint use of different techniques

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within the same experiment. It is rather a correlation of the simulation results of each of the paradigms to obtain a general concept of the behavior of the system. Although the very concept of combining different types of modeling is not particularly complex and original, in recent years its popularity has continued to grow. There are reasons to believe that this fact is connected with the need for a more complete study of the systems of interest using separate approaches to modeling. The popularity of the hybrid approach is also increasing when modeling sociotechnical systems for management optimization — this is confirmed by the positive dynamics of the number of publications in the domains of operations research, control systems, and industrial engineering. Hybrid modeling is a collective term for various approaches that have their application features. Different classes of hybrid models solve different tasks. In this paper, the classification of hybrid models is considered on two grounds: by the type of combined basic approaches and by the type of interdependencies of the basic approaches within the hybrid. The classification of hybrid modeling (see Fig. 1) [18].

Fig. 1. The classification of hybrid modeling.

Hybrid modeling is divided into four types according to a set of combined basic approaches to modeling. The most popular in the study of operations is the combination of a system-dynamic and discrete-event approach [19, 20] — it accounts for almost half of all cases of hybrid modeling. Combinations of the agent approach with the discrete-event approach [21] and the system-dynamic approach [22] are used in approximately equal proportion. There are fewer cases of combining three approaches at once, which can be explained both by the complexity of such modeling and the lack of a real need to study systems using such approaches. The ratio of publications in the bibliographic database of Web of Science on request by type of combined approaches (as of October 2021) is shown in Fig. 2.

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Distribuon of publicaons by model types

SD+DES

DES+ABS

ABS+SD

SD+DES+ABS

Fig. 2. Distribution of publications by model types.

There is another approach to classifying hybrid modeling. The authors of the article [23] propose to separate the cases of hybrid modeling based on the interdependencies of the basic approaches within the hybrid. So, according to this criterion, the models are divided into four classes: conjugate, sequential, augmented, and integrated. In mating hybrid modeling [24], models of different classes are developed and applied separately from each other (see Fig. 3). The advantage of this approach is that at different stages of development and application of models (including at the moment of obtaining results), there is a possibility of cross-validation of models, comparison of intermediate or total results to identify opportunities for improvement, or addition.

Fig. 3. The flowchart of interaction type of hybridization.

In sequential hybrid modeling [25, 26], the basic approaches are applied one after the other, the models themselves are not connected, but the results of the previous method are used in the following method (see Fig. 4). This hybrid modeling approach makes it possible to model systems with complex multilevel behavior that is difficult to describe using only one of the basic approaches.

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Fig. 4. The flowchart of sequential type of hybridization.

With complementary hybrid modeling [27, 28], one of the basic approaches used is the main one, and in the process of its application, the results obtained in the modeling process using other approaches are used (see Fig. 5). This hybrid modeling approach is applicable if, to achieve the main goal of modeling, there is a need to consider the system or its elements at different levels of abstraction and detail.

Fig. 5. The flowchart of enriching type of hybridization.

In integrating hybrid modeling [29], different approaches are used in modeling different subsystems, but modeling is not performed separately, there is a constant relationship between models of different types (see Fig. 6). This approach of hybrid modeling is rationally applied when working with complex systems, the elements of which differ in qualitative heterogeneity, but at the same time are largely interrelated. That is why this class of approaches is the most frequently encountered in the modeling of sociotechnical systems.

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Fig. 6. The flowchart of integration type of hybridization.

3.2

Modeling Tools

For socio-economic systems, simulation modeling is the most adequate way to study systems and processes in the absence of complete, accurate, and reliable information about their properties. However, despite the emphasis on socio-economic and sociotechnical models, it is worth integrating the tools and approaches used to model technological systems. This approach is conditioned by the need for the maximum possible determinism and transparency of the results. Within the framework of the work, the following tools for modeling technical systems were identified, which need to be integrated into models of socio-economic and socio-technical systems: • mathematical modeling and dynamic forecasting — completion of a series of virtual tests (simulation experiments); • interval approach — reducing the influence of uncertainty conditions in the initial data for modeling on the final result; to provide the possibility of forming a family of forecasts; • sensitivity analysis — assessment of the impact of possible inaccuracies in the original data on the simulation results; • calibration — updating forecasts based on current discrepancies between forecast and actual data series; • verification — confirmation of compliance of the results of simulation experiments with existing mental models; • validation — confirmation of compliance of the results of simulation experiments with real data. Dynamic modeling makes it possible to trace continuous changes in the parameters of the model over time, as well as to assess the impact of the velocity characteristics of parameter changes on the modeling object as a whole. Verification and validation are necessary at the stage of comparison of analytical and numerical results. Accordingly, the purpose of verification may be to determine the quantitative value of the error for a given model. Validation will serve as an indicator of the correspondence of the numerical model to the real object of research. Within the framework of modeling socio-economic systems, validation and verification processes

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can serve as a source of information for including additional parameters that determine the behavior of the system.

4 Discussion The following areas of application of models are distinguished [30]: • • • • • • •

R — reasoning (determination of conditions and inference of logical consequences); E — explanation (providing explanations of empirical phenomena); D — development (selection of characteristics of simulated objects); C — communication (transfer of knowledge and ideas); A — action (ensuring the choice of actions and management decisions); P — prediction (obtaining forecasts of future phenomena); E — exploration (study of possibilities and hypotheses).

Existing models are often aimed at application in several areas at once. At the same time, the construction of any model is impossible without taking into account the first four blocks, which represent the descriptive part of the system, taking into account its key parameters and their interrelations. Special attention should be paid to the process of knowledge transfer between the elements of the system. In this case, it is necessary not only to determine the mechanisms of knowledge transfer but also the speed characteristics of this process. The development of the system as a whole will directly depend on this. In comparison with simulation modeling, graphical models represent a visual representation of the system under consideration and often become the basis for the same simulation models. Network modeling can be considered as an example of graphical models [31]. The network model allows you to analyze learning and distribution in networks, decision-making by individual network elements influenced by their social neighbors, game theory and markets in networks, as well as many related topics. The next stage in the development of network models can be considered neural network models that have the ability to self-study and develop. The structural model, in contrast to the simulation model, is aimed at ensuring the transfer of knowledge between the objects of the model, taking into account their characteristics. The purpose of the structural model is to identify critical elements of the system. Thus, neither structural nor graphical models allow testing hypotheses, taking into account the simulation of various source data to obtain predictions of the behavior of the system, and most importantly, they do not provide the possibility of choosing actions. Each of the models has its advantages and can be implemented as part of the preparatory stages for creating a simulation model as the final result. Simulation modeling is exclusively aimed at working with predictive and prescriptive analytics. Forecasting in this case is considered not only as an analysis of possible events but also as part of determining trends. It is important to ensure that the goals of forecasting and explanation are met together since even an accurate forecast of the occurrence of an event has little value without analyzing the causes of the occurrence of this event. This implies an equally important area of application of models — research, processing of intuitive hypotheses. The most common application of prescriptive analytics is the

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analysis of the investment attractiveness of projects, especially in the field of state support. This feature allows simulation models to be used for all types of modeling tasks. Within the framework of modeling socio-economic and socio-technical systems, the use of hybrid-type simulation modeling, depending on the nature of the described system, seems to be the only way to provide complete analytical information for making competent and timely management decisions, taking into account the analysis of risks and slippery spots.

5 Conclusions This article discusses approaches to the modeling of socio-technical, and socioeconomic systems. The basic paradigms of simulation modeling are considered: system dynamics, discrete-event and agent modeling. Based on these three modeling paradigms, concepts on various ways of hybridization of these paradigms are presented: conjugate, sequential, augmented, and integrated. Each of these concepts has its advantages, so the choice depends on the characteristics of the modeling object. Another classification of hybrid models is based on a combination of different paradigms. The most popular combination is a system-dynamic approach with a discrete event approach - about 50% of the analyzed articles. Thus, it is necessary to ensure a two-step selection of the model: the choice of paradigms depending on the goals of modeling, the choice of the method of hybridization depending on the characteristics of the modeling object. This paper also provides an overview of the modeling tools of technical systems that are applicable in the modeling of socio-economic systems: dynamic forecasting, interval approach, sensitivity analysis, calibration, verification, validation. Using these tools will improve the accuracy and quality of the model. The presented research will serve as an analytical basis for modeling socio-technical and socio-economic systems. Acknowledgment. The research is funded by the Ministry of Science and Higher Education of the Russian Federation (contract No. 075–03-2021–050 dated 29.12.2020).

References 1. Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L.K., Young, T.: Simulation in manufacturing and business: A review. Eur. J. Oper. Res. 201(1), 1–13 (2010) 2. Jain, S., McLean, C.R.: Components of an incident management simulation and gaming framework and related developments. SIMULATION 84(1), 3–25 (2008). https://doi.org/10. 1177/0037549708088956 3. Maidstone, R.: Discrete event simulation, system dynamics and agent based simulation: Discussion and comparison. System, pp. 1–6 (2012) 4. Guimarães, A.M.C., Leal, J.E., Mendes, P.: Discrete-event simulation software selection for manufacturing based on the maturity model. Comput. Ind. 103, 14–27 (2018). https://doi. org/10.1016/j.compind.2018.09.005 5. Zeigler, B.P., Muzy, A.: From discrete event simulation to discrete event specified systems (DEVS). IFAC-PapersOnLine 50(1), 3039–3044 (2017)

Hybrid Simulation as a Key Tool for Socio-economic Systems

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6. Malandri, C., Briccoli, M., Mantecchini, L., Paganelli, F.: A: discrete event simulation model for inbound baggage handling. Transportation Research Procedia 35, 295–304 (2018) 7. Antonelli, D., Litwin, P., Stadnicka, D.: Multiple system dynamics and discrete event simulation for manufacturing system performance evaluation. Procedia CIRP 78, 178–183 (2018) 8. Coelho, P., Silva, C., Ferreira, L.M., Franca, B.: Operational improvement of an industrial equipment rental system using discrete event simulation. IFAC-PapersOnLine 51(11), 478– 483 (2018) 9. Rebs, T., Brandenburg, M., Seuring, S.: System dynamics modeling for sustainable supply chain management: a literature review and systems thinking approach. J. Clean. Prod. 208, 1265–1280 (2019) 10. Franco, E.F., Hirama, K., Carvalho, M.M.: Applying system dynamics approach in software and information system projects: a mapping study. Inf. Softw. Technol. 93, 58–73 (2018) 11. Zhang, L., Antony Chettupuzha, A.J., Chen, H., Wu, X., Abourizk, S.M.: Fuzzy cognitive maps enabled root cause analysis in complex projects. Appl. Soft Comput. 57, 235–249 (2017) 12. Stadnicka, D., Litwin, P.: Value stream mapping and system dynamics integration for manufacturing line modelling and analysis. Int. J. Prod. Econ. 208, 400–411 (2019) 13. Castellacci, F.: Co-evolutionary growth: a system dynamics model. Econ. Model. 70, 272– 287 (2018) 14. Hansen, P., Liu, X., Morrison, G.M.: Agent-based modelling and socio-technical energy transitions: a systematic literature review. Energy Res. Soc. Sci. 49, 41–52 (2019) 15. Abar, S., Theodoropoulos, G.K., Lemarinier, P., O’Hare, G.M.P.: Agent based modelling and simulation tools: a review of the state-of-art software. Computer Science Review 24, 13– 33 (2017). https://doi.org/10.1016/j.cosrev.2017.03.001 16. Utomo, D.S., Onggo, B.S., Eldridge, S.: Applications of agent-based modelling and simulation in the agri-food supply chains. Eur. J. Oper. Res. 269(3), 794–805 (2018) 17. Ahmed, F.D., Majid, M.A.: Towards agent-based petri net decision making modelling for cloud service composition: a literature survey. J. Netw. Comput. Appl. 130, 14–38 (2019) 18. Brailsford, S.C., Eldabi, T., Kunc, M., Mustafee, N., Osorio, A.F.: Hybrid simulation modelling in operational research: A state-of-the-art review. European J. Operational Res. 278(3), 721–737. Elsevier (2019). DOI: https://doi.org/10.1016/j.ejor.2018.10.025 19. Burger, K., White, L., Yearworth, M.: Developing a smart operational research with hybrid practice theories. Eur. J. Oper. Res. 277(3), 1137–1150 (2019) 20. Jamalnia, A., Feili, A.: A simulation testing and analysis of aggregate production planning strategies. Prod. Plann. Cont. 24(6), 423–448 (2013) 21. Abduaziz, O., Cheng, J.K., Tahar, R.M., Varma, R.: A hybrid simulation model for green logistics assessment in automotive industry. Procedia Engineering 100, 960–969 (2015) 22. Swinerd, C., McNaught, K.R.: Design classes for hybrid simulations involving agent-based and system dynamics models. Simul. Model. Pract. Theory 25, 118–133 (2012) 23. Farsi, M., Erkoyuncu, J.A., Steenstra, D., Roy, R.: A modular hybrid simulation framework for complex manufacturing system design. Simul. Model. Pract. Theory 94, 14–30 (2019). https://doi.org/10.1016/j.simpat.2019.02.002 24. Barbosa, C., Azevedo, A.: Hybrid simulation for complex manufacturing value-chain environments. Procedia Manufacturing 11, 1404–1412 (2017) 25. Scholl, H.J., Phelan, S.E.: Using integrated top-down and bottom-up dynamic modeling for triangulation and interdisciplinary theory integration. In: The 22nd International Conference of the System Dynamics Society, July 25–29, 2004, Oxford, England, pp. 25–29 (2004)

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26. Vasudevan, K., Son, Y.-J.: Concurrent consideration of evacuation safety and productivity in manufacturing facility planning using multi-paradigm simulation. Comput. Ind. Eng. 61(4), 1135–1148 (2011) 27. Jain, S., Lindskog, E., Andersson, J., Johansson, B.: A hierarchical approach for evaluating energy trade-offs in supply chains. Int. J. Prod. Econ. 146(2), 411–422 (2013) 28. Renna, P.: Controllable processing time policies for job shop manufacturing system. Int. J. Adv. Manuf. Technol. 67(9–12), 2127–2136 (2013) 29. Pawlewski, P.: DES/ABS Approach to Simulate Warehouse Operations. In: Bajo, J., et al. (eds.) PAAMS 2015. CCIS, vol. 524, pp. 115–125. Springer, Cham (2015). https://doi.org/ 10.1007/978-3-319-19033-4_10 30. Page, E.S.: The Model Thinker: What You Need to Know to Make Data Work for You. Basic Books, New York, NY (2018) 31. Jackson, M.O.: Social and economic networks. Princeton University Press, Princeton, NJ. p. 520 (2010)

Balancedness of Economic, Legal and Social Macrosystems Based on Decision Making Modeling Lidiya V. Zhukovskaya(&) Central Economics and Mathematics Institute of the Russian Academy of Sciences, 47 Nakhimovsky Avenue, 117418 Moscow, Russia [email protected]

Abstract. Does the existing market-oriented economic model develop science, education, culture, health care, the population’s social security and support, and other social branches, particularly the entire social sphere? How does this model correlate with the legal doctrine that Russia is a social state ensuring the wellbeing of its citizens? These questions have been answered by studying the interaction of the economic, legal, and social macrosystems. In Sects. 1, 2 we present the basic concepts for constructing a game-theoretic model of the three national macrosystems (economic, legal, and social). The concepts of Nash and Berge equilibria for the economic, legal, and social macrosystems are defined in Sects. 3 and 4, respectively. Section 5 justifies and formalizes a balanced equilibrium model based on Berge equilibrium (decisionmaking according to the GR) and Nash equilibrium (rational decision-making) to balance the three macrosystems of the complex metasystem. Keywords: Macrosystem
Strategic decisions
Balancedness
Berge equilibrium
The Golden Rule of ethics
Nash equilibrium
Economic doctrine

1 Introduction The Golden rule (GR) is “one of the most ancient, specific, and widespread moral generalizations expressing the experience accumulated by mankind”. It instructs: “Do to others as you would like them to do to you.” Academician A.A. Guseinov noted: a) “Relations between people are moral only if they are interchangeable as the subjects of individually responsible behavior.” b) “The culture of moral choice is the ability to put oneself in the place of another.” c) “One must commit only the acts approved by the objects of activity.” (See [11, 20, 24]). These rules can be assumed one of the main ethical adjusters of social relations. Within a general philosophical approach, ethics and humanity underlie the culture of strategic decision-making. This principle is the main prerequisite and ultimate goal when developing the national economic system and increasing the population’s © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 273–287, 2022. https://doi.org/10.1007/978-3-030-98832-6_24

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welfare. The social state is treated as a state serving the interests of society [1, 15]. With such an interpretation, the operation of the economic, legal, and social systems should be balanced and aimed at increasing the population’s welfare. The concept used in this work assumes that a balanced equilibrium based on the concept of Berge equilibrium (the GR) [24] structurally reflects the social state’s essence and changes the constitutional doctrine in this domain. It is proposed to use game theory as a mathematical theory of decision-making in conflict conditions. With the help of this theory, the task is set to balance the three considered macrosystems.

2 Equilibrium Models of the Complex National Metasystem At the conceptual level, the interaction model of the three macrosystems is as follows [21]. Both objective and subjective factors determine the state and operation of the economy as a macrosystem. Here, one of the instruments is public administration (regulation) of the economy by national legislation through establishing a definite order of economic relations and adopting various economic criteria and indices (tax rates, tariffs, etc.). Economic relations, in some sense, are reflected in legal relations, filling them with economic content. At the same time, the incomes produced in the economy, particularly using taxation legislature, are redistributed into the social sphere formed by social legislation. The population is a “common denominator” that unites the three macrosystems. Quite expectedly, in the economic macrosystem, a model focused on the market and profit maximization matches the interests of economic agents. Transferring the existing market model to the social sphere causes several integral problems: unbalanced actions of the three macrosystems and the destruction of social sectors due to changes in the national economic and legal macrosystems. The third problem follows from the first two ones: poverty of the population in all forms (poverty zones, poverty of the working population, persistent poverty). Justifying the mechanism of interaction between the three macrosystems, we assume that all economic, legal, and social agents reach some equilibria. Equilibrium means the coordinated or balanced interests, capabilities, and actions of the three macrosystems. The neoliberal economic doctrine is mathematically described by the concept of Nash equilibrium, also called “the selfish equilibrium”: each of the three macrosystems acts in its interests and separately achieves its goals, solving individual problems, i.e., implements its strategies to improve its performance. At the conceptual level, an equilibrium is when each macrosystem cannot improve its performance by changing its strategy if the other participants continue using the same strategies. The set of Nash equilibria is internally unstable, which makes the macrosystems unbalanced. For example, let the legal macrosystem implement its goal and strategies, disconsidering the operation of the other two macrosystems. Then the metasystem may become unbalanced due to the following reasons:

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– In the economic macrosystem, the growing density of economic law overregulates the activity of agents. This has a negative impact on their income and the state of the social sphere. (Recall that the incomes produced in the economy using the instruments and mechanisms of law are redistributed into the social sphere). – In the social macrosystem, the growing density of law makes high-quality services (healthcare, education, culture, and sports, etc.) unavailable for particular categories of the population. Further, the adjuster applies, for example, the “legal guillotine” to balance the activity of the macrosystems. When constructing a formal model, we obtain a new equilibrium, not necessarily better than the previous one. For solving these integral problems, we propose a new mechanism for balancing the economic, social, and legal macrosystems based on the concept of Berge equilibrium (the altruistic equilibrium). This mechanism expresses the Golden Rule of ethics in its positive statement: “Do to others as you would like them to do to you” [11]. Combining the methodology of systems analysis and the game-theoretic approach, we reveal the definition of Berge equilibrium [21–25] as a strategy profile, any deviation from which will contradict the Golden Rule of ethics. This rule guides the behavior and relations of the three macrosystems: each macrosystem acts to improve the performance of the other two macrosystems, and a unilateral deviation from such an equilibrium is impossible due to the philosophical and moral principles. With this approach, morality and humanity should underlie the culture of strategic decision-making. The new Berge equilibrium models [21] are intended for implementing the social state idea, thereby contributing to the development of science, education, the population’s social security and support system, and other social branches in the long run. The interaction model of the three macrosystems is based on the Golden Rule of ethics, proposed as a new doctrine for strategic decision-making. Consider the Berge equilibrium model at the substantive level. The national legal macrosystem affects the economy through establishing a definite order of economic relations and adopting various economic criteria and indices. The national law, as a macrosystem, fixates the existing economic relations within a new ethical economic model focused on the growth of the population’s well-being. Economic relations are reflected in legal relations, filling them with real economic content. The incomes produced in the economy are redistributed into the social sphere, which can be formed using the new social legislation. The structure of the social sphere allows for no privatization processes, and the price of social products (goods and services) is determined not by market laws but the philosophical and moral concepts. The proofs were presented in [21]. In the real public metasystem consisting of the three macrosystems, it seems possible to use hybrid approaches: the selfish equilibrium in the economic macrosystem and the altruistic equilibrium in the social one. Within the currently used economic doctrine (“every man for himself”), assume that they play a non-cooperative game. At the same time, they must have considered national tasks, of a higher hierarchy level system (e. g., the Government). Concerning the performance of control systems, the key criterion is control effectiveness, which cannot be completely equated with the performance of the entire

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macrosystem: effectiveness refers primarily to what should be achieved (the consistency between the goal and result). In reality, good goals lead to the opposite results since the control and controlled systems conflict. The next section is devoted to static non-cooperative games.

3 A Nash Equilibrium of Economic, Legal, and Social Macrosystems Nowadays, the generally accepted approach in decision-making is to use the concept of Nash equilibrium [12, 13]. We construct a non-cooperative three-player game in the form: D E C3 ¼ f1; 2; 3g; fXi gi¼1;2;3 ; ffi ðxÞgi¼1;2;3 ;

ð1Þ

where the players are public authorities (control systems) of the three interconnected macrosystems: economic, legal, and social. They choose strategies xi 2 XRni to increase their performance, i. e., their payoffs fi ðxÞ in a given strategy profile x ¼ ðx1 ; x2 ; x3 Þ 2 X1  X2  X3 R3 . A Nash equilibrium ðxe ; f e ¼ ðf1 ðxe Þ; f2 ðxe Þ; f3 ðxe ÞÞÞ 2 X  R3 is given by the three Eqs. (2):   f1 ðxe Þ ¼ max f1 x1 ; xe2 ; xe3 x1 2X1   f2 ðxe Þ ¼ max f2 xe1 ; x2 ; xe3 x2 2X2   e f3 ðx Þ ¼ max f3 xe1 ; xe2 ; x3

ð2Þ

x3 2X3

According to Eqs. (2), each player seeks to achieve its goals. The goals of the control system in the economic sphere can be realized with prejudice to the interests of the social sphere by legal tools. The resulting strategy profile of the three-player game will satisfy the following property: no participant can increase its payoff by a unilateral deviation if the other participants continue using the same strategies. This concept of equilibrium was proposed by J. Nash in 1949. Note that in contrast to Berge equilibria [24], Nash equilibria satisfy individual rationality, not facilitating the peaceful resolution of conflicts [20].

4 The Berge Equilibrium of a Complex Metasystem A mathematical model of the GR based on Berge equilibrium was developed and justified in the recent monograph [24]. Also, see [2, 3]. In particular, the existence of Berge equilibrium was established under standard assumptions of mathematical game theory. A practical method for designing Berge equilibria was proposed; Berge

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equilibria under uncertainty were investigated; finally, several simplified applications to competitive economy models were presented. Note that the authors did not consider the issues of designing metasystem equilibrium. Let us construct a formal mathematical model for the interaction of the economic, legal, and social macrosystems based on Berge equilibrium. A Berge equilibrium ðxB ; f B ¼ ðf1 ðxÞ; f2 ðxÞ; f3 ðxÞÞÞ 2 XR3 is given by the three Eqs. (3): f1 ðxB Þ ¼ f2 ðxB Þ ¼ f3 ðxB Þ ¼

max

f1 ðxB1 ; x2 ; x3 Þ;

max

f2 ðx1 ; xB2 ; x3 Þ;

max

f3 ðx1 ; x2 ; xB3 Þ:

ðx2 ;x3 Þ2X2 X3 ðx1 ;x3 Þ2X1 X3 ðx1 ;x2 Þ2X1 X2

ð3Þ

Unlike (2), each player (control system) in (3) strives to increase the payoff functions of the other two, i.e., to improve their performance. Such an aspiration of the players has moral foundations (the GR) and matches the equilibrium desired by most society members: the economic system operates only in the legal field and aims to satisfy the interests of the social system. Berge equilibria do not satisfy individual rationality. The players’ payoffs in some Berge equilibria are greater compared to Nash equilibria [24].

5 Balanced Berge Equilibrium of the Complex Metasystem 5.1

The Concept of Berge and Nash Equilibrium

The economic-mathematical model presented below is based on the concept of Berge equilibrium; see the book [24, 25]. The Germeier convolution of the players’ payoff functions was constructed therein using the original game; it was proved that the minimax strategy at the saddle point is a Berge equilibrium in the original game. The same technique was adopted to establish an existence theorem for Berge equilibria considering its internal instability. (There may be several Berge equilibria such that the payoffs of all players in one equilibrium exceed those in the other [24, 25].) The requirement of Pareto maximality was therefore added, and the existence of a Paretomaximal Berge equilibrium was proved in the class of mixed strategies. Below, using the results of earlier research [24], we define Slater-minimal guarantees for each strategy profile in the “game of guarantees” and modify Wald’s (maximin) principle to introduce the concept of Slater-guaranteed Berge equilibrium (balanced Berge equilibrium) and prove existence theorems. In the previous book [26], the saddle point, Wald’s principle [17, 19], and other techniques of operations research and multicriteria choice were employed to design a Nash equilibrium in an N-player game and prove its existence in pure strategies and mixed strategies. In what follows, we use the definitions of a Berge equilibrium and a Paretomaximal Berge equilibrium (in pure and mixed strategies), together with the concepts

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of Slater-guaranteed Nash equilibria and Slater-minimal uncertainties, the existence of which was proved in [26], to construct a Slater-guaranteed balanced Berge equilibrium as an analog of the saddle point. 5.2

Berge Equilibrium in a Non-cooperative N-player Game

Further presentation needs some background in operations research. The assertion below was proved in [21]. Assume that: 1. A scalar function Fðx; yÞ is continuous on the product of compact sets X  Rn and Y  Rm, where Y is convex. 2. For each x 2 X, the function Fðx; yÞ is strictly convex in the variable y on the set Y, i.e., the inequality Fðx; ayð1Þ þ ð1  aÞyð2Þ Þ\aFðx; yð1Þ Þ þ ð1  aÞFðx; yð2Þ Þ holds for each x 2 X, 8yð1Þ ; yð2Þ 2 Y, and 8a ¼ const 2 ð0; 1Þ: Then the m-dimensional vector function yð
Þ : X ! Y given by min Fðx; yÞ ¼ Fðx; yðxÞÞ; 8x 2 X; y2Y

ð4Þ

is continuous as well. Also, we will use Slater- and Pareto-minimal uncertainties [26] and the following notations of multicriteria choice problems: for two vectors f ðjÞ ¼ ðf1j ; :::; fNj Þ; ðj ¼ 1; 2Þ: (1) (2) (1) (2)  f  f    f i  f i (i  )  ;

 f (1)  f (2)    f (1)  f (2)  ;  f (1)  f (2)    fi (1)  fi (2) (i  )  ;

 f (1)  f (2)    f (1)  f (2)    f (1)  f (2)  ;

ð5Þ

 f (1)  f (2)    f (1)  f (2)  ;  f (1)  f (2)    f i (1)  f i (2) (i  )  ;  f (1)  f (2)  

f

(1)

 f (2)  .

Let the n-dimensional vector x 2 X be an alternative, the m-dimensional vector y 2 Y be an uncertain factor, and yð
Þ 2 YX be a counteralternative, where YX denotes the set of all m-dimensional vector functions yðxÞ with the domain of definition X and the codomain Y: The analysis below will be confined to the counteralternatives yð
Þ : Y ! X continuous on X.

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Definition 1. Consider the N-criteria choice problem C ¼ hY,f ðx; yÞi with a fixed alternative x 2 X. An uncertainty yS 2 Y is said to be Slater-minimal in the problem C if for all 8y 2 Y. The uncertainty yP 2 Y is said to be Paretominimal in the problem C if for 8y 2 Y.  Consider the N-criteria choice problem CðxÞ ¼ YX ; f ðx; yÞ defined 8x 2 X. A counter alternative yS ðxÞ 2 YX is said to be Slater-minimal if for each x 2 X; 8y 2 Y. A counteralternative yP ðxÞ 2 YX is said to be Pareto, 8y 2 Y: is true. minimal if for each x 2 X; the equation Proposition 1. Consider the multicriteria choice problem Cðx Þ ¼ hY; f ðx ; yÞi: 1) If the set Y is compact, and the function f ðx ; yÞ is continuous on Y, then the set YS of all Slater-minimal uncertainties yS is non-empty and compact [14]. 2) The uncertainty yS 2 Y given by. min y2Y

8ai ¼ const  0, where

P i2N

X i2N

ai fi ðx ; yÞ ¼

X

ai fi ðx ; yS Þ;

ai [ 0 is Slater-minimal in the problem Cðx Þ[24].

3) The uncertainty yP 2 Y given by. X X min ai fi ðx ; yÞ ¼ ai fi ðx ; yP Þ; y2Y

ð6Þ

i2N

i2N

ð7Þ

i2N

8ai ¼ const [ 0 is Pareto-maximal in the problem Cðx Þ, see in [21, 24]. 4) Due to (6), the sets of Slater-minimal yS and Pareto-minimal yP uncertainties in the problem Cðx Þ satisfy the relation YS YP . 5.3

Nash Equilibrium in the Non-cooperative N-player Game

Consider a non-cooperative N-player game of the form   N; fXi gi2N ; Y; ffi ðx; yÞgi2N ;

ð8Þ

where: N ¼ f1; :::; Ng denotes the set of players, Xi Rni is the set of admissible strategies xi of player i, and Y 2 Rmi is the set of strategic uncertainties y. In the game (4), all players independently and simultaneously choose their strategies xi without creating coalitions. As a result, a strategy profile x ¼ ðx1 ; :::; xN Þ 2 Q Xi is formed in this game. An uncertainty y 2 Y is realized independently of the

i2N

players’ strategies. For each player i 2 N, a payoff function fi ðx; yÞ is defined on the pairs ðx; yÞ 2 X  Y. Each player i 2 N seeks to maximize his (its) payoff fi ðx; yÞ using

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an appropriately chosen strategy xi 2 Xi . Note that the players expect any (a priori unknown) realization of the uncertainty y 2 Y. A Nash equilibrium xe ¼ ðxe1 ; :::; xeN Þ 2 X was defined in [26], and its positive and negative properties were explored. As is known, a Nash equilibrium xe ¼ ðxe1 ; :::; xeN Þ 2 X of the non-cooperative game (8) is stable to unilateral deviations of players and satisfies individual rationality [26]. Several existence theorems in pure and mixed strategies were established in [26]. In particular, the set of Nash equilibria is internally unstable and improvable; unlike the saddle point, such equilibria are neither interchangeable nor equivalent [26]. A modification of this concept, the Slater-optimal Nash equilibrium, was studied in [26]. Using these auxiliary results, we introduce the following equilibrium. Definition 2. A strategy profile xB ¼ ðxB1 ; :::; xBN Þ 2 X is called a Berge equilibrium [21, 24] in the game (8) if max fi ðxkxBi Þ ¼ fi ðxB Þ (i 2 N), where ðxkxBi Þ ¼ ðx1 ; :::; x2X

xi1 ; xBi ; xi þ 1 ; :::; xN Þ. The Berge equilibrium of the non-cooperative game (8) with N = {1, 2} will coincide with the Nash equilibrium if the players exchange their payoff functions and choose their strategies based on the latter concept; see [24]. This fact, together with the existence theorem of Nash equilibrium [26], proves the existence of a Berge equilibrium in the game (8) with N = {1, 2}. Next, we define the concept of Pareto–optimal Nash equilibrium in the game (8) as a strategy profile x 2 X such that: x 2 X is a Berge equilibrium in the game (8), i.e., max fi ðxjjxBi Þ ¼ fi ðxB Þ ði 2 NÞ; x2X

and x 2 X is a Pareto-maximal alternative in the N-criteria choice problem. 

 XB ; ffi ðxÞgi2N ;

i.e., the system of inequalities fi ðxÞ  fi ðx Þ (i 2 N), with at least one strict inequality, is inconsistent [24]. 5.4

Slater-Guaranteed Balanced Berge Equilibrium of the Complex Metasystem

Using the auxiliary results from Subsect. 5.1 and the guaranteed solution [26] (see the proof of existence in [26]), we introduce the concept of balanced Berge equilibrium. Definition 3. A pair ð~xB ; ~yS Þ 2 X  RN is called a Slater-guaranteed balanced Berge equilibrium [24] in the game (8) if there exists an uncertainty yS 2 Y such that: 1) The strategy profile xB is a Berge equilibrium in the game   N; fXi gi2N ; ffi ðx; yS Þgi2N ; which is obtained from (8) with y ¼ yS , i.e., by Definition 2,

ð9Þ

Balancedness of Economic, Legal and Social Macrosystems

max fi ðxkxBi ; yS Þ ¼ fi ðxB ; yS Þ ði 2 NÞ: x2X

281

ð10Þ

2) The uncertainty yS is a Slater-minimal alternative in the N-criteria choice problem 

 Y; ffi ðxB ; yÞgi2N ;

ð11Þ

which is obtained from (8) with x ¼ xB , i.e., by Definition 1, ð12Þ 3) The pair ð~xB ; ~f S Þ is Slater-maximal in the N-criteria choice problem  B  fx ; yS g; ffi ðx; yÞgi2N ;

ð13Þ

where each element ðxB ; yS Þ of the set fxB ; yS g simultaneously satisfies (10) and (12), i.e.

˜

˜

˜

ð14Þ

Accordingly, the Berge equilibrium xB is called Slater-guaranteeing in the game (8), and ~f S is called the guaranteed vector payoff (the vector guarantee, or the guaranteed solution) [26]. This solution of the non-cooperative game under uncertainty has the following advantages: 1. Using their strategies from the profile ~xB , the players obtain the vector guarantee ~f S since, due to (12), for xB ¼ ~xB the components of the vector fi ð~xB ; yÞði 2 NÞ cannot be simultaneously smaller than those of fi ð~xB ; ~yS Þði 2 NÞ. In addition, by (14), this guarantee is Slater-maximal among all other guarantees f ðxB ; yS Þ achieved on any pairs ðxB ; yS Þ under conditions 1 and 2 of Definition 3. 2. The equilibrium ð~xB ; ~f S Þ is defined under the worst-case (maximally counteracting) uncertainty, i.e., rests on the principle of guaranteed result [26]. 3. This concept is quite general and incorporates the basic concepts of game theory and multicriteria choice as special cases. Moreover, it can be equipped with Pareto, Borwein, Joffrion, and A-optimality [26]. The connection between such solutions was established in [26]. 4. The concept of Slater-guaranteed equilibrium is adequate for the practical construction and proof of existence [24]. The central result of the paper is the construction of the balanced equilibrium for the three macrosystems: economic, legal, and social. Consider the control structure of the economic macrosystem as a player that can generate or supply dysfunctional processes and uncertainties in the complex metasystem. It chooses strategies y 2 Y and has a payoff function of the form

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wecon ðy; xÞ ¼  where ai ¼ const  0ði 2 NÞ ^

P i2N

X

ai fi ðy; xÞ;

i2N

ai [ 0. The other players are the control structures

of the social and legal macrosystems with the payoff functions X X wSoc ðy; z; xÞ ¼ maxffi ðy; zi kxÞ  fi ðy; zÞði 2 NÞ; fj ðy; xÞ  fj ðy; zÞg j2N

j2N

and wleg ðy; z; xÞ ¼ wsoc ðy; z; xÞ ¼ wðy; z; xÞ; respectively. Well, player 1 is the control structure of the economic macrosystem, further referred to as Econ; with the payoff function wecon ðy; xÞ. Players 2 and 3 are the control structures of the legal (Leg) and social (Soc) macrosystems with the payoff functions wleg ðy; z; xÞ and wsoc ðy; z; xÞ, respectively. Let the strategies of players Soc and Leg be x 2 X and z 2 Z ¼ X, respectively, in the game (8). As repeatedly mentioned above, the economic macrosystem determines the specifics of the national legal and social macrosystems and causes transformation processes. Hence, let the strategy of player Econ be y 2 Y. Consider the auxiliary threeplayer game D

E fEcon; Leg; Socg; fY; Z; Xg; fwi ðy; z; xÞgi¼1;2;3 :

ð15Þ

A Nash equilibrium in the game (15) is given by the three equalities max wEcon ðy; ze ; xe Þ ¼ wEcon ðye ; ze ; xe Þ; y2Y

max wLeg ðye ; z; xe Þ ¼ wLeg ðye ; ze ; xe Þ;

z2Z¼X

ð16Þ

max wSoc ðye ; ze ; xÞ ¼ wSoc ðye ; ze ; xe Þ: x2X

Due to the form of wi ðy; z; xÞði ¼ 1; 2; 3Þ in the first equality, we obtain ye ¼ yS , and the pair ðze ; xe Þ is a saddle point of the zero-sum two-player game [24]: hwðyS ; z; xÞ ¼ wSoc ¼ ðyS ; z; xÞ; Z ¼ X; Xi: Recall that if there exists a saddle point in the zero-sum two-player game, then the minimal strategy is a Pareto-maximal Berge equilibrium in the non-cooperative game [24]. Therefore, if the game (15) has a Nash equilibrium, then ðze ; f S ¼ f ðye ; ze ; xe ÞÞ is the Slater-guaranteed balanced Berge equilibrium under the nonmandatory requirement (13) of Definition 3.

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The balanced equilibrium under consideration has a negative property: by condition 1 of Definition 3, astrategy profile ~xB 2 X is a Berge equilibrium if max fi ð~xkxBi ; yS Þ ¼ fi ð~xB ; yS Þ; x2X

ð17Þ

where the uncertainty is fixed. However, according to the problem statement, the uncertainty y takes arbitrary values from the set Y. The possibility of a particular realization yS is minimal, and equalities (17) do not necessarily hold for other y 6¼ yS . If y 2 Y y 6¼ yS is realized in the game (8), Berge equilibrium actually vanishes: the strategy profile xB provides the vector guarantee ~f S ¼ f ð~xB ; ~yS Þ only if all players follow their strategies from xB without any deviations.

6 Modeling Decision Processes Under Uncertainty by Combining Berge Equilibrium with Minimax Regret Control systems are usually more complex by content than controlled ones. Control systems fix goals and strategies and determine behavioral rules in the noncooperative game model. Recall that this model consists of the following elements: the set of players (control systems); the set of strategies for each player; a scalar functional for each player, defined on the set of their strategies, which measures the player’s performance (the achievement of his (its) goal). This functional is called the player’s payoff function. Consider again the noncooperative N-player game under uncertainty:   G1 ¼ N; fXgi2N ; Y; fðfi ðx; yÞgi2N ;

ð18Þ

where N ¼ f1; 2; :::; N  2g denotes the set of players. In this game, each player i 2 N independently and simultaneously chooses his (its) strategy xi 2 Xi  Rni ði 2 NÞ without coalitions. As a result, a strategy profile x ¼ ðx1 ; :::; xN Þ 2 Q creating X¼ Xi Rn , n ¼ n1 þ n2 þ ::: þ nN , is formed in the game. An uncertainty y 2 i2N

YRm is realized independently of the players’ strategies. For each player i 2 N, a payoff function fi ðx; yÞ is defined on the pairs ðx; yÞ 2 X  Y. Its value is called the payoff of player i. In addition, let X 2 compRn , Y 2 compRm , and fi ðx; yÞ 2 compðX  YÞ; see [26]. We define the Savage risk functions Ri ðx; yÞ ¼ max fi ðz; yÞ  fi ðx; yÞ ði 2 NÞ and z2X

construct the guaranteed payoff fi ½x ¼ min fi ðx; yÞ and guaranteed Savage risk Ri ½x ¼ x2X

max Ri ðx; yÞ of player i, ði 2 NÞ; see [26]. y2Y

Consequently, we pass to the game of guarantees   G2 ¼ N; fXi gi2N ; ffi ½x ; Ri ½x gi2N

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and the auxiliary game     G3 ¼ N; fXi gi2N ; fFi ½x ¼ fi ½x  si Ri ½x gi2N ¼ N; fXi gi2N ; fFi ½x gi2N ;

ð19Þ

where si 2 ð0; 1Þ is some constant. Letting ½xkzi ¼ ½x1 ; :::; xi1 ; z; xi þ 1 ; :::; xN and applying the methodology of [24, 26], we give the following notion. Definition 4. “A strategy profile xB ¼ ðxB1 ; :::; xBN Þ 2 X is called a Berge equilibrium in the game (19) if

If the players in the two-player game (N ¼ f1; 2g) exchange their payoff functions, then the Nash equilibrium of the modified game will become the Berge equilibrium of the original game [24]. Due to this fact, the features of Nash equilibrium will also hold for Berge equilibrium. In particular, the set of Berge equilibria will be internally unstable and improvable [21, 24].

7 Results We have formalized the Pareto-guaranteed Berge equilibrium, proved its existence in mixed strategies. This equilibrium has the following positive features [21–25]: 1. When using Berge equilibrium, even in the game of guarantees, the players need no preliminary agreements regarding the choice of a specific equilibrium from the set X B : decision-making is based, and each player independently chooses his (its) strategy without any negotiations with other game participants in advance. This fact matches the noncooperative character of the game. 2. Like the set of Nash equilibria, the set of Berge equilibria is internally unstable: there may exist two or more Nash equilibria such that the payoffs of all players in one equilibrium are strictly greater than in the other(s) [21–24]. Internal instability can be eliminated by adopting the following solutions Pareto-maximal Berge equilibrium. 3. Berge equilibria are generally improvable. This feature can be eliminated by adopting as new solutions Pareto-maximal strategy profiles or Pareto-maximal Berge equilibria, i.e., Berge equilibria that are simultaneously Pareto-maximal. Nevertheless, the listed features do not diminish the advantages of Berge equilibrium. Thus has discussed new game-theoretic models of decision processes based on increasing the outcomes (payoffs) and simultaneously reducing the associated Savage risk. They have been used to construct guaranteed solutions and the corresponding risks and investigate the features of Berge equilibrium. To identify the specific features of Berge equilibrium (particularly stability and unimprovability), we have formalized the Pareto-guaranteed Berge equilibrium, proved its existence in the class of mixed strategies.

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8 Discussion The GR shows how to be moral, and not why an individual should be moral. Hence, this rule can be assumed one of the main ethical adjusters of social relations. Among the important theoretical problems caused by the reorientation of public goals, a key one is finding a new balance of the three macrosystems (economic, legal, and social) and concretizing the moral basis of modern economics. To what extent is it reasonable to consider a moral economy? Berge equilibrium is a formal mathematical reflection of the GR. It represents another type of equilibrium corresponding to the strategic goals of the social state. In the social state, the operation of the economic, legal, and social systems should be balanced and aimed at increasing the population’s welfare. In this context, a balanced equilibrium based on a Berge equilibrium (the GR) formally reflects the social state’s essence; as a mathematical theory of optimal decision-making under conflict, game theory allows balancing the three macrosystems under consideration. Thus, we provide a formal answer to the main question addressed by Stiglitz, Sen, and Fitoussi in their report.

9 Conclusion The article has presented the application of game-theoretic tools to the socio-economic problems of society allows solving complex strategic problems, in particular, balancing the three macrosystems that are significant from the society’s perspective: economic, legal, and social. A new concept of equilibrium for the economic, legal, and social macrosystems based on Slater-guaranteed balanced Berge equilibrium is presented [20]. This type of equilibrium has been designed and justified. The equilibrium model considers the effect of uncertain factors: the modern economic system has such intrinsic properties as uncertainty, inconsistency, multiple criteria, and incomplete information. Opposing to the neoliberal economic doctrine and supplementing the individual rationality-based Nash equilibrium, Berge equilibrium suggests a way of balancing based on philosophical and moral principles. Some positive and negative properties of Berge equilibrium have been discussed (particularly internal instability). This property is undoubtedly important but not critical: it serves as a driver for the transition from one stage of system operation to another. Moreover, the existence theorem [21] deals with internal instability by incorporating Pareto maximality into the concept of Berge equilibrium. The existence of the Pareto-maximal Berge equilibrium is proved in the case of continuous payoff functions and compact sets of mixed strategies of the players.

References 1. Aristov, E.V.: Pravovaya paradigma sotsial’nogo gosudarstva. [A Legal Paradigm of the Social State.] Yuniti-Dana, Moscow (2016). (in Russian) 2. Berge, C.: Théorie générale des jeux de plusieurs personnes. Gauthier Villars, Paris (1957)

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3. Berge, C.: Sur une Convexite Reguliere et ses applications a la Theorie des Jeux. Bull. Soc. Math. France 81, 301–315 (1954) 4. Borel, E.: La théorie du jeu et les equations intégrales à noyau symétrique gauche. Comptes Rendus de l’Académie des Sci. 173, 1304–1308 (1921). [Translated by L.J. Savage under the title “The Theory of Play and Integral Equations with Skew — Symmetric Kernels. Econometrica, 21, 97–100 (1953)] 5. Borel, E.: Sur les systèmes de formes linéaires a determinant symétrique gauche et la théorie generale du jeu. Comptes Rendus de l’ Académie des Sciences 184, 52–53 (1927). [Translated by L.J. Savage under the title “On Systems of Linear Forms of Skew Symmetric Determinant and the General Theory of Play”. Econometrica. 21, 116–117 (1953)] 6. Borel, E.: Sur les jeux ou le hasard se combine avec l’Habilite joueurs. Comptes Rendus de l’Académie des Sciences 178, 24–25 (1924) 7. Borel, E.: Traite du calcul des probabilites et ses applications. In: Applications aux jeux de hasar, vol. 4. Gauthier Villars, Paris (1938) 8. Chernogor, N.N., Pulyaeva, E.V., Chesnokova, M.D., Glazkova, M.E.: Monitoring of efficiency of legal mechanism of rendering social services. J. Russ. Law 8, 66–76 (2010) 9. Germeier, Y.: Non-antagonistic Games. Reidel, Netherlands (1986) 10. Glicksberg, I.L.: A further generalization of Kakutani’s fixed point theorem with application to Nash equilibrium point. Proc. Am. Math. Soc. 3(1), 170–174 (1952) 11. Guseinov, A.A.: Zolotoe pravilo nravstvennosti. [The Golden Rule of Ethics.] Molodaya Gvardiya, Moscow (1988). (in Russian) 12. Nash, J.F.: Equilibrium points in n-person games. Proc. Nat. Acad. Sci. USA 36, 48–49 (1950) 13. Nash, J.F.: Non-Cooperative Games. Ann. Math. 54, 286–295 (1951) 14. Podinovskii, V.V., Noghin, V.D.: Pareto-optimal’nye resheniya mnogokriterial’nykh zadach. [Pareto-Optimal Solutions of Multicriteria Problems.] Fizmatlit, Moscow (2007). (in Russian) 15. Sinyukov, V.N.: Rossiiskaya pravovaya sistema. Vvedenie v obshchuyu teoriyu. [Russian Legal System. Introduction to General Theory.] Infra-M, Moscow (2016). (in Russian) 16. Vasil’ev, F.P.: Metody optimizatsii. [Optimization Methods.] Faktorial Press, Moscow (2002). (in Russian) 17. Wald, A.: Sequential Analysis. Wiley, New York (1947) 18. Wald, A.: Generalization of a theorem by von Neumann concerning zero-sum two-person games. Ann. Math. 46, 281–286 (1945) 19. Wald, A.: Statistical decision functions which minimize the maximum risk. Ann. Math. 46, 265–280 (1945) 20. Zhukovskaya, L.V.: Sistemnyy analiz i teoretiko-igrovoy analiz povedeniya ekonomicheskoy, pravovoy i sotsial'noy sistemy makrosistem. [Systemic Analysis and TheoreticalGame Tools of Interaction between Economic, Legal and Social National Macrosystems.] Actual Probl. Econ. Law 13(3), 1287–1300 (2019). (in Russian). https://doi.org/10.21202/ 1993-047X.13.2019.3.1287-1300 21. Zhukovskaya, L.V.: The Balancedness of the Social, Economic, and Legal Macrosystems Based on Modelling Decision Processes, Doctoral (Econ.) Dissertation, Moscow (2021) 22. Zhukovskiy, V.I., Kudryavtsev, K.N.: Pareto-optimal Nash equilibrium: sufficient conditions and existence in mixed strategies. Autom. Remote Control. 77(8), 1500–1510 (2016) 23. Zhukovskiy, V.I., Gorbatov, A.S., Kudryavtsev, K.N.: Nash and Berge equilibria in a linearquadratic game. Mat. Teor. Igr Prilozh. 9(1), 62–94 (2017)

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24. Zhukovskiy, V.I., Salukvadze, M.E.: The Berge Equilibrium: A Game-Theoretic Framework for the Golden Rule of Ethics. Springer, Switzerland (2020). https://doi.org/10.1007/978-3030-25546-6 25. Zhukovskiy, V.I., Salukvadze, M.E.: The Golden Rule of Ethics. A Dynamic GameTheoretic Framework Based on Berge Equilibrium. CRC Press, London (2021) 26. Zhukovskiy, V.I., Zhukovskaya, L.V.: Risk v mnogokriterial’nykh i konfliktnykh sistemakh pri neopredelennosti. [Risk in Multicriteria Choice and Conflict Systems under Uncertainty.]. URSS, Moscow (2003). (in Russian)

Numerical Implementation of an Adapted k-means Algorithm for Solving the Problem of Russian Industrial Regions Classification Olga M. Shatalova(&)

and Ekaterina V. Kasatkina

Kalashnikov Izhevsk State Technical University, Studencheskaya Street 7, 426069 Izhevsk, Russia [email protected]

Abstract. The article presents the results of the development and numerical implementation of the cluster analysis algorithm, applicable to classify the industrial regions of the Russian Federation by economic specialization. The proposed algorithm is based on the use of the k-means method and the hierarchical agglomerative method, and is adapted to the conditions of the research problem, including taking into account the existing data of state statistics and specifics of the spatial development in the Russian economy. Using of the algorithm is allowed to form homogeneous groups of regions and carry out meaningful interpretations based on statistical data of the regional socioeconomic indicators. The results of clustering industrial regions are also served as the basis for a generalized description of the country’s economic space in the concept of Clark Fisher model of the three-branch economy. The numerical implementation of the adaptive algorithm is showed the stability of the solution, as evidenced by the results of assessing the quality of clustering, compiled on the basis of pairwise graphs of the scatter between the shares of industries in regions belonging to different clusters, as well as based on using an alternative clustering method (the similarity of clustering results by different methods is 97.5%). The results of the numerical implementation of the adapted clustering algorithm made it possible to conclude that there are high possibilities to identify significant regularities of the spatial organization of the Russian economy, which have importance in the formation of industrial, innovation, investment, fiscal policy government levels. Keywords: Regional economy
Clustering
Mathematical methods Sustainable development
Industry
Innovation policy




1 Introduction The national economy development is largely determined by manufacturing industries state and technological and economic growth possibilities [1]. This position is due to the fact that the manufacturing sector is of key importance for ensuring employment of the economically active population, this sector forms incentives for scientific and technical activities, creates a material basis for trade development, etc.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 288–299, 2022. https://doi.org/10.1007/978-3-030-98832-6_25

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Assistance to the industrial economy sector is an important element of investment, innovation, scientific, technical and fiscal policy of states focused on sustainable development. Government policy should be based on the current state and the current laws of the national economy. In this case, it is necessary to take into account the regional specifics, since the economic space of the Russian Federation is characterized by a significant heterogeneity of regions, including in their economic specialization. This approach makes it possible to form the necessary and substantiated synthesis of knowledge about the state, trends, factors of economic development on a national scale. Interregional comparisons, analysis of trends and resources of socio-economic development of regions, identification of effective mechanisms of state management of economic processes become possible with the correct division of the aggregate of regions into homogeneous groups [2]. Cluster analysis methods are widely used for solving such a problem.

2 Theoretical Foundations of the Research With a high level of mathematical apparatus development of cluster analysis, its application to economic research, as a rule, requires additional methodological solutions. The construction of targeted typologies of economic objects should be based on a preliminary study of the relevant theoretical foundations and heuristic analysis of the existing practice in a specific field of research, as well as the possibilities of numerical measurement of clustering criteria, etc. Cluster analysis and classification of Russian industrial regions according to the format of economic (sectoral) specialization was based on theoretical provisions on the impact of industry on economic development: the concept of industrial clusters [3, 4], the Clark Fisher model of the three-branch economy [5], spatial polarized development [6], neo-industrial development [7]. The possibilities of measuring data on the spatial organization of the economy are outlined in the works of R. Haining [8], V. Balash et al. [9], B. Boots et al. [2]. Economically significant solutions of cluster analysis, relevant in our research, are contained in the works of S.A. Ayvazyan et al. [10], T. F. Slaper et al. [11], Sölvell Ö. et al. [12].

3 An Adapted k-means Algorithm In addition to the existing scientific approaches to cluster analysis and classification of industrial regions, a clustering algorithm was proposed that allows one to classify such regions, according to their economic specialization in line with the Clark Fisher model. The developed algorithm is adapted to the Russian system of statistical measurements and takes into account the current specifics of the spatial organization of the Russian economy. Within the framework of the developed algorithm, the general logic of the research is described by the following structural diagram (see Fig. 1).

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Statistical data

Formation of the initial sample Filling in missing data regression estimates

expert judgment

Calculation of clustering features Selection of significant features Clustering by k -means method Checking the stability of the formed clustering by the hierarchical algorithm Meaningful interpretation of clustering results Formation of cluster groups based on the sectoral structure of the economy

Fig. 1. Block diagram of cluster analysis and classification of industrial regions according to their economic specialization.

The initial sample for the cluster analysis was formed according to the data of the state regional statistics1 on the volume of shipped industrial products for consolidated types of economic activities (TEA)2 and on the sectoral structure of shipped products as part of these TEA. The parameters of the regional sectoral structure were calculated using the following Equation: ðrÞ

Vi ðrÞ xij ¼ yij
P ; i ¼ 1; N; j ¼ 1; L: m ðr Þ Vi

ð1Þ

r¼1

where N is the number of industrialized regions included in the sample; L is the ðr Þ number of clustering features; Vi is the consolidated TEA; m is the number of industry-related TEA, m ¼ 4:

1 2

https://gks.ru/bgd/regl/b20_14p/Main.htm. “Extraction of minerals”, “Manufacturing”, “Provision of electricity, gas and steam…”, “Water supply, wastewater disposal, waste collection and disposal, activities to eliminate pollution” appear in this capacity based on the Russian Classification of Types of Economic Activity.

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For the selection of significant clustering features, it is possible to use the principal component method, which in this case makes it possible to reasonably reduce the dimension of the criteria space. Clustering by the k-means method is aimed at dividing the set of objects, included in the sample, into homogeneous groups in such a way as to minimize the total distance between the objects and the center of the cluster. The object of cluster analysis is a set of industrially oriented regions X ¼ fxi gi¼1;N defined by a vector of categorical features   xi ¼ xij ¼ ðxi1 ; :::; xiL Þ. Euclidean distance is used as a measure of distance: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u L uX  2 qð x i ; x k Þ ¼ k x i  x k k ¼ t xij  xkj ; i; k ¼ 1; N;

ð2Þ

j¼1

where xij ; xkj are values of the j-th categorical feature for the xi ; xk regions. The objective function of clustering is presented in the considered algorithm as follows: F¼

nk  K X X  xk  lk  ! min; i

ð3Þ

k¼1 i¼1

  where F is the total deviation of cluster regions from cluster centers; lk ¼ lk1 ; :::; lkL is the k-cluster center; xki is the i -th region, included in the k -cluster; K is the clusters number (K ¼ 12); nk is the number of regions included in the k -cluster. The content of the k-means algorithm, adapted to solve the clustering problem of industrial regions according to their economic specialization, is described by the following provisions: 1. The number of clusters acceptable for research is determined by experts. The implementation of this stage is based on the position of the concept of Clark Fisher model of the three-sectoral economy on the structuring of industrial sectors into two groups: a) primary and b) secondary group; at the same time, secondary industries are classified on the basis of the existing specificity of the territorial organization of the industrial sector (the results of the solution we obtained for this stage are reflected in the table). 2. The cluster centers are initialized using the “k-means++” method: a. We randomly initiate the first centroid: lk ¼ xp ; p ¼ randomð1; NÞ; k ¼ 1:

ð4Þ

b. We calculate the distance of each object to the nearest centroid:    Ri ¼ min q xi ; lk ; i ¼ 1; N: k

ð5Þ

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c. We consider the probabilities of choosing a region as the center of a cluster: R2 Pi ¼ PNi 2 : j Rj

ð6Þ

d. We calculate the accumulated probabilities of choice: Si ¼

i X

Pj :

ð7Þ

j¼1

e. We generate a uniform random variable Rnd ¼ randomð0; 1Þ. f. We increment the number of the cluster ðk ¼ k þ 1Þ, for which the centroid is determined, and define the centroid as a region to which the interval of accumulated frequencies corresponds, containing the value Rnd : p : Sp1  Rnd \Sp ; lk ¼ xp :

ð8Þ

g. If k\K, then we return to point 2b, otherwise all cluster centers are initiated and go to point 3. n o

3. Each region xi is assigned to a specific cluster T k ¼ xkj , k 2 f1; 2; :::; K g, on the assumption of the minimum distance to cluster centers:    xi ! xkj ; k ¼ arg min q xi ; lk ; i ¼ 1; N:

ð9Þ

k¼1;K

4. The centers of the clusters are recalculated: nk   1X lk ¼ lk1 ; :::; lkL ; lkj ¼ xk ; j ¼ 1; L: nk l¼1 jl

ð10Þ

5. If there is a change in the centers of clusters or redistribution of regions, then the transition to stage 3 is carried out. Otherwise, the resultingn cluster centers lk ; k ¼ o 1; K and the distribution of objects across clusters T k ¼ xkj ; j ¼ 1; nk are considered optimal. The verification of the results obtained by the k-means method is carried out: a) by constructing and evaluating pairwise graphs of the scatter between the shares of industries in the regions included in different clusters, namely, graphs of the scatter of the values of criteria attributes for each cluster, as well as one-dimensional and two-dimensional graphs of the distribution of clustering attributes obtained from using nuclear density estimation [13]; b) using an alternative clustering method; in this capacity, the hierarchical agglomerative method can be used. The objectivity and stability of the obtained solution can be said by the closeness degree of the results [14].

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The stage of meaningful interpretation of the results is key in the entire procedure of cluster analysis. The clustering of regions leads to a reasonable decrease in the dimension of the studied set of objects to several homogeneous groups, which allows differentiated (in the context of formed clusters) to evaluate each such group and identify relevant patterns (regularity) of the territorial organization of the Russian economy. A substantial interpretation was carried out using data from state regional statistics3. The obtained estimates can be important for the analysis of national indicators of achieving the sustainable development goals within the framework of the goal “Industrialization, innovation and infrastructure”4. It is advisable to combine the identified clusters in accordance with the conceptual sectoral Clark Fisher model for a generalized description of the structure of the national economy. This research stage is aimed at synthesizing knowledge about the structural content of the industrial sector of the economy.

4 Results and Discussion The described above algorithm was used by the authors for cluster analysis of the Russian industrial regions based on statistical data from 2019. The numerical implementation of clustering methods was carried out in Python programming language using the Scikit-Learn machine learning library5. The most significant results can be represented by the following points. The analyzed aggregate is represented by 40 regions (taking into account the provisions on the composition of industrial regions set forth in the article by S.A. Ayvazyan et al. [10]). The criterion space was reduced to 13; for this, the method of principal components. The use a of method of principal components made it possibleto establish that 13 out of 23 features explain 99.9% of the variance. The result of the cluster analysis was the identification of twelve clusters, each of which includes regions homogeneous in terms of economic specialization. The composition of industry clusters is shown in Table 1. To assess the quality of clustering, pairwise graphs of the scatter between the shares of industries in the regions included in different clusters were constructed. Figure 2 shows, for example, scatter plots (plots above the diagonal), as well as one-dimensional (diagonal plots) and two-dimensional (plots below the diagonal) distributions of clustering features obtained using the kernel density estimate [13]. Figure 2 demonstrates that the distributions of variables differ depending on the cluster, which confirms the effectiveness of the clustering obtained by the k-means method.

3 4 5

https://gks.ru/bgd/regl/b20_14p/Main.htm. https://rosstat.gov.ru/sdg/national. https://scikit-learn.org.

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O. M. Shatalova and E. V. Kasatkina Table 1. Results of clustering industrial regions according to economic specialization.

Sectors of Cluster characteristics the economy

Cluster cipher

Regions (region number in the database)

1

2

3

4

5

Primary

Mono-industry resource-oriented economy

The regional economy is based on the “Oil and Gas production” (OG) industry

(0)

Khanty-Mansiyskiy Autonomous Okrug (AO) (26); Yamalo-Nenetskiy AO (27); Nenetskiy AO (9); Sakhalinskaya obl. (38); Astrakhanskaya obl. (14); Resp. Yakutiya (36)

The regional economy is based on the “Coal mining” and metallurgy (CM) industry

(4)

Resp. Khakasiya (30); Kemerovskaya obl. (33)

The regional economy is (2) based on the “Mining of metal ores” and CM industry

Magadanskaya obl. (37); Chukotskiy AO (39)

Economic specialization includes “Food production” and “Mining of metal ores”

Belgorodskaya obl. (0); Kurskaya obl. (3)

Secondary

Weakly diversified economy dominated by resource-based industries

Diversified economy with significant influence from resource-based industries

Secondary

Diversified economy dominated by hightech industries

(11)

The regional economy is (6) based on the “Woodworking” industry

Resp. Kareliya (7); Arkhangelskaya obl. without AO (10)

The key industries are the OG industry and manufacturing industries (low-tech)

(3)

Resp. Komi (8); Irkutskaya obl. (32); Orenburgskaya obl. (22); Tomskaya obl. (35); Udmurtskaya Resp. (19)

The key industry is “Oil refining”

(9)

Volgogradskaya obl. (11); Omskaya obl. (34)

The basis of the economy is (5) fuel and industrial complex: “Oil Production”, “Oil Refining”. The industry “Mechanical Engineering” is also “dominant” of the economy

Tyumenskaya obl. without AO (28); Resp. Bashkortostan (16); Resp. Tatarstan (19); Permskiy kray (20)

The economy is based on the (7) CM industry. The industry “Mechanical engineering” is also developed

Krasnoyarskiy kray (31); Vologodskaya obl. (15); Sverdlovskaya obl. (25); Chelyabinskaya obl. (29); Lipetskaya obl. (4); Tulskaya obl. (6)

“Mechanical Engineering” is (8) pronounced economic dominant

Kaluzhskaya obl. (2); Samarskaya obl. (23); Nizhegorodskaya obl. (21)

(1) There are wide industry diversification and significant role of high-tech industries

Vladimirskaya obl. (1); Novgorodskaya obl. (13); Saratovskaya obl. (24); Leningradskaya obl. (12); Resp. Mariy El (17); Smolenskaya obl. (5)

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Fig. 2. Graphical characteristics of the distribution of clustering features: a) joint distribution of indicators D2 (oil and natural gas production), OP5 (production of coke and oil products), and OP6 (production of chemical products) for clusters (0) and (5); b) joint distribution of indicators OP4 (production of paper and paper products), OP8 (metallurgical production), and OP10 (manufacture of machinery and equipment) for clusters (1) and (8).

Fig. 3. Dendrograms of hierarchical clustering of industrially oriented subjects of the Russian Federation by the economic specialization.

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Fig. 4. Numerical characteristics of industry specialization/differentiation by class of regions.

To test the stability of the obtained solution on a set of industrially oriented regions, a hierarchical agglomerative method was implemented and a dendrogram was constructed (see Fig. 3). The similarity of the clustering results by different methods was 97.5%, which confirms the stability of the solution obtained by the k-means method.

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To identify significant industries within each cluster, a numerical assessment of the level of connection between each cluster and the clustering criterion was carried out (see Fig. 4). This estimate is based on the correlation coefficients between the shares of industries in the volume of shipped products and dummy variables that characterize the attitude of the region to a certain cluster. Based on the results of assessing the communication levels of the clusters and the clustering criteria (shown in Fig. 4), the sectoral clusters were combined into groups of the sectoral structure of the economy (column 2 of the table); when carrying out this grouping, the concept of the three-sectoral Clark Fisher model and the actual results of the cluster analysis were taken into account. The obtained classification of regions as a result of cluster analysis made it possible to make an assessment of the sectors of the economy identified according to the sectoral criterion, according to the main socio-economic indicators, and in the structural aspect. The homogeneity of the aggregate of regions within each cluster, proved by the low level of variation of the studied indicators across regions within each cluster, made it possible for the necessary generalizations and conclusions. In particular, a significant lag was shown for a number of the most important socio-economic indicators of the regions of the cluster (8) and cluster (1) from the national average values: the level of profitability (lower by 26%), the level of income and wages (lower by 29%), GRP per capita (27% lower); it is also necessary to note the very low level of investment activity in these regions (5–6% of the amount of fixed capital). In assessing these values, it is necessary to take into account that it is these clusters that represent the high-tech sector of the Russian economy. If we assess the current situation from the standpoint of the UN concept of sustainable development, then we can conclude that there are some threats: a) low wages determine the threat of negative migration of qualified personnel from those regions that provide the production and technological potential of the country’s industry; b) the existing level of profitability of products may indicate insufficient investment opportunities of industrial production enterprises to ensure their technological development; c) the level of production investment does not provide even a simple reproduction of fixed assets. The results of cluster analysis make it possible to draw up a structural characteristic of the existing territorial organization of the economy. Its graphic representation is shown in the form of a color chart in Fig. 5. Based on the color chart in Fig. 5, it should be concluded about the high share of industrially oriented regions in the territorial structure of the economic space of the Russian Federation; at the same time, most of the industrial regions are represented by primary industries, and regions with high-tech industries occupy an insignificant share in the territorial scheme.

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Fig. 5. Graphical diagram, representing the structural characteristics of the Russian territorial organization.

5 Conclusions The presented in the article algorithm for clustering regions according to their sectoral specialization is based on the use of traditional mathematical methods of cluster calculations and is adapted to the conditions of the organization of state regional statistics; also, the developed algorithm makes it possible to implement the theoretical concept of a three-sectoral model of the economy (Clark Fisher model). The use of such an algorithm allows us to identify important regularities of the spatial organization of the economy, which may be important in the formation of industrial, innovation, investment, fiscal policy, both at the regional and federal levels of government. The development of the research is seen in the need to expand the space of clustering criteria. It also seems that conducting a cluster analysis of the economic specialization of regions in a long-term retrospective will make it possible to compile additional characteristics and assessments of structural changes in the economy. Further study of the problem of clustering regions by economic specialization, aimed at developing methods for predicting the consequences of certain measures of state industrial and innovation policy, is urgent.

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References 1. Our Common Future: Report of the World Commission on Environment and Development. Official Report of the UN General Assembly (1987). https://www.un.org/ru/ga/pdf/ brundtland.pdf. Last accessed 1 Sept. 2021 2. Boots, B.: Typology of Russian Regions. Moscow (2002) 3. Porter, M.E.: The competitive advantage of nations. Harv. Bus. Rev. 73–91 (1990) 4. Becattini, G.: Le district industriel: milieu créatif. Espaces et sociétés 1992/1 (n°66), pp. 147–164 (1992). https://doi.org/10.3917/esp.1992.66.0147 5. Fisher, A.G.B.: Production, primary, secondary and tertiary. Econ. Rec. 15(1), 24–38 (1939) 6. Perroux, F.: Les investissements multinationaux et l’analyse des poles de developpement et des poles d’integration. Revue Tiers-Monde 9(34), 239–265 (1968) 7. Bodrunov, S.D., Silina, Y.P., Ryazanova, V.T., Animates E.G. (eds.): Novaya industrializaciya Rossii: strategicheskie prioritety strany i vozmozhnosti Urala: Monografiya [New Industrialization of Russia: Strategic Priorities of the Country and the Opportunities of the Urals: Monograph.] USUE Publishing House, Yekaterinburg (2018). (In Russian) 8. Haining, R.: Spatial data analysis in the social and environmental sciences. Spatial Data Analysis in the Social and Environmental Sciences (1990) 9. Balash, V., Balash, O., Faizliev, A., Chistopolskaya, E.: Economic growth patterns: spatial econometric analysis for Russian regions. Information, Switzerland, 11(6) (2020). https:// doi.org/10.3390/INFO11060289 10. Ayvazyan, S.A., Afanasiev, M.Y., Kudrov, A.V.: Clustering methodology of the Russian federation regions with account of sectoral structure of GRP. Appl. Econom. 1(41), 24–46 (2016) 11. Slaper, T.F., Harmon, K.M., Rubin, B.M.: Industry clusters and regional economic performance: a study across U.S. metropolitan statistical areas. Econ. Dev. Q. 32(1), 44–59 (2018). https://doi.org/10.1177/0891242417752248 12. Sölvell, Ö., Ketels, C., Lindqvist, G.: Industrial specialization and regional clusters in the ten new EU member states. Compet. Rev. 18(1–2), 104–130 (2008). https://doi.org/10.1108/ 10595420810874637 13. Jorda, V., Sarabia, J.M., Jäntti, M.: Inequality measurement with grouped data: parametric and non-parametric methods. J. R. Stat. Soc. Ser. A Stat. Soc. 184(3), 964–984 (2021). https://doi.org/10.1111/rssa.12702 14. Oldenderfer, M.S., Blashfield, R.K.: Cluster Analysis. Factorial, Discriminant and Cluster Analysis. Finance and Statistics, Moscow (1989)

Solving Fuzzy Equations Based on Fuzzy Interval Bisection Method for Intelligent Data Processing in Cyber-Physical Systems Konstantin Semenov(&)

and Anastasiia Tselishcheva

Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya Street, 29, 195251 St. Petersburg, Russia [email protected]

Abstract. This paper proposes the fuzzy interval bisection method for solving nonlinear fuzzy equations that is applicable to cyber-physical systems. The presented method is based on the authors’ previous research in solving equations that have inaccurate coefficients and expands the interval bisection approach from the interval data to fuzzy one. The proposed method allows the construction of the approximation for the membership function of the equation’s root that involves all available input fuzzy information on the equation’s coefficients. The conceptual details of migrating from ordinary to fuzzy intervals are briefly discussed. The advantages of the new method are considered: the possibility to detect the optimal moment to stop the iterative procedure of fuzzy equation solving and a way to propagate the nested intervals of membership functions when searching the root. An example of approach using is presented for the cyber-physical system containing nonlinear sensors for obtaining information from the object under control. Keywords: Solving fuzzy equations CPS


Fuzzy intervals
Interval bisection


1 Introduction Since the impressive development of computational means, cyber-physical systems have widely spread in different areas of technical control and processes and object monitoring [1–4]. The detailed elaboration and deep-designed comprehensive intellectualization of algorithms used by them require the standardization of different typical computational problems that occur during the cyber-physical systems applications: solving the equations, local or global optimization performing, reasonable decisionmaking, etc. The harmonization of corresponded software tools, which provide means universal enough to be treated as standardized, with the obtained data on the problem to be solved brings us to the requirement of using the complicated data types for representing all available knowledge on the process or object under control, observation, or study. Really, let us use the ordinary data representation formats like double-precision floating-point numbers representation as a basic data type. Then for success in solving the problem we face, it is necessary to develop a way to express using this type all the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 300–310, 2022. https://doi.org/10.1007/978-3-030-98832-6_26

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knowledge we have about the problem. To achieve the maximum degree of standardization during the software development for cyber-physical systems, it is preferable to use the more sophisticated data types that can be used to represent different-nature data: of objective nature like measurements results or of entirely or semi-subjective nature like experts’ opinions or knowledge poor-formalized in the mathematical sense. One of the most popular ways to provide the mentioned harmonization is to use the fuzzy representation because of its well descriptive capacity. These considerations lead to the situation when means intended for solving fuzzy equations, performing fuzzy optimization, and fuzzy decision-making are highly desirable to be developed to be used as mathematical primitives when designing intelligent software for cyber-physical systems. This paper presents the approach for searching roots of fuzzy equations that satisfies all the requirements arising from the practice of cyber-physical systems using.

2 Requirements for Soft- and Algorithmic-Ware Used in Cyber-Physical Systems for Solving Equations Cyber-physical systems are computer systems in which physical and software components are integrated to ensure synergy [1]. These systems are widely used to automate processes in various fields (for example, they include transport or traffic control systems, automated control in manufacturing and agriculture, power supply management, management in medicine, etc.) [2–4]. The interaction between computational and physical elements of cyber-physical systems is carried out to achieve a better quality of obtained results. In the software part, intelligent processing of data from sensors (hardware part) is performed to make decisions; expert information can also be used (for example, in a “smart” house, etc.). As it was mentioned in the introduction, the coming data, as well as the calculations’ results, should be expressed in the form with the highest descriptive capacity. This reasoning should include the fact that the data used by cyber-physical systems and intelligent control and monitoring systems is inaccurate. We suppose that, from these two viewpoints, using fuzzy variables is a good choice. In this case, when processing such data or using it to develop a solution to the problem the cyber-physical system faces, it is necessary to take into account that this information is expressed in the form of fuzzy sets that allow reasoning considering uncertainty. 2.1

A Fuzzy Approach in Cyber-Physical Systems

Why is the fuzzy approach preferable? In practice, the main approach to express some quantity’s inaccuracy is to use an interval of its possible values constructed according to a given confidence probability. This interval can be used in itself or as a nested interval for the membership function of a corresponded fuzzy set representing the quantity’s uncertainty. The latter variant has a higher descriptive capacity — the membership function consists of the set of nested intervals, each of which is accompanied by its individual metric with a meaning related to the confidence probability. In contrast, the single interval cannot contain information corresponding to more than one confidence level. One of the most effective ways to quantitatively describe the uncertainty of different

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nature is to use the generalization of interval continued to a fuzzy set: to a fuzzy interval, which differs from the ordinary interval in that its boundaries are fuzzy, not precise. Another important circumstance is that fuzzy models are already widely used in cyber-physical systems. For example, the paper [5] discusses several approaches of their applying in environmental systems for evaluating risks. In [6], a method is given for robot control systems to detect faults using a fuzzy model. The paper [7] proposed a fuzzy model for control in engineering systems. In [8], a fuzzy logic technique is used in cyber-physical systems in healthcare. Summing all discussed considerations, we conclude that representation of the data used in cyber-physical systems’ soft- and algorithmic-ware by fuzzy sets theory is the right direction to provide standardization of commonly used program modules and to increase further the degree of intellectualization and complexity of cyber-physical systems. 2.2

Solving Equations Performed in Cyber-Physical Systems Software

As it was mentioned earlier, one of the typical problems that occur during intelligent data processing for technical control purposes is equations’ solving. Let us consider the cyber-physical system used to control or monitor some technical object. Let the object under control be described by some mathematical model, and it is necessary to solve the equations obtained from this model to generate control signals for the object. Measurement results of the parameters’ values of the object’s current state or other available relevant empirical data can be used as part of these equations. If the equations’ parameters are the results coming from the measuring channels of the cyberphysical system, then they will be distorted by the measurement errors, or if they are expert estimates, then they are initially of approximate nature. If this inaccuracy is not taken into account by the system when decision-making, then their quality, as well as the quality of the performed control, will be unclear. Since the data, the cyber-physical system deals with, is inaccurate, then, when solving equations including these data as parameters, the numerical process of solution obtaining may lose convergence if ignoring the mentioned uncertainty because of bias caused by unaccounted information influence. This may lead to failure, like an absence of the result of system operating, and this is unacceptable. If the required solution is not obtained, then the further actions of the cyber-physical system will be impossible. That may lead to system unreliability — short- or long-term, or even standing. To ensure information compatibility, the solution should be expressed in the same format as the input data provided to the software of the cyber-physical system. For a moment, a wide set of methods for solving equations is developed. They can be divided into several groups for the convenience of further analysis of applicability in cyber-physical systems. We can highlight the group of techniques for solving linear algebraic equations within the framework of interval analysis [9–13]. They always bring us to the solution but are excessively sophisticated and usually overestimate the uncertainty of the results. For a situation when the equations are nonlinear, modifications of the Newton interval method are applicable [11]. These methods do not guarantee that the equation root will obtain (this may take place if there is no global convergence or if it is impossible to calculate the derivatives). At last, in [14], a

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modification of the bisection method is presented applicable for nonlinear equations with inaccurate parameters. This approach proposed by the authors of this paper allows obtaining an estimate of the root that guarantees a given accuracy and considers the uncertainty of the equation’s coefficients. The original method from [14] requires to specify the errors of processed data as intervals of all possible values. To extend this approach to cases when data inaccurateness is represented as intervals corresponding to a confidence probability less than one, some bisection-based methods were developed for solving nonlinear equations with fuzzy numbers. So, in [15–17], modifications of the bisection method for finding roots using fuzzy numbers are given — but in these methods, only the desired variable is a fuzzy number, and its membership function has a certain form (triangular), the coefficients of the equations are given as real numbers without any errors. This meets some of the discussed requirements for solving equations in cyber-physical systems software, but not all. The variant of bisection from [14] better suits the problem: it considers all the information about the parameters’ uncertainty of the equation and guarantees that the solution of adequate quality will be obtained. The only exception is that it operates with the single interval, and that is why it does not provide the result in the form of the equation parameters, i.e., fuzzy variable. In this paper, we describe the modification that eliminates this discrepancy — it extends the interval bisection method to the case of fuzzy intervals. This paper presents an approach to solve fuzzy equations, which satisfies all requirements that are derived from specifics of cyber-physical systems using. The proposed method is based on the bisection technique, guarantees the obtaining of the nonlinear equations’ roots, and reconciles their accuracy with the fuzziness of the equation and its parameters. The using example is discussed.

3 Fuzzy Interval Bisection Approach for Solving Fuzzy Equations 3.1

Bisection for Solving Equations with Ordinary Interval-Valued Coefficients

Let the solution to the equation f(x, p) = 0 be sought, where x is the unknown, vector p = (p1, p2, …, pn)T contains the parameters of the equation. In the original interval bisection [14], vector p was filled with ordinary interval-valued variables, but now we suppose that all its elements pi are fuzzy variables. The root localization interval is initially taken as I1 = [a1, b1]. The proposed algorithm supposes by default function f’s monotonicity, and it is also necessary that f takes values of different signs at the boundaries of interval I1. The algorithm is divided into two parts. Its first part is to execute the traditional bisection method. In the second part, the boundaries of the set of roots’ possible values that arise from the equations’ coefficients errors are determined. The algorithm output is the sequence I1  I2  … In of narrowing intervals, each of which localizes the desired root of function f with a guarantee.

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Let us start with the original interval bisection description [14]. In the first stage, the midpoint ci = (ai + bi)/2 of the interval Ii is calculated at each iteration step i = 1, 2,…. After, the interval for root searching is shortened. If the signs of the function’s values at the points ci and ai are different (i.e., f(ai, p)
f(ci, p) < 0), then the root will be in the left half of the localization interval – so, Ii+1 = [ai, ci] is selected for the next step. Otherwise, when the mentioned signs are the same (i.e., f(ai, p)
f(ci, p) > 0), then the root will be on the right side, and localization interval Ii+1 = [ci,bi] is chosen for the next iteration. To consider the influence of the equation coefficients’ inaccuracy, we calculate value Df(ci, p) representing the error margin of the function f’s value in mentioned point ci. It may be calculated using the metrological self-tracking method [18] for accompanying inaccurate data processing. This approach consists of the automatic differentiation [19] and fuzzy intervals’ calculus and provides mainly the linearized estimates but is usable for generating second- and higher-order estimates as well. Thus,  n  X @f ðci ; pÞ  Df ðci ; pÞ ¼  @p 
Dpk ; k¼1

k

where Dpk is the error margin for function f’s parameter pk represented by the ordinary or fuzzy interval, the derivatives ∂f(ci, p)/∂pk are calculated using the referred automatic differentiation, the operations “ +” and “ ” are understood in the sense of interval or fuzzy calculus. Next, the condition |f(ci, p)| < Df(ci, p) is checked. If the condition is met, then the function sign in interval midpoint ci cannot be determined exactly. Therefore, a further reduction of the interval Ii is impossible since its half containing the equation’s root cannot be selected. After such a situation arises, the transition to the algorithm’s second stage is performed, where the left and right boundaries of the root localization interval are searched for separately. Let the condition for the transition to the algorithm’s second part be satisfied at iteration i = k, and the root localization interval be equal to Ik = [ak, bk] respectively. Now, its left half is used to study the left border of the root’s possible values that is localized at the first step of the algorithm’s second stage with a1 = [ak, ck]. Similarly, b1 = [ck, bk], the right part of Ik, becomes the localization interval for the right border. Let j denote the independent indexing of iterations inside the second stage of the interval bisection. Further, the intervals aj ¼ ½aj ; aj  and bj ¼ ½bj ; bj  are reduced at each iteration. The midpoints of these intervals (mid aj ¼ ðaj þ aj Þ=2 and (mid bj ¼ ðbj þ bj Þ=2 and the error margin of the function f’s value in these points caused by equation parameters’ inaccuracy (Df(mid aj, p) and Df(mid bj, p)) are calculated. Then the conditions for shortening intervals aj and bj are checked. For the left border of the root’s possible values, if |f(mid aj, p)| < Df(mid aj, p), then aj+1 = [aj , mid aj] is selected for the next step, otherwise aj+1 = [mid aj, aj ]. For the right border, if |f(mid bj, p)| < Df(mid bj, p), then bj+1 = [mid bj, bj ] is chosen as the next estimation, otherwise bj+1 = [bj , mid bj]. Intervals aj and bj are refined until the stop condition is satisfied.

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At each iteration, an estimate of the equation root’s possible error margin Dj is calculated. According to the practical considerations, this value is rounded to one, maximum to two, significant digits. The iterative process should be stopped when the following condition is met: roundðDj Þ ¼ roundðDj 1 Þ; where “round” is the rounding operator. 3.2

From the Ordinary Interval to Fuzzy Interval

The described algorithm can be naturally extended for use with fuzzy sets. The fuzzy set for some quantity x is defined as fðx; la ð xÞÞjx 2 X g, where la(x) is a membership function that characterizes the degree of uncertainty in the discussed application; its values are called the degree of belief and are related to the confidence level; X is a universal set of possible values of x. Function la(x) takes values from the range [0, 1] as it is usually taken in practice [20]. The set of values x, for which inequality la(x)  a holds, form the interval that is called the nested interval of the membership function. In the context of the discussed problem, it has the additional meaning of such an interval Ja that satisfies the equation ProbðJa ½Dj ; þ Dj Þ ¼ a:

ð1Þ

Here, “Prob” is an operator of probability assessment — objective in the case of probabilistic data used to construct membership function, or subjective in other cases (like expert knowledge processing or using the approach of subjective probabilities). Such interval has fuzzy boundaries, and that is the reason for calling him the fuzzy interval. Let us show that this concept is natural for cyber-physical systems applications. In cyber-physical systems, uncertain data such as measurement results, expert data, and others can be expressed as intervals of possible values with a certain confidence level. If these values are represented as fuzzy sets, then the membership functions of corresponding fuzzy numbers can be described as families of nested intervals. The nested intervals at different belief degrees will include the set of possible values of the described variable for related confidence levels. For the degree of belief equal to a = 1, the respective nested interval J1 contains values that exactly fall into the set of variable values that are possible. When value a is lowered, the corresponding nested intervals may not include only possible values but values that possibly may not occur (we do not know exactly). When a = 0, Ja is such that it is absolutely wider than the analyzed set. Thus, the degree of belief is the subjective measure of confidence that the nested interval is entirely contained within the set of possible values of the variable. Such definition is very successful: we do not need to consider the nature of uncertainty that fuzzies the interval’s boundaries but just describe these boundaries’ fuzziness. This makes the fuzzy intervals calculus useful for application when dealing with different types of inaccuracy. Its descriptive capacity is near the maximum. With equal simplicity, the fuzzy interval can express all kinds of uncertainties corresponding to some

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variable: the errors caused by rounding and known factors’ influencing are bounded by the nested interval at a = 1; the additional fuzziness inherited from poorly accounted knowledge on the inaccuracy of mathematical formalization of the variable under considerations is determined by intervals at levels a < 1. The discussed terms are illustrated graphically in Fig. 1 and show the difference between traditional fuzzy variable interpretation and fuzzy interval benefits for cyberphysical applications.

Fig. 1. Membership functions for traditional fuzzy number (a) and fuzzy interval (b).

The usual fuzzy number treats the membership function of the measured variable x as the probabilities that its corresponding values are possible. In Fig. 1a, we see the measurement result xR is accompanied by the degree of belief equal to 1 — really, we have already obtained the value xR, and we know that this value can occur. In contrast, nested intervals Ja for the fuzzy interval presented in Fig. 1b does not set the probability to every potentially possible value of the variable but sets the confidence level to the boundaries of its values range. Obviously, the smaller value a is, the wider the corresponded interval Ja is. Since we assume that the fuzzy interval boundaries should change continuously, Ja1 Ja2 . . .Jam for a1 < a2 < … < am (see Fig. 1b). 3.3

Bisection for Solving Equations with Fuzzy Interval-Valued Coefficients

To extend the described above interval bisection to solving the fuzzy equations or equations those parameters are fuzzy intervals, the following simple approach is proposed. The degree of belief a has interpreted as the measure of our confidence that the studied set of possible values covers the corresponding nested interval Ja. Let us perform calculations with several variables p1, p2, … pn that are presented by corresponded single nested intervals Ja1 ; Ja2 ; . . .; Jan , where values a1, a2, …, an may be different. Let x be the result of computations, then the interval of its values obtained during calculations is the nested interval too denoted as Jb. The question is, what its degree of belief b is equal to. If the operator “Prob” in (1) has theQmeaning of objective probability and all uncertainties act independently, then b m i¼1 ai . If the probability is subjective and

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all membership functions were assessed by one team of experts, then b = min(a1, a2, …, am). These variants correspond to two popular rules of dealing with fuzzy variables. The second variant is preferable because the definition of the fuzzy interval includes the mentioned above requirement of the continuity of the nested intervals’ width changing. Thus, for case when a1 = … = am = a, we have b = a. Then when solving equation f(x, p) = 0, where vector p of parameters is filled with fuzzy intervals p1, p2, …, pn, we can apply the interval bisection for a set of nested intervals for elements of p for the given degree of belief a. The result will be the nested interval of the root’s membership function respective to the same belief degree. The next section provides an example of how this method can be used to solve an nonlinear equation the cyber-physical system can face.

4 Application Example As an example, we will consider a cyber-physical system, the simplified diagram shown in Fig. 2. Let sensors of this system be included in a direct current electric circuit and based on the diodes. When processing data from them, an indirect measurement of the voltage in this circuit must be performed, while the incoming data (i.e. direct measurements) are inaccurate. The current-voltage characteristic of the diode sensors is as follows:  qe
U  I1 ¼ I0
e k
T  1 :

ð2Þ

In expression (2), the current I represents the current flowing in the circuit and equals 0.10114 A; the diode reverse current I0 is 25 lA; temperature T equals 300 K; the Boltzmann constant k is 1.38
10–23 J⋅K−1; the elementary charge qe equals 1.6⋅10−19 C.

Fig. 2. Cyber-physical system and its connections with the object.

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In the electrical circuit in Fig. 2, the linear resistance r2 equals 25 Ω. The estimates of the maximum possible absolute errors of the given values are as follows: DIi = 0.15
i mA, DI0i = 0.15
i lA, DTi = 0.5
i K, Dr2i = 0.1
i Ohm, i = (1, 2, …,5). Different values of index i are related to the following confidence levels l: for i = 1 l = 1, for i = 2 l = 0.75, etc. up to i = 5 l = 0. Figure 3 shows the membership functions of fuzzy numbers for these values (I, I0, T, r2). The equation for determining the value of voltage U has the form  qe
U  U I0
e k
T  1 þ  I ¼ 0: r2

ð3Þ

The root value will equal U* 0.213 V if we solve Eq. (3) without considering the coefficient errors. The interval [–1.0; 1.0] V was chosen as the initial interval of root localization for the interval bisection method. The obtained approximations for U and the limits of the boundaries of their intervals are in Table 1. Figure 4 shows the obtained limits for different values of i. The estimate of the solution limits obtained using the interval bisection method has a left boundary in the interval [0. 2099, 0.2119], and a right one — [0.2134, 0.2158]. The resulting interval contains the U* value. Every nested interval in Table 1 contains the value U* as it should be.

Fig. 3. Memberships functions of the fuzzy parameters of Eq. (3).

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Table 1. Estimates of the value of the root of the equation and the limits of its error. Degree of belief

Approximation of the root

1.00 0.75 0.50 0.25 0.00

0.2126 0.2126 0.2129 0.2127 0.2129

Estimation of the interval boundaries Left Right 0.2119 0.2134 0.2114 0.2139 0.2109 0.2148 0.2104 0.2150 0.2099 0.2158

Number of performed iterations

12 12 10 13 11

Fig. 4. Membership function of fuzzy interval for the root of Eq. (3).

5 Conclusions This paper presents a novel method for solving nonlinear fuzzy equations based on fuzzy interval bisection. This approach allows matching the accuracy of the roots obtained with the uncertainty of the initial data specified as fuzzy intervals. The advantages of the proposed approach include that the solution is guaranteed to be obtained and that we stop the equation solving in full compliance with equation fuzziness. It is shown that the formalization of inaccurate data, the cyber-physical systems face, by the fuzzy interval has the maximum descriptive capacity to be used as the universal data type. The main disadvantage of fuzzy interval bisection is the method’s rate of convergence (linear), but the computations are nevertheless accelerated due to the timely stopping of iterations. Acknowledgments. The study was partially funded by grant No 19–71-00127 of the Russian Science Foundation (fuzzy interval approach for solving fuzzy equations) and by grant No 19– 31-90165 of the Russian Foundation for Basic Research (code development, computations carrying out).

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References 1. Baheti, R., Gill, H.: Cyber-physical systems. The impact of control technology 12(1), 161– 166 (2011) 2. Bordel, B., Alcarria, R., Robles, T., Martín, D.: Cyber–physical systems: extending pervasive sensing from control theory to the Internet of Things. Pervasive Mob. Comput. 40, 156–184 (2017) 3. Seshia, S.A., Hu, S., Li, W., Zhu, Q.: Design automation of cyber-physical systems: challenges, advances, and opportunities. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 36(9), 1421–1434. IEEE (2016) 4. Lee, E.A., Seshia, S.A.: Introduction to embedded systems: a cyber-physical systems approach. MIT Press, Cambridge, MA (2017) 5. Akinina, N.V., Gusev, S.I., Kolesenkov, A.N., Taganov, A.I.: Issues of applying fuzzy situational models of decision making for identifying ecological risks. In: 2017 6th Mediterranean Conference on Embedded Computing (MECO), pp. 1–5. IEEE (2017) 6. Zhang, J., Li, M., Guo, P.F., He, L., Bo, Y.M.: Fault detection of robot control systems based on available wireless network measurements. Appl. Mech. Mater. 300, 604–610 (2013) 7. Jovančić, P.D., Tanasijević, M., Milisavljević, V., Cvjetić, A., Ivezić, D., Bugarić, U.S.: Applying the fuzzy inference model in maintenance centered to safety: case study–bucket wheel excavator. In: Applications and Challenges of Maintenance and Safety Engineering in Industry 4.0, pp. 142–165. IGI Global (2020) 8. Kumar, M.S., Dhulipala, V.S., Baskar, S.: Fuzzy unordered rule induction algorithm based classification for reliable communication using wearable computing devices in healthcare. J. Ambient. Intell. Humaniz. Comput. 12(3), 3515–3526 (2021) 9. Shashikhin, V.N.: Metody interval’nogo analiza v sinteze robastnogo upravleniya. [Methods of interval analysis in synthesis of robust control.] J. Vychislitelnye tekhnologii [Comput. Technol. J.] 6(6), 118–123 (2001). (In Russian) 10. Moore, R.E.: Methods and applications of interval analysis. Society for Industrial and Applied Mathematics, Philadelphia (1979) 11. Moore, R.E., Kearfott, R.B., Cloud, M.J.: Introduction to interval analysis. Society for Industrial and Applied Mathematics, Philadelphia (2009) 12. Jaulin, L., Kieffer, M., Didrit, O., Walter, E.: Interval analysis. In: Applied Interval Analysis, pp. 11–43. Springer, London (2001) 13. Sharyj, S.P.: Konechnomernyj interval’nyj analiz. [Finite-dimensional interval analysis.] IVT SO RAN, Novosibirsk (2010). (In Russian) 14. Semenov, K.K., Tselishcheva, A.A.: Interval method of bisection for a metrologically based search for the roots of equations with inaccurately specified initial data. Meas. Tech. 61(3), 203–209 (2018) 15. Saha, G.K., Shirin, S.: Solution of fuzzy non-linear equation using bisection algorithm. Dhaka Univ. J. Sci. 61(1), 53–58 (2013) 16. Nasr Al Din, I.D.E.: Bisection method by using fuzzy concept. Int. J. Sci. Innov. Math. Res. 7(4), 8–11 (2019) 17. Senthilkumar, L.S., Ganesan, K.: Bisection method for fuzzy nonlinear equations. Glob. J. Pure Appl. Math. 12(1), (2016) 18. Semenov, K.K., Solopchenko, G.N.: Combined method of metrological self-tracking of measurement data processing programs. Meas. Tech. 54(4), 378–386 (2011) 19. Griewank, A.: On automatic differentiation. In: Mathematical Programming: Recent Developments and Applications, vol. 6, no. 6, pp. 83–107 (1989) 20. Klir, G., Yuan, B.: Fuzzy sets and fuzzy logic, vol. 4. Prentice hall, New Jersey (1995)

The “Growing” of System Concept and Its Further Development Aleksandra V. Loginova1(&) , Alla E. Leonova2 , Svetlana V. Shirokova1 , and Yu. Yu. Chernyy3 1

3

Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya St, 29, 195251 St. Petersburg, Russia [email protected] 2 Scientific Research Center for Electronic Computing JSC “NITSEVT”, Varshavskoe sh. 125, 117587 Moscow, Russia [email protected] Institute of Scientific Information for Social Sciences of the Russian Academy of Sciences, Nakhimovsky prospect 51/21, 117418 Moscow, Russia

Abstract. The analysis of the features and laws of functioning of open systems with active elements is carried out. The limited representation of them by formal mathematical models and the change in the concept of “project” are substantiated. The behavior of open systems with active elements approaches a mobile equilibrium (according to A.A. Bogdanov and L. von Bertalanffy), similar to the behavior of living organisms, and that system is cannot be assembled from parts. Consequently, the traditional principles of organizing the assembly process from parts are inapplicable. It is necessary to develop a different approach to the creation of such systems. The history of the emergence of an approach based on the composition of the main components of the system (a “gene” of F.Ye. Temnikov) and “growing” the system is considered. Variants of the implementation of this approach are presented. Described of the implementation of this concept are given on the basis of “switching” humanitarian and formal thinking and using the methods related to them. Other examples of the implementation of the proposed concept are given. Prospects for the further development of the concept are considered. Keywords: Growing


System
“Gene”
Gradual formalization

1 Introduction The study of the features and regularities of the functioning of modern enterprises and organizations shows that to manage them it is necessary to represent them as open systems with active elements, especially when introducing emergent information technologies of Industry 4.0. The functioning of such systems approaches the behavior of living organisms, which are characterized by a state of movable equilibrium (according to A.A. Bogdanov [1] and L. von Bertalanffy [2, 3]). Such a system cannot be assembled from parts (a living organism cannot be “assembled”, it is grown from seeds, embryos, eggs, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 311–321, 2022. https://doi.org/10.1007/978-3-030-98832-6_27

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which will contain a hereditary factor containing information about certain characteristics and functions of a future living object (a plant, organism). Thus, we can conclude that the traditional principles of organizing the assembly process from parts are not applicable. It is necessary a different approach to the projection of the system. For the first time, such a conclusion about the need to extend the principles of functioning of living organisms to organizational systems was made by A.A. Bogdanov in his three-volume work “Tectology” [1], which he comprehended from 1903 to 1922. Later in 1937, L. von Bertalanffy [3] proposed an approach that he called “organismical”, and connected it with the concept of an open system with active elements. Interest in this works has intensified in revitalization with the development of automated information systems for the management of enterprises and organizations, which began in the 1960s. Attempts to entrust this work to mathematicians were unsuccessful. A number of approaches to the creation of such systems were proposed, discussed in Sect. 1. The experience of applying these approaches has led to the rejection of the creation of a single integrated automated enterprise management system and “assembling” it from separate subject-oriented information systems that provide automation of individual functions of the control system, and usually little related to each other. The analysis of this experience leads to the expediency of turning to history and recalling the approach that was proposed in the 1970s by the head of the first department of systems engineering in the USSR, Professor F.E. Temnikov [4] (Sect. 3). The first implementation of this approach, proposed by a graduate student of that period V.N. Volkova [5], based on switching of the humanitarian and formal thinking and the methods related with them, is briefly described in Sect. 4. The development of the approach and examples of its implementation are given in Sects. 5 and 6.

2 Analysis of Approaches to the Research of Complicated Systems 2.1

Development of Approaches to Modeling Systems

Initially, at theory systems was used approach, based on the study of input actions and output results, called “black box” by W. Ross Ashby, was proposed [6]. Then we tried to apply the behaviorist approach [7, 8]. In 1967 on the Conference on Systems Science and Cybernetics, which was in the Institute of Radio Engineers (Boston, Massachusetts) M. Mesarović proposed two approaches to the study of systems: a) purposeful and b) terminal [9]. When characterizing the second approach, he suggested using the term “state space” borrowed from mathematics [10]. R. Kulikowski used more convenient terms: decomposition and composition. F. Zwicky [11] suggested a morphological approach. Yu.I. Chernyak [12] called the approaches based on the method of the “language” of a system, the linguistic approach. In philosophy, the approaches were called axiological and causal (from “cause”). For ease of use in practice, the approaches were called “from the top” and “from the bottom”.

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In the theory of organizational management, the following approaches have been proposed (see links in [13]): normative-functional, functional-technological, and system-target. In the 1990s, a process approach [14, 15] was proposed, which can be considered the development of a functional and technological one. 2.2

Methodologies for Management

To develop systems analysis methodologies were used that are focused on organizational management in the socio-economic sphere, the origins of which are cybernetics, operations research, and sociology: Viable Systems Methodology Staf. Bir [16]; the methodology of critical systems of W. Ulrich [17]; the methodology in operations research, systems analysis and ethics of U. Churchman [18]; the strategic assumptions methodology in operations research, systems analysis and ethics of U. Churchman [19]; the methodology of interactive planning by the American scientist Russell Ackoff, who had a great influence on the development of operations research, systems analysis, management [20]; Methodology Soft Systems Methodology of Peter Check-land [21].

3 The F.E. Temnikov’s “Gene” and the Idea of a System “Growing” 3.1

The Story of the F.E. Temnikov’s “Gene” and System “Growing” Ideas

In 1970, Professor of the Moscow Power Engineering Institute, Doctor of Technical Sciences F.Ye. Temnikov proposed the composition of the main functions of any complicated system [22]: S ¼ \C; M; K; R; P [ ;

ð1Þ

where C = f(G) means communication, collection, and transmission of information, its movement in space G; M = f(G, t) means memory, storage of information, transferring it in time t; K = f(G, t, J) means calculation (computer), processing, obtaining new information J; R means reason (mind); P means politics. In living systems, the ability to process information, such as calculations, is not enough, there is also reason or mind R. And in more complexly organized social systems, there is also a function of politics P. For a simple Information system it is enough to use the representation: IS ¼ \C; M; K [ : To explain the suggested approach to creating an information system F.E. Temnikov was proposed an experiment with placing the creator of the information system in a room with zero initial information, that is, in a situation with large initial uncertainty. The room has facilities for means of communication C, memory M, and calculation K.

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The first phone call comes: “In which group is student Petrov?” Thus, there are “students”, “groups” in the system, and you need to start creating the base of students, again using a means of communication. The new questions (for example from the external and internal environment about teachers, scientific researchers, etc.) will force to create new data missives and communication between them. This is how the information system will gradually develop, will be “growing”. Thus, instead of analyzing the existing complex control system, “enumerating” all its elements, from which the development of automated information systems usually began, F.Ye. Temnikov proposed an approach of gradual accumulation of information about the system, which is based on a set of functions required for the system of this class. Subsequently, the set (1), which can be detailed, began to be called Temnikov’s “gene”. Temnikov’s “gene” was not immediately understood. Especially, the inclusion of a “policy” in the model. Based on this idea, a postgraduate student of F.E. Temnikov has proposed a concept that is presented in Sect. 3.2. 3.2

The Concept of Gradual Formalization Model of Decision-Making, Based on “Switching” Between Humanitarian and Formal Thinking

In 1973, in the further development of idea F.Ye. Temnikov about the “gene” of the information system and, on the basis of understanding the role of humanitarian and formal knowledge the in works of F.Ye. Temnikov’s graduate student V.N. Volkova [5, 22] was proposed the concept of gradual formalization of the decision-making model based on the switching of humanitarian knowledge and formal methods. Formal methods do not allow revealing the content of the processes under study. Therefore, we need methods that help to activate the intuition and experience of specialists (i.e., humanitarian thinking) to identify the content, and then display the understood principles of interaction of components, obtained empirically, using formal methods, which can be done using prompts. To test this idea in 1972, an “experiment” was carried out with a fifth-grader, one of the authors of the work [22]. In the puzzle section of one of the popular magazines a problem that could not be solved by the methods of mathematics known to the girl was presented. The task was formulated as follows: a group of visitors entered the dining room, and initially, they sat down at several tables for 6 and 7 people; and then settled equally, 11 people each, occupying z tables. It was required to determine how many visitors entered the dining room if there were more than 100 and less than 150 persons. Formally, the situation proposed in the puzzle is described by the equation 6x þ 7y ¼ 11z;

ð2Þ

and the limitation 100 < 11z < 150. In Eq. (2), the number of unknowns (three) is greater than the number of equations. Consequently, the usual methods of solving algebraic equations are not applicable to it. The fifth-grader could not have known artificial techniques. There remains random selection, for which the puzzle is designed. To speed up such an enumeration, it can be guided by using hints that provide stepby-step assistance in obtaining the result.

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Tip 1: “You may apply what you know. The multiplication table, for example”: 6x þ 7y ¼ 11z 6  17  111  1 6  27  211  2 ... After waiting for a little (until about multiplication by 15), we can recommend applying the following hint. Tip 2: “don’t get carried away with listing items.” In this case, taking for the elements “6x”, “7y”, “11z”, you can do further with the resulting columns. Tip 3: “go back to the problem.” Necessary to organize the enumeration of the sums of the columns “6x”, “7y” and their comparison with the products “11z”. However, the enumeration in this case (in the given example, this is the number of placements with repetitions) in the case of 15 products will be 153 = 3375 (!). Another hint for limiting the enumeration is contained in the problem statement, in the constraint 100 < 11z < 150. Therefore, only this range of sums should be considered. If you cut out a range of columns from the columns of the products that meet the constraints of the third column, you can quickly find a solution: x = 11, y = 11, z = 13. But maybe there are more solutions? To check, it is necessary to again slightly expand the range of feasible solutions, and again there may be brute force problems. And then she suggested herself a technique that she often used at school: not to calculate the sums in full, but to check first the sums of the last digits of the summands for coincidence with the last digit of the constituents on the right side of the equation. After that, in a matter of minutes, the following three solutions were received: 1) x = 8, y = 12, z = 12; 2) x = 9, y = 8, z = 10; 3) x = 11, y = 11, z = 13. In the answer to the puzzle, there was only the third solution, which can be obtained using a special technique: an equation with three unknowns mx + ny = kz is solved for any x, y, and z if the sum of the coefficients for variable terms is equal to the coefficient for z, i.e., m + n = k. More solutions can be obtained by summing the terms that are closer to the beginning and the lower limit, further expanding the range of feasible solutions. At the same time, the enumeration of options increases. The area of feasible solutions should be increased gradually until the last possible solution is obtained. To speed up the search for solutions, an automated procedure can be developed, which at present can be written by almost every student. Based on this example and a number of others suggesting, the concept was developed [5, 22]. This concept is based on the idea of gradual formalization of the model for setting and solving a problem by alternately using means from the class of methods, implementing humanitarian and formal thinking.

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The process of “growing” a model (a system) begins with listing the components that are known to the person making the decision. Then, the rules for transforming the resulting “space of states” are introduced (rules for decomposition or “measures of proximity”, and combinatorics). The resulting new structures and components are included in the original set. The decision to include new components and rules is made by the researcher, i.e. using his intuition and experience of specialists (“humanitarian” thinking). The newly obtained components are included in the original set and the considered procedure is repeated until a satisfactory solution is found. Automated dialog procedures can be used to support this enumeration process. The approach can be useful when starting to investigate problems with large initial uncertainty. It was used experimentally when justifying the structure of the automated information system [5, 22] when creating an organization’s information and control complex when choosing innovations. Using a growing system approach, methods have been developed using other variants of the “gene” of a system, discussed in the Section “Results”.

4 Results 4.1

Development of the “Gene” of the Enterprise Management System

According to one of the simplified understandings, the “gene” is a composition of the main components of the system. In the works of management schools’ ideologists, the different compositions of management functions are determined as the main components. The formation of the main functions of the organization’s management by different management schools was been going on for quite a long period. Thus, H. Fayol considered 5 functions to be the main ones (1916) [23]: F= .

Taking the Fayol management functions as a basis, L. Gyulik and L. Urvik significantly modified their composition, taking into account the needs of practice [24]: F ¼ \planning; organization (creation of structures); staffing; leadership; coordination; reporting; budgeting [ : According to C. Bernard’s opinion, the main tasks of management are [25]: F ¼ \1Þ definition of goals; 2Þ creation of a communication system; i:e:; a hierarchical and accountable structure; information transfer systems; 3Þ development of a set of incentives [ :

P. Drucker in his theory of “Management by Objectives” (1954) proposes to group functions into 4 main groups [26]:

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F ¼ \1: Development of goals: 2: Development of plans to achieve them: 3: Control; measurement; and evaluation of results: 4: Corrective actions to achieve planned results [ :

When automating control in the 1970s, a set of functions was adopted [27]. F ¼ \forecasting; forward planning; current planning: : organization; control; analysis; accounting [ A system analysis of the process of formation and transformation of the main composition of functions [28] allows us to conclude that this process can be regarded as the development of the “gene” of the organization’s management system. Perhaps, goal structuring techniques based on system definitions can be regarded as the “genes” of the “equifinality” of the system [13, 29, 30]. 4.2

The “Gene” for the Library of the Future

Since the 1970s, the approach has taken shape in the theoretical idea of the library as a system consisting of four elements: L ¼ \LK; CU; LP; MTB [ ; where LK is the library collection; CU is the contingent of users; LP is the library personnel; MTB is the material-technical base. In 1972, the Russian librarian Yu.N. Stolyarov outline his understanding of the library as a four-element system [31]. In the 2000s, Yu.N. Stolyarov substantiated a multilevel (multi-circuit) library model [32, 33]. In the current of state the Fourth Industrial Revolution (digital economy), the transformation of libraries will also imply their movement towards cyber-physical systems. The library of the future, considered as a CPS, should be built on the basis of a structure that has developed over millennia, taking into account the development trends of the global information environment and the integration of technologies of the Fourth Industrial Revolution. In its broadest sense, it will become a digital, real-time, closedloop institution that collects data and builds predictive analytics from it. With the introduction of cognitive-information technologies, the structure of the library as a system, consisting of four named elements, must be preserved. In this case, each of the elements can be developed independently, but the interaction between the components must be ensured, taking into account the preservation of the integrity of the system [34]. The four-component model of the library can be considered as the “gene” of the system, which helps to “grow” the library of the future, fulfilling the role of pre-serving the human lifeworld.

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The Concept of a System “Growing” Based on the Idea of a “Living Cell” by E. Bauer

The scientist form Leningrad, E. Bauer, when exploring the regularity of the fundamental disequilibrium of living systems, explained it by the fact that all structures of living cells at the molecular level are charged with “excess” energy, i.e. a cell has biological potential energy (or information) and use this potential, not for work, but for maintaining itself in a disequilibrium state, or when a target appears [35]. If we consider the organization as a “living system”, then E. Bauer’s approach is consistent with the concept of “engineering” in the original sense of this term (from the Latin “ingenium” – ingenuity, invention, knowledge), where “engineering” means “knowledge for design”, that is, engineering is, first of all, an engineering activity based on scientific knowledge, i.e. of needed information. Taking into account E. Bauer’s research, for the development of any organization, a kind of “living cell” that accumulates energy and information in order to invent innovations is needed. Based on this, it can be concluded that for the development of an organization is need science-technical information, engineering tasks should be solved by teams of qualified professionals specializing in the relevant types of professional activity. And for the management of this activity, the coordination of the relevant departments or organizations performing this work is necessary [36, 37]. Such coordination requires the creation of a system of information support at all stages of the life cycle of an activity, for which the engineering concept is applied, i.e., it is necessary to create a unified information and control complex, including software for engineering activities, accompanying regulatory and methodological, regulatory and technical, organizational and administrative documentation. In particular, to create such an environment, it is proposed to use the intelligent knowledge representation system proposed in [38]. The development of such a system and the coordination of engineering work at the enterprise should be included in the functions of the appropriate unit dealing with the organization of the strategic development of the enterprise (organization).

5 Discussion Obviously, the works, presented in Sect. 4, evolved from different needs, in parallel and independently of each other. However, the proposed concept of “growing” complicated systems with active elements allows them to be rethought from a single point of view. The proposed concept helped to understand the history of based components of the management-theory development in a new way [28], to propose a concept of “growing” and its further development, based on the ideas of E. Bauer [36], to propose the idea of developing a library as a cyber-physical system [34]. We hope that this concept will contribute to the comprehension and creation of concepts for the further development of complex systems of various types and purposes.

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6 Conclusions The limited representation of the features and laws of functioning of open systems with active elements by formal mathematical models are substantiated. Consequently, the traditional principles of organizing the assembly process “project” from parts are inapplicable. It is proposed to develop a different approach to the creation of such systems. The history of the emergence of an approach based on determining the composition of the main components of the system (the “gene” of F. Ye. Temnikov) and the “growing” of a system concept are considered. Variants of the implementation of this approach are presented. Descriptions of the implementation of this concept are given on the basis of humanitarian and formal thinking [5, 22]. Other examples of the implementation of the proposed concept are given in Sect. 4 [28, 34, 36]. The concept is used to develop the Theory for Complicated Systems [39], the Theory of cyber-physical systems development [40–42], problems of Sustainable Development of Socio-Economic Systems [43, 44]. Prospects for the further development of the concept of a complicated system “growing” are considered.

References 1. Bogdanov, A.A.: Vseobshchaya organizatsionnaya nauka: Tektologiya [General Organizational Science: Tectology. V 2-kh kn. Berlin-Sankt-Peterburg (1903–1922). Pereizdaniye: V 2-kh kn.], Ekonomika Publ., Moscow (1989) (in Russian) 2. von Bertalanffy, L.: General System Theory. Foundations, Development, Applications. George Braziller, New York, NY (1st Publ., FRG, 1945) (1968) 3. von Bertalanffy, L.: General System Theory – A Critical Review General System, vol. VII, pp. 1–20 (1962) 4. Temnikov, F.Ye.: Voprosy teorii i metodologii sistem [Questions of the theory and methodology of systems]. V sb. trudov Moskovskogo Energeticheskogo instituta. Vyp. 158. Sistemotekhnika [Proceedings of MEI. Issue 158. Sistemotechnika], pp. 3–9. Moscow (1873) (in Russian) 5. Volkova, V.N.: K metodike proyektirovaniya avtomatizirovannykh informatsionnykh sistem [To the methodology for designing automated information systems]. Avtomaticheskoye upravleniye i vychislitel’naya tekhnika, p. 289–300 (1975) (in Russian) 6. Ashby, W.: Ross: general systems theory as a new discipline. Gen. Syst. III, 1–6 (1958) 7. Baer, D.M., Wolf, M.M., Risley, T.R.: Some current dimensions of applied behavior analysis. J. Appl. Behav. Anal. 1, 91–97 (1968) 8. Powers, R.B., Osborne, J.G., Anderson, E.G.: Positive reinforcement of litter removal in the natural environment. J. Appl. Behav. Anal. 6(4), 579–586 (1973) 9. Mesarović, M.: General systems theory mathematical foundation. In: Conference on Systems Science and Cybernetics. Institute of Radio Engineers Boston, Massachusetts. October 1967, pp. 11–15 (1967) 10. Mesarović, M., Takahara, Y.: Mathematical Theory of General Systems. Academic Press (1972)

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A. V. Loginova et al.

11. Zwicky, F.: Discovery Invention, Research Through the Morphological Approach. McMillan (1969) 12. Chernyak, Yu.I.: Sistemnyy analiz v ekonomike [Systems Analysis in Economics]. Ekonomika, Moscow (1975) (in Russian) 13. Volkova, V.N., Kozlov, V.N. (eds.) Sistemnyy analiz i prinyatiye resheniy: Slovar’spravochnik [System analysis and decision making: Dictionary-reference book]. Vysshaya shkola, Moscow (2004) (in Russian) 14. Hammer, M., Champy, J.: Reengineering the corporation: a manifesto for business revolution. Bus. Horiz. 36(5), 90–91 (1993) 15. Mayer, R., de Witte, P.: Delivering Results: Evolving BPR from Art to Engineering. www. idef.com. Accessed 08 Aug 2021 16. Beer, S.: Management Science. Albus Books, London (1967) 17. Cherchman, C.W.: The Systems Approach and Its Enemies. Basic Books, New York, NY (1979) 18. Ulrich, W.: Critical Heuristics of Social Systems Design. Haupf, Berne (1983) 19. Ackoff, R.L.: The Democratic Corporation. Oxford Univ. Press, Oxford (1994) 20. Checkland, P.B., Scholes, I.: Soft Systems Methodology in Action. Wiley, Chichester (1990) 21. Temnikov, F.E., Volkova, V.N., Makarova, I.V.: Systematik, Informatik und Intellektik als neue Verfahrender Datenverarbeitung. Rechentechnik Daten verardeitung, r.Iahrgang Beiheft, 1/2. Die Elektronisch Datenverer-beitung im Hochshulwe-senvert-Rage der wis senschaftlichen: Konferenz der DDR. Berlin, p. 18–22 (1970) 22. Volkova, V.N.: Postepennaya formalizatsiya modeley prinyatiya resheniy [Gradual formalization of decision-making models]. Izd-vo SPbGPU, St. Petersburg (2006) (in Russian) 23. Fayol, H.: Administration industrielle et générale. Dunod et Pinat, Paris (1917) 24. Urwick, L.F.: The Elements of Administration. New York, NY (1943) 25. Sheldrake, J.: Management Theory: From Taylorism to Japanization. International Thomson Business Press (First Published in September 1996) (1998) 26. Drucker, P.F.: The Practice of Management (1954) 27. Volkova, V.N.: Teoriya informatsionnykh protsessov i sistem [Theory of Information Process and Systems]. Yurait, Moscow (2014) (in Russian) 28. Volkova, V.N.: Otkrytyye sistemy: Kak zhit’v usloviyakh podvizhnogo ravnovesiya [Open Systems: How to Live in Movable Equilibrium]. Kurs, Moscow (2021) (in Russian) 29. Volkova, V.N., Denisov, A.A.: Teoriya sistem i sistemnyy analiz [Systems Theory and Systems Analysis]. Yurayt, Moscow (2014) (in Russian) 30. Volkova, V.N., Kozlov, V.N. (eds.). Modelirovanie sistem i protsessov [Modeling of Systems and Processes]. Yurayt, Moscow (2015). (In Russian) 31. Stolyarov, Yu.: Glazami bibliotekoveda. Retsenziya. [Through the Eyes of a Librarian Review]. Bibliotekar’ 11, 56–59 (1972) (in Russian) 32. Stolyarov, Yu.: Biblioteka: strukturno-funktsional’nyy podkhod [Library: StructuralFunctional Approach]. Кniga, Moscow (1981) (in Russian) 33. Stolyarov, Yu.. Biblioteka kak sistema. Issledovaniya i materialy [Library as a System], vol. 49, pp. 59–79. Kniga, Moscow (1984) (in Russian) 34. Chiornyy, Yu.Yu.: Biblioteka budushchego kak kiberfizicheskaya sistema [A library of the future as a cyber-physical system]. In: Volkova, V.N., Kozlov, V.N. (eds.) Sistemnyy analiz v proyektirovanii i upravlenii: Sbornik nauchnykh trudov XXIV Mezhdunar. nauch. i uchebno-praktich. konf, vol. 1., pp. 209–215. Izd-vo Politekh-Press, St. Petersburg (2020) (in Russian) 35. Bauer, E.S.: Teoreticheskaya biologiya [Theoretical Biology]. Izd. VIEM, MoscowLeningrad, 206 p. (1935) (in Russian)

The “Growing” of System Concept and Its Further Development

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36. Volkova, V.N., Leonova, A.E., Romanova, E.V., Chernyy, Y.Y.: Engineering as a coordinating method for the development of the organization and society. In: Bylieva, D., Nordmann, A., Shipunova, O., Volkova, V. (eds.) PCSF/CSIS -2020. LNNS, vol. 184, pp. 12–21. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-65857-1_2 37. Volkova, V.N., Leonova, A.E., Loginova, A.V.: Models for development of the informationcontrol complex of the enterprise. Syst. Res. Inf. Technol. 1(2021), 121–130 (2021) 38. Kuzin, Ye.S.: Predstavleniye znaniy i resheniye informatsionno-slozhnykh zadach v komp’yuternykh sistemakh: monografiya [Representation of knowledge and solution of information-complex problems dachas in computer systems: monograph]. Novyye tekhnologii, Moscow (2004) (in Russian) 39. Volkova, V.N., Fleishman, B.S., Tarasenko, F.P., Loginova, A.V.: Further development of potential feasibility theory for complicated systems according to the unified general-system principle. In: Bylieva, D., Nordmann, A., Shipunova, O., Volkova, V. (eds.) PCSF/CSIS 2020. LNNS, vol. 184, pp. 446–453. Springer, Cham (2021). https://doi.org/10.1007/978-3030-65857-1_37 40. Vasiljev, Y.S., Volkova, V.N., Kozlov, V.N.: The concept of an open cyber-physical system. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 146–158. Springer, Cham (2020). https://doi.org/10.1007/978-3-03034983-7_15 41. Tarasenko, F., Kozlov, V., Volkova, V., Kudriavtceva, A.: On future development of the control theory of automated complexes in the information-communication technologies implementation. In: CSIS’2019: Proceedings of the XI International Scientific Conference Communicative Strategies of the Information Society, October 2019, St. Petersburg. ACM International Conference Proceeding Series, Article No. 7 (pp. 1–6) ACM, New York, NY (2019). https://doi.org/10.1145/3373722.3373772 42. Volkova, V., Gorelova, G., Pankratova, N.: The development of the cyberphysical system concept on base of the interdisciplinary theories. In: 2020 IEEE 2nd International Conference on System Analysis and Intelligent Computing, SAIC-2020, 9239213. IEEE (2020). https://doi.org/10.1109/SAIC51296.2020.9239213 43. Volkova, V.N., Loginova, A.V., Chernenkaja, L.V., Romanova, E.V., Chernyy, Yu.Yu., Lankin, V.E.: Problems of sustainable development of socio-economic systems in the implementation of innovations. In: Proceedings of the 3rd International Conference on Human Factors in Complex Technical Systems and Environments, Ergo 2018, pp. 53–56 (2018) 44. Volkova, V.N., Loginova, A.V., Leonova, A.E., Chernyy, Y.Y.: Development of the theory of sustainability based on the concept of an open system. In: Proceedings of 2019 3rd International Conference on Control in Technical Systems, CTS 2019, p. 15–18 (2019)

Modeling the Effectiveness of an Investment Strategy in Conditions of Insufficient Information Anatolii Smetankin1(&) , Sergei Efimenko1 , Dmitrii Garanin1, Irina Malihina2, Vladimir Shilkin2, and Igor Chernorutsky1 1

2

Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya St. 29, 195251 St. Petersburg, Russia [email protected] The Admiral Makarov State University of Maritime and Inland Shipping, Dvinskaya St. 5/7, 198035 St. Petersburg, Russia

Abstract. An original approach to the issue of estimation and implementation of the strategies of economic reliability of an enterprise is presented in the article . An algorithm for forecasting various economic parameters under uncertainty lies at the base of the approach. The dynamics of interest rates and other factors influencing the development and achievement of the optimal state of any enterprise can be modeled using the economic forecast. The optimal state of an enterprise is the probability of its presence in various information situations or its balanced probabilistic model. A non-asymptotic formulation of the problem is typical for the theory of statistical decision making using small samples. Coupling and integration of heterogeneous data sources involve probabilistic and statistical interpretation (simulation) of various information situations. The approaches to the development of strategy models described in the article cover a wide range of information situations. Moreover, acceptable solutions under high uncertainty with a very limited amount of data are obtained using these approaches. Keywords: Efficiency
Profit
Demand Probabilistic model
Shannon’s entropy


Optimal state
Information


1 Introduction The matter of survival of an enterprise in a competitive environment depends on the proper identification of an investment strategy. In a real-case scenario, this statement means that a search for ways to solve a number of applied problems under uncertainty in enterprise resource planning should be conducted in order to develop a research algorithm and identify the “proper” economic strategy [13, 14, 21]. This issue can be reduced to the task of achieving an optimal state of an enterprise or a certain dynamic “equilibrium” when the ratio of expenditures and profits is ideal in given economic circumstances. It is worth noting that the optimal state of an enterprise is the probability of its presence in various information situations or its balanced probabilistic model [13, 14]. The concept and types of demand forecasting. In a certain sense, the achievement of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 322–331, 2022. https://doi.org/10.1007/978-3-030-98832-6_28

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balanced state of an enterprise’s economic model under external perturbations can be described in terms of the concept of “Nash equilibrium”. Moreover, the uncertainty of the state of the external environment can be expressed by the means of probabilistic models [5, 15, 16, 18]. The amount of the initial data determines the method of development, studying, and forecasting of probabilistic models. Therefore, coupling and integration of heterogeneous data sources involve simulation and probabilistic and statistical interpretation of various information situations. The research and forecasting are based on the methods of probabilistic statistics, the theory of queueing systems, certain provisions of information theory, and etc.

2 Materials and Methods 2.1

The Concept and Types of Demand Forecasting

A forecast, in a general sense, is a system of scientifically based ideas about the possible states and the paths of an object’s dynamics in the future, and alternative ways of its development. Consequently, the process of developing a forecast is called forecasting. The forecasts of consumer demand are necessary for the development of enterprise strategies and data output in order to calculate forecasts. The ideas consumers base their purchases of certain products on should be considered in order to understand the market. Demand forecasting is a scientific prediction of the total volume and structure of demand for goods and services in the required period under specific conditions of changes in the solvency of consumers and the supply of goods on the market [3, 7, 10]. Demand (market capacity in economics) is the relationship between the price (P) and the quantity (Q) of goods that consumers can buy at a strictly defined price in a given period of time. The total demand for goods is aggregate demand for these goods at different prices. Demand depends on the market prices for similar products. The supply depends on the price of the product and on the variables impacting the costs of the production. Elastic demand is the demand formed under the condition that the change in its level (in %) exceeds the percentage expression of the decline in prices. The law of demand states that the value (level) of demand decreases as the price of the product increases. In mathematical terms, it means that there is an inverse relationship between the demand and the price (however, it is not necessary expressed in the form of hyperbola). An increase in the price leads to a decrease in the demand, while a decrease in the price leads to an increase in the demand [12, 19, 20]. 2.2

Types of the Forecast and Its Basis

The forecast of sales quantity of the enterprise’s goods in the future gives an idea of the level of the demand. Demand is influenced by many factors. These factors can be identified by making a forecast of sales quantity (demand). There are different types of forecasts grouped according to their projection horizons: operational, short-term, medium-range, long-range, advanced. [13, 16, 19, 21].

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Different indicators are used as a base for the forecasting: the level of demand in the preceding period (review, retrospective), the dynamics of political and economic circumstances, advertising effectiveness, behavior of competitors, and etc. The planning and implementation of economic calculations are based on such forecasting. 2.3

Methods of Conducting Economic Design and Modeling

Economic and Mathematical Methods. These are methods of statistical modeling (simulation). The forecast model is designed, which describes the dependence of the studied variable on the number of factors. The calculation of the coefficient of demand elasticity is carried out, as well as extrapolation, which is based on past experience and extended to the future. Special Methods. These are trend models in graphic or mathematical form. Trend is a time factor, which describes the tendency of change of the parameters considers the characteristics of the demand for various goods (durable goods, market testing, panel sampling, one-time goods — method of trial purchases, repeat purchase) [3, 7, 14, 19, 20]. 2.4

Impact of Demand on the Economic Strategy of the Enterprise

The development of an enterprise directly depends on the demand. An enterprise should determine the production volume, as well as assess the market and understand which product will be in demand among consumers. In order to do this, the market, the consumer demand, the competitors’ goods should be studied. The strengths and weaknesses of the competitors should be identified. An efficient strategy should be developed based on the obtained information. The proper forecasts allow an enterprise to minimize costs and achieve required results [12–14, 21]. 2.5

Company Strategy

A.A. Thompson and A. J. Strickland have defined company strategy in their works as (quote): “company strategy is a combination of competition and business organization methods aimed at customer satisfaction and organizational goals”. Strategic objectives are a kind of reaction to unforeseen events inside and outside the firm that can significantly affect the achievement of its goals. The essence of integrated systems of strategic management is that companies, on the one hand, have a clear, dedicated, and organized, so-called “formal” (embodied in special documents) strategic planning. On the other hand, the corporate governance structure, systems, and mechanisms of its interaction of individual links are constructed so that: 1. to ensure the development of a long-term strategy for winning the competition; 2. to create management tools to transform these strategies into current production and economic plans to be implemented in practice [7, 14, 19, 21].

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In theory, it is very difficult to forecast demand; the level of consumer demand also depends on the purchasing power of the population. Sales forecasting is the cornerstone of any business organization’s strategy.

3 Results General Provisions. Enterprise profit is calculated as the difference between the total revenue and total costs. The information on the total revenue (the amount of income received by the company from the sale of a given volume of manufactured products), TR, is needed in order to plan the optimal production volume. Stochastic Income Averaging. Formally, the relationship between income and time is expressed as follows: Z

1

TR ¼

PðQÞdQ;

ð1Þ

0

where P (Q) is a demand price function; Q is the current demand for a product. The level of demand and the price are considered to be random variables in conditions of a dynamic market and competitive environment. The distribution laws of such variables are difficult to determine. Therefore, the probability of receiving revenue TR greater than a given one should be determined. The desired probability can be identified by averaging the law of distribution of the price of demand considering the density function of the demand level, [11, 17] when the distribution laws of the demand level and price are given: Z P¼ Gð pÞ/ð pÞdp; ð2Þ X

where Gð pÞ is the distribution law (for the level of demand); /ð pÞ is a density function (level of demand in the price of goods); X is a domain of a random variable p. Usually analyzing the real situation on the market does not provide any opportunity to identify distribution laws adequately. In practice either there is data describing the distribution laws of the demands volume and incomplete data on the demands price or the data is completely lacking [11]. In either case the results of equilibrium analysis are such that it is possible to determine average demands values m_p and m_Q R p [11, 17]. Originally, a sample of the law (law and (or) 0 /ð pÞdp should be determined using the relationship (2). The principle of maximum entropy [1] can be used when the problem of choosing GðpÞ is given in such form. We describe the distribution law of the demand level using the Rayleigh distribution, as follows:

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  p p2 /ð pÞ ¼ 2 exp  2 ; r 2r

ð3Þ

where r is a distribution parameter defined in terms of variance. Shannon’s entropy in differential form is used as a measure of uncertainty [5, 11, 17, 18,]: Z

1

He ¼  0

! /ð pÞGð pÞ /ð pÞGð pÞ R1 ln R 1 dp: 0 /ð pÞGð pÞdp 0 /ð pÞGð pÞdp

ð4Þ

At this stage, it becomes possible to solve the variational problem that provides the maximum of functional (4), under additional conditions: Z

1

Z

0

0

Z

1

1

½1  Gð pÞ dp ¼

gð pÞdp ¼ 1;

ð5Þ

gð pÞpdp ¼ mp :

ð6Þ

0

½1  Gð pÞ0 pdp ¼

0

Z

1

0

Further, the probability (2) can be estimated (“weighted”), with the corresponding initial data, by calculating an integral of the form 1 P¼  c1

Z

1

Rh

m2 1 c1 /ð pÞ

i exp;

ð7Þ

0

1 where m2 can be determined using condition (5); c1 ¼ Pmin : Let us conditionally assume that the quantity

Z p¼

1

/ð pÞGð pÞdp

0

can vary within [0,1], then, given the required accuracy, the problem can be solved by an approximate method in the following sequence. Step 1. Let us assign p a minimum positive value to the value Pmin . Then functional (4) takes the form Z He ¼ 

1

c1 /ð pÞGð pÞ ln½c1 /ð pÞGð pÞdp;

ð8Þ

0 1 where c1 ¼ Pmin : Step 2. We maximize the expression for finding the extremal G ðpÞ considering L ¼ c1 /ð pÞGð pÞ ln½c1 /ð pÞGð pÞ  m1 gð pÞ  m2 gð pÞp, (5) and (6), according to the existing theorems of the calculus of variations, where m1 ; m2 are indefinite Lagrange multipliers.

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Step 3. The Euler equation is obtained using the partial derivatives of the Lagrange function: @L ¼ c1 uðpÞ ln½c1 uðpÞGðpÞ  c1 uðpÞ; @G @L ¼ m1  m2 p: @g The Euler equation for the extremal is obtained: c1 /ðpÞ ln½c1 /ðpÞGðpÞ þ c1 /ðpÞ  m2 ¼ 0:

ð9Þ

Step 4. The desired expression for the extremal is determined using Eq. (8) after a number of algebraic transformations: G1 ð pÞ ¼

  1 m2 exp 1 : c1 /ð pÞ c 1 / ð pÞ

ð10Þ

Step 5. We use condition (5) and determine m2 . Step 6. At each subsequent step, by increasing the parameter ci  ci1 þ Dc; according to dependence (9), we determine the extremal Gi ðpÞ. Step 7. Take the criterion as a basis   He ¼ max Hei ; i¼1;n

and we get the expression of extremal G ðrÞ: Step 8. Further, the probability (2) can be estimated (“weighted”), with the appropriate initial data, by calculating an integral of the form 1 P¼  c1

Z

1

Rh

m2 1 c1 /ð pÞ

i exp;

ð11Þ

0

1 where m2 can be determined using condition (5); c1 ¼ Pmin :

Comment. The static formulation is a “classical” mathematical model and has only theoretical value, since it is not very suitable for predicting informational situations. For this, economic characteristics must be fixed in time, which is quite rare in practice. In our case, it is of interest to study the development of the dynamics of processes in the market.

4 Discussion 4.1

Demand and Supply Dynamics

The dynamic interaction of processes of the “supply-demand” type can be formalized in the framework of the theory of QS (queuing systems).

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For example, the relationship of demand and supply can be represented as a model of incoming orders as it is described in the public service theory. In order for it to match the theory’s rules the following requirements must be met: • Common events (occur independently) • Events with no relevant outcomes (at a given interval all events are independent at crossing intervals). A characteristic feature of the QS is the probability of its being in a free state. The theoretical solution can be expressed in the form of Eq. (3) for a given probability. The probability of the QS being in a free state is the probability of unmet demand for a given type of product. In this case, it is the dynamic characteristics of the system that are of interest, and we are not talking about its stationarity. The most developed, for such a situation, is the same apparatus of queuing systems (QS), which allows analytical solutions in the case of the simplest flows of requests and services. [13, 21, 22]. The analytical solution for a given probability (that is, for service “with failures”) can be written as PðtÞ ¼

  l k 1 þ exp½ðk þ lÞt ; kþl l

ð12Þ

where k is the intensity of demand for a product; l is a demand satisfaction rate. Note. The Eq. (12) shows that: a) the dependence is exponential in time; b) the dependence has a stationary solution in the steady state mode. When choosing investment strategies (in the process of assessing the efficiency of investments), solutions of predictive problems are necessary, therefore, the nonstationary domain of solutions presented as a function of time is of particular interest for analysis in this setting. 4.2

Statistical Formulation

The standard criteria used for making statistical decisions are type I and type II errors. These criteria present the consequences, expressed in lost profits, up to economic ruin, when choosing investment strategies. The fundamental difference between the strategic task and the task of speculative investment, when the balanced decision-making is considered, is not an increase in profit, but minimization of possible losses. In fact, there are certain risks when decisions on investment strategies are made. However, it is possible to make statistical decisions when the data on the distributions of supply and demand is available [4, 9, 14, 21]. Suppose that the investment process can be presented in terms of statistical formulation. Then, let the random demand for the product be expressed by the distribution density f(x), and a random offer of a given product, at the same time, is described as the distribution density q(x).

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Naturally, there is no doubt that the decisions made can be described in terms of statistical decision making which inevitably comes with risks. Figure one suggests that an ideal state may occur for an investor when supply and demand for investment match in their parameters. Furthermore, there is a match in dispersions under conditions of full equilibrium. In this case neither side of the deal takes any risks or losses [11, 17]. Other extremes are described by two states: 1. full coverage of demand, supply in excess; 2. excess of demand over supply, causing a deficit. The current state of the market can be characterized not only by different dispersion, but also by a shift in the supply and demand distribution functions, which is shown in Fig. 1.

Fig. 1. Statistical decision-making on investment. (Take the figure from the calculation in the static package “STATISTICA”).

An investor may suffer losses depending on the strategy implemented. An insufficient investment or an incorrect estimation of demand (there is a risk of overproduction or “overstocking”) may be the reasons for losses in this case. According to the theory of statistical decision making, if there is an investment with the amount of V, then there is a risk of losses for an investor due to underinvestment [11, 17]. The probability of the risk is calculated as follows: Z

1

a¼ V

f ð xÞdx:

ð13Þ

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The probability of risk of losses due to an incorrect estimate of demand: Z

1

b¼

f ð xÞdx:

ð14Þ

V

A non-asymptotic formulation of the problem is typical for the theory of statistical decision making using small samples [1, 4, 5, 9]. Moreover, the acceptable values of type I (6) and type II (5) errors should always be balanced. Such balance in terms of investments means the equilibrium state. Therefore, the solution depends on the accuracy of given distribution laws f(x) and q(x) in the suggested formulation. Thus, the approaches to the development of models for estimating the efficiency of investment strategies described in the paper cover a wide range of information situations. Moreover, acceptable solutions under high uncertainty with a very limited amount of data are obtained using these approaches [13, 15].

5 Conclusion For many problems, within the framework of the theory of statistical decision making based on a small number of observations, a non-asymptotic setting is typical. The problem of determining the best estimates when using a small amount of statistics depends on the sample size and the type of distribution. Moreover, under the conditions of non-asymptotic formulation, the problem is not an object of general mathematical theory. [11, 17] Currently, the theory of making statistical decisions using small samples lacks scientific justification and development.

References 1. Abraham, A., Grosan, G.: Swarm Intelligence in Data Mining, 267 p. Springer, Berlin (2006) 2. Anderson, T.W.: An Introduction to Multivariate Statistical Analysis, 3rd edn, p. 752. Wiley-lnterscience, Hoboken, NJ (2003) 3. Ahern, K.R., Daminelli, D., Fracassi, C.: Lost in translation? The effect of cultural values on mergers around the world. J. Financ. Econ. 117(1), 165–189 (2015). https://doi.org/10.1016/ j.jfineco.2012.08.006 4. Conaver, W.J.: Practical Nonparametric Statistics, 3rd edn, p. 584. Wiley, New York, NY (1999) 5. Cover, T.M., Thomas, J.A.: Elements of Information Theory, 2nd edn, p. 748. John Wiley & Sons Inc., New York, NY (2009) 6. Scott, D.W.: Multivariate Density Estimation. John Wiley & Sons Inc., New York (1992) 7. Duchin, R., Schmidt, B.: Riding the merger wave: Uncertainty reduced monitoring, and bad acquisitions. J. Financ. Econ. 107(1), 69–88 (2013) 8. Engelbrecht, A.: Computational Intelligence: An Introduction, p. 597. John Wiley and Sons Ltd., Sidney (2007)

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9. Epps, T.W., Pulley, L.B.: A test for normality based on the empirical characteristic function. Biometrika 70(3), 723–726. Oxford University Press, Oxford (1983). DOI: https://doi.org/ 10.2307/2336512 10. Kardes, F.R., Posavac, S.S., Cronley, M.L., Herr, P.M.: Consumer Inference. Chapter 6, p. 27. In: Haugtvedt, C.P., Herr, P.M., Kardes, F.R. (eds.) Handbook of Consumer Psychology, p. 1296. eISBN 9781315648361. Routledge, New York, (2008). DOI: https:// doi.org/10.4324/9780203809570 11. Garanin, D.A., Lukashevich, N.S.: Modelirovanie parametrov investicionnogo proekta na osnove informacionno-statisticheskogo podhoda. [Modeling of investment project parameters based on information and statistical approach.] Ekonomicheskij analiz: teoriya i praktika [Econ. Anal. Theory Pract.] 33(384), 37–48 (2014). (In Russian) 12. Giannotti, M., Yafeh, Y.: Do cultural differences between contracting parties matter? Evidence from syndicated bank loans. Manage. Sci. 58(2), 365–383 (2012). https://doi.org/ 10.1287/mnsc.1110.1378 13. Ivchenko, B.P., Martyschenko, L.A., Ivantsov, I.B.: Informacionnaya mikroekonomika. Chast’1. Metody analiza i prognozirovaniya. [Information microeconomics. Part 1. Methods of analysis and forecasting.], p. 159. Publishing house “Nordmed-Izdat”, St. Petersburg (1998). (In Russian) 14. Ivchenko, B.P., Martyschenko, L.A., Tabukhov, M.E.: Upravlenie v ekonomicheskih i social’nyh sistemah. [Management in economic and social systems.], p. 247. Publishing house “Nordmed-Izdat”, St. Petersburg (2001). (In Russian) 15. Ivchenko, B.P., Martyschenko, L.A., Monastyrsky, M.L.: Teoreticheskie osnovy informacionno-statisticheskogo analiza slozhnyh system. [Theoretical foundations of information and statistical analysis of complex systems.], p. 319. Publishing house “Lan’ ”, St. Petersburg (1997). (In Russian) 16. Klavdiev, A., Pasevich, V.: Adaptive Technologies of Information-probabilistic Analysis of Transport Systems, p. 305. Publishing house “North-West Customs Administration of the Russian Federation”, St. Petersburg (2009) 17. Klavdiev, A., Trushnikov, V., Garanin, D., Efimenko, S., Vdovin, O.: Informacionnostatisticheskie metody ocenki effektivnosti investicionnyh proektov. [Information-statistical methods of an estimation of efficiency of investment projects.] GIAB — Gornyj Informacionno-Analiticheskij Byulleten’ (Nauchno-Tekhnicheskij ZHurnal) [The Mountain Information-Analytical Bulletin (Scientific and Technical Magazine], vol. 1, pp. 68–76. Moscow (2016) 18. Park, S.Y., Bera, A.K.: Maximum entropy autoregressive conditional heteroskedasticity model. J. Economet. 150(2), 219–230. Elsevier (2009). DOI: https://doi.org/10.1016/j. jeconom.2008.12.014 19. Pyka, A., Foster, J.: The Evolution of Economic and Innovation Systems, 641 p. Springer, Heidelberg (2015) 20. Reus, T.H., Lamont, B.T.: The double-edged sword of cultural distance in international acquisitions. J. Int. Bus. Stud. 40(8), 1298–1316 (2009) 21. Schumpeter, J.: The Theory of Economic Development. Harvard University Press, Cambridge, MA (1934) 22. Wentzel’, E.S., Ovcharov, L.A.: Teoriya veroyatnostej i ee inzhenernye prilozheniya. [Probability theory and its engineering applications.], p. 480. Publishng house “Nauka”, Moscow (1988). (In Russian)

Innovation Technologies in Technical and Socio-Economic Systems

Digitalization as a Basis for Transformation of the Enterprise Organizational Management System Galina P. Chudesova(&) ITMO University, Kronverksky Pr. 49, bldg. A, 197101 St. Petersburg, Russia [email protected]

Abstract. The article is centered on the tendency to occupy leading positions in the developing world through the digitalization of business. The interest of the society is warmed by significant expectations, which are based on practical results. Primary steps in the implementation of digitalized business demonstrate that digitalization is actually capable of offering significant competitive advantages to enterprising activities. Digitalization is accompanied by “the race of scientific and technical repowering”. Its leaders explicitly show that the acquisition of particular advantages with the use of recent technologies is impossible under the conditions of the absence of deepest changes in business, including its coordination and human resource. As time goes by, numerous technological processes will be quickly developing and improving. Under such conditions, many directors of enterprises try to use the most up-to-date technological processes as well as to evaluate business risks, associated with transformation. It is shown in the article that all structural transformations can be carried out with regard to the fact that digitalization affects human, organizational, and computer resources. In exploring the object on the whole the tools of digitalization help to find a radically new solution. Keywords: Digitalization
Human Organizational management system


Organizational
Computer resources


1 Introduction Practical activity of creating digital organizations within the basis of newest informational technologies starts to develop actively. The interest of the society is warmed by great expectations based on practical results. Primary operations performed as part of the implementation of digital business demonstrate that digitalization is really capable of offering significant competitive advantages to it. Digitalization is accompanied by a “race of scientific-and-technical re-equipment”. The favorites of this race explicitly show that the acquisition of competitive positive aspects due to application of the newest technologies is impossible under the conditions of absence of deepest changes in business proper, including the coordination and human resource. The transformation affects not only enterprising institutions, but also financial and social institutions [5, 9]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 335–346, 2022. https://doi.org/10.1007/978-3-030-98832-6_29

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As time goes by, numerous technological processes will dramatically develop and improve. Under the given circumstances numerous leaders of enterprises try to “taste” the newest technological processes, which include: acquiring the experience of applying new technologies, their competitive advantages, direction and scale of their business, and also evaluating their own business risks, associated with this transformation.

2 Development of Systems for Management of Organization and Mutually Complementary Assets Worldwide and also Russian leaders of digitalization explicitly show that it often leads to the deepest changes of the entire business [7, 21]. Such transformation is able to fundamentally change the logic of the business itself, as well as the result, to change the structure of its material and non-material assets [e.g. 14–16]. Due to this reason, the research of digitalization of the company requires detailed analysis. It is necessary to see the entire business as a whole in every element of business. Many business analysts speak about mutually complementary connections between the components of the company strategy and about the interdependent kinds of activity within the frame of the basic strategy [15, 16]. With all differences in terminology, the reasoning is reduced to one and the same thing — diversity of mutually complementary connections in practical activities, as well as qualitative characteristics of human, organizational, and computer resources. Understanding of the significance of mutually complementary connections, as well as coordination-related practical activities in the company (among them IT and conditions of human resources) is widely spread both in economy and in management. The human resource contains explicit and hidden knowledge of the employees, their ability to learn, motivation, the language of communication, values as well as mutual trust. It forms an informal structure within the enterprise, where human relations play the major role [20]. The organizational resource contains combinations and basics of organization of work — organizational and technical procedures, practice of decision-taking, distribution and transfer of responsibility, business processes and principles, scientific-andengineering development, and besides, the habits and skills, which are applied in the work — technology of performing functions within the system of organization management. These assets are related to the different levels of hierarchy of management: level of employees of the company, of its subdivisions, a team of intracorporate subdivisions, and the company as a whole, or to the level of interaction of the company with external contractors. The computer resource contains information concepts — a list of sources, concepts for processing, transfer, and saving of information; concepts affecting practical activities — automated control systems, information search systems, intranet works, as well as dynamics of applying these systems and besides, information, which is created by information concepts [e.g. 12, 13]. An interconnection between the above-mentioned resources is expressed in the fact that they mutually complement each other like pieces of a puzzle, creating an

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individual face of the company. Three categories of mutually complementary active resources are contained in any organization. If the enterprise functions at the market for a long time and its functioning is stable, then given assets are mutually complementary [1]. In dynamics, everything is much more complicated. The problem is that mutually complementary resources manifest their mobility in a different way: each of them has its particular level of variability or, vice versa, stability. Because of that a number of assets are modified quicker than the others and constitute a locomotive of changes. Their modifications, according to a chain of mutually complementary interconnections, generate changes in other mutually complementary assets. In the same way in dynamics, the organizations contain, on the one hand, most changeable assets and on the other hand, other assets, deficient and prohibiting the changes (through mutually complementary connections). According to the degree of approximation, a new line of mutually complementary connections is constantly formed, which correspond to modified assets, due to what the newest kind of mutually complementary assets of the company appears. Not only the variety of structures but also general tendencies of forming them are reflected in the profile of mutually complementary assets. As the chronicle shows (various practical activities of one of three companies were explored during a fairly long period) mutually complementary assets become most mobile [1]. Dynamics of changing these assets pre-conditions not only the formation of structures but also the competitive advantages.

3 History of Enterprise Evolution and Development of Digitalization 3.1

Changes in Human Resource

Up to the formation of manufacture-based production, the efficiency of work and competitive advantages of business were summed up not only out of the number of technologies, but also out of the variety of individual experience of practitioners, and besides, out of informal relationships inside the company. All required data were transferred in oral form and were stored in paper form. Dynamics of the generation of human resources directly characterized the development and forming of the company. Further on, the universal basic resource of the company became team-based. The team responsible for transformation includes a small but highly qualified “core” of digital talents, and the lack of such specialists is currently the biggest problem, a bottleneck in human resources. Such persons are needed who are able to adapt, implement, train, and optimize the processes based on new technologies [2]. 3.2

Changes in Organizational Resource

Since the end of the 19th century (origination of manufacturing system), the most moveable category of mutually complementing assets of human resource is substituted for a coordination-based basic resource. The origination of large-scale and social

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manufacturing led to the appearance of new structures in company management. Significant dynamics of coordination assets led to the popularization of belt-line social and large-scale production in the first half of the 20th century [6]. Dynamic of forming organization assets was low, in spite of the fact that new office technological processes affecting paperwork, aimed at reduction of paper document flow, came into existence. 3.3

Changes in Computer Resource

Extensive use of digital computers by the companies at the end of the 20th century for the purpose of automation of particular functions and business processes became the basis for the intensive increase of another category of mutually complementing assets — the computer resource. Quick growth of data, which the companies encountered, is the result of significant dynamics of the computer resource. It accelerated the automation of business processes of the enterprises [18, 19] and then — the appearance of digital products [1, 2]. As of today, the dynamics of changes in computer resource does not decrease in any way, which also contributes to the transition of business processes of the companies to digitalization [17, 19]. It also refers to procedures of forming relationships, into which the companies enter. It offers the basis for considering a digital system to be a system, in which the computer resource is a cluster of assets, which is most susceptible to changes. In the same way, it is possible to single out the basic definitions, on which the development of the theme is based. The digitalization of the company shall be understood by us as such modification of the company, in which the driving force of changes is the mutually complementary asset of the computer resource. Another component, which is analyzed as part of this theme, is the digital organization, which is understood as a system, in which two assets are considered to be most susceptible to changes and mutually complementary — organizational and computer resources, which use a broad spectrum of cyber-physical systems and breakthrough technologies (large data arrays, artificial intellect, technologies of VR/AR reality, Internet of things, etc.) in keeping with the digitalization of certain functions [8]. Besides variability, there is also another method of evaluating mutually complementary assets. While exploring this theme, it became evident that mutually complementary assets acquire the quality of corresponding to the goals of the company. The reverse is an exception. Coordination of work within the company could be implemented in different ways. The main significance of this or that method of coordination consists in the fact that it leads to particular stable structures of enterprises. Each method of coordination in keeping with an extended function should be based on one or several mutually complementary assets. The stable appearance of all (without any exceptions) mutually complementary assets should also be completely formed. In order to be comparable, they should be shaped in the same way (i.e., according to the same methodology), since the importance of this or that action is assumed to be one unit — a functional link. For example, in ordinary manufacturing works the “key” mutually complementary assets

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are the loyalty of the personnel and external contractors (the share of the human resource). In organizations, mechanical manufactures, the “key” assets are understood as principles and business processes (share of coordination resource). A specific feature of running an enterprise, formed on the basis of information technologies, consists in the separation of organizational and automation components. They mutually complement each other but don’t substitute each other. The highest point of their joint activity is the Intranet, in which the content is the technology of organizational management, while the technical support is provided by ACS (informational retrieval system and other systems like this). In the most general case, the “key” mutually complementary assets are also more susceptible to change. Their mutual correspondence in the epoch of digitalization is an object for further study. However, it is possible to assume that the stability of the company is utterly simple and is associated directly with the stability of the “key” mutually complementary assets. Due to that reason, the research of mutually complementary assets of the company (identification of essence of practical activities and specific features of their interconnections) is also a dominant factor and is considered to be an important indicator of its diagnostics, modeling of stability and planning of changes. The method of mutually complementary assets implemented through identifying the mutual connections between different practical activities of the company offers excellent orienting points in searching for elements of acting of modern information technologies upon the probable competitive advantages and business risks of the company. The orientation of the company at the maximum mobility of computer facilities will inevitably lead to the restructuring of mutually complementing connections between the assets, as well as assets themselves. It also leads to the biggest possible transformation of business, which can be allowed by the digitalization of the company.

4 Basic Problems of Studying Digital Organization As of today, there are dozens of scientific-and-technical IT-methods, which are applied at various stages of management, from E-mail to cyber-money. In a great jump to modern business, a great number of mutually dependent concepts causes the presence of a large number of obstacles encountered on this way. 4.1

Difficulty

During the process of the newest industrial changes, the majority of enterprises introduces new necessary technological processes. Any subdivision can implement various external concepts for the purpose of analysis, management of software, prototyping, and besides, use any digital devices of a subdivision’s sphere. Such a situation generates significant difficulties. A large number of concepts is characterized by mutual overlapping thereby decreasing the efficiency of work on the

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whole. Also, a transition between concepts is observed for the purpose of quicker fulfillment of separate business processes, which leads to an overload of the employees. 4.2

Introduction of Digital Technologies Is an Issue of Digital Changes

Application of digital devices means the adaptation of employees to the most recent technologies. This goal pursued by the digital changes consists in emphasizing the global conditions of coordination-and-industrial concepts. Efficient digital transformation will require efforts, enabling to establish a common language, which can be used for solving similar problems in any situation. The solution to invest in most modern technologies cannot be taken without reason. Having studied your own data, you can take a decision that this is an optimum process aimed at forming your business, that it increases the efficiency of work of the employees as well as contributes to retention of the customers. If this concept is not accepted and the employees lack the required level of skills, you cannot hope for maximizing the potentiality of your own digital assets. 4.3

Civilized Changes

Digital transformation is not simply digitalizing of documents. The goal of digital transformation initially consists in the following: in order to transform the mode of own existence and activity, it is necessary to make it digital. The employees, who are used to spend their main time on direct interaction with customers, will now spend it on working with a personal computer and apply other methods of communication, which will mainly change the civilization of the work zone: general activity, servicing, clearness. The presence of natural human negation of changes is thereby considered to be a real psychological barrier, which should be treated seriously. The issue of digital transformation in the civilized world of mutual relations consists in the revision of own importance of employees. A person who previously only entered the data is able to be a more important specialist in this field. 4.4

Rate

The only requirement, which is given by the digital transformation, is to keep pace with new technologies. However, this formula has an opposite side — to preserve corporate traditions, which exist inside the company. As of today, fundamentally new and newest difficulties have appeared which are associated with the application of artificial intellect, machine-based training, and other innovative directions in the development of scientific-and-technical progress. These settings should be formulated at the very beginning of digital transformations. It is necessary to forecast the origination of the newest roles as part of forthcoming digital automation and to understand, where the technological processes broaden the function, not simply substitute it. In order to reduce production areas (work zones), the newest devices should attract people and stimulate the use of digital technologies by them.

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341

Competition

When the problem attains a certain level according to the conditions of digital transformation, any contractor is able to create and offer a certain profit. It is necessary to generate a large number of initiatives, without a priori concentrating upon something known and without squandering away the means of the company. The goal consists in the selection of such ideas, which can be realistically implemented. It is necessary to remember that the digital transformation can provide for an advantage in the competition, however, not in a sprint, i. e., not during a short time. Analyzing the top branches of scientific-and-technical development, it is necessary to state, which of them will be of the greatest significance for the company’s business in the nearest future. One should constantly trace the trends in the newest technologies and certain changes in the customers’ demands. Numerous companies leave the correction of their own concepts up to the time when it is already late. It means that first of all the companies should identify and eliminate these vulnerable issues. 4.6

Protection

In order that digital transformation should be performed successfully, it is necessary to concentrate the attention upon the degree of protection of the company. There is no business of past years in the content of a digital society of collective networks. In the process of solving the problem of protectiveness, all additions without exception should be analyzed. According to the data of selected interrogation made by 451 Research to the order of CenturyLink Inc., the main interest should be concentrated upon the increase of business adaptiveness to external challenges, improvement of risk management, and increase of elasticity of scientific-and-technical activity.

5 Current State of a Digital Organization Conception Digital organization is not only the number of new technological processes but also a new organization of business. Digitalization led to the origination of new tactics of management and to the modification of old ones. Having learned a great amount of theoretical and practical research concerning the creation of elements of digital organization, it is possible to identify a number of their characteristic features. The features of the company completely reflect the administrative practices of the organizational resource: • Digitalization of products All products without exception pass from material form to digital form. The presence of a material model of the product does not simply vanish; the application of the product is made unrealizable under the conditions, when its digital notion is absent. Such idea of the present object got the name of “digital alter ego”. For example, in engineering, the demonstration of a digital model of the product acquires more importance than the demonstration of direct material result or

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documents. The electronic model of the product is enriched with a great number of services, which also manifest themselves as digital products. • Digitalization of business processes The probability of applying a “digital alter ego” of complicated equipment combined with constant monitoring of absolutely all its components and actions led to the origination of new business processes — digital ones. They orient the company not only at deepest digitalization of absolutely all company’s internal functions or organizational procedures for performing these functions [4] but also at creation of close partnership almost with all its contractors, which take part in the performance of successful projects. An important component of such deep partnership is the formation of unified built-in information and communication resource in the form of new technology for organization management developed with the use of cyberphysical systems. The presence of a large number of constant interactions of participants of this procedure could contribute to transfer (into the grade of digital) of services, offered by the external organizations (consultations of specialists, outsourcing, inquiries, orders, prescriptions, etc.). • Digital management of creation of values by the enterprise Business activity of the digital company is practiced and is included with the cooperation network with absolutely all its contractors and buyers. Within this network, the enterprise is incorporated into market procedures for the formation of values. In this case, one should be guided by the priority profitable business not only of the company itself but also of all participants for epy procedure of values formation [1]. As an example, let us quote the chain of forming values in engineering, where everything begins with the management of the company, then goes the contractor, then the experimental design bureau, manufacturers of accessories, after which comes the scale production and then — the buyers and after-sale service. Any enterprise is unique and develops individually. It enters into a large number of interrelations and, due to this reason, it has to agree not only its operational work, but also its interactions with absolutely all participants of any procedure. Of late many companies started to form business models, in which their contractors have a large number of opportunities to quickly form their own procedures, to attract new participants. The specialists of the company thereby acquire skills of managing these procedures.

6 The Difference Between Automation and Digitalization Digital companies don’t appear right out of the blue. Their appearance is preceded by a significant history of their automation. Several problems manifest themselves here and that is what makes automation of the company’s functioning different from its digitalization [3]. Does the company acquire some new specific features, implying the application of the newest IT, or are these just quantitative changes? During the first stage of automation the automated lines, sections, workshops, business management systems, information search systems, automated management systems, intranet works, etc. are created.

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During the second stage of automation in business — digitalization — such data is used in information conceptions, that describe real objects, all of which are (without any exceptions) “digital alter egos” of actual objects. What is the reason for being unable to characterize this model as a digital one, if it implies the use of information conceptions for management of interactions with the buyers and/or suppliers (CRM/SRM), moreover, jointly with the concepts of planning the resources of the company (ERP)? A new stage of automation will require significant retraining of personnel and formation of its motivation, it will require changes within the system of organizational management, its coordination structure, and business procedures. While the automation of business formed the role and place of information in the company, and only partly reflected the state of reality, digitalization generates such a structure, in which an information system has to be a “digital alter ego” of actual reality. Reflection of reality corresponds to such a situation in the society, which consists of information conceptions and appears to be similar to reality. As of today, only individual companies can assert that the data contained in their informational concepts reflect the situation inside the company — many-sided and rich for events, as well as absolute understanding by the company of real conditions. Automation of industry in Russia was carried out in the past — in the 1960s, through the introduction of various ACS, which practically recorded the existing situation within the management system of the enterprise, however, they did not analyze it and did not install it [18]. In digital organizations, existing discrepancies should be fundamentally reduced, which will lead to qualitative changes in management and in coordination resource [10, 11]. As time goes by, digitalization “pulls” (creating corresponding conditions) into its own information system not only actual reality, in which the human exists but also the human himself. This situation not only contributes to the formation of intellectual robots for the enterprise but also supports digital “prosthetics” of its organizational and intellectual abilities. It is necessary to note that digitalization “dared to affect” the main human resource of the company and that it will possibly become the reason for qualitative changes not only in the management culture, but also in the human culture as a whole. Thus, in a digital company, the main resource becomes the locomotive of changes, because of which all other mutually complementary assets (without exception) start to change. All this will lead to the practically fundamental change of financial means. It looks like a period has come, which is characterized by quick accumulation of digital changes and processing of large volumes of information, miniaturizing of devices, high rates of their mobility. The growth of the number of participants of data exchange, as well as their activity, lead to high-quality fundamental changes within the system of organization management, its structure, and business of the companies. The directors of many companies deeply understood that digitalization is in no way an opportunity to hide and to wait. The fundamental difference of the fourth industrial revolution from all the preceding ones consists in the rate of dissemination of technologies and the universal character of applying them [7]. Like all the preceding scientific-and-technical revolutions, which led to the development of the newest reality, modern digitalization forms this newest reality. Qualitative and quantitative growth of characteristic features of a digital company is a

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proof of signs of the newest reality. Naturally, each managing director would like to find his own approach under the conditions of this newest reality. The target is to see these potential changes, taking into account that only now some traits of future changes are becoming visible. However, the number of such signs is rather small for the managing director to be able to form a convincing strategy of modifying his own business. He will have to make a selection “by feel”, using the method of trials and errors. During this process, the actual practical activity consisting in digital transformation largely influenced the academic idea of actual changes in financial, administrative, and social spheres. These disciplines will only have to prove their advantages to directors– practitioners by offering them all possible support in the field of searching for ways of digital transformation and business of the company. The closest to digital transformation are IT-oriented companies and the companies, which offer services. It appeared that it is easier for them to work with “digital alter egos” of actual reality. More and more interested in digital transformation are resource-intensive businesses with a huge amount of material assets, which use different kinds of cyberphysical systems, creating collaborative spaces, using cloud, 3D/nD-technologies, and other intellectual cyber-resources.

7 Conclusion The tendency to occupy leading positions in the highly technological and developing world is based on digitalizing of business. Its main essence does not so much consist in the number of newest information technologies, which have been introduced, but in the number of digital transformation of business. All structural transformation should be implemented with regard to the fact that digitalization affects basically technological transformations, i.e. the ones, which answer the question — how? However, considering the object on the whole, it is possible to find a fundamentally new solution. The task of the specialist is not to miss such an opportunity. Such a problem can be solved only by top specialists in the field of the 4D industry. The changes in the system of organizational management accompanied by the introduction of information technologies were carried out earlier as well. But before, under the conditions of automation (informatization) the transformation meant the transition of a business from one stable resource to another more stable position. What concerns digital changes, they trigger the constant procedure of changing business, in which its sustainability and stability will be more and more indistinct, therefore all companies without exception will be unstable. The notions, methods, and tools of management should be revised in keeping with the newest reality. Digital transformation affects not only industrial companies, but also all social institutions without exception.

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References 1. Ananyin, V., Zimin, K., Lugachyov, M., Gimranov, R., Skripkin, K.: Cifrovoe predpriyatie: transformaciya v novuyu real’nost’. [Digital enterprise: transformation into a new reality.] Biznes-informatika [Bus. Inform.] 2(44), 45–54 (2018). (In Russian) 2. Arenkov, I.A., Smirnov, S.A., Sharafutdinov, D.R., Jaburova, D.V.: Transformaciya sistemy upravleniya predpriyatiem pri perekhode k cifrovoj ekonomike. [Transformation of the enterprise management system in the transition to the digital economy.] Rossijskoe predprinimatel’stvo [Russ. J. Entrep.] 19(5), 1711–1722 (2018). (In Russian) 3. Voloshina, V.: Revolyuciya v mozgah, ili chem cifrovizaciya otlichaetsya ot avtomatizacii. [A revolution in the brain, or how digitalization differs from automation.] Upravlenie proizvodstvom: delovoj portal [Business portal “Manufacturing control”], Moscow (2018). (In Russian). http://www.up-pro.ru/library/strategy/tendencii/cyfra-avtomat.html. Last accessed 21 Sept. 2021 4. Borkova, E.A., Burtsev, D.S., Gavriliuk, E.S., Kazanskaya, N.N., Chudesova, G.P.: Preobrazovanie sistemy organizacionnogo upravleniya naukoemkim predpriyatiem v usloviyah cifrovoj transformacii. Uchebnoe posobie. [Transformation of the system of organizational management of a knowledge-intensive enterprise in the context of digital transformation. Tutorial.], p. 140. ITMO University Press, St. Petersburg (2020). https:// books.ifmo.ru/file/pdf/2649.pdf. Last accessed 21 Sept. 2021 5. Keshelava, A., Khaet, I.: Predmet cifrovoj ekonomiki i rol’ cifrovykh instrumentov. [The subject of the digital economy and the role of digital tools.] Setevoe izdanie “Cifrovaya ekonomika” [Digit. Econ. J. (online)] 2(6), 87–95. CEMI RAN, Moscow (2019). (In Russian). https://doi.org/10.34706/DE-2019-02-13. http://digital-economy.ru/images/ easyblog_articles/485/DE-2019-02-13.pdf. Last accessed 21 Sept. 2021 6. Popov, I.V., Kiseliova, M.M., Tolochko, I.A.: Vliyanie cifrovyh tekhnologij na biznesprocessy predpriyatiya. [The impact of digital technologies on the business processes of an enterprise.] J. UEPS: Upravlenie, ekonomika, politika, sociologiya [MEPS: Manag. Econ. Polit. Sociol. J.] (3), 29–37 (2019). (In Russian). https://doi.org/10.24411/2412-2025-201900037 7. Safronchuk, M.V.: Vliyanie cifrovoj transformacii na biznes i delovuyu sredu. [The impact of digital transformation on business and the business environment.] Ekonomika i upravlenie: problemy, resheniya [Econ. Manag. Probl. Solut.] 3(2), 38–44 (2018). (In Russian) 8. Volkova, V., Loginova, A., Kudriavtceva, A.: Management of enterprise cyberphysical systems sustainable development while undergoing a digital transformation. IOP Conf. Ser. Mater. Sci. Eng. 940(1), 012009 (2020) 9. Filippov, V.P., Muzalevsky, A.V.: Potencial’nye ugrozy informacionnoj bezopasnosti cifrovoj ekonomiki v sisteme metrologicheskogo obespecheniya. [Potential threats to the information security of the digital economy in the metrological system.] Trudy Krylovskogo gosudarstvennogo nauchnogo centra [Proc. Krylov State Scientific Center] 1(391), 217–226 (2020). (In Russian) 10. Zachman, J.P.: The Zachman Framework Evolution. 2009–2011. Zachman International Inc., Monument, CO (2011). https://www.zachman.com/. Last accessed 21 Sept. 2021 11. Madanskaya, Y.M.: Scheme of Zakchman in the development of requirements for IP. In: Project Design Practice–2015 (2015). http://reqcenter.pro/zachman-framework/. Last accessed 21 Sept. 2021

346

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12. Zachman, J.A.: A framework for information systems architecture. IBM Syst. J. 26(3), 276– 292 (1987). https://www.zachman.com/images/ZI_PIcs/ibmsj2603e.pdf. Last accessed 21 Sept. 2021 13. Sowa, J.F., Zachman, J.A.: Extending and formalizing the framework for information systems architecture. IBM Syst. J. 31(3), 590–616 (1992). https://www.zachman.com/ images/ZI_PIcs/ibmsj1992.pdf. Last accessed 21 Sept. 2021 14. Zinder, E.Z.: Novoe sistemnoe proektirovanie: informacionnye tekhnologii i biznesreinzhiniring. [New system design: information technology and business reengineering.] Part 1. J. DBMS 4, 37–49 (1995); Part 2. Business reengineering. J. DBMS 1, 55–67; Part 3. Methods of new system design. J. DBMS 2, 61–76 (1996). (In Russian) 15. Daniline, A., Slyusarenko, A.: Arhitektura i strategiya: “In’” i “Yan’” informacionnyh tekhnologij predpriyatiya. [Architecture and Strategy. “Yin” and “Yan” Information Technologies of the Enterprise.], p. 503. Internet-Universitet Informacionnyh Tekhnologij, Moscow (2005). (In Russian) 16. Kalyanov, G.N.: Konsalting: ot biznes-strategii k korporativnoj informacionnoupravlyayushchej sisteme: uchebnik. [Consulting: From a Business Strategy to a Corporate Information Management System. Textbook.], 2nd edn, p. 210. Goryachaya liniya – Telekom [Hotline–Telecom], Moscow (2016). (In Russian) 17. Kudryavtsev, D.V., Arzumanyan, M.Y., Grigoriev, L.Y.: Tekhnologii biznes-inzhiniringa: uchebnoe posobie. [Business Engineering Technology. Tutorial.] Kudryavtsev, D.V. (ed.), p. 426. Polytechnic University Publishing House, St. Petersburg (2014). (In Russian) 18. Trofimov, V.V., et al.: Informacionnye sistemy i tekhnologii v ekonomike i upravle-nii: uchebnik. [Information Systems and Technologies in the Economy and Management: Textbook.] Trofimov, V.V. (ed.), p. 480. Higher Education, Moscow (2006). (In Russian) 19. Repin, V.V., Eliferov, V.G.: Processnyj podhod k upravleniyu: modelirovanie biznesprocessov. [Process Management Approach. Modeling Business Processes.] (Series “Practical Management”), p. 404. Standarty i kachestvo [Standards and Quality], Moscow (2004). (In Russian) 20. Team responsible Digital economy and skills (Unit F.4) European Commission. https://ec. europa.eu/digital-single-market/desi. Last accessed 21 Sept. 2021 21. Rolling Plan on ICT Standartisation-European Commission-Brussels. http://ec.europa.eu/ newsroom/dae/document.cfm?doc_id=9136. Last accessed 25 Sept. 2021

Analysis of Options for a Smart City Architecture Description Alexander N. Danchul(&) Moscow Metropolitan Governance University Named After Yury Luzhkov, Sretenka st. 28, 107045 Moscow, Russia [email protected]

Abstract. An extension of the conceptual model for describing the system architecture according to the ISO/IEC/IEEE 42010: 2011 standard, which allows describing two-dimensional multilayer architectures, is considered. Two geometric metamodels for visual presentation of the introduced concepts and their interrelationships are presented. The main provisions of the concept of smart cities are considered. By analogy with the architecture of the enterprise, it is proposed to distinguish business architecture and IT architecture in the architecture description of a smart city. A brief overview, based on the issues of various international organizations, and the analysis of options for describing the business architecture and IT architecture of smart cities and their interaction with each other are given. References are given to some sources that touch on these issues in relation to two Russian smart city projects. Keywords: Smart city
Architecture of system
Architecture description Business architecture
IT architecture and dimensions of smart cities




1 Introduction A conceptual model for describing the architecture of any software-rich system was described in the ISO/IEC/IEEE 42010: 2011 “Software and Systems Engineering Architecture description” standard [1]. The main idea behind this conceptual model is to list stakeholders with specific concerns and points of view regarding the system of interest. In terms of systems theory, stakeholders are considered as subjects of activity in a complex active system with a common object of activity, but with different goals and representations of this system. In [2] the author showed that this conceptual model is not intended to describe the specifics of multidimensional architectures, as well as systems for which the architecture of subsystems is considered. To remove these limitations in [3, 4], it was proposed to introduce a new group of architectural description elements into the conceptual model. For a visual representation of the introduced elements of the architectural description (concepts) and their interrelationships, the author has developed two geometric metamodels [2, 5]. The generalized architectural description of enterprises as complex organizational and technical systems with significant use of information technologies includes two interrelated components: business architecture and the architecture of information © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 347–357, 2022. https://doi.org/10.1007/978-3-030-98832-6_30

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technology (IT architecture or ICT architecture) [6]. This generalized description can be considered as the basis, the initial stage of the architectural description of the smart city, reflecting the points of view of the main groups of stakeholders — the city administration and IT specialists. Detailed smart city architecture models can be developed using the architecture description formalisms contained in the standard [1]. The degree of elaboration of the architectural description of a smart city can be considered from the standpoint of the proposed additional concepts of the architectural description. It is characterized by the number of levels of description of business architecture and IT architecture, as well as the presence of interrelationships between them. Thus, architectural descriptions of smart cities can be divided into groups, each of which can be associated with a certain geometric metamodel.

2 Architecture Description The main idea of the conceptual model presented in [1] is to form a list of stakeholders, whose interests in relation to the system are expressed in the form of points of view that allow concluding agreements necessary for the further creation, interpretation, and use of architectural representations (views) of the system. Sets of certain types of models (model kind) correspond to different points of view of stakeholders on the system. Sets of models of these types of components form the representation of the system (view) corresponding to this point of view, reflecting a certain set of system properties and relationships. The new group of elements of the architectural description added in [3, 4] to the conceptual model from [1] to remove its limitations consists of 4 concepts. The main one is the “architectural frame” concept reflecting at different levels of the description parts of the architecture (“aspect subsystems”) corresponding to the existing points of view, their coordination, as well as structuring concerns of stakeholders. The architectural frame includes the following three elements (“classes”) of the architectural description that govern the architecture viewpoint: • an “architecture configurator” class, which defines sets of aspects used by different stakeholders; • an “architecture level” class, which defines a set of levels of descriptions used for various aspects; • a “frame correspondence” class is a class derived from class “correspondence” and reflects the relationship between the elements of the architectural framework. An “architecture configurator” class is based on the concept of a configurator used in systems theory [6], which is understood as a list of aspects (sets of properties) of a system that describes it. The configurator defines the first dimension of the architectural description of the system. Each viewpoint on the system of certain specialists or their groups, considered as stakeholders, can be associated with a set of system properties that interest them from this viewpoint. The description of this set of properties (aspect) of the system in the

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form of a set (hierarchy) of models can be considered as an “aspect subsystem” corresponding to the architectural concept “view”. The second dimension is related to the regularity of hierarchy [7], which implies that the system can be described in more detail at a lower level of hierarchy. As a result, sets of two-dimensional hierarchical aspect representations are formed. The analysis of the text of the Programme “Digital Economy of the Russian Federation” [10] based on the proposed extended conceptual model of architecture description is given in [8].

3 Geometric Metamodels for Describing System Architecture The author has developed two geometric metamodels that correspond to two-dimensional descriptions and allow you to visually display concepts and their relationships. The disadvantage of the geometric metamodel of the system architecture (see Fig. 1), first published in [5], is that the number of presented aspects is limited to three.

Fig. 1. “Pyramidal” geometric metamodel for describing system architecture.

As an alternative in [2], the “wire” metamodel was proposed. In the “wire” model, each aspect (there are four of them in Fig. 2) corresponds to a line segment. Points on a line segment (two on each) correspond to different levels of the hierarchy of the corresponding representation. This geometric metamodel will be used in Sect. 5 to analyze options for smart city architecture.

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Fig. 2. “Wire” geometric metamodel for describing system architecture.

4 Smart City In 2015 the United Nations in its document [9] used a broad term — Smart Sustainable City (SSC), which emphasizes the importance of sustainable urban development. Although the program “Digital Economy of the Russian Federation” [10] uses the term “smart city”, that is, the word “sustainable” is excluded from the translation of the English-language term, it remains in the definition of a smart city. Sustainable urban development must take into account demographic, sociocultural and other points of view. In doing so, it is important not to lose sight of the problems caused or created by cities. As examples, we will point out the following two problems: 1) a decrease in the number of able-bodied population in other territories of the country, primarily in rural settlements and small towns, causing a depression in their socio-economic development; 2) the introduction of “green” transport, such as electric vehicles in large cities, leading to an improvement in the environmental situation in them, causes a deterioration of the environmental situation in places of additional electricity production at the leading thermal power plants operating on fossil fuel (gas, coal, fuel oil, etc.). It is also necessary to bear in mind the environmental harm associated with the production of powerful batteries and their subsequent disposal.

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Thus, the most important task at the strategic level is to define and concretize development directions within the SSC concept, taking into account the specifics of the city for which it is being developed. The development of smart cities goes in two ways: the modernization of existing cities and the construction of new ones. The first direction is the most widespread in domestic and world practice and covers a substantially large part of the population. Examples of the application of smart city technologies can be found in cities with a population of over 5 million people (New York in the USA, Shanghai in China, Tokyo in Japan, Singapore), in cities with a population of one million (Barcelona in Spain), in large metropolitan areas; in cities with a population of several hundred thousand people (Amsterdam in the Netherlands, Stockholm in Sweden, Tel Aviv in Israel), and in medium-sized (with a population of about 200 thousand people) non-capital cities (Milton Keynes and Southampton in England) [11]. The second direction is represented by several dozen cities built on a new site, representing samples of modern planning culture and focused on the maximum use of the latest technologies for organizing urban life. representing samples of modern planning culture and focused on the maximum use of the latest, primarily information and communication technologies for organizing urban life. The most famous examples of cities built on the principles of “smart city” are Songdo (Korea), Masdar (United Arab Emirates), Iskander (Malaysia), Neom (Saudi Arabia). In Russia, these are the Skolkovo innovation center near Moscow and Innopolis in the Republic of Tatarstan, focused on the digital economy. The organization of their life is based on the use of information and communication technologies [12].

5 Architecture Description of Smart Cities The schema (see Fig. 3) is usually used for an integrated architectural description of enterprises as complex organizational and technical systems with significant use of information technology. This schema can be considered as the basis, the initial stage of the architectural description of the smart city.

Fig. 3. Integrated architecture description.

Descriptions of the functional areas and business models of the city’s life, including the city management system, as well as the placement of traditional urban infrastructure

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facilities and their integration, refer to business architecture. The organization of support by innovative information and communication technologies for the life of a smart city is determined by the architecture of information technology (IT architecture or ICT-architecture). Unlike even large enterprises, the business architecture of a city, especially a “smart” one, is much more complicated, it includes a variety of interconnected functional subsystems — complexes of the city economy, such as housing and communal services, transport, construction, socio-cultural, energy, etc. It is in the business architecture that the direction of smart city development should be reflected. “Smart Cities”, according to standard [13] are “systems of systems” intensively using information technology. Most of the smart city models given in [11] use the architecture description formalisms contained in the standard [1] and only one model is based on the description according to the ISO / IEC19505 standard “Information technology — Unified Object Management Group Modeling Language (OMG UML)”. As noted above, smart city models using standard [1] include elements of their architectural description that reflect different views of the smart city from the viewpoints of stakeholders with specific interests and goals. The city administration and IT specialists can be considered the main groups of stakeholders. Thus, two classic components are reflected in these architectural descriptions: business architecture and IT architecture. The above models can be divided into the following groups: 1. The point of view of only one group of stakeholders (city leaders or technical specialists) is presented. The view is one-dimensional and contains 2 to 8 aspects. There are no relationships between aspect representations. Aspects are usually referred to as levels (see Fig. 4).

Fig. 4. One-dimensional presentation of the one-stakeholder architecture description.

2. The point of view of only one stakeholder group (A1 or A2) is presented. The architecture description from this viewpoint is two-dimensional and contains from 3 to 5 aspect subsystems, each with 2–3 levels of presentation. There are no intralevel connections (see Fig. 5).

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3. The views of two stakeholder groups are presented. There are no connections between them. Representation of each stakeholder is one-dimensional, containing from 2 to 4 aspect subsystems, each with 1–2 levels. There are no relationships between aspect representations (see Fig. 6).

Fig. 5. Two-dimensional presentation of the one-stakeholder architecture description.

Fig. 6. Two one-dimensional presentations of the two-stakeholder architecture description.

4. The views of two stakeholder groups are presented. There is no connection between their views. The view of the city leadership is one-dimensional and one-level. IT view is two-dimensional, it contains of 2 groups of interrelated aspects, for which 1–2 levels are distinguished (see Fig. 7).

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Fig. 7. Architecture description in the form of a two-dimensional representation with 2 groups of interrelated aspects of one stakeholder without links with a one-dimensional representation of another stakeholder.

5. The points of view of two stakeholder groups are presented. The links between their views are indicated. Both views are one-dimensional and sibling (see Fig. 8).

Fig. 8. Architecture description in the form the two-dimensional presentation of two-stakeholder one-dimensional presentations with links between them.

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6 Urban Dimensions as Basis of Business Architecture In the technical specifications [14], the development directions of the architecture of a smart city under consideration are called “needs”. They are interpreted as the urban dimensions (in our definition — “aspects”) that are affected by the innovation. The proposed in [14] urban dimensions are summarized in Table 1. Table 1. Urban dimensions. No 1 2 3 4 5

Dimensions People Living Environment Governance Economy

Points of view Identifying and meeting the needs of today’s and future generations Improving the quality of life and social balance Safety, waste disposal, and emission control to prevent climate change Provision of state, city (municipal), and communal services Sustainable growth and competitiveness

Based on Table 1 in these specifications, the author in [15] verified the compliance of the listed dimensions with Key Performance Indicators (KPIs) of smart cities, which are consistent with aspects of the living environment identified by the UN. These KPIs are as follows: ICT, Quality of life, Physical infrastructure, Environmental sustainability, Productivity, Equity, and social inclusion. It concludes that the first three KPIs can be applied across all dimensions. Document [11] notes the possibility of other approaches that emphasize the importance of such additional aspects as transport mobility, resilience (to natural disasters, pandemics, terrorist attacks, accidents). The presented dimensions and the sub-dimensions included in them reflect the degree of urgency for a given city of the problems associated with them. The substantiation of this statement, the examples, as well as other interesting materials can be found in a video lecture [16] by Michinaga Kohno, a leading Japanese expert on smart cities. In another document [17], the ITU Focus Group on SSC presented the key performance indicators of smart cities, divided according to dimensions and subdimensions that differ from those given in [15]. There is also a third level of KPIs grouping, which is a level of categories. There are 91 indicators in total, which are divided into 3 groups: 20 indicators characterize a “smart city”, 39 — a “sustainable city”, there are also 32 indicators of a “structural” type. Additionally, all indicators are divided into 27 categories.

7 Conclusion The draft strategy of the city of Moscow “Smart City — 2030” [18] defines the goals, principles, architecture, and directions for the further development of Moscow as a smart city. Compared to the dimensions proposed in [14] the draft strategy has added the “digital mobility” direction, which characterizes its great importance for Moscow. Based on a comparison of this draft with the proposals in [17], the author came to the conclusion that directions and sub-directions of Moscow’s development can be

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considered as a description of the business architecture, which refers to the second version of the smart city architecture described in Sect. 5. In contrast to the draft strategy, the ITU Focus Group on SSC proposals highlight separate sub-dimensions of KPIs for ICT and non-ICT urban infrastructure. Mixing them in one subdirection, as, for example, in “housing and communal services” or “Transport”, in our opinion, indicates an insufficiently clear study of the links between business architecture and IT architecture and threatens the danger of digitalization for the sake of digitalization. The direction of development “Urban Economy” in the draft program is largely inconsistent with the proposals of the ITU Focus Group on SSC. In this direction, the indicators are not sufficiently developed, and in the sub-direction “Industry” they are not given at all. This indicates the need for a better study of the business architecture of Moscow as a smart city driving the digital economy. On March 4, 2019, the Deputy Minister of Construction, Housing, and Utilities of the Russian Federation approved the Basic and Additional Requirements for Smart Cities (Smart City Standard) [19]. This document, in our opinion, basically contains a description of the business architecture of a smart city with a partial indication of the ICT elements that are designed to implement them. Currently, work in this direction is being developed within the framework of the program “Smart City”. It is a part of the departmental project of the Ministry of Construction, Housing and Utilities within the framework of the national project “Housing and Urban Environment”, and the national program “Digital Economy of the Russian Federation” [20].

References 1. ISO/IEC/IEEE 42010:2011. Systems and software engineering — Architecture description. ISO/IEC (2011), IEEE (2011), https://www.iso.org/standard/50508.html. Accessed 21 May 2019 2. Danchul, A.N.: Modeli metaopisanii arkhitektury. [Meta-description models of architecture.] In: 18th Russian Scientific-Practical Conference Enterprise Engineering and Knowledge Management, vol. 1, pp. 100–108. State University of Economics Statistics and Informatics, Moscow (2015). (In Russian) 3. Danchul, A.N.: Metamodel’ opisaniya mnogomernoy arkhitektury sistemy. [The metamodel of the description of the multidimensional architecture of a system.] In: 19th Russian Scientific-Practical Conference Enterprise Engineering and Knowledge Management, vol. 1, pp. 16–22. Plekhanov Russian University of Economics, Moscow (2016), http://conf-eekm. ru/wp-content/uploads/2016/01/eekm16.pdf. Accessed 21 May 2019 (In Russian) 4. Danchul, A.N.: Modeli i metamodeli opisaniya arkhitektury slozhnoy aktivnoy sistemy. [Models and meta models describing the architecture of a complex active system.] In: Proc. of the 20th International Scientific and Practical Conference on Systems Analysis in Engineering and Control (SAEC-2016), vol. 1, pp. 72–82. Polytechnic University Publ. House, St. Petersburg (2016). (In Russian) 5. Danchul, A.N., Efremov, M.V., Lazarev, V.N.: Razrabotka informatsionnykh modeley na osnove aspektnoy dekompozitsii. [Development of information models based on aspect decomposition.] In: Informacionnye tehnologii v strukturah gosudarstvennoy sluzhby [Information Technology in the Structures of the Public Service], pp. 53–62. Russian Academy of Public Administration Publ. House, Moscow (1999). (In Russian)

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6. Peregudov, F.I., Tarasenko, F.P.: Osnovy sistemnogo analiza. [Fundamentals of Systems Analysis.], 2nd edn. NTL Publishing, Tomsk (1997). (In Russian) 7. Volkova, V.N., Denisov, A.A.: Teoriya sistem i sistemnyi analiz: Uchebnik. [Systems Theory and System Analysis: Textbook.] URAIT Publ. House, Moscow (2010). (In Russian) 8. Danchul, A.: The “digital economy of the Russian Federation” programme: An analysis based on architecture description. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 726–734. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34983-7_72 9. The UNECE-ITU Smart Sustainable Cities Indicators. United Nations, Economic and Social Council (2015), https://www.unece.org/fileadmin/DAM/hlm/projects/SMART_CITIES/ ECE_HBP_2015_4.pdf. Accessed 12 Sept 2019 10. Programme “Digital Economy of the Russian Federation”. Approved by Order No. 1632 –R of the Government of the Russian Federation, 28 July 2017, which became invalid in accordance with Order No. 195 –R of the Government of the Russian Federation, 12 Feb. 2019. http://static.government.ru/media/files/9gFM4FHj4PsB79I5v7yLVuPgu4bvR7M0. pdf. Accessed 12 Sept 2019 (In Russian) 11. Smart cities. Preliminary Report 2014. ISO/IEC JTC 1 Information technology (2014) https://www.iso.org/files/live/sites/isoorg/files/developing_standards/docs/en/smart_cities_ report-jtc1.pdf. Accessed 21 May 2019 12. Esaulov, G.V.: “Umnyy” gorod v tsifrovoy ekonomike. [“Smart” city in the digital economy.] Academia. Archit. Constr. 4, 68–74 (2017). (In Russian) 13. ISO/IEC/IEEE 15288:2015 Systems and software engineering — System life cycle processes. ISO (2015) 14. Setting the framework for an ICT architecture of a smart sustainable city. Focus Group Technical Specifications, https://www.itu.int/en/ITU-T/focusgroups/ssc/Documents/website/ web-fg-ssc-0345-r5-ssc_architecture.docx. Accessed 21 Aug 2021 15. Danchul, A.N.: Ispol’zovaniye standartov arkhitekturnogo opisaniya dlya analiza kontseptsii umnogo goroda. [The use of architectural description standards for analyzing the concept of a smart city.] Bull. Univ. Gov. Mosc. 4, 35–43 (2019). (In Russian) 16. Kohno, M.: Strategy of applying latest concept of “smart cities” in the metropolis (challenges and tasks for Moscow), https://www.youtube.com/watch?v=4aYXb3stdoA. Accessed 27 Aug 2021 17. Collection Methodology for Key Performance Indicators for Smart Sustainable Cities, https://www.itu.int/en/publications/Documents/tsb/2017-U4SSC-Collection-Methodology/ files/downloads/421318-CollectionMethodologyforKPIfoSSC-2017.pdf. Accessed 21 Aug 2021 18. Proyekt strategii goroda Moskvy “Umnyy gorod – 2030”. [Draft strategy of the city of Moscow “Smart City – 2030”.] https://www.mos.ru/upload/alerts/files/3_Tekststrategii.pdf. Accessed 21 Aug 2021. (In Russian) 19. Bazovyye i dopolnitel’nyye trebovaniya k umnym gorodam (standart “Umnyy gorod”). [The basic and additional requirements for smart cities (Smart City Standard).] Approved by the Deputy Minister of Construction, Housing and Communal Services of the Russian Federation on Mar 4, 2019, http://www.minstroyrf.ru/docs/18039. Accessed 21 Aug 2021. (In Russian) 20. Umnyj gorod bazovye trebovaniya poetapnogo vnedreniya v period 2019-2024 godov. [Smart City: basic requirements for phased implementation in the period 2019-2024.] (presentation), https://minstroyrf.gov.ru/upload/iblock/323/Prezentatsiya-_Umnyi_-gorod_. pdf. Accessed 21 Aug 2021. (In Russian)

Evaluating the Performance of the Electricity Sector in Iraq and its Relationship to Sustainable Development Tatiana A. Makarenya1(&) and Ahmed Ibrahim Hussein Obaidi1,2 1

Southern Federal University, Bolshaya Sadovaya St. 105/42, 344006 Rostov-on-Don, Russia [email protected], [email protected] 2 Ministry of Higher Education and Scientific Research, Rusafa Road 52, 55509 Baghdad, Iraq

Abstract. In this article, the researchers sought to employ indicators of sustainable development in auditing the performance of the Iraq’s electricity sector; as well as indicators for auditing the performance of this sector issued by the Federal Office of Financial Supervision of Iraq in accordance with sustainable development. The General Electricity Authority of Iraq contributed to ensuring the achievement of sustainable development, and the authors explore the steps that are being taken in the country in this direction. The systematic presentation of the problems of the development of the Iraq’s energy complex is presented; and the model for auditing the electricity sector’s efficiency is proposed. The electricity sector of Iraq is considered as a complex system, and the techniques of system analyses are implemented. To achieve sustainable development, the two researchers authors reached a set of conclusions and recommendations, the most important of which is the necessity of having an audit program that includes auditing the performance of public electricity institutions within the framework of achieving sustainable development, for the purpose of promoting the status of activity electricity to serve the present and future periods. Keywords: Electricity sector
Sustainable development Electrical complex
Power complex


System analyses


1 Introduction The issues of improving the technical and information security of the electricity complex are urgent and topical for the Iraq’s economy. The electricity (power) system is worn out morally and physically. Therefore, the study and identification of systemic problems of the development of the electricity complex of Iraq is an important and urgent task. Therefore, the issues of energy security in terms of development, modernization of the energy economy are relevant for researchers who are interested in issues of both the macroeconomic development of Iraq and the issues of energy complexes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 358–366, 2022. https://doi.org/10.1007/978-3-030-98832-6_31

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Unfortunately, a systematic study of this problem is not visible in all researchers. This article attempts to suggest a systematic presentation of the problems of the development of the energy complex.

2 Materials and Methods Among the problems of sustainable development are the following problems: obsolescence of machinery; lack of a unified electricity supply system, which is the reason for low levels of induction; performance that prevents achieving sustainable development. Therefore, it is not possible to ascertain the efficiency and effectiveness of the electricity sector without auditing its performance. The power sector efficiency and effectiveness’ audit, including audit of its various activities according to sustainable development, helps to develop an audit program. In such a program the performance of the electricity sector is evaluated using the indicators of sustainable development and indicators evaluating the performance of the electricity activity issued by the Federal Office of Financial Supervision of Iraq. This approach allows ensuring the effectiveness of investment in the power sector. The electricity sector plays a vital role in various fields (economic, social, environmental, institutional); that is why it is so crucial to manage the performance of this sector, according to the sustainable development concept. Investing in this sector enables the service of the economy and the service of development to advance the reality of the country without harming the production capacity for the future. 2.1

Objectives of Research

To study the development of the Iraqi power complex, the authors are: 1. Explaining the concept of sustainable development. 2. Ensuring the efficiency and effectiveness of investment in the electricity sector to be relied upon in qualifying other government sectors. 3. Preparing and implementing a proposed program for auditing the performance of the electricity sector and ensuring sustainable development. 2.2

Hypothesis of Research

The research will be based on the main hypothesis based on a systematic approach to studying the development of the Iraqi electric complex and identifying systemic development problems, developing a mechanism for increasing the efficiency of the electric power complex functioning as a unified Iraqi energy system. 2.3

Theoretical Aspects

Electricity Sector and Sustainable Development. Electricity is one of the components for the economic and social development of the macroeconomic level of all countries. The progress of the electricity sector shoes the development of civilization,

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as the electricity supplies represent a key factor in advancing the wheel of production and achieving stability and growth. Thus, there is a need to open the doors of investment to advance the reality of this sector, and then an effective audit investment and the role of auditing the performance of the electricity sector will contribute to achieving sustainable development goals. However, achieving sustainable development still faces many obstacles and difficulties due to low levels of performance [1–3]. The factors of the deterioration of the electricity system and the direct damages arising from the recent conflicts and the great shortage of investments during the past ten years have contributed to a major shortage of supplies. At present, new technologies in the field of energy are being introduced, and “smart” approaches to energy use are spreading. They are used in many fields, including the medicine, the agricultural sector, and the industrial field. Electricity provides the vital needs of people, ensures the safety of people, and ensures the ability to work for all economic agents, including enterprises. However, the alternative energy is underdeveloped in Iraq [4–6]. Energy is closely correlated with the development currencies of the populated countries. The more energy resources are available in the country, the more powerful that is towards the launch and strength towards the progress of the country, not the opportunity to increase the strength of the state, and vice versa. This is one of the main confrontations faced by the country. Governments in most developing countries have the task of improving the efficiency and reliability of energy supplies while making energy services modern is accessible to all and affordable. The importance of energy comes through taking measures to ensure the efficiency of energy use in urban, city centers, and transport planning [7, 8]. The most important goals that the electricity sector seeks to achieve are the following [9, 10]: 1. Providing electric energy that meets the needs of society and the national economy. 2. It regulates the operating activities of production, transmission, distribution, and purchase of energy. 3. Regulates investment activities such as building and rehabilitating projects related to the provision of electric energy. 4. Regulates the entry of the national and foreign private sector, investment in the field of production and distribution, and the provision of the environment and the institution necessary for that. 5. Support and encourage for use of renewable energies in various fields and the localization of their industries. The Gradual Transition from Centralized to Decentralized Management in the Activities of Operating and Maintaining Production and Distribution Facilities. Sustainable development uses many resources, as well as economic, social, environmental, and institutional repercussions. This is for the possibility of providing the needs of the coming term, rehabilitating the deteriorating environment, trying to change the quality of economic growth and addressing the problems of poverty, and fulfilling the basic human needs in a way that achieves a balance between economic growth and the requirements of protecting the environment, through restoration. Sources analysis showed [11–16]:

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1. Dimension Economic: it is a general picture that aims to find a solution to the problem of economic underdevelopment over time. Poverty reduction, and then it means a more comprehensive and efficient use of the available economic resources reconstruction and the advancement of the human standard. To improve the quality of life, acquire knowledge, and access the resources necessary for an adequate standard of living. 2. Dimension Environmental: the introduction of environmental estimation in the field of economics has led to a change in the concept of economic development. From the mere increase in the exploitation of scarce economic resources to realize the multiple and renewable human needs, to the concept of “development.” Continuous or sustainable development. 3. Social Dimension: social enumeration is represented in the constituent elements of society, such as family, religion, and customs. customs, traditions, beliefs, behavioral patterns, social systems, care, and preparation of the secret element, and it is also defined as the natural human right to live in a clean and safe environment through which he practiced all activities while ensuring his right to a fair share of Nature’s wealth and secret development. 4. The institutional dimension: this dimension is represented in public administrations and institutions, which are considered the executive arm of the state, through and through which it draws and implements its development policy, and therefore the achievement of sustainable development and the steady progress of societies. Raising the level and quality of individuals’ lives, securing their human rights, and providing a valid framework for their commitment to their duties towards society and the state. It all depends on the extent to which its institutions and departments succeed in performing their functions and missions. 2.4

Application Side of Research

The electricity distribution sector is considered the starting point for reform because of its negative effects on the electricity sector as a whole, in terms of financial sustainability, harm the economic sustainability of electricity generating companies and transportation companies. One of the strategic directions of the Government of Iraq policy is to encourage the private sector to participate in the development of the electricity sector in Iraq. In accordance with the package of the reforms launched by the Government of Iraq, bids are submitted to companies that have succeeded in qualifying. The above call includes assigning the companies to manage, operate, maintain, and rehabilitate the electricity distribution companies in the covered residential neighborhoods. All over Iraq, in addition to collecting energy costs from consumers and replacing the traditional measurements for consumers with smart ones. And all the requirements necessary for the sustainability of the work of distribution companies, and the ministry aims to support the private sector and eliminate administrative losses.

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Rationalizing energy consumption and controlling the price of liquid energy wages for developing a technical performance of the system and reducing technical losses. Contribute to employing manpower and reducing unemployment, which is one of the most important indicators of sustainable development that contributes to the elimination of unemployment. Poverty (the first goal of sustainable development), and accordingly, the investor was contracted with the company (Um for Engineering Contracting) under the contract No. (4) in the year 2017, which is subject to Investment Law No. 13 of 2006 - Article (33-b) and the term of the contract covers five years ago.

3 Results After reviewing the director’s records and personal articles with stakeholders, the following observations were made (see Table 1): 1. Contract employees (temporary owners) were employed in the covered areas due to the low salaries they receive and their lack of experience and training for the purpose of carrying out maintenance work. 2. In the field of securing the machines necessary to carry out maintenance work, onsite detection, and untie bottlenecks, machines that are not specialized were brought in Old (expendable) and frequent holidays, and the investor did not abide by the terms of the contract that the type and number of machines are commensurate with the size and nature of the region covered. 3. The investor is committed to providing the necessary necessities to complete the work, such as (control desks, servers, computers, paper) in the headquarters of the directors. 4. The increase in the percentage of losses to the energy received during the year (2017), which amounted to (58%) (regarding the years (2016–2015–2014). which are (23%, 27%, 39%), which indicates the overrun on electric power lines, which in turn leads to an increase in losses in received electrical energy. 5. Exceeding the percentage of losses in the received energy for the permissible limit (12%) as it reached (15%, 27%, 46%) (except for one year) (2014), where it was recorded (11%), which is a permissible rate. This indicates that the director did not take the necessary measures. We show the received and selling capacity of electricity in Iraq from 2014–201, it shows the loss percentage to received capacity and exceeding to limit (12%). The received capacity is shown in Fig. 1. The resulting capacity has a constant tendency to increase, which indicates an increase in the demand for electricity from the Iraqi economy. We can illustrate selling capacity in Fig. 2.

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Table 1. Amounts of lost electrical energy received from 2014 to 2017. Statement Received capacity Selling capacity Lost Lost percentage to received capacity Exceeding to limit (12%)

3.5E+09 3E+09 2.5E+09 2E+09 1.5E+09 1E+09 500000000 0

2014 M.W 2240658099 171595289 524672810 23%

2015 M.W 2343528920 1704803553 638725367 27%

2016 M.W 2621304328 1604244241 1017060087 39%

2017 M.W 3060767771 1296435390 1764332381 58%

11%

15%

27%

46%

year 2014

year2015

year2016

year2017

Fig. 1. Received capacity.

1.8E+09 1.6E+09 1.4E+09 1.2E+09 1E+09 800000000 600000000 400000000 200000000 0

year 2014

year2015

year2016

Fig. 2. Selling capacity.

year2017

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Despite the generation of electricity, sales volumes are declining, which indicates theft of electricity. What makes it difficult to modernize Iraq’s electricity system. Electricity losses indicate physical deterioration of power transmission lines. Nobody reimburses the losses of electric companies, and they do not direct financial resources for development.

4 Discussion The most important conclusions reached by the researchers through the study of the theory and practical application of induction can be summarized as follows: 1. The absence of an audit program at the Internal Audit Department in the Ministry of Electricity and its formations and the Federal Financial Supervision Bureau, which includes Performance audit procedures for each number of rapid sustainable development (economic, social, environmental, institutional). 2. Weak awareness of the importance of the investment project among citizens, which leads to their reluctance to pay the sums they owe for their rejection of the project. 3. A decrease in the percentage of the actual amounts of fluid energy from the planned energy, due to the lack of planning tools available to the sector in the estimates Forecasting the growth of electric energy needs and the inaccuracy in drawing up plans that are commensurate with the generation capacities of electric power This is difficult to achieve sustainable development. 4. Lack of coordination between the Ministry of Electricity, universities, and stimulating centers for the development of midwives of cadres working in the electricity sector and conducting Research and studies supporting this vital sector, which is positively reflected in the institutionalization of sustainable development. 5. It is necessary to develop standards for all economic entities, standards for losses, since a loss of 12% is a lot. Losses in electrical networks should not exceed 5–7%. This requires the appropriate specialists.

5 Conclusion In this article, the problem of evaluating the performance of the Iraq’s electricity industry on the basis of system analyses’ methods was discussed. The approach reflecting the relationship between electricity industry management and sustainable development conception was proposed. The main hypothesis suggested by the authors was based on a systematic approach to studying the development of the power complex of Iraq. The problems of this complex were presented as system development problems; that helps to develop a mechanism for increasing the efficiency of the electric complex functioning as a unified Iraqi energy system.

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As a result, the authors of the article recommend the following points: 1. The necessity of having an audit program that includes the audit of the general directors of electricity to ensure the achievement of sustainable development (economics). (Social, Environment, and Institution), to show the extent of the director’s commitment to preparing reports deals with sustainable development and the extent of its contribution to protecting the environment from pollution, and to maintain the needs of all human generations. 2. Availability of an appropriate climate for investment in terms of security, political and administrative stability, and the establishment of awareness campaigns regarding the importance of investment. And what it achieved in terms of providing the level of service required for citizens in terms of maintaining the network or the new connections for the electric current, which participates to the sustainability of the electricity sector to achieve sustainable development. 3. Accuracy in drawing up plans for gas energy, in proportion to the generation capacities of the required electric energy and activating the tools the planning available for the sector in the forecast estimates for the growth of electric energy needs. 4. Strengthening coordination between the Ministry of Electricity, universities, and research centers to develop midwives of cadres working in the electricity sector and conducting research; and the studies supporting this sector to achieve the institutional dimension of sustainable development. 5. To realize the reasons for such huge losses, which are increasing every year, it is necessary to conduct a comprehensive audit. According to the results of which it is necessary to form a budgeting system for the entire energy complex of Iraq. 6. To develop the electric power complex, it is necessary to form a unified system of energy supply for Iraq, to modernize fixed assets, and that is, equipment. To carry out these activities, it is necessary to use the tools of system analysis. 7. It is necessary to develop the Iraqi energy sector, which included a modernization plan, a plan, a financial plan of receipts and outflows of funds, a research plan in conjunction with universities for the needs of Iraqi energy.

References 1. Smith, A.: The Theory of Moral Sentiments (Great Books in Philosophy), pp. 1–20. Prometheus Books, US (2000) 2. Application of sustainable development indicators in ESCWA countries, pp. 3–10. United Nations, New York (2001) 3. Asia Electricity Study, pp. 15–25. OECD publications, Paris (1997) 4. Bosselmann, K.: The Principle of Sustainability, pp. 201–210. Ashgate Publishing Company, US (2008) 5. Carmichael, D.R.: Auditing concepts and methods: A guide to current theory and practice. In: Schaller, C. (ed.) 6th International edition, pp. 251–258. McGraw-Hill Education, New York (1995)

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6. Holt, D.: Management Principles of Practice Englewood Cliffs, pp. 55–60. Prentice Hall, New Jersey (1993) 7. Hunger, D., Wheelen, T.L.: Strategic Management and Business Policy, pp. 145–155. Prentice Hall-Inc., UK (2004) 8. Document issued by the World Bank, Diwan of Financial Supervision, pp. 1–30. World Bank, Washington (2015) 9. Final document of the RAW+2 Conference, The Future We Want, pp. 25–40. Rio de Janeiro, Brazil, (2012) 10. International Monetary Fund (IMF) Report, pp. 5–15. IMF, Washington (2015) 11. Ivancevich, J.M., et al.: Managing for Performance, pp. 185–190. Business publications, Inc., USA (1983). Revised edition 12. Performance auditing guidelines, Iraq, pp. 1–10. Financial Supervision Bureau, Baghdad (2014) 13. The International Energy Agency (IEA) Report, pp. 1–40. IEA, Paris (2013) 14. The United Nations Organization, Report of the Millennium Development Goals, pp. 1–28. U.N, New York (2010) 15. United Nations General Assembly resolution “The Transformation of Our World” Agenda 2030 on 25 September, pp. 20–25. U.N, New York (2015) 16. United Nations Program, Eighth Special Session of the Governing Council. In: Global Ministerial Environment Forum, 29–29 March 2004, pp. 20–30, Jeju, Republic of Korea (2004)

Development Challenges of Remote Rural Terrians: Network Ontology Olga Berestneva1 , Alexei Tikhomirov2 , Andrey Trufanov3(&) , Maria Kuklina3 , Vera Kuklina4 , Dmitriy Kobylkin4 , Natalia Krasnoshtanova4 , Victor Bogdanov4 , Elena Istomina4 , Eduard Batotsyrenov5 , Erdenebaatar Altangerel6, and Zolzaya Dashdorj6 1

Tomsk Polytechnic University, 30 Lenina Avenue, 634050 Tomsk, Russia 2 Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Korea 3 Irkutsk National Research Technical University, 83 Lermontov Street, 664074 Irkutsk, Russia [email protected], [email protected] 4 V.B. Sochava Institute of Geography SB RAS, 1 Ulan–Batorskaya St., 664033 Irkutsk, Russia 5 Baikal Institute of Nature Management SB RAS, 8 Sakhyanovoy St., 670047 Ulan-Ude, ZAB, Russia 6 Mongolian University of Science and Technology, 34 Baga toiruu, 14191 Ulaanbaatar, Mongolia [email protected]

Abstract. Residents of any territory strive for improving their living conditions in all respects: cultural, economic, social and physical ones by developing technologies, producing goods and services, and simultaneously taking care of nature conservation. The study proposes network-based platform aimed at clarification of programs for governance and sustainable development of specific remote rural territories. The case study includes the area of Okinskiy District, Republic of Buryatiya, Russian Federation. Having performed the research, the methods of social geography and network science were utilized. Interviews with residents provided qualitative data to understand the expectations of local people. Our previous network model that combines primitive elements — nodes into more complex constructions stems and bouquets were extended to more novel versions. These are useful for interpretations of socio-economic development of territories as a whole: business scope, environment conservation, and personal and community needs of residents. The enhanced network model comprises not only three sub-spaced groups of local (internal) entities: individual actors, infrastructural systems, and natural resources with pertinent interconnections that might be observed, so that each group forms network subspaces in network space. It also pays attention to external elements, individual or aggregated both, within the same-named network subspaces (e.g. nonresident investors, or transcontinental infrastructures, global climate changes…). Interest-oriented scope on network centralities introduces ranking elements

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 367–381, 2022. https://doi.org/10.1007/978-3-030-98832-6_32

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O. Berestneva et al. along with the preferences of conscious actors and their communities. Such an ontology with enhanced spaced combined stem network model supported by interest-oriented ranking of model elements has been suggested as a platform for comprehensive analysis of programs for prospective development of remote rural territories. Keywords: Hard-to-Reach terrains
Sustainable development
Crossdisciplinarity
Social geography
Network science
Enhanced spaced combined stem networks
Interest-oriented centralities

1 Introduction Numerous areas on the Earth still need to be developed for the benefit of local people. It regards remote rural places as well. Usually, these are difficult to reach terrains hardly populated but often with enough natural resources. Such a case is Okinskiy District, Republic of Buryatiya, RF (see Fig. 1). According to our estimates, on the terrain one can find 600 houses for summer living and 520 km of tertiary roads (dirt and track ones), this roads mostly go along riverbeds. Its land cover map is represented by forests (33%), agricultural grounds, pastures, steppes and mountain steppes (25%), sparsely vegetated, stones and sands (23%), sparse forests and shrubs (18%), and lava plateaus (1%). In recent decades a new vision of socioeconomic development of remote terrains has been discussed — within the scope of sustainability [4], which implies meeting the needs of local societies along with efforts for cultural and environmental conservation. The current study tries to implement network vision for clarification of effective and efficient ways in prospective development of remote territories, including internal and external participants, individuals and enterprises, i. e. multinational ones external investments. Few examples of successful economic activities of relatively poor areas with no external aid have been observed. Anyway, traditional land use, mining, and tourism industry might be considered as sources of economic prospects in remote rural areas. (Even it contradicts the position that sustainable development of terrains through tourism is unrealizable [13]). Any economic activity brings changes or threats for the cultural or natural environment to some extent [6, 10, 11]. This work aims on building a platform to assess optimal ways of territory governance with the selection of pertinent projects for its development. Geographical details, concomitant multifaceted interviews which clarify the needs of residents, and advanced network-based models promote to move further in understanding the development prospects.

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Fig. 1. Land cover (2020) of the research area: 1 — settlements, 2 — houses for summer living, 3 — tertiary roads, 4 — gravel roads, 5 — rivers, land cover types: 6 — sparsely vegetated, stones, sand, 7 — agricultural grounds, pastures, steppes, mountain steppes, 8 — forests, 9 — sparse forests, shrubs, 10 — lava plateaus.

2 Materials and Methods The current interdisciplinary study utilizes methods of social and economic geography and network science.

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General Considerations on Socio-economic Issues

The postulate of limited resources in the economy permeates all spheres of public life. Municipal budgets are no exception. Realizing the fiscal function assigned to them, the municipal authorities are looking for additional sources for the formation of the revenue side of their budgets. The revenues of the consolidated budget of the Okinsky municipality in 2019 amounted to 401 million rubles. Of these, 210.5 million rubles. (52.5%) were received in the form of transfers from the higher budget. The remaining 190.5 million rubles. (47.5%) were obtained from their own tax and non-tax sources. Consolidated budget expenditures of the municipal formation amounted to 424 million rubles, which is 23 million rubles. more budget revenues. The size of the budget deficit turned out to be insignificant and amounted to only 0.7% of the volume of regional GDP for the analyzed period. In 2019, the gross product of the municipality increased by 712 million rubles. and reached 3.29 billion rubles, which is 28% more than in the previous year. Such a significant increase in the gross product of the Okinsky municipality was due to the inclusion of the Republic of Buryatia in the Far Eastern Federal District and the receipt of additional preferences in the implementation of national projects, inter-budgetary relations, support of small businesses, agricultural producers and the social sphere. After the Republic of Buryatia joined the Far Eastern Federal District, the financial resources at the disposal of the Okinsky Municipal Formation have increased significantly. The total volume of investments in the economy and social sphere of the municipality exceeded 1.3 billion rubles, including off-budget investments — 985 million rubles. (76% of the total), budget — 327 million rubles. At the expense of budgetary funds on the territory of the Okinsky municipality, six national (including the national projects “Healthcare” and “Demography”) and 15 regional projects are being implemented, targeted subsidizing of agricultural producers is carried out, various activities are carried out to support representatives of small businesses, the informatization of educational institutions has begun. Expenditures on modernization of the social sphere have been significantly increased. Despite this, the district administration continues to search for options for the harmonious development of the region’s economy. The realities are such that the administration cannot significantly influence the intensity of the work of the mining industry. Although these enterprises are the main payers to the regional budget, and also play a major role in creating jobs on its territory. Therefore, the efforts of the district administration are aimed at the development of such types of business that can use the absolute advantages of the district in the creation of certain types of products and services [16]. 2.2

Mining Industry

The study also revealed the concern of the local population with the problem of employment and the outflow of young people. “My husband goes on shift work in Yakutia. Everyone travels, it happens that families move to Yakutsk in order to earn money. Our family left for America to earn money, they have been living there for 4 years… They also leave for Neryungri to

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work, to Ulan-Ude, to Irkutsk, mostly leave with their families. … in Moscow, many work, in South Korea there are, 3–4 people went to Israel, arrived recently.” (Female, local, 47 years old, Orlik). “Some work (in mining enterprises). Our subsoil users are not against our local ones, but we do not have qualified personnel. Now and then recently, children have begun to enter the Baikal College of Subsoil Use, that has never happened before… In Samarta (a name of the village), out of 1500 employees, 20–30 are local.” (Female, administration worker, 49 years old, Sorok). Therefore, the extractive industry is hardly considered as a source of development of the local economy and community. 2.3

Tourism

All-season active tourism in the Okinsky district includes mountaineering and skiing, ecotourism, medical and recreational ones, religious tourism, hunting, rural tourism, and some excursions [8]. Together they represent a promising foundation that makes sense to create concomitant tourist infrastructure. In this regard it is necessary to develop both each type of tourism separately, and to organize a comprehensive and interconnected harmonious structure of tourism in the region, just to bring a positive socio-economic impact. But it is especially important to note prospects for health tourism, which is popular among local residents and vacationers. The analysis of the interviews shows that the local population understands that tourism should be responsible, not damaging the environment. And it is noted that the local population is not yet paying attention to this perspective, developing a traditional economy. One of the respondents emphasized that tourism still generates income for people who provide services to tourists. “We are very developed: they take tourists from Orlik to the pass, they also earn money, that is, they provide a lorry. horse 2.5 thousand there back, there you need to go through the pass on horseback. This was the hostess of the hotel, her son built there in a cafe, or whatever base, they also meet at home. There is a dining room, and there are men who deliver freight transport, and there on the way halfway they stop to dine there at the base and continue on, they already provide a horse under the pass.” (from an interview with a woman, representative of culture, Orlik, 47 years old). Despite the numerous attractions and natural prerequisites of the Okinsky region (the presence of natural monuments, mineral, and medicinal springs, etc.), tourist activity in the region is not sufficiently developed. According to the official data of the administration, 4 collective accommodation facilities with a total of 99 beds are registered in the Okinsky District. One of them is the well-known and popular Shumak tourist center with 48 places. According to these placements, the total number of visitors to the area in 2020 was 1,700, due to restrictions on entering the area during the peak seasons of the spread of COVID. In general, tourism also is not considered as a regular source of income.

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Government and Traditional Lifestyle

In 2020, a roadmap for the development of tourism was elaborated at the Buryatia administration, where, among the allocated tourist zones, and other things, the Baikal (RF)-Khubsugul (Mongolia) corridor was presented [2]. Since this route also passes through the Okinsky district, there may be new opportunities for the development of tourism in the study area. In 2020, in the village of Orlik, a multifunctional site was launched for the preparation and reception of standards for the all-Russian physical culture and sports complex “Ready for Labor and Defense” worth 2.5 million rubles [1]. In August 2020, a new diesel power plant with a capacity of two megawatts was launched in the village of Orlik. The inhabitants of the mountainous Oka have been waiting for this event for several decades. Due to worn-out networks, outages occurred here regularly. It happened that an entire district with a population of five thousand was left without electricity for a long time. But now it’s in the past. The first launch of a new diesel power plant took place in Orlik. Matvey Madasov, head of the Okinsky District Administration: “In our region, the new diesel power plant is a great help for our residents, fellow countrymen who work and live in more comfortable conditions. This is that during the entire shutdown time, this power plant can operate and supply the entire population, all settlements and even livestock breeding points.” [12]. In 2020 municipality has also created a visitor center with the aim to promote tourism. However, not even some local residents are aware of its existence and what services it offers. 2.5

Mining Industries vs Traditional Land Use

Local residents are very concerned about the environmental situation associated with the development of existing industrial enterprises and plans to develop new deposits. “From the fact that we are remote nature has been preserved in its original form. But recently, more than 30 licenses for the development of minerals were issued on the territory of the Okinsky District. Only on the territory of our settlement, there are about 20. We were always proud that we have the cleanest air, the cleanest water, yet water comes from here. And now all the licenses will work, I don’t know what will happen.” (from an interview with an administration representative, 2020). “But, unfortunately, we will not make it to the park, we have gold mining work here, nature is spoiled. In Samarta there is a gold mining factory, a cyanide cushion that can burst at any moment, and the cyanide will run and everything will go to Oka, throughout the Irkutsk region. It’s not only Oka residents who suffer, but in the Tunkinsky district all the effluents from the factory go into the water, so there are a lot of cancer patients…” (from an interview with a medical worker, woman, 65 years old, 2000, in Orlik).

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Tourism vs Traditional Land Use

Tourists become the customers of local residents buying yak soles and other products. However, the flow of tourists is quite modest and does not allow residents to rely only on this source of income. Usually, transactions between tourists and locals take place informally. Moreover, some people get annoyed with the growing flow of tourists due to overfishing and causing wildfires: “…I think it’s good that we didn’t make the road, anyway there is a deterrent factor. Even now, people are driving in bulk, and even though the road is bad… they also catch our natural resources, kindle fires. It has become very fashionable to come by motorboats and to ride frightening fish.” (Female, cultural worker, 47 years old, Orlik). “Of course, I understand, on the territory of the Russian Federation, please, but I want the Okinsko-Tunki park to be made and banned from sailing along the rivers with their windmills. So that tourists come just to see. They act in a barbaric way. Sometimes fish are taken out in whole barrels. And we don’t know, maybe they hunt, we can’t check everyone, it’s not allowed by law. There used to be a border zone, until the 1990s, there was a strict regime here and they did not have the right to travel. Tunkinsky, Okinsky, Zakamensky districts — all border areas, if they restored it, I would be very pleased….” (from an interview with administration representative, 2020). 2.7

Network Scope

To apply network scope to remote rural territories with a huge number of actors which represent different nature classes, a combined stem network series was considered as a principal concept [3]. To describe any complex multi-nature system with diverse types of relations between actors this concept represents the system with a set of triplets Bn = (Sn, Tn, Cn), which are defined as “beds” of nature n 2 N, where S is set of stems, L is a set of layers of the network, corresponding to a specific profile of links, C = (C1, C2,…, Ct), t 2 T, so that Cl elements are binary relations in layer l 2 L on the S. Within such a network — stem network (SN-network), specific aggregated elements are represented — stems which join multiple nodes of several layers by pertinent links of the same nature. Then being gathered in one physical place several stems of the same or different nature combine a “bouquet” q which is a set created by collecting stems s “without replacement” on N beds, ordering does not matter: qm ¼ f s1;m 2 Sk1 ; s2;m 2 Sk1 ; . . .sH;m 2 SKm g;

ð1Þ

so that 1  Km  ∣Sn∣, 1  n  N. H is a number of stems in a bouquet (1  H  ∣ S1 + S2 + … SN∣, and Km is a number of considered beds 1  Km  N. Along with this, consideration human beings, biological and machine entities might be put in one network silo and thus combined on a comprehensive network platform [17]. The number of stems in a bouquet might be 1 or even close to ∣S1∣ + ∣S2∣ + … + ∣SN∣.

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Each stem fastens nodes that correspond to one entity: a human, ICT, a biological or a machine actor. Thus, sensor and actuator might be nodes adjoined to a machine stem. It makes sense to differentiate links within stem nodes, between nodes in the same layer of interaction, and between stems that are tied into a bouquet. To distinguish the links we call them B-, C-, D- links respectively (see Fig. 2).

Fig. 2. Toy example of a bouquet of two-bed stems of two- and three-layered interactions.

In general, within the combined stem network concept the bouquets correspond to the physical vicinity of their stems; each stem inside a bouquet is composed of nodes that belong to diverse interaction layers. To make the idea clearer, it is of sense to note that individuals build numerous multifaceted relations in a society: kinship, friendship, professional, administrative, neighbor, and many others. So, stems-humans might be presented as nodes linked in diverse layers through pertinent social relations. ICT devices portray a “bed” with multilayer C-links provided by diverse communication operators. A stem-computer can’t be an entity of human social links, nor a stem-person does not generate electric signals and interact with a computer indirectly: through a keyboard, a mouse, etc. These are stems of different beds, but being placed in one closed space they represent a bouquet and might co-work as a whole.

3 Results Even being applied, however, the model (1) does not take into account such a factor as a spatial scale and pertinent functionality the latter impacts on. Thus, the three traditionally considered spheres of livelihood on a territory concern social, economic, and environmental aspects. To find the balance between socio-economic development of territories in a whole, environment protection, and personal hopes of locals for the future a combined stem network model was improved.

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Fig. 3. Three groups of entities: actors, infrastructures, and assets, which form bouquets and their respective subspaces.

The specific case study is distinguished with its separate actors: human, Information and communication, technical, biological ones deployed on a terrain that contains such natural assets (with concomitant climate conditions) as landscape, water, and mineral resources. It also implies that a terrain is provided with some infrastructure to support an adequate standard of living over there. We suggest that these three groups of entities: individuals and supplement facilities, infrastructure complexes, and natural systems might be observed through network prism, so that each group forms its separate network subspace (marked with A-, F-, and R- letters respectively) in network space (see Fig. 3):
 QA ¼ BA 1 ; BA 2 ; . . . BA NA ;
 QF ¼ BF 1 ; BF 2 ; . . . BF NF ;
 QR ¼ BR 1 ; BR 2 ; . . . BR NR : Each one-nature network (bed) in pertinent subspace is described with:
 BA nA ¼ SA nA ; T A nA ; C A nA ;
 BF nF ¼ SF nF ; T F nF ; C F nF ;
 BR nR ¼ SR nR ; T R nR ; C R nR ; where nA 2 NA, nF 2 NF, and nR 2 NR, while NA, NF, and NR are numbers of beds in respective subspaces.

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Subspace BAnA is represented by a combined stem network, in which stems impact on human beings through C- and D-links, i.e. in a severely different way than elements from other subspaces do. Subspace BFnF includes stems-objects that constitute infrastructures. In practice subspace BRnR is displayed on maps or in time series data these might be converted into network structures by several transformation techniques, e.g. [9, 14, 19, 20]. Interdependence between elements of all these three subspaces is realized through socio-economic and natural processes. Thus, individuals that are actors in A- subspace use facilities networked by means of infrastructure lines. Infrastructural F- complexes are nested into the territorial landscape and depend on natural phenomena. Environment (R-subspace) might be extremely vulnerable to economic activity, and so on. These additional interdependency effects that are not taken into account in previous stem network models we denote by U-links. Moreover, the network bouquets of diverse subspaces can be located on the same square cell (see Fig. 4).

Fig. 4. Bouquets of diverse subspaces located within the same geometry cell.

Also one should understand that local spaced networks are components of pertinent larger systems (external or global networks) (see Fig. 5).

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Fig. 5. Spaced combined stem network as part of a global network.

4 Discussion Residents of the territory (authorities and laypeople), as a rule, are concentrated on problems connected to their place of living. On one hand, being interested in higher socioeconomic level, residents form a demand for external actors who are able to cooperate and supply local community requirements to mutual benefit. On the other hand, economic actors as some sort of corporations might be interested in expansion on new terrains. Regarding remote rural territories with rich natural resources, two sorts of industries are principally considered as promising partners for locals — those of mining and tourism. Terrains suitable for the lumber industry, as a rule, have been exploited now. The studied area hardly can be involved in forestry (see Fig. 6).

Fig. 6. Example of the terrain landscape.

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The spaced combined stem network (SCSN) model might be applied to compare possible ways to reach principal objective socioeconomic development of remote rural terrains with modest environmental damage. Depending on where the investments are coming from, land use plans and pertinent economic expectations are distinguished and have specific values for the industrybased investors. Municipal party and local representatives assess socio-economic and natural assets of the terrain in line with their interests. Taking an SCSN platform for such an analysis it is of sense to identify the most important actors in the sophisticated tangled network. These actors might be represented not only by nodes as in traditional networks but additionally by node aggregations—stems in stem networks, and bouquets as aggregated stems in combined stem networks. Among numerous network metrics, centralities occupy a special place. Thus based on shortest-paths (SP) betweenness centrality (SPBC) in a multilayer network [15] is described by: X ðvl Þ ¼

XN q;s¼1;q6¼s6¼v

 rq;s ðvl Þ rq;s ;

ð2Þ

where rq,s = |P  [q ! s]| is the number of SP which start from node q and finish in node s, and rq, s(vl) is the number of times an SP from q to s contains the node vl. If the deal with networks of diverse nature it is of sense to follow [7] with the idea that modular centrality of a network node is a vector. In our case, ZB(vi) = (zBL(vl), zBG(vl)) defines the vector containing Local and Global centralities of the node vl on bed B, corresponding to one of subspaces: A-, F-, or R-. Similar to the definition in [15], the SPBC centrality of a stem, in a stem network placed on bed B, can be presented by: Z ð SÞ ¼

XL l¼1

Z ð Sl Þ

ð3Þ

(this matches to Manhattan distance in L- dimension space). It should be noted, that such a centrality concentrates the property of the same nature (e.g., social relations in a multilayer social network). To go further we assert that scalar centralities are valid and correspond to the very considered domain. If one would like to give ranks to bouquets through SPBC centralities a pertinent aggregated centrality might be prescribed by the collection of stem SPBCs: Z ðQiÞ ¼ ðZ ðSi ; B1 Þ; Z ðSi ; B2 Þ; . . . Z ðSi ; Bm Þ; . . . Z ðSi ; BM ÞÞ;

ð4Þ

where m 2 (1, 2, …M), and M is the cardinality of set B. In some cases, it is possible to consider “super” stems if those are a combination of several stems of the same nature. Thus a centrality is given to each bouquet in a vector form. Further, to rank bouquets we may use Euclidean or Manhattan distance YEu and YMa respectively:

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ffi, qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PM qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 2 P PM IQ m¼1 wm Z ðSi ; Bm Þ YEu ðQi Þ ¼ wm Z ðSi ; Bm Þ2 ;

ð5Þ

, PIQ PM w Z ð S ; B Þ i m m¼1 m

ð6Þ

m¼1

i¼1

and YMa ðQi Þ ¼

PM

i

m¼1

wm ðSi ; Bm Þ;

where IQ is the cardinality of a set of bouquets. It is of value to pay attention to the weights wm which define the importance of the beds in the ranking procedure. It severely depends on the interest of actors who perform such an assessment. We call these centralities “interest-oriented” ones. Thus, for example, mining corporations and tourism enterprises focus on local entities in line with their industrial preferences and consequently discern the significance of network stems in each A-, F-, and R-subspaces (see Fig. 7).

Fig. 7. Local SCSN from mining and tourism points of view.

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Depicted bouquets and relations of the SCSN are aggregated inwardly and denoted as rings and links respectively. (Ring radiuses are proportional bouquet ranks, industrial preferences are marked with dashed lines of different types and colors). Naturally, local society gives ranks to the bouquets its own way. Also, the suggested network scope might be productive to clarify such social effects as corporate and group egoism [5, 18, 21] just to focus the actors on social responsibility.

5 Conclusions A new ontology with an enhanced spaced combined stem network (ESCSN) model supported by the interest-oriented ranking of model elements has been suggested as a platform for comprehensive analysis of programs for prospective development of remote rural territories. The ESCSN allows to separate elements and processes of real systems on the territories and to clarify the relations of internal and external actors. Through ranking aggregated actors: nodes, stems, and bouquets the approach distinguishes stakeholder-related interests and makes development programs more transparent in the context of challenges, capacities, and prospects. And it is of value to note on applicability of the ontology for revealing the program weaknesses and vulnerabilities. Moreover, the ESCSN might be useful for detailing plans for collaborative activities of local communities and businesses. Acknowledgment. The study was funded by RFBR and MECSS, project number 20-57-44002 “Interdisciplinary network platform for modeling socio-economic and environmental processes in the cross-border territories of the Russian Federation and Mongolia with limited transport accessibility”.

References 1. A new Ready for Labor and Defense site willss appear in the Okinsky district of Buryatia. https://bgtrk.ru/news/society/194638/. Accessed 06 Aug 2021. (in Russian) 2. A roadmap for tourism development has been developed in Buryatia. https://www.baikaldaily.ru/news/15/403159/. Accessed 05 Aug 2021. (in Russian) 3. Ashurova, Z., et al.: Comprehensive Mega Network (CMN) platform: Korea MTS governance for CIS case study. In: Proceedings of the 2016 Conference on Information Technologies in Science, Management, Social Sphere and Medicine, pp. 532–535. Atlantis Press (2016) 4. Chelan, M.M., Alijanpour, A., Barani, H., Motamedi, J., Azadi, H., van Passel, S.: Economic sustainability assessment in semi-steppe rangelands. Sci. Total Environ. 637–638, 112–119 (2018) 5. Deryugin, P.P., Tarasova, O.O.: Tipologiya vidov korporativnogo egoizma: konceptual’naya prezentaciya. [Typology of types of corporate egoism: a conceptual presentation.] Vestnik Sankt-Peterburgskogo universiteta. Sociologiya [Bull. St. Petersburg Univ. Sociol.] 4, 154– 163 (2015). (in Russian)

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6. Finding Balance: Cultural Preservation and Tourism. https://chemonics.com/blog/findingbalance-cultural-preservation-tourism/. Accessed 20 Dec 2021 7. Ghalmane, Z., El Hassouni, M., Cherifi, C., Cherifi, H.: Centrality in modular networks. EPJ Data Sci. 8(1), 1–27 (2019). https://doi.org/10.1140/epjds/s13688-019-0195-7 8. Imetkhenov, A.B., Sharapstepanov, B.D., Imetkhenov, O.A.: Gornaya Oka. Geografiya Vostochnogo Sayana: ucheb. posobie. [Gornaya Oka (geography of the Eastern Sayan): textbook.] 192 p. Publishing House of the Buryat State University, Ulan-Ude (2008). (in Russian) 9. Lacasa, L., Luque, B., Luque, J., Nuno, J.C.: The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion. Europhys. Lett. 86, 30001– 30005 (2009) 10. Newbold, T., et al.: Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015) 11. Polasky, S., et al.: Role of economics in analyzing the environment and sustainable development. Proc. Natl. Acad. Sci. 116(12), 5233–5238 (2019) 12. Residents of the most remote region of Buryatia, Okinsky, will no longer experience problems with uninterrupted power supply. https://bgtrk.ru/news/society/192604/. Accessed 07 Aug 2021. (in Russian) 13. Sharpley, R.: Tourism, sustainable development and the theoretical divide: 20 years on. J. Sustain. Tour 28, 1–15 (2020) 14. Silva, V.F., Silva, M.E., Ribeiro, P., Silva, F.: Time series analysis via network science: concepts and algorithms. WIREs Data Min. Knowl. Discovery 11(3), e1404 (2021) 15. Solé-Ribalta, A., De Domenico, M., Gómez, S., Arenas, A.: Centrality rankings in multiplex networks. In: Proceedings of 2014 ACM Conference on Web Science—WebSci’14, pp.149– 155, 23–26 June 2014, Bloomington, IN, USA (2014) 16. The Republic of Buryatia. In: Investment platform of Russian regions. https://www. investinregions.ru/en/regions/03/. Accessed 24 Aug 2021 17. Tikhomirov, A., Trufanov, A., Grigoryev, S., Berestneva, O., Burkatovskaya, Yu.: Global brain and beyond: a concerted model of interacting networks. J. Phys. Conf. Ser. 1680 (012049), 1–6 (2020). IOP Publishing. https://doi.org/10.1088/1742-6596/1680/1/012049 18. Tomaszewski, M.: Egoism and cooperation in economic development — a historical approach, Economic Research-Ekonomska Istraživanja (2021). https://www.tandfonline. com/doi/full/https://doi.org/10.1080/1331677X.2021.1874461. Accessed 20 Dec 2021 19. Trufanov, A., Kinash, N., Tikhomirov, A., Berestneva, O., Rossodivita, A.: Image converting into complex networks: scale-level segmentation approach. In: Proceedings of IV International Conference on “Information Technologies in Science, Management, Social Sphere and Medicine” (ITSMSSM 2017) (ACSR), vol. 72, pp. 417–422 (2017) 20. Tsiotas, D., Magafas, L., Argyrakis, P.: An electrostatics method for converting a time-series into a weighted complex network. Sci. Rep. 11, 11785 (2021) 21. Tullberg, J.: Group egoism; investigating collective action and individual rationality. J. Socio-Econ. 35(6), 1014–1031 (2006). https://doi.org/10.1016/j.socec.2005.11.022

Prospects for Digital Transformation of Public Administration Galina S. Tibilova1 , Stanislav V. Kazarin2 , Anastasiya V. Potapova3(&) , Andrey V. Ovcharenko4 and Natalia V. Diachenko5 1

3

,

St. Petersburg State Unitary Firm “St. Petersburg Information and Analytical Centre”, 59 Cherniakhovskogo St., 191040 St. Petersburg, Russia [email protected] 2 Government of St. Petersburg, St. Petersburg, Russia [email protected] Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya St, 29, 195251 St. Petersburg, Russia [email protected] 4 St. Petersburg, Russia [email protected] 5 Russian State Hydrometeorological University, Malookhtinsky Pr., 98, 195196 St. Petersburg, Russia

Abstract. The papers discusses the prospects and directions of applying of system analysis in the digital transformation of public administration at the stage of digital transformation and in assessing the potency and efficiency of digital transformation measures based on its influence on the social capital of the region. The principles of digital transformation of public administration are determined; the concepts of subsidiarity, proactivity and omnichannel are given. The problems of digitalization of decision-making processes for public services and other services are described. The possibilities of using elements of artificial intelligence, namely, expert systems, in conditions of limited or unreliable information are considered. Social capital as a criterion for assessing the potency and efficiency of digital transformation. Methods for studying social capital are described, and the strata of its measurement in the region are highlighted. A methodology for assessing the digital maturity of public services has been developed. It is planned to test the proposed approach, by performing a series of pilot studies at the levels of specific social capital and individual services. Keywords: Digital transformation
Public services
Social capital
Digital maturity
Expert systems
Proactivity
Subsidiarity
Omnichannel

1 Introduction The digital transformation of public administration (hereinafter referred to as the DT PA) is one of the priorities of state development for the coming years in accordance with the national program “Digital Economy of the Russian Federation” [1] and the Decree of the President of the Russian Federation dated July 21, 2020 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 382–391, 2022. https://doi.org/10.1007/978-3-030-98832-6_33

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No. 474 “On national goals development of the Russian Federation for the period up to 2030” [2]. The concept has become widespread in connection with the implementation of national projects and now has supplanted the concept of “electronic government”. The main differences between DT PA and e-government are presented in Table 1 [3]. Table 1. Comparison of the concepts of digital transformation of public administration and egovernment. E-government Automation is the transfer of existing processes into an electronic form; replacing paper documents with electronic ones Industry automation, industry isolation of information resources In the center is the state: the interaction of the state with citizens and organizations and the interaction of executive authorities with each other Of the horizontal links, only interdepartmental information interaction is automated

Digital transformation of public administration Building new processes and complete modification of existing ones exclusively based on data stored in digital form and suitable for intelligent machine processing Cross-sectoral approach, digital transformation above departmental boundaries The citizen is in the center: his interaction with the state, organizations, other citizens (invisible state) Any horizontal communications are digitized, including links between citizens and nongovernmental organizations (there is no emphasis on the powers of individual executive authorities, since digital transformation can go beyond the powers of individual executive authorities if the interests of the client require it)

DT PA requires the achieving of systemic socio-economic effects, which must be measured, evaluated and used for further managerial decision-making. At the same time, traditionally used target indicators are local non-system metrics of individual projects or areas in digitalization (for example, the availability and quality of communication networks, the number and other characteristics of any processes converted to digital form, etc.). These indicators characterize, rather, the volume of work performed than socio-economic results. To assess the economic effects, as a rule, the working time saved due to automation of the interaction participants and/or material resources (for example, paper) is used, however, these calculations are often speculative, since timing measurements, paper, etc. cause many practical difficulties. The social effects of DT PA are currently assessed using opinion polls on user satisfaction with certain public services. These methods do not allow us to assess the impact of DT PA on the relationship between the population and the government as a whole and its contribution to the well-being of society.

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This article discusses the prospects and possibilities of applying system analysis in DT PA at the following stages: – when implementing DT PA (analytics, work with information, digitalization of processes); – in assessing the potency and efficacy of DT PA.

2 Methods 2.1

Implementing of DT PA

DT PA is based on the following principles [4]: 1. Proactivity. Public services, services and useful information are provided to citizens not at their direct request, but based on an analysis of information available about them in state information systems, social networks, and other sources and based on an analysis of user behavior. In the case of public services, proactivity means moving away from the declarative nature of the services. In the case of other services, we are talking about their contextual, targeted promotion and warning of users’ needs. Now, a citizen, as a rule, interacts with the authorities, being in a state of stress — he either expresses dissatisfaction, or achieves the realization of his legal rights, acting as the initiator of interaction. Proactivity is designed to reduce this negative component and provide a positive context in the interaction of citizens and authorities. 2. Subsidiarity. Subsidiarity meets the tasks of building an inconspicuous and effective state and consists in the maximum possible transfer of decision-making when interacting with citizens to the lower level, namely to the level of information systems. For public services, this means the exclusion of a civil servant from the decision-making process (full automation of decision-making). For other requests and services, this means the use of artificial intelligence to generate proactive recommendations and process requests. 3. Omnichannel. Omnichannel means reducing the distance to the citizen (the state in the smartphone). The number of points of access to city services is increasing. They are available not only through official portals and state mobile applications, but also on a priority basis — in the channels of daily presence of citizens, in social networks, instant messengers, commercial mobile applications that citizens trust — wherever it is convenient for the citizen. Omnichannel also implies the identification and priority development of services of frequent use (daily needs). The main problem in the implementation of these principles is the inaccessibility, incompleteness and/or inaccuracy of user data. There are regulatory and legal reasons for this (regulatory prohibition on access to data about a citizen outside the strict framework of certain processes), organizational and political (departmental closeness, competition) and technical reasons (for example, non-harmonized data in state information systems or their absence in digital form). At the same time, the requirements for

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the accuracy and correctness of work for public services are extremely high. Irrelevant contextual advertising and errors of artificial intelligence of commercial systems do not cause such a negative reaction as they do with public services. In addition, in the case of public services, mistakes are in principle unacceptable, since we are talking about compliance with the law and the legitimate interests of citizens, as well as the use of state budget funds. Thus, in DT PA, there is an acute problem of full or partial digitalization of decision-making processes for public services and other services (including, in terms of well-grounded, relevant recommendations to their citizens) in conditions of limited or inaccurate information. This problem was still at the early stages of building e-government (2008–2010), but at that time, it arose not due to the need to prevent the needs of citizens, but in connection with the need for effective planning of the executive branch’s activities to transfer public services into electronic form [5]. To determine the best option for transferring to electronic form of the service delivery option, it was proposed to use a process-oriented approach and information potential, calculated according to the criterion of A.A. Denisov [6–8]. The optimality of the plan was determined by the balance between the demand for the option of providing a service from applicants, the availability of information necessary for a civil servant to make a decision on the service, and the available funding for development. The information process of rendering a service was presented in the form of a directed graph, the vertices of which are information arrays — as an nstep task. Two main classes of characteristics of the information array were identified: – The need for an information array (the probability of demanding an information array); – The availability of the information array, which was assessed qualitatively (yes or no) and quantitatively (the cost of carrying out work to automate one or another source of information, namely, setting up an automated workstation for providing information or creating a web service and full integration with the departmental information system). The task of dynamic programming was set, the target criterion of which took into account both the probability of demand and the availability of information arrays. As an objective function in determining the optimal version of the information process, the information potential of the information process version, calculated according to the criterion of A. A. Denisov [9–11], was used. Currently, the focus has shifted from optimizing the activities of civil servants to a high-quality solution and prevention of life situations for citizens. For this, in conditions of incomplete and unreliable data, today it is planned to use elements of artificial intelligence, namely, expert systems.

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Assessment of the Potency and Efficiency of Measures for DT PA

An approach is proposed to assess the potency and efficiency of DT PA in the region based on social capital, as a regional asset, directly and largely dependent on the quality of the regional information and communication environment. Thus, there was a need to create several ES, the signs for which can both intersect and differ from each other. Social capital is a relatively young concept, which is still used, mainly in sociology and economics [14, 15]. In the definition of social capital, three of its main components can be distinguished: trust (including respect, willingness to help, tolerance, solidarity); social networks (group membership, constructive collective action); norms (values). Social capital can be vertical (interaction between citizens and authorities) and horizontal (interaction between citizens). In general, social capital characterizes the quantity and quality of ties in society and has both political and economic expression (viewed as a resource). This study is limited to vertical social capital (interaction between the population and the government), while it is understood as the level of trust between citizens and the government and the ability of citizens and government to take constructive joint actions. The study formulated and adopted the axiom that trust between citizens and the government arises and increases when the government meets the expectations of citizens, namely, ensures the adoption of fair laws and control over their implementation. The role of digital transformation in building trust between citizens and government is expressed in: – Regarding the adoption of fair laws: the use of IT technologies to involve citizens in law-making activities; – Regarding control over the implementation of laws:: control over the implementation of laws using digital tools, while control is understood as the inevitability of punishment for violations, and assistance to citizens in the implementation of their legal rights in full (for example, proactive recommendations and provision of services – providing citizens the benefits given to them without their request, at the initiative of the state). The ability of citizens and authorities to take constructive joint actions is manifested in two ways: – The state directly involves citizens in solving problems of city management (through voting, consideration of private initiatives, etc.); – The state creates conditions for the self-organization of citizens for various purposes. In both cases, digital transformation acts as an intermediary. In the accepted practice, the main method for studying social capital is various sociological studies and polls, however, within the framework of the study, however, within the framework of the study, it is planned to use strictly data from state information systems and various webometrics to measure vertical social capital, its dynamics and the impact on it of certain areas and projects of digitalization.

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There are three strata for measuring social capital in the region: 1. The city as a whole (total social capital). The main indicators at this level should be indicators characterizing the electoral behavior of citizens and their political activity, political moods. Sources of data: data from election commissions, results of monitoring of social networks. Restrictions. In the total social capital, it is difficult to delineate the degree of influence not only of individual projects in informatization, but of the entire industry as a whole (with the exception of ensuring the participation of citizens in lawmaking activities). 2. Subject area (project, sphere) (specific social capital). These indicators characterize social capital in individual subject areas (for each specific project, sphere or area of application). Specific social capital is intended to show how IT contributes to the growth of trust between the population and the government and how IT is used to ensure joint constructive actions of citizens and government. Restrictions. Specific social capital is measured in different units for each subject area (project, sphere), and therefore projects cannot be compared with each other on the basis of specific social capital. It is impossible to say which priority and the contribution of which project to the increase of social capital will be greater. 3. Separate service, public service (unified metrics). Metrics that do not depend on the subject area or the specifics of a service or public service, which allow determining priorities in the development of services and public services and assessing their contribution to social capital (measuring all services and public services in the same units). The data sources here are webometrics that characterize the volume and composition of the target audience of the service, the frequency of its use, the presence/absence of seasonality in terms of access points (channels of interaction with the user), the type of users and subprocesses. Partially, these metrics can be collected using Yandex.Metrica or other similar tools; however, for a number of indicators, the functionality for collecting metrics should be developed at the stage of creating each specific service. In addition, due to the presence of a large number of specific requirements for the digital transformation of public services, indicated in the administrative documents and strategic planning documents, a corresponding strata No. 4 was added and a methodology for assessing the digital maturity of public services was developed.

3 Results 3.1

Using Elements of Artificial Intelligence in DT PA

An experimental expert system (hereinafter referred to as ES) [12, 13] was created, which contains the mechanism of recognition and classification of objects according to Bayes (naive Bayesian classifier). To test the ES on public services, four services were taken concerning the birth of a child: Lump-sum benefit upon the birth of a child, Lump-sum compensation payment to women who gave birth at the age of 20 to

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24 years inclusive of their first child in the period from January 1, 2018, Monthly payment in connection with the birth (adoption) of the first child, Lump-sum compensation payment at the birth of a child). Since on the same grounds the applicant can apply for several services at the same time, an ES network was created. At the first stage, with more than a hundred training cycles, the system was trained and successfully determined which groups of services the applicant applied for according to the given criteria, however, with a large number of services, training would be extremely laborious, and there is also a high probability of an expert’s error during training. To minimize these risks, it was proposed to create an ES for each service separately (see Fig. 1). In this case, the signs of classification of objects for different ES can both overlap and differ from each other. In this case, the ES, based on the input characteristics (data of the digital profile of the citizen), determines whether the applicant applies for this service or not. In the case of incomplete data, the trained ES determines the probability with which a citizen can apply for this service. Depending on the magnitude of this probability, a recommendation of a service may or may not be sent to a citizen. In addition, it was found that: – signs of object classification have a different degree of influence on the final probability with which a citizen can apply for a service, therefore, this parameter should also be taken into account when setting up an ES; – in view of the complex structure of requirements for applicants, some services must be divided into several ES (sub-services).

Pretends to with a probability of 100%

ES 1 – PS 1 Sert 1

Sert 1

Sert 3

Sert 6

Sert 2 Pretends to with a probability of more than 80%

ES 2 – PS 2 Sert 3

Sert 4

Individual

Sert 2

Sert 4

You are eligible for PS 1 + we recommend that you consider PS 2 (you may be eligible)

Sert 6

No Pretends to with a probability of less than 50%

ES 3 – PS 3 Sert 5

Sert 1

Sert 2

PS 3 and PS 4 are not offered

Sert 7

Sert 6

No Sert 7

Pretends to with a probability of less than 30%

ES 4 – PS 4

Sert 3

Sert 5

Sert 7

No

Fig. 1. ES network for proactive public service recommendations.

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389

Research of Social Capital

Figure 2 shows the general structure of the research of social capital in order to assess the potency and efficiency of DT PA.

Trust between cizens and authories

Joint construcve acon

Public services

Separate service, public service

Subject area, sphere

City

Electoral behavior of cizens Mass polical senment Atude towards authories based on monitoring of social networks

Parcipaon of cizens in legislave acvity

Enforcement of laws: compliance with traffic rules, payment for parking, taxes, etc. (Before and aer digital transformaon)

Analysis of populaon appeals (Regional Management Center)

The efficiency of urban services for the self-organizaon of cizens (volunteering, tourism, etc.)

Respect for cizens' rights, such as provision of public services, observance of quotas for people with disabilies, provision of social food, etc. (before and aer digital transformaon)

Efficiency of votes and public discussions by industry (project)

Efficiency and frequency of proacve recommendaons (response) Potenal, projected actual Availability of direct feedback tools (service quality assessment), Seasonality availability audience size (acve, passive, composion of indicators and their values (if tools are available) Number of access formal) , Related services points (channels, end Predicted and actual frequency applicaons) By types of users, access points, subprocesses of use of the service

Digital maturity of the public services portal as a whole • Assessment of the overall "digital maturity" of the Public Services Portal, its convenience and modernity

Digital maturity of individual services •

Assessment of extraterritoriality

•

Assessment of digital maturity of public services in accordance with the regional maritza of digital maturity

Fig. 2. The framework for assessing the effectiveness and efficiency of digital transformation based on social capital.

There are two blocks in the research: 1) trust between government and citizens, and 2) the ability of citizens and government to take constructive joint actions; and four strata — a city, a subject area, a separate service/public service and specific strata for assessing the digital maturity of public services.

4 Discussion It is important to note that, although the ultimate goal of the implementation of digital transformation of public administration is the acceptance-free provision of the necessary public services and the complete exclusion of a person from the decision-making process, as well as the prevention of user requests with high accuracy, the digital maturity of state information resources and regulatory legal the base has not yet been reached. At the current stage of digital maturity and in connection with the specifics of regulatory regulation, we are only talking about providing users with mathematically sound

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recommendations for obtaining services or services, as well as relevant targeted satisfaction of the information needs of the population in terms of interaction with the authorities. The methodology for assessing the potency and efficiency of DT PA based on social capital includes a regional matrix of digital maturity of public services and the regional Portal of public services as a tool for obtaining them. Since the specific requirements for the digital transformation of public services are formalized in administrative documents and are finite, scales were developed for assessing the compliance of public services with these requirements in conventional units — points. The methodology allows assessing the current status of digital transformation of public services and planning further development, as well as assessing the progress achieved.

5 Conclusion The paper considered the possibilities of using system analysis in the implementing of DT PA. Since the centralized control center of DT PA is acutely faced with the task of full or partial digitalization of decision-making processes for public services and other services in conditions of incomplete and unreliable data, today it is planned to use elements of artificial intelligence, namely, expert systems. At present, networks of expert systems can be used to determine the probability of a user receiving a particular public service, but since the digital maturity of state information resources and the regulatory legal framework has not yet been reached for the acceptance-free provision of public services, the user can only be provided with recommendations on receiving or services. The article also proposed an approach to assessing the potency and efficiency of the DT PA in the region based on social capital. Currently, it is planned to test the proposed approach, namely a series of pilot studies at the level of specific social capital and at the level of individual services.

References 1. “Passport of the national project” National Program “Digital Economy of the Russian Federation” approved by the Presidium of the Presidential Council for Strategic Development and National Projects. (Minutes dated 24.12.2018 No. 16) 2. Decree of the President of the Russian Federation of 21 Jul 2020, No. 474 “On the national development goals of the Russian Federation for the period up to 2030” 3. Tibilova, G.S., Ovcharenko, A.V., Stankova, E.N., Dyachenko, N.V.: The electronic government of St.-Petersburg as relevant experience of construction of digital economy. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11621, pp. 357–371. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24302-9_26 4. Tibilova, G.S., Ovcharenko, A.V., Potapova, A.V.: Proactivity and subsidiarity as the basic principles of digital transformation of state interaction with citizens and businesses. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 544–553. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34983-7_53

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5. Tibilova, G.S.: Primenenie informacionnogo podhoda k analizu sistem A. A. Denisova pri proektirovanii elektronnyh gosudarstvennyh uslug. [Application of the A.A. Denisov information approach to analysis of systems in the design of electronic public services.] In: Sb. nauch. trudov XXIII Mezhdunar. nauch.-prakt. konf. Sistemnyi analiz v proektirovanii i upravlenii. [System Analysis in Engineering and Control: collection of scientific papers of the 23rd International Scientific and Practical Conference.] St. Petersburg, Russia, pp. 539– 549. POLYTECH-PRESS, St. Petersburg (2019). (In Russian) 6. Volkova, V.N., Denisov, A.A., Temnikov, F.E.: Metody formalizovannogo predstavleniya sistem: Uchebnoe posobie. [Methods of formalized representation of systems: tutorial.] 107 p. SPbSTU Publishing House, St. Petersburg (1993). (In Russian) 7. Volkova, V.N., Denisov, A.A.: Osnovy teorii sistem i sistemnogo analiza. [Fundamentals of the theory of systems and system analysis.] 3rd edn. Publishing house of Polytechnic un., St. Petersburg (2004). (In Russian) 8. Denisov, A.A.: Vvedenie v informacionnyj analiz sistem : Tekst lekcij. [Introduction to information analysis of systems: text of lectures.] 52p. Leningrad Polytechnic Institute, Leningrad (1988). (In Russian) 9. Denisov, A.A.: Informacionnye osnovy upravleniya. [Information management bases.] 72 p. Energoatomizdat, Leningrad (1983). (In Russian) 10. Denisov, A.A.: Informaciya v sistemah upravleniya: uchebnoe posobie. [Information in control systems: a tutorial.] 67 p. Leningrad Polytechnic Institute, Leningrad (1980). (In Russian) 11. Denisov, A.A.: Sovremennye problemy sistemnogo analiza. [Modern problems of system analysis.] 295 p. Publishing house of Polytechnic. un., St. Petersburg (2005). (In Russian) 12. Potapova, A.V., Tibilova, G.S., Ovcharenko, A.V.: Primenenie ekspertnyh system v proektirovanii proaktivnyh gosudarstvennyh uslug. [Using of expert systems in the design of proactive public services.] In: Communicative Strategies of the Information Society: Proceedings of the XI International Scientific-theory Conference, 25–26 Oct 2019, St. Petersburg, Russia, pp. 143–152. POLYTECH-PRESS, St. Petersburg (2019). (In Russian) 13. Potapova, A.V., Tibilova, G.S., Ovcharenko, A.V., Diachenko, N.V.: Designing a network of expert systems for identifying recipients of public services. In: Bylieva, D., Nordmann, A., Shipunova, O., Volkova, V. (eds.) PCSF/CSIS -2020. LNNS, vol. 184, pp. 136–148. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-65857-1_14 14. Bourdieu, P.: Forms of capital. http://bourdieu.name/bourdieu-forms-of-capital. Accessed 10 Mar 2021 15. Polishchuk, L.I.: Porozn’ ili soobshcha. Social’nyj kapital v razvitii gorodov. [Separately or Together. Social Capital in the Development of Cities.] Strelka press, Moscow (2014). (In Russian)

Features of Developing the Concept of Digital Transformation Using Simulation Modeling Approaches Alexey V. Boykov(&) , Michail B. Uspensky and Marina V. Bolsunovskaya

,

Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya St, 29, 195251 St. Petersburg, Russia {alexey.boykov,mikhail.uspenskiy, marina.bolsunovskaia}@spbpu.com

Abstract. The purpose of this work is to develop a concept for the digital transformation of a typical production area based on the performed enterprise survey and the study of international experience in the design and implementation of digital transformation projects in similar industries. The study is aimed at forming a list of regulatory documents and determining the requirements for the development of these documents. In this paper, the authors are discussing the development of a concept. As part of this study, an analysis of the subject area was conducted, during which several relevant publications from the RSCI and Scopus databases in the field of digital transformation in various industries were reviewed with a detailed study of case studies, including those involving the use of simulation modeling as a means of assessing changes in complex technical systems. Targets were formed and constraints for their calculations were defined. As a result of this study, a universal simulation model of the production area was developed and a comparative analysis of the estimated indicators of the digital transformation of the area was carried out. Keywords: Digital transformation Analysis
Production


Digitalization
Simulation modeling


1 Introduction Digital transformation leads to significant changes in business processes through the use of digital technologies, such as analytics, cloud computing, the Internet of things, machine learning technologies to improve the quality of doing business and introduce innovations in management models. In the realities of the existing global competition on the part of business, there is a constant demand to improve the efficiency of the provision of services or the production of goods. One of the options for increasing efficiency is the digital transformation of the business as a whole or its sections with a low degree of digitalization. The industrial sector has particularly high demands on the accuracy of design and planning of modernization work due to the large investment required. Sufficient © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 392–400, 2022. https://doi.org/10.1007/978-3-030-98832-6_34

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accuracy can be ensured by methodological elaboration of design processes, implementation of a digital object, and assessment of the target state of the object [1]. This article explores how the concept of digital transformation has evolved using the example of a typical production unit and the use of simulation methods to assess the achievement of target indicators.

2 Problem Statement 2.1

A Subject Area Description

Digital transformation processes have been relevant and widespread in modern society for more than a century since the advent of computer technology. In fact, in different spheres of life, it is going at different rates. Such processes appeared much earlier and are more developed in the field of production activities than in other areas at the moment due to the direct relationship with the receipt of economic benefits. To conduct a study, it is necessary to unambiguously define the terms digitalization and digital transformation. In the sources studied, they are often used in different meanings. It is customary to understand digitalization as the material process of converting analog streams of information into digital data [2]. Digital transformation is a comprehensive transformation of business activities aimed at a successful transition to new business models, channels of communication with customers and contractors, products, business, production, and technological processes, to significantly increase its efficiency and long-term sustainability. This is based on fundamentally new approaches to data management using digital technologies. This article takes the production department as an example to discuss comprehensive digital solutions for digital transformation [3]. Digital transformation processes should be viewed as a cross-disciplinary phenomenon, which combines research in economics, management, information technology, and systems analysis. The first use of the term digital transformation can be found in a 1971 essay published in the North American Review [4]. The subject of “digital transformation” has been extensively studied, and there are many published books on the subject. Based on the world experience, each case is conditioned by a variety of tasks, and managers often have to be guided by hands-on experience and knowledge in the industry rather than traditional approaches [5]. The article discusses the types and performance indicators of an enterprise using the example of the digital transformation of an agro-industrial enterprise [6]. The approach to index formation is based on an analysis of a company’s strategic documents and its economic model. The digital transformation process is penetrating many industries, but industries do not embrace transformation at the same time and at the same level of implementation and use [7]. This article is mainly based on hands-on experience in developing the concept of digital transformation for a typical production unit, as well as a review of articles from the RSCI and Scopus scientific publication databases from the past five years (2017–2021) and earlier.

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2.2

Problem Statement

There are currently many different recommendations and method manuals for digital transformation, for example, method recommendations for the digital transformation of corporations under public law and companies with state participation [8]. The conditions of a given situation will vary greatly. To solve a specific task of digitizing a production system, it is necessary to carry out a methodological study within the framework of existing conditions [9]. In this situation, the enterprise is faced to carry out a global digital transformation and at the initial stage implementing a pilot project at one typical production enterprise. The unit is equipped with equipment that was produced in the middle of the last century and is not adapted for interaction in the digital ecosystem. Also, the unit is characterized by a high proportion of manual labor and the need for many workers, the absence of electronic document management, and a certain level of defects in production. To carry out the digital transformation, it is, therefore, necessary to update the equipment pool, the possibility of digital control and commissioning with a high degree of automation of the production and technological processes. Moreover, the possibility of organizing the electronic document flow as well as the implementation of organizational modernization lead to reducing the number of and retraining of the unit’s workforce. The first step is to complete the conceptual design phase. It includes analysis and description of the current situation at the enterprise, the formation of the target image of the unit, and the assessment of the target indicators set by the business. [10] The target indicators of digital transformation include: • Availability of the unit; • Number of production errors; • The degree of automation of the unit. Availability is the percentage of the unit's uptime relative to the unit's maximum possible uptime. The number of rejects is measured in piece indicators of finished products that have not passed quality control. The relative indicator to the total output volume is also calculated. The degree of automation is measured as the ratio of time spent on manual operations to all technological operations in the production of finished products. The approach to the KPI formation is based on the decomposition of requirements on the part of the management apparatus. In this study, specific examples of digitalization of production activities are analyzed to highlight theirs. There are several common and important requirements, based on examples of digitalization of production activities: 1 Digital transformation must start from the top. It is necessary to include the management staff in the modernization of processes to comply with that the architecture of the object is being formed based on the strategic goals of the organization; 2 Digital transformation must be carried out by a team of qualified employees. The design and implementation of a digitally controlled facility imposes certain requirements for competencies in information technology, system analysis, and management;

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3 Digital transformation must be supported by sufficient resources of the organization. High demands on the automation of production equipment, hardware, and software entail high implementation costs. Often, replacing solutions with cheaper analogs significantly limits the possibilities and reduces the effect of digital transformation, thereby reducing its economic feasibility; 4 Digital transformation should not be done just for the sake of digital transformation. The design of a digital production unit, starting from the concept stage, should be carried out to achieve the performance targets determined by the economic organization of the enterprise [11–13]. An article by the VTT Technical Research Center of Finland identifies 4 levels of process change in digital transformation: 1. 2. 3. 4.

Processes; Organization; Business sphere; Society [14].

This article discusses the changes affecting the first two levels. As a result, two groups of requirements were formulated, which should be reflected in the concept of digital transformation: 1. Technological requirements (level “Processes”): • Target digital architecture of the unit; • Requirements for the integration of production equipment with control information systems; • Requirements for software and technical solutions; • Description of digital twins and requirements for their creation; • Requirements for updating the production equipment of the unit. 2. Organizational requirements (organization level): • Requirements for the composition of the personnel at the unit, the personnel retraining program; • A list of organizational measures and a roadmap for digital transformation. Simulation methods allow assessing the possibility of achieving the target performance of a unit during digital transformation. Simulation modeling provides an opportunity to study complex systems, including technical ones, because it enables the calculation of a large number of interrelated indicators, taking into account the probability of various events, which is necessary for the more effective design of such systems [15–17]. The article of the Institute of Information Technologies of the National Academy of Sciences of Azerbaijan describes approaches to the design of production units using mathematical modeling [18]. Science and technology parks in local communities use this approach in their activities. There are cases where simulation modeling is used to analyze how to improve the process of technical modernization at a textile production unit.

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In this study, the comparison of the indicators of the initial unit of state and the target unit of state was carried out by comparing the results of simulation models “as is” and “as should be”.

3 The Proposed Solution 3.1

Composition of the Conceptual Design

The basic composition of the concept was formed based on a combination of several approaches from related industries [20, 21]. The conceptual design of the digital production unit was carried out based on the formed requirements. Conceptual design project documentation consists of: • • • •

Research report of the current situation in the unit; Explanatory note to the conceptual design of the digital production unit; Roadmap for the digital transformation of the unit; Draft regulatory documents: • Description of the processes and procedures performed to ensure the functioning of the digital unit; • Requirements for input and output data; • Requirements for information systems and software and hardware; • Requirements for production and technological equipment; • Terms of reference for individual components of a digital unit; • The program for repurposing the personnel of the unit. The survey report is formed based on an analysis of technological, production, and organizational documentation and general and individual interviews with unit employees and process owners involved in technological and information chains at the enterprise. The survey identifies all information systems with which integration must be ensured or which must be replaced or eliminated. The conceptual design of the digital production unit is carried out and the explanatory note is formed based on the input data collected during the audit phase. Draft regulatory documents contain the target digital architecture, requirements for input and output data, digital twins and the process of their development, requirements for integration with existing information systems, requirements for organizational changes. The digital transformation roadmap includes the stages and timing of organizational activities. The roadmap is based on the strategic documents of the enterprise. The functional and technical requirements for all components of the architecture are formed to ensure the functioning of the unit based on the requirements for the target state of the unit and taking into account the existing and updated digital architecture. Terms of reference for the development or purchase of components are developed based on regulatory documents. The retraining program for the unit’s employees is developed by taking into account the formed requirements for the processes of the digital unit’s functioning. This activity provides a detailed description of the source and target states of the production unit.

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Simulation Modeling of the Production Unit

Simulation modeling is a method for studying complex systems, containing a logical and algorithmic description of the system elements, their behavior, and the rules of their interaction, reflecting the sequence of events that occur in the system. The simulation-based approach is often used in the study of various complex systems with a discrete nature of work. One of the options for such systems is mass production systems. For such systems, the most common tool is timing diagrams. The main task that the simulation model of production and technological processes solves is to assess the functioning of the unit in terms of achieving the target indicators of digital transformation. The simulation model can also be used as a tool for solving production planning problems such as: • Assessment of the ability to ensure the planned release of finished products; • Determination of the maximum output for certain products and labor resources; • Determination of optimal launch batches to achieve planned or maximum production volumes. The model uses two main types of parameters - characterizing the duration of processes and characterizing material flows. Performance targets are defined as follows. The availability of a unit is highly dependent on the condition of equipment, infrastructure, and personnel and appears to be a variable parameter in the simulation model. Unit availability is parameterized by setting regular and one-time technological breaks. The number of rejects in production is determined by the technological procedure and the state of equipment and tooling, as well as by the qualifications of personnel, and in the simulation model, it also appears to be a variable parameter. The degree of automation is defined as the share of manual labor in the overall production process and is a calculated indicator in the simulation model. When developing a simulation model, the following conditions are taken into account: • Existing and target layout of the unit and its environment; • The composition and location of the main and auxiliary equipment in the current and target state; • Existing and planned personnel of the unit; • Existing and planned mode of work of main and auxiliary workers; • Technological processes and technological routes in the current and target state; • Time characteristics of technological operations, including preparatory and final, auxiliary and machine time; • Time characteristics of production and logistics operations and quality control operations. The unit simulation model in the initial and target state can be used to simulate various scenarios of changes in production and technological processes to assess the impact of planned changes on performance indicators, including target indicators. There are two main scenarios for the model. In the first case, restrictions in the form of a certain volume of finished product output are set to estimate the required time

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costs. In the second case, the time limit for the unit’s operation is set to assess the maximum possible volume of production in the desired nomenclature. Table 1 shows the results of simulation modeling of the production unit. Table 1. Results of the simulation of the production area in the initial state and the target state. Indicator Output of finished products, pcs Automation of production unit, % Time spent on the production of finished products, h Time spent on manual operations, h The share of faults at the unit, % The number of bounces, pcs

SM of the initial state 15626 79,43 10516

SM of the future state 44200 98,16 7214

2163 5,59 56

133 0,6 17

The indicator “Time spent on the production of finished goods” also takes into account the idle time of the workpiece waiting for the next production operation. Production processes include only those processes that are carried out directly by the working personnel of the unit.

Fig. 1. Comparative graphs of the results of the simulation of the production area in the initial and target states.

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Figure 1 shows comparative graphs of the values of the calculated parameters of the simulation. The values of the calculated modeling indicators, incl. target indicators are used to assess the achievement of these indicators and will be used to make decisions on digital transformation and detail implementation requirements.

4 Conclusion This study explored publications from RSCI and Scopus databases, described the experience of digital transformation in various industries with detailed case studies and explained the use of modeling as a means of evaluation changes in the complex are the result of the reversal of the evolutionary process. Technical systems are used to analyze data of varying types. Based on the enterprise survey, a concept of digital transformation was formed, which is discussed in this article. Based on the analysis of the results of the initial and target states, a conclusion was made about the feasibility of digital transformation in terms of achieving the targets. Also, in the process of modeling, a tool in the form of a simulation model was developed, which can be used in production planning to assess changes in the range of products, technologies, process routes, and cycles of manufacturing of finished products, personnel. Acknowledgement. The research is funded by the Ministry of Science and Higher Education of the Russian Federation (contract No. 075–03-2021–050 dated 29.12.2020).

References 1. Liere-Netheler, K., Packmohr, S., Vogelsang, K.: Drivers of digital transformation in manufacturing. In: The Digital Supply Chain of the Future: Technologies, Applications and Business Models (2018) 2. Avdeeva, I.L., Polyanin, A.V., Golovina, T.A.: Digitalization of industrial economic systems: problems and consequences of modern technologies. News Sar Un. New Ep. Ser Econ., Man., Law. no. 19(3), 238–245 (2019) 3. Bertola, P., Teunissen, J.: Fashion 4.0. Innovating fashion industry through digital transformation. Res. J. Text. Apparel 22(4), 352–369 (2018) 4. Schallmo, D.R.A., Williams, C.A.: History of digital transformation. In: Digital Transformation Now! Springer Briefs in Business. Springer, Cham (2018) 5. Linderoth, H., Elbanna, A., Jacobsson, M.: Barriers for digital transformation: The Role of Industry. In: ACIS 2018 Proceedings, no. 84 (2018) 6. Shamin, A., Frolova, O., Makarychev, V., Yashkova, N., Kornilova, L., Akimov, A.: Digital transformation of agricultural industry. IOP Conf. Ser. Earth Environ. Sci. 346, 012029 (2019). https://doi.org/10.1088/1755-1315/346/1/012029 7. Gobble, M.: Digital strategy and digital transformation. Res. Technol. Manag. 61(5), 66–71 (2018) 8. Mugge, P., Abbu, H., Michaelis, T., Kwiatkowski, A., Gudergan, G.: Patterns of digitization. Res. Technol. Manage. 63(2), 27–35 (2020) 9. Albukhitan, S.: Developing digital transformation strategy for manufacturing. Procedia Comput. Sci. 170, 664–671 (2020)

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10. Warner, K., Wäger, M.: Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Plan. 52(3), 326–349 (2019) 11. Kutnjak, A., Pihiri, I., Furjan, T.M.: Digital transformation case studies across industries – literature review. In: 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1293–1298 (2019) 12. Gomes, S.B., Santoro, F.M., Mira da Silva, M., Iacob, M.: A reference model for digital transformation and innovation. In: IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), pp. 21–30. IEEE (2019) 13. Genzorova, T., Corejova, T., Stalmasekova, N.: How digital transformation can influence business model, case study for transport industry. Transp. Res. Procedia 40, 1053–1058 (2019) 14. Parviainen, P., Tihinen, M., Kääriäinen, J., Teppola, S.: Tackling the digitalization challenge: how to benefit from digitalization in practice. Int. J. Inf. Syst. Proj. Manag. 5(1), 63–77 (2017) 15. Novikov, S., Sazonov, A.: Digital transformation of machine-building complex enterprises. J. Phys. Conf. Ser. 1515(3), 032021 (2020). https://doi.org/10.1088/1742-6596/1515/3/ 032021 16. Kaidalova, J., Sandkuhl, K., Seigerroth, U.: How digital transformation affects enterprise architecture management — a case study. IJISPM Int. J. Inf. Syst. Project Manage. 6(3), 5– 18 (2018) 17. Ivanov, R., Sherstennikov, Y., Porokhnya, V., Grynko, T.: Mathematical model for imitation of management of the enterprise’s logistical system. SHS Web Conf. 107, 10004 (2021). https://doi.org/10.1051/shsconf/202110710004 18. Aliyev, A., Shahverdiyeva, R.: Application of mathematical methods and models in product – service manufacturing processes in scientific innovative technoparks. Int. J. Math. Sci. Comput. (IJMSC) 4(3), 1–12 (2018) 19. Morakanyane, R., Grace, A., O’Reilly, P.: conceptualizing digital transformation in business organizations: a systematic review of literature. In: Proceedings of 30th Bled eConference Digital Transformation — From Connecting Things to Transforming Our Lives, Bled, Slovenia (2017). https://doi.org/10.18690/978-961-286-043-1.30 20. Baiyere, A., Salmela, H., Tapanainen, T.: Digital transformation and the new logics of business process management. Eur. J. Inf. Syst. 29(3), 238–259 (2020) 21. Brown N., Brown, I.: From digital business strategy to digital transformation — How: A systematic literature review. In: SAICSIT’19: Proceedings of the South African Institute of Computer Scientists and Information Technologists 2019, September 2019, Article No: 13. ACM (2019). https://doi.org/10.1145/3351108.3351122

Analysis of Innovative Technologies for the Formation of a Cyber-Physical System of an Enterprise Arina Kudriavtceva(&) Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya St., 29, 195251 St. Petersburg, Russia [email protected]

Abstract. This article examines the structure of the cyber-physical system of an enterprise, as well as an analysis of information technologies that form the structure of the cyber-physical system. A brief overview of existing scientific works on the assessment of innovative technologies is carried out. It is concluded that the existing models for managing the innovative activity of an enterprise are able to assess only the economic contribution of innovation, which is not enough for a comprehensive assessment of innovations for an industrial enterprise. To achieve the goal set in the article, first, a brief analysis of technological innovations of the third and fourth industrial revolutions is carried out, which forms the basis for creating an area of intended innovations for implementation. In this work, an algorithm has been created that allows you to narrow the continuously expanding set of feasible solutions. The algorithm is based on the use of methods for organizing complex expertise, namely information models by A.A. Denisov to assess innovative technologies for creating a cyber-physical system. It is shown that the selected models make it possible to assess the degree of the innovation development over time, as well as the mutual influence of innovations during their simultaneous implementation. Information models also allow us to assess the significance of an innovation based on the likelihood of its implementation and the degree of this innovation relevance. Application of the created algorithm makes it possible to systematically assess the significance of innovations for creating a cyber-physical system of an enterprise. Keywords: Information technology
Cyber-physical system Industrial revolution
Industrial enterprise


Innovation


1 Introduction It is required to determine the relationship between a set of information technologies and other
potential innovations  hinn1 ; inn2 . . .; inni ; . . .; innm i and a set of organizational goals z1 ; z2 ; . . .; zj ; . . .; zm , and select a subset of technologies for implementation from the set of proposed innovations:

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 401–412, 2022. https://doi.org/10.1007/978-3-030-98832-6_35

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 z1 ; z2 ; . . .; zj ; . . .; zm Whinn1 ; inn2 . . .; inni ; . . .; innm i;

where w is a complex functionality implemented in the interactive mode using the program. Thus, it is necessary to choose INNm ¼ hinn1 ; inn2 ; . . .; inni ; :::; innm i from INNn ¼ hinn1 ; inn2 ; . . .; inni ; . . .; innn i, m 2 INNm , n 2 INNn , INNm  INNn , where n is the number of proposed innovations, m is the number of innovations introduced. To accomplish the task, it is necessary to carry out a comparative analysis of the existing methods of technology assessment.

2 Overview of Enterprise Innovation Management Models To substantiate the choice of models for managing the innovative activity of an enterprise, a brief review of existing studies on models of managing the innovative activity of an enterprise is carried out. In foreign articles, the assessment of innovation goes through the term measurement of innovation. In his model, H. Tang [1] relies on the relationship between the management of personnel competence and increasing the potential for innovation. The right amount of intellectual capital must be created and maintained to withstand the inevitable changes in technology. Continuous development of skills and collective knowledge, timely exchange of information, and proper management of the firm’s intellectual assets are the basis for ensuring innovation potential. In article [2], T. Broekel introduces the measurement of technology through the concept of complexity of the technology. The article discusses two main approaches to measuring technological complexity: the reflection method and the assessment of the combinatorial complexity of technologies. A reflection-based assessment of complexity measures begins with the calculation of the Regional Technological Advantage (RTA) of a region r by technology c per year t.

RTAr;c;t

Ppatentsr;c;t patentsr;c;t ¼ Pr patentsr;c;t c PP c

r

patentsr;c;t

Next, using a binary link, the incidence matrix is constructed M between areas (rows) and technologies (columns) if the region r has RTAr;c;t [ 1, i. e. he is above average specialized in technology c, otherwise, there is no connection. The number of links in each region (the sum of the lines) represents its diversity ðKr ; 0Þ, and the links of each technology represent its ubiquity ðKc ; 0Þ(column sum). According to A. Hidalgo and R. Hausmann [3], estimates of diversity and ubiquity are sequentially calculated by evaluating the following two equations simultaneously using iterations. KCIr;n ¼

1 X Mr;c Kr;n1 Kr;0 r

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KCIc;n ¼

403

1 X Mr;c Kr;n1 ; Kc;0 c

where KCI c;n represents the complexity of technology. Estimates based on calculating a measure of complexity are presented in [4, 5]. A comparative analysis of methods for assessing innovative technologies was also carried out in domestic studies [6, 7]. The most suitable methods for assessing innovations are the methods of organizing complicated experts [8, 9], namely the method of decision matrices [9], the PATTERN method [10], and the informational assessments of A.A. Denisov [11]. In the PATTERN methodology, three groups of evaluation criteria are distinguished: relative importance; mutual usefulness; state and terms of development (“state — term”). Assessing relative importance based on multiple criteria kx and their weights qx carried out by experts using the standardization method. Next, there is an assessment of the relative importance of innovation for each criterion sjx , after which estimates of the relative importance of the Pmj innovation are calculated by the x criterion using the following formula: rj ¼ j¼1 qx sjx , where m is a number of criteria [11]. When using the criterion of state and development time, the “state — term” coefficient is quantitatively determined as follows: Rx f ð xÞdx rs ¼ R0s ; f 0 ð xÞdx 0

Rs Rx where 0 f ð xÞdx is the full resource consumption for development; 0 f ð xÞdx are costs required to complete development. When using the criteria for assessing the mutual utility, a complete enumeration of the interrelationships of all components of the corresponding level of the “goal tree” and the assessment of their mutual utility is a very laborious procedure. In applications of the assessment system in various developments in our country, the assessment of mutual utility was sometimes interpreted as an assessment of interconnection without determining the numerical coefficient of the strength of the connection [11]. A.A. Denisov, have the following advantages over the PATTERN method and the decision matrix method: 1. The model of the 1st type provides the ability to take into account not only the degree (probability) of influence pi for the implementation of goals, but also the likelihood of implementation of this innovation in specific conditions. The assessment is carried out by individual experts. When transforming the estimate p0i in Hi, the computation of the generalized estimate is obtained by simple summation. 2. The method of assessing innovations, taking into account the change in significance over time into account the dynamics of the introduction of innovations. 3. The method of assessing innovations, taking into account their mutual influence, makes it possible to refine the assessments Hi based on taking into account the mutual influence of the assessed components [12].

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Comparing the methods of organizing complicated expertise [13, 21], it can be concluded that the main advantage of informational assessments of A.A. Denisov is an opportunity to compare dissimilar innovations, using the opinions of individual experts who are well aware of the proposed innovations. Experts assess the degree of influence of the innovation on the implementation of the goals of the enterprise, as well as the likelihood of using this innovation in certain conditions. Information assessments also measure changes in the contribution of innovation over time-based on measurable deterministic parameters and assess the mutual impact of innovation.

3 Algorithm for Evaluating Innovations 3.1

The Choice of Technologies

A cyber-physical system for a manufacturing enterprise may include the following complex (technologies): a CAD/CAE computer-aided design system, industrial robots, and computer vision systems coordinating their interaction, 3D printing for prototyping and manufacturing small batches of products, virtual (AR) and augmented reality technologies (VR) for the creation of visual “instructions-prompts” in the workplace, as well as for the promotion and sale of products, big data analysis tools to support online decision-making, a system for integrating supplier-customer pairs into a single control loop of end-to-end business processes and data exchange, and other technologies [14]. In such a working mechanism, sensors, equipment, and information systems are connected using standard Internet protocols for predicting, self-tuning and adapting to change. Thus, a cyber-physical system is not a separate autonomous operating device but is viewed as a network of relatively autonomous interacting technologies. The choice of innovations must be carried out taking into account their usefulness for the implementation of the goals of the organization. For an overview of existing innovations, one can refer to the works of D. Rifkin “The Third Industrial Revolution” [15, 16], K. Schwab “The Fourth Industrial Revolution” [17, 18]. Based on the analysis of these works, as well as on the text of the Decree of the President the RF “On the Strategy for the Development of the Information Society in the Russian Federation for 2017–2030” [22], and the recommendations contained in [19], the technologies shown in Fig. 1 were selected to create a cyber-physical system of an enterprise. Having selected a variety of prospective innovative technologies for implementation, it is necessary to determine the priority technologies for implementation, which will form the basis of cyber-physical systems.

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Fig. 1. Technologies of Industry 4.0.

4 Algorithm for Evaluating Innovations The cyber-physical system unites industrial and information technologies (see Fig. 2). It is practically impossible to compare this of dissimilar technologies (especially when introducing innovative digital technologies) using traditional methods of expert assessments (ranging, rationing, etc.). Therefore, it is proposed to apply methods based on information assessments [11, 12]. These methods are based on a survey of individual experts who are well aware of the proposed technologies, and the methods of combining these opinions with the use of rationing at the stage of processing estimates. Setting the problem in this form is a very difficult task and has a lot of uncertainty. To gradually narrow the range of feasible solutions, you can compose the algorithm presented below. Step 1. Obtaining a set of innovations based on the analysis of industrial revolutions and the selection of innovations for the product life cycle. Let us denote the set of innovations as INN ¼ hinn1 ; inn2 ; . . .; inni ; . . .; innn i; i ¼ 1; . . .n, where INN is the set of innovations selected on the basis of the analysis of industrial revolutions’ technologies [e. g. 15–18] taking into account the life cycle of products; and each of the selected innovations inni is compared with the elements of a set Z ¼ hz1 ; z2 ; . . .; zi ; . . .; zk i; k ¼ 1; . . .l, where Z is the set or structure of organizational goals. Step 2. Pre-selection of innovations by comparing innovations and the goals and functions of the enterprise. Then it is necessary to make a preliminary selection of innovations INNo ¼ hinno1 ; inno2 ; . . .; innoi ; . . .; innom i on the basis of matching the sets Z and INN: ZWINN. Step 3. Inverting the hierarchical structure and obtaining indirect quantitative estimates, i. e. it is necessary to obtain indirect quantitative estimates for the innovations INNo ¼ hinno1 ; inno2 ; . . .; innoi ; . . .; innom i.

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Fig. 2. Correlation of innovations and goals and functions of the cyber-physical system.

Step 4. Application of the selected methods of organizing complicated experts using the program. To obtain and process expert assessments of the proposed innovative technologies, it is proposed to use the informational assessments of A.A. Denisov. The information assessments are based on 3 models. Models of the 1st Type. In accordance with the informational approach, the effect of each innovation is calculated as: Hi ¼ qi logð1  p0i Þ; where pi′ is the estimate of the i-th innovation (technology) feasibility degree; qi is the potential (significance) of the i-th technology[12]. The cumulative effect of the innovations of a certain group (for example, united by a common sub-goal): H¼

n X

qi logð1  p0i Þ:

i¼1

Models of the 2nd Type. Models are based on a comparative analysis of innovations during a certain initial period of their implementation by comparing changes in information estimates over time.

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Given the process of innovation and its dynamics. Hi ¼ Ji =ni þ si dJi =dt þ Li d 2 Ji =dt2 ; where Ji ¼ Ai =DAi , and Ai is interpreted as the number of new technology being introduced units; determines with what degree of accuracy it is necessary to consider; ni is the volume of the concept of innovation necessary to obtain the potential for the chosen one; dJi =dt is the speed of the introduced innovation (i. e., the number of innovations of a given type introduced per unit time); si is the minimum implementation time; d 2 Ji =dt2 is the acceleration, increment of the innovation’s speed; Li is the characteristic of system rigidity, resistance to innovation; can be calculated as the reciprocal of the ratio of the difference in the speed of innovation to the time interval between them, that is, it is interesting in the case of the process of mass introduction of innovation. It is easier for an expert to evaluate the predicted pik′ at the end of the innovation implementation stage than to give pit′ estimates at the moment when monitoring the implementation of the innovation. Models of the 3rd Type. Models describe the assessment of situations described, taking into account the mutual influence of technologies: H1 ¼ f ðH11 ; H12 ; H13 Þ; H2 ¼ f ðH21 ; H22 ; H23 Þ; H3 ¼ f ðH31 ; H32 ; H33 Þ: where H1, H2, H3, … are the significances (essences) of the 1st, 2nd, 3rd, etc. technologies respectively; H11, H22, H33… Hii, …are the values of the intrinsic importance of technologies in the absence of other technologies that affect their value; H12, H13, H21, … Hij, … are the changes in the values of the i-th technology (innovation) in the presence of the j-th innovation on the market [14]. Step 5. To compare the estimates obtained by these methods and indirect quantitative estimates, it is necessary to construct and analyze histograms. Step 6. Economic assessments of proposed innovations. Step 7. At the final stage, an optimization model for control and managing of the innovative activity of the enterprise may be formed: n X m X

Hij xij ! max

i¼1 j¼1 n X

xij  1(innovations);

i¼1 n X m X i¼1 j¼1

aij xij  Z(cost constraint);

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tij xij  T(time limit);

i¼1 j¼1 n X m X

mij xij ! max(material constraint);

i¼1 j¼1

i ¼ 1; n; j ¼ 1; m: Step 8. The making a decision on the implementation of innovations for the cyberphysical system.

5 Results To implement the algorithm presented in Sect. 3, a program was written in C#. In the course of the program developing, the methods based on the informational assessments of A.A. Denisov were used. To implement the algorithm, the following innovations were taken into account: laser and plasma technologies, high-tech welding technologies, automated non-contact measuring systems, complex mechanized production lines, 3D-printer, 3D-scanner, thermoelectric systems, warehouse equipment automation. As a result of assessing innovations using information models of the 1st type, the results are shown in Fig. 3. To assess the dynamics of the development of innovations over time, models of the second type were applied (see Fig. 4). As a result, the set of feasible solutions has narrowed. To calculate the significance of innovations, taking into account their mutual influence, models of the third type were used. The calculation result is shown in Fig. 5. Studies have shown that the application of the three ways of assessing the relative importance of innovation can vary significantly. If we take into account the mutual influence of innovations, the significance can increase. When applying the second method of assessing innovations over time, due to the non-linearity of processes, small initial differences can increase or decrease, and in some situations, innovation, which, according to the first method, were not the best, over time may turn out to be more significant due to the problems of implementation that have arisen innovations, that were prioritized initially.

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Fig. 3. Report on the significance and normalized significance of the significance of each innovation using the 1st type model.

Fig. 4. Significance of innovation for the current period.

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Fig. 5. Significance of innovations taking into account their mutual influence.

6 Discussion As a result of the execution of the algorithm, the initial set, consisting of 10 innovations, after applying the models, taking into account the assessment of the appropriateness and the likelihood of use, was reduced to 6 innovations with the highest significance indicators. After evaluating the selected innovations, taking into account the dynamics of development over time, it was decided to evaluate 3 innovations taking into account the mutual influence. After performing the assessment with models of the 3rd type, the expert makes a decision on the priority implementation of innovations with the highest results of effectiveness. Taking into account the results of the calculations, we can conclude that the information models of A.A. Denisov make it possible to calculate the significance of innovations using the system-target approach, and also allow evaluating the development of innovations over time and taking into account the mutual influence of innovations, which cannot be assessed by any from the learned methods. Due to the initiatives of active elements and innovations, problems arise of maintaining the sustainability of production processes, which, when innovative technologies interact, possess the specific properties of open systems, which requires a revision of the methods and criteria for assessing their sustainability. Moreover, the sustainability management system should reflect changes in the enterprise’s activities assess their dynamics, and predict the future state of the system [20]. To solve the problem of determining the sustainability of production processes when introducing a

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cyber-physical system, it is proposed to use a combination of the principles of stabilization proposed in the theory of automatic control and the experience of assessing sustainability used in the practice of enterprise management. Such a combination is proposed to be made on the basis of an informational assessment of the degree of integrity, which makes it possible to provide emergent properties arising in a cyberphysical system as a result of the interaction of components [13, 21].

7 Conclusion In the current study aimed at studying decision management models when creating a cyber-physical system of an industrial enterprise, it is shown that modern methods of assessing innovations do not allow taking into account the laws of systems theory and systems analysis. The considered methods make it possible to assess only the economic contribution of the introduction of innovation. Therefore, an algorithm for evaluating innovations was proposed, based on a systems approach and using the laws of systems theory and methods of systems analysis. Further research involves the study of the patterns of development of complicated systems with active elements, on the basis of which a decision should be made about the feasibility of introducing innovations, taking into account the preservation of the stability of the operation of the enterprise.

References 1. Tang, H.K.: An inventory of organizational innovativeness. Technovation 19, 41–51 (1999) 2. Tom, B.: Measuring technological complexity — Current approaches and a new measure of structural complexity. Utrecht Universit (2018) . Hidalgo, A., Hausmann, R.: The building blocks of economic complexity. PNAS 106 (26) (2009) 4. Caldarelli, G., Cristelli, M., Gabrielli, A., Pietronero, L., Scala, A., Tacchella, A.: Network analysis of countries’ export flows: firm grounds for the building blocks of the economy. PLoS ONE 7(10), 1–11 (2012) 5. Balland, P.-A., Rigby, D.: The geogrpahy and evolution of complex knowledge. Econ. Geogr. 93, 1–23 (2017) 6. Volkova, V.N., et al.: Modelirovanie sistem i protsessov. [Modeling of Systems and Processes.] Yurait Publ., Moscow (2015). (in Russian) 7. Volkova, V.N., et al.: Modelirovanie sistem i protsessov: Practicum. [Modeling of Systems and Processes: Workshop.], pp. 96–125. Yurayt Publishing House, Moscow (2016). (in Russian) 8. Pospelov, G.S., Ven, V.L., Solodov, V.N., Shafranskiy, V.V., Erlikh, A.I.: Problema programmno-tselevogo planirovaniya i upravleniya. [The Problem of Program-Target Planning and Management.] Nauka, Moscow (1980). (in Russian) 9. Moiseev, N.N.: Matematicheskiye problemy sistemnogo analiza. [Mathematical Problems of System Analysis.], pp. 404–409. Nauka, Moscow (1981). (in Russian) 10. Lopukhin, M.M.: PATTERN (Metod planirovaniya i prognozirovaniya nauchnykh rabot) [PATTERN. Method of Planning and Forecasting Scientific Works.], 160 p. Sov. Radio, Moscow (1971). (in Russian)

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11. Denisov, A.A.: Sovremennyye problemy sistemnogo analiza. [Modern problems of system analysis]. 3rd edn. Izd-vo Politekhn. un., St. Petersburg (2008). (in Russian) 12. Volkova, V.N, et al.: Sistemnyy analiz innovatsionnykh tekhnologiy promyshlennykh revolyutsiy. [System analysis of innovative technologies of industrial revolutions.] In: Trudy III Mezhdunarodnoy konferentsii “Chelovecheskiy faktor v slozhnykh tekhnicheskikh sistemakh i sredakh” (Ergo-2018), 4–7 July 2018, St. Petersburg, Russia (2018). (in Russian) 13. Kudriavtceva, A.: Cyber-physical system as the development of automation processes at all stages of the life cycle of the enterprise through the introduction of digital technologies. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 601–607. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34983-7_59 14. Commission of European Communities: Information. In: Communications Technologies (ICT) in Horizon 2020, Brussels, November 2011 (2020) 15. Rifkin, J.: The Hidrogen Economy: The creation of the World-wide energy Web and the redistribution of power on Earth, 304 p. NY Tarcher (2002) 16. Rifkin, J.: The Third industrial revolution; How lateral power is transforming energy, the economy, and the World, 304 p. Palgrave Macmillan (2011) 17. Schwab, K.: The Fourth Industrial Revolution. “E” Publishing House, Moscow (2017). (in Russian) 18. Schwab, K., Devis, T.: Shaping the Fourth Industrial Revolution. “E” Publ., Moscow (2018). (in Russian) 19. Masyutin, S.A.: Opyt razrabotki strategii predpriyatiya dlya realizatsii otraslevykh strategiy (na primere kontserna “Ruselprom”). [Experience in developing an enterprise strategy for the implementation of industry strategies (on the example of the Ruselprom concern).] In: Plenarnyye doklady Trinadtsatogo Vseros. simp. “Strategicheskoye planirovaniye i razvitiye predpriyatiy” (2012). (in Russian) 20. Volkova, V., Loginova, A., Kudriavtceva, A.: Management of enterprise cyberphysical systems sustainable development while undergoing a digital transformation. IOP Conf. Ser. Mater. Sci. Eng. 940(1) (2020) 21. Volkova, V.N., Kudryavtseva, A.S.: Modeli dlya upravleniya innovatsionnoy deyatel’nost’yu promyshlennogo predpriyatiya. [Models for managing innovative activities of an industrial enterprise.] Otkrytoye obrazovaniye 22(4), 64–73 (2018). (in Russian) 22. Ukaz Prezidenta Rossijskoj Federacii ot 09.05.2017 № 203 “O Strategii razvitiya informacionnogo obshchestva v Rossijskoj Federacii na 2017 – 2030 gody”. [Decree of the President of the Russian Federation No. 203 of 9 May 2017 “On the Strategy for the Development of the Information Society in the Russian Federation for 2017–2030”.] Moscow (2017). (in Russian)

Expert Systems in Innovation Project Management: Architecture and Application Nikita B. Kultin(&) Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya st. 29, 195251 St. Petersburg, Russia [email protected]

Abstract. In modern conditions of the global economy, the competitiveness of an enterprise is determined by the level of innovativeness of both the enterprise itself and the level of innovativeness of its products. The activities of most modern enterprises are of a project nature. The success of an enterprise largely depends on how successfully the enterprise implements innovative projects. As an object of management, an innovative project differs from the so-called conventional projects by a high degree of uncertainty about the types and terms of work, time and financial costs. The success of a project is largely determined by the quality of the decisions made by the project manager. The manager of project is frequently forced to make decisions in the condition of inaccurate or incomplete information, which limits the ability to apply analytical methods, including those based on machine learning technology, to make the best or optimal decision. Therefore, experts are usually involved in solving problems of analyzing a problem, forming options for solving a problem, choosing and justifying the best solution. Solving problems with the involvement of experts requires significant time and financial costs. In addition, an expert’s decision is often subjective. It is possible to reduce the price and shorten the examination time, reduce the influence of the human factor, and improve the quality of decisions made by using the expert system as a decision support tool. Using an expert system, a manager of project can assess the commercial potential of an innovation, analyze risks, and evaluate the effectiveness of investments. The use of the expert system as a decision support tool in the management of an innovative project will reduce the time and financial costs for conducting examinations, improve the quality of project management, and reduce the likelihood of erroneous decisions. Keywords: Expert system
Decision support system
Artificial intelligence Innovation management
Innovation project
Project management




1 Introduction The competitiveness of an enterprise in the domestic and foreign markets is determined by the level of innovativeness of both the enterprise itself and the level of innovativeness of its products. The activity of most innovative enterprises is of a project nature. The success of an enterprise largely depends on how successfully the enterprise implements innovative projects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 413–423, 2022. https://doi.org/10.1007/978-3-030-98832-6_36

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An innovative project is a project whose goal is to create and launch innovation on the market—a fundamentally new product created on the basis of advanced achievements of science and technology. Characteristic features of the innovation project are a large amount of research and development work, high uncertainty by type, time and cost of work, significant risks, high costs, big gains in case of success, and large losses in case of failure [4]. The price of management decisions in the implementation of innovative projects is high. Management of innovative projects is one of the most difficult areas of management. This is explained, first of all, by the specifics of an innovative project as an object of management. In the process of preparing and implementing the project, the manager is forced to make decisions in the face of uncertainty, using incomplete or not sufficiently accurate information about the current state of the project and the prospects for its development. The quality of management decisions is largely determined by the experience and intuition of the project manager and team. Decisions are made under conditions of uncertainty, which often makes it impossible to apply analytical models and methods. Many problems are solved by the method of expert evaluation, involving the involvement of third-party experts, which makes the decision-making process long and expensive. Often, an expert council is formed to solve problems—a group of experts in different subject areas. Usually, the examination consists of the following stages: preparation, direct examination, processing of the results. At the preparatory stage, a pool of experts is formed, questions are formulated, the answers to which must be obtained from the experts. At the processing stage, expert responses are processed. Attention should be paid, the human factor has a significant influence on the result of the examination (the wording of the questions, the choice of experts, the addiction and qualifications of the experts, methods of processing the results). As an alternative to expert assessment, it is possible to use an artificial intelligence system [5, 13, 15]. The goal is to reduce time and financial costs, reducing the influence of the human factor.

2 Artificial Intelligence in Project Management Artificial intelligence is a section of computer science associated with the development of intelligent programs for computers [1]. The systems of artificial intelligence (AI) include word processing systems, pattern recognition, speech recognition, expert systems, as well as systems based on machine learning technology. In this study, the possibility of using expert systems and machine learning systems as a tool to support decision-making in the management of innovative projects is considered. An expert system (ES) is a computer program that uses expert knowledge to provide highly efficient problem solving in a narrow subject domain [1]. There are other definitions. An expert system is a system that combines the capabilities of a computer with the knowledge and experience of an expert in such a way that the system can offer sensible advice or carry out a reasonable solution of the task. ESs are designed to solve those problems where, as is commonly believed, it is impossible to do without a human expert [3]. Expert systems are used to solve various problems arising in the process of project management [6, 7, 15]. With the help of

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an expert system, a project manager can, without resorting to expert assistance, promptly receive advice or recommendation on solving a problem, for example, assess the commercial potential of a project product, assess project risks, select a contractor or supplier [5, 8, 14]. The basis of machine learning systems (Machine learning, ML) is the approach to create a rule (algorithm) for data processing by the program itself that performs data processing. Machine learning systems are effectively used to solve problems of classification and clustering of objects [9]. Classification is the assignment of an object to one of two possible classes. For example, according to the results of an examination of a business plan, a project can be assigned to one of two “classes”: “accepted” or “rejected”. The solution of the clustering problem involves assigning an object to one of several classes, for example, according to the results of a business plan examination, a project can be assigned to one of three classes: rejected, candidate, or accepted. It should be noted that the use of machine learning implies the availability of data necessary for the “learning” of the system—the calculation of the coefficients of the function.

3 Project Lifecycle Tasks and AI Tools Consider the tasks arising at different stages of managing an innovative project that can be solved using artificial intelligence systems. The project life cycle consists of the following steps: • Analysis • Planning • Implementation The peculiarity of the tasks of the analysis and planning stages is that they are usually unique, poorly formalized, and when making decisions, the project leader, and then the project team, rely on their experience and intuition, taking into account expert advice and recommendations. It is also necessary to pay attention to the fact that the initial phases of the project determine a large part of its result since the main decisions are made at these stages. At the same time, 30% of the contribution to the final result of the project is made by the preparation stage, 20% by the planning stage, and 50% by the project implementation stage [4]. The solution of many problems arising in the process of implementing an innovative project, in principle, can be brought to the solution of the classification problem and solved using a machine learning approach [9]. However, it is necessary to understand that a sufficiently large amount of information is necessary for the application of the machine learning approach, which allows one to train the decision support system, i. e. statistics is needed—information about the conditions (parameters) and the results of previous decisions. However, due to the uniqueness of innovative projects, such statistics for most tasks cannot be, except perhaps for the tasks of selecting contractors (suppliers) or evaluating standard projects [9]. The tasks of the stages of the life cycle of an innovative project and AI technology that can be used to solve them as a tool for decision support are given in Table 1.

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Stage

Tasks

Analysis

Analysis of the commercial potential of innovation Risk analysis Estimation of the duration of the stages (works) Cost estimation (budget development) Selection of sources and financing schemes for the project Analysis of indicators of efficiency of investments

Planning

The formation of the project team Selection of the performer (counterparty, supplier)

Implementation

Analysis of commercial proposals Risk analysis Estimation of the duration of the stages (works) Cost estimate Analysis of the reasons for the violation of the implementation schedule Development of recommendations for eliminating the consequences of risks

AI tool ES ES ES ES ES ES, ML ES, ML ES, ML ES ES ES ES ES ES

4 The Expert System Architecture The expert system consists of a knowledge base, an inference engine, an explanation module, a developer interface, and a user interface. The basis of the expert system is the knowledge base about the subject area, which contains knowledge about the object. In most cases, knowledge is heuristic and probabilistic in nature. Knowledge in the expert system can be represented in the form of a set of rules of inference, a semantic network, or in the form of a set of frames [1, 2]. The knowledge representation based on the rules is based on the use of expressions of the form IF a condition THEN conclusion, reflecting the course of reasoning of a person-expert when solving a problem. Rules provide the most natural way to describe a decision-making process. Semantic networks and frames, as a rule, are used to solve the fundamental problems of artificial intelligence. In addition to the rules, the knowledge base contains facts—information about the current state of the object. Facts appear in the knowledge base during the consultation process, as a result of the user’s answers to the expert system’s questions, and are also generated by the inference mechanism through the fact and rule agreement process. The inference engine searches the knowledge base for appropriate rules and matches them with facts. The inference engine is an interpreter of rules that uses rules and facts to solve the problem. It generates problematic hypotheses and tests them for the purpose.

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The developer interface is used by the knowledge engineer in the process of creating an expert system to enter and correct the knowledge base. The user interface allows the user of the expert system to interact with the system during a consultation. The explanation subsystem allows the user to see and understand the chains of logical inference, which increases the user’s confidence in the recommendations of the expert system. In a minimal configuration, an expert system can be built from an inference engine module, a knowledge base, and a developer interface. When starting to develop an expert system, it is important to choose the right system architecture and programming language. Consider the approaches to the software implementation of an expert system (see Fig. 1 and Table 2). Implementation of expert system

Logic programming language

Universal programming language

Embedded knowledge base

External knowledge base

Fig. 1. Approach to the implementation of expert system.

Table 2. Comparison of approaches to the implementation of an expert system. Property

Implementation Type 1

Type 2

Programming language

Special (e.g. Prolog)

Universal Type 2.1

Knowledge base

Embedded in the program code – Changes in the program code – Requires programmer participation

Embedded in the program code – Changes in the program code – Requires programmer Participation

High

Average

Correction of the knowledge base

Complexity of developing software code Universality

Type 2.2 External text file – Changes only in an external text file – Does not require the participation of the programmer Average

For a new subject area a For a new subject area a For a new subject area only a new development is new development is new knowledge base is needed needed needed

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A system of type 1 involves the use of a logic programming language [10, 11]. A systems of type 2 as e rule based a universal object-oriented programming language like C/C++. When using the universal programming language, the knowledge base may be a part of program code or may be presented as an external plain text file or XML file. Experience shows that at the stage of creating an expert system it is impossible to foresee all possible situations that the user will have to face in the process of using the expert system. The user often needs to adjust the behavior of the system. To do this, you need to make changes to the knowledge base. If the knowledge base is part of the code, then it is necessary to involve programmers to make changes. If the knowledge base is separated from the code, then the user can make changes to it himself. The second approach is obviously less expensive. Thus, the expert system must be an open system. This requirement is met by the architecture of the second type (Type 2.2 in the Table 2), in which the knowledge is represented by a set of logic rules that are in a separate external text file.

5 Knowledge Base Organization There are a lot of freeware third-party solutions that allow you to create an expert system. To solve the task of creating an expert system focused on problems arising in the innovative project managing process, the UNGIN shell [13] was selected. The properties of the UNIGIN to the requirements formulated above. A knowledge base in UNGIN syntax is represented by a set of rules, which are looks as:

rule (N) Object1 = Value1 Object2 = Value2 . . . Object = Value then Objecti = Valuei,cf = k;

where: Objecti is a subject area object; Valuei is a value of the object; cf is the confidence factor; k is the value of the confidence factor (a confidence factor that value may be from 0 to 1.0 shows the confidence degree that the object has the specified value.

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Table 3 shows the objects of the expert system for solving the problem of including a project in the portfolio of projects. It should be noted that the list of object values, as well as the list of objects themselves, can be expanded in the process of using the expert system. Table 3. Objects of the expert system of technology portfolio formation. Object

Object description

Possible value

Profit-making

Assessment of the possibility of making a profit Lower, average, above the average, from the implementation of the project high, very high Solution level Degree of changes to the previous system Fake, minor improvements, fundamental improvement, new generation Technique Level of technique needed to produce the project Low, average, hi-Tech level product by rivals Manufacturing Impact degree on the existing production New manufacturing process, big process process changes, minor changes, without changes Employees Costs of new highly skilled staff Very high, acceptable, not required Innovative The degree of effect of the project on the level of Unaffected, insignificant, medium, potential the innovative potential of the firm significant Market Product market Expands the product line, creates a new product line Investment The cost of R&D acquiring assets, building Insignificant, acceptable, high, very creation and modernization of product high production Project finance The project financing sources Equity, debt, government grants Payback period The amount of time it takes to recover the cost of Short, medium, long an investment Internal rate of A metric used in financial analysis to estimate Low, medium, high return the profitability of potential investments Risk Project risk assessment Negligible, medium, significant, high, very high Links Links with other ongoing or pending projects None, acceptor, weak donor, donor, multi donor Project Decision to accept or reject a project Accept, candidate, clarify, reject

The following is presented to see a knowledge base snippet of the research prototype of the projects analysis expert system.

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rule () patent_protection = patent market = creates_a_new technology_level = high then commercial_potential = very_high; rule () patent_protection = industrial_sample market = expands_current technology_level = medium then commercial_potential = sufficiently_high; rule () patent_protection = no market = expands_current technology_level = medium then commercial_potential = medium; rule () commercial_potential = very_high investment = significant innovative_potential = increase then project = accept, cf = 75; rule () commercial_potential = enough_high investment = significant innovative_potential = increase then project = candidate, cf = 80; rule () commercial_potential = low then project = reject, cf=90;

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6 Verification In order to verification the developed prototype of the expert system, it is necessary with the help of the expert system to conduct an examination of the projects that have previously passed the usual examination and compare the opinions of the experts with the conclusions obtained using the prototype. Four innovative projects were selected for testing. The results of the usual and executed using the prototype expert system of examinations are shown in Table 4. The data in the table show that the conclusion of the expert system and the conclusions of the human expert are the same. Thus, the experiment carried out underpins the possibility of using the expert system for carrying out an express examination of projects of various types. Table 4. Recommendations of the expert and the expert system. Project description Production of LEDs for outdoor lamps Production of turbine blades for aircraft engines. Development of technology and creation of production Serial production of special hydraulic equipment and tools Production of crumb rubber coatings

Expert recommendation Project may be accepted Project may be accepted

ES recommendation Project = accept, cf = 75 Project = candidate, cf = 80

Project may be accepted Project must be rejected

Project = accept, cf = 75 Project = reject, cf = 85

7 Discussion Constructing of an ES requires the involvement of recognized experts in the subject area, for the solution of the problems of which an expert system is created. Knowledge engineering specialists are also needed. It is necessary to understand that the creation of an ES is a long process that requires significant financial investments. It is difficult to estimate the efficiency of investments in the project of creating an expert system, since it is impossible to predict the cash flow generated as a result of the use of ES. However, the efforts and funds invested in the creation of ES will avoid losses associated with making erroneous decisions. The conducted research, the created prototype and using it, confirm the correct choice of architecture and the possibility of using ES to solve problems arising in the process of implementing innovative projects.

8 Conclusion In the paper, the idea of an expert system for managing an innovative project was proposed. The main conclusions of the work are as follows:

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• When solving problems arising in the process of managing an innovative project, it is possible to use an expert system as a tool to support decision-making. Using the expert system, the project manager can analyze the commercial potential of an innovation, analyze risks, estimate the duration of stages, costs, estimate the cost of individual stages and the total cost of the project. • The use of the expert system at various stages of the project implementation makes it possible to increase the objectivity of the examination, reduce the influence of the personal preferences of experts, and significantly reduce the financial and time costs associated with the preparation, conduct and processing of the results of the examination. • Decision support systems based on machine learning technology for solving innovative project management problems may have the limited application (analysis of investment performance indicators, choice of supplier, co-performer), provided there is a sufficient amount of statistics necessary for learning the decision-making algorithm.

References 1. Giarratano, J.C., Riley, G.D.: Expert Systems: Principles and Programming, 4th edn., 288 p. Course Technology (2004) 2. Baral, C.: Knowledge Representation: Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003) 3. Tecuci, G., Dorin, M., Boicu, M., Schum, D.: Knowledge Engineering Building Cognitive Assistants for Evidence-Based Reasoning. Cambridge University Press, Cambridge (2016) 4. Tukkel’, I.L., Surina, A.V., Kultin, N.B.: Upravlenie innovacionnymi proektami [Management of innovative projects.]. In: Tukkel’, I.L. (ed.) BHV-Petersburg, St. Petersburg (2017). (In Russian) 5. Kultin, N.B.: Ekspertnaya sistema kak instrument podderzhki prinyatiya upravlencheskih reshenij [Expert system as a tool to support management decisions.] Nauchno-tekhnicheskie vedomosti Sankt-Peterburgskogo gosudarstvennogo politekhnicheskogo universiteta [Scientific and Technical Reports of Saint Petersburg State Polytechnic University] 3(121), 139– 141 (2011). (In Russian) 6. Schuyler, J.R.: Expert systems in project management. PM Netw. 14, 27–29 (2000) 7. Alves, R., Alvelos, F., Sousa, S.D.: Resource constrained project scheduling with general precedence relations optimized with SAT. In: Correia, L., Reis, L.P., Cascalho, J. (eds.) EPIA 2013. LNCS (LNAI), vol. 8154, pp. 199–210. Springer, Heidelberg (2013). https://doi. org/10.1007/978-3-642-40669-0_18 8. Ahuja, A., Rödder, W.: Project risk management by a probabilistic expert system. In: Leopold-Wildburger, U., Rendl, F., Wäscher, G. (eds.) Operations Research Proceedings 2002, pp. 329–334. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-642-555374_53 9. Kultin, N.B., Kultin, D.N., Bauer R.V.: Application of machine learning technology to analyze the probability of winning a tender for a project. Proc. ISP RAS 32(2), 29–35 (2020). https://doi.org/10.15514/ISPRAS-2020-32(2)-3. Trudy ISP RAN (the Institute for System Programming of the RAS)

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10. Jones, J.D.: Logic programming for intelligent systems. In: Encyclopedia of Information Science and Technology, 4th edn. Chapter 411, pp. 4736–4745. Liberty University, USA (2018). https://doi.org/10.4018/978-1-5225-2255-3.ch411 11. Khosrow-Pour, M.: Advanced methodologies and technologies in artificial intelligence. Comput. Simul. Human Comput. Interact., 879–890 (2019). https://doi.org/10.4018/978-15225-7368-5.ch065 12. Bramer, M.: Logic Programming with Prolog, 2nd edn. Springer, London (2013). https://doi. org/10.1007/978-1-4471-5487-7 13. Expert system shell UNGIN. https://www.microsoft.com/store/apps/9PHPDLLRDX4P. Accessed 5 June 2021 14. Kultin, N.B.: Iskusstvennyj intellect v upravlenii innovacionnymi proektami [Artificial intelligence in management of innovation projects]. Innovations 12(254), 99–103 (2019). https://doi.org/10.26310/2071-3010.2020.254.12.014. (In Russian) 15. Surina, A., Kultin, D., Kultin, N.: An expert system as a tool for managing technology portfolios. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 746–753. Springer, Cham (2020). https://doi.org/10.1007/978-3030-34983-7_74 16. Sackey, S., Kim, B.-S.: Development of an expert system tool for the selection of procurement system in large-scale construction projects (ESCONPROCS). KSCE J. Civ. Eng. 22(11), 4205–4214 (2018). https://doi.org/10.1007/s12205-018-0439-2

Experience in Design of Artificial Neural Network for Object Detection on Monochromatic Images Anton V. Kvasnov(&) , Nikolai A. Nikitin and Vyacheslav P. Shkodyrev

,

Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya St. 29, 195251 St. Petersburg, Russia [email protected]

Abstract. The paper considers the design of artificial neural networks (ANN) for the detection and classification of vehicles on monochrome images obtained from unmanned aerial vehicles (UAV). The main problem based on the existence of miscellaneous images, which are obtained for various distances and different object profiles. The aim of this paper is an estimation of the efficiency of artificial neural networks as a function of different metrics. A mathematical statement of the problem in the context of binary decision is given. The results were evaluated using simulation in Matlab2020, where images were marked, and a neural network was implemented based on the YOLO technology. The average precision archives 48% in terms of detection and classification of vehicles for various profiles of objects and distance. As a way to improve the accuracy of vehicle detection, it recommends increasing the volume of the training sample. It is advisable to use the entire number of object profiles in order to achieve the upper limit of recognition. As a way to improve the accuracy of vehicle detection, it recommends increasing the volume of the training sample. It is advisable to use the entire number of object profiles in order to achieve the upper limit of recognition. Keywords: Neural networks
Object recognition Monochrome image
Unmanned aerial vehicles


Vehicle recognition


1 Introduction Over the last years, computer-based vision technologies have been introduced extensively in many areas of activity. For example, due to the accomplishment of the concept “Smart City”, and increased traffic jams, automatic vehicle detection, and control systems on the roads are required for the management of the transport network in metropolises [1]. There is a round-the-clock video for surveillance of vehicles, which uses both the means of fixed photo cameras and the means of video cameras. This equipment allows us to estimate automatic detection and classification of vehicles with high reliability. Unfortunately, widespread use of these systems is limited by the high cost, the associated data storage and transmission infrastructure. One of the ways to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 424–430, 2022. https://doi.org/10.1007/978-3-030-98832-6_37

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reduce the cost of automatic vehicle detection and control systems on the road is to implement photosystems based on unmanned aerial vehicle (UAV) observation [2, 3]. However, a significant problem of UAV detection remains the significant influence of perspective on the potential accuracy of recognition. A so-called binary decision problem appears when a detector can observe a false object (False Positive error) or omit a true target (False Negative error). Thus, the aim of this research is to study the accuracy of the object detection on monochrome images in the case of photo receiving at different aspect angles and altitudes, when the false positive and false negative recognition errors are given.

2 Research Review Although there have been advances recently with deep learning, the results of detecting small objects are far from ideal. In most cases, detection carries out with several assumptions, for example, height limit, aspect angle, fixation of the point observation, and so on. One of the ways to improve the detection result is to use high-resolution images. However, this approach has high costs in terms of computing power and finances. For example, the authors in [4] proposed their own architecture for the artificial neural network QueryDet, which uses a new query mechanism to increase the speed of object output based on pyramids of functions. That allows reducing the time of detection of small objects. At the same time, the authors managed to have obtained an average detection accuracy of about 40–60% on the VisDrone [8] and COCO [9] datasets, in which high-resolution color images of vehicles are widely represented. In the work [5], the problem of detecting vehicles removed from UAVs is considered. To improve the detection results, the authors proposed their own preprocessing, which implies adaptive resizing. In their work, they also used a dataset from VisDrone and UAVDT [10]. As a result, the authors managed to significantly increase the detection accuracy, but work was also carried out with color images. In the work [6], the authors proposed their SyNet ensemble network to solve the problem of detecting objects removed from the UAV. This network combines a multistage method. The main goal of the authors was to reduce the frequency of falsenegative errors. The authors were able to obtain an average accuracy of 75% on the COCO and VisDrone datasets. The analysis of work in the field of detection and classification of vehicles on the road showed that, although there are many solutions for the detection of vehicles, all of them are implemented only for color images, which greatly simplifies the detection process.

3 Mathematical Formulation The implementation of the recognition algorithm provides for the choice of a criterion according to which we must evaluate its effectiveness. To consider the correctness of detection and classification of objects, the Recall metric was chosen as a numerical metric:

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R¼

TP ; TP þ FN

ð1Þ

where TP is the number of true positive results; FN is the number of false positives. On the other hand, it is possible to apply the Precision metric according to the expression: P¼

TP ; TP þ FP

ð2Þ

where TP is the number of true positive results; FP is the number of skips of objects. In the first case, the assessment characterizes the degree of object recognition against the background of interference; the second case makes it possible to judge the ability to detect objects without prejudice to missing the target. Often, both criteria are combined into one common aggregated F-Measure: F ¼ 2

PR : PþR

ð3Þ

The F-Measure characterizes the harmonic mean of two quantities. In fact, in the conditions of illumination of the ground situation, we need to require some compromise option, when, with a fixed value of false alarms, the maximum of detected objects is reached: 8fP ¼ constg 2 0. . .1 : F ðRÞ ! max

ð4Þ

Obviously, it is not a trivial task to find the analytical dependence for the indicated expressions considering the convolutional ANNs. For this reason, we will try to establish an experimental estimate of the ANN efficiency for various types of networks.

4 Practical Implementation As a methodological basis for solving the problem of detecting and classifying vehicles on the roads using monochrome images obtained from UAVs, we used the tools of the MATLAB software product - Image and Video Ground Truth Labeling and the readymade architecture of a neural network for detecting objects in images: YOLOv2 [6, 10], from MATLAB Deep Learning Toolbox. At the beginning, data were collected using a UAV. As a result, 245,305 monochrome images were captured, but in most of the images either the desired objects are absent, or it is difficult to clearly identify them. Therefore, image filtering was carried out, as a result, 48935 images were extracted. And then the image processing was done. First, to simplify training, the images were cropped from 1024  512 to 256  256, thereby increasing the number of images that are suitable for training (see Fig. 1). After that, the marking of various images was carried out. At the moment, 1640 images are marked for medium and long distances (height from 300 m).

Experience in Design of Artificial Neural Network

Intermediate data

IniƟal data number: 245 305

FILTRATION

Image size: 1024x512

number: 48 935

427

Final data IMAGE PROCESSING

Image size: 1024x512

number: 1640 Image size: 256x256

Fig. 1. Data transformation steps.

We used the markings of 2 classes of vehicles: cars and trucks. The markup data is written in the Ground Truth structure, the frames of the marked objects are represented as an integer vector—[left bottom width height], that is, the coordinate of the lower-left point of the frame, the width and height of the frame itself. The architecture of the used YOLOv2 model is shown in Fig. 2.

Fig. 2. YOLOv2 ANN architecture.

This model starts with a feature extraction network that can be pretrained, but in this case, a non-pretrained network was used. Next comes the discovery subnet. As this network, the popular architecture of the convolutional neural network ResNet50 [12] was used, which has proven itself well in computer vision problems. It is followed by the transformation and output levels: yolov2TransformLayer and yolov2OutputLayer. The yolov2TransformLayer object transforms the raw output of the convolutional neural network into the form necessary for object detection. And the yolov2OutputLayer object defines the parameters of the anchor box and implements the loss function that is used to train the detector.

5 Results and Discussion So far, the detector has been trained for only one class “Car” in a batch mode of 4 images in batch with the Adam optimizer [12] and lr = 0.005. The entire dataset was divided into 3 parts: train *75%, test = 10% and validation *15%. The detector was trained for about 500 epochs. Based on the test results, the graph in Fig. 3 was viewed. According to this graph, we can say that on average the detector detects about 30% of objects in the image with an accuracy of almost 90%. F-Measure metric = 0.48, which is comparable to the results of vehicle detection in color images. From this the

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Fig. 3. Precision/Recall Chart.

detector practically does not make false-positive errors, however, the low quality of the images affects, because of which only about half of the objects are detected at most. Figure 4 below shows the results of vehicle detection from the test sample. The results show that the detector confidently recognizes light vehicles at different angles and different heights [13–15]. False-positive errors are made in images with a large number of objects such as people, other vehicles, elements of houses that look like elements of vehicles. However, when building automatic control systems, the operator will be able to vary the “threshold”, thereby excluding false positives. Despite the accuracy of results, we take into account a number of other problems. The important problem is how the rotation matrix is defined and, thus, if this mathematical entity matches the physical meaning one is giving to it [16]. The article [17] considers the hierarchical deep cosegmentation approach, which improves the properties of objects detected by UAV. Moreover, we can use radar technologies for the recognition of ground objects [18].

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Fig. 4. Obtained results of detecting cars on optical images.

6 Conclusion In the paper, we try to estimate the recognition effectiveness of ground objects in case of detection with different profiles by unmanned aerial vehicles. As a result, FMeasure = 0.48, Precision = 0.88, and Recall = 0.29 were obtained. On average, approximately a third of objects on the image are detected, however, the detection is performed with high accuracy (about 90%). These results are comparable to the results of detection in color images. A few percentages of correct detection can be explained as an essential distance to the target and potential low resolution of photo cameras. As a way to improve the accuracy of vehicle detection, it recommends increasing the volume of the training sample. It is advisable to use the entire number of object profiles in order to achieve the upper limit of recognition.

References 1. Puentes, M., Novoa, D., Delgado Nivia, J., Hernández, C.B., Carrillo, O., Le Mouël, F.: Datacentric analysis to reduce pedestrians accidents: a case study in Colombia. In: International Conference on Sustainable Smart Cities and Territories. arXiv preprint arXiv: 2104.00912 (2021) 2. Jain, A., et al.: AI-enabled object detection in UAVs: challenges, design choices, and research directions. IEEE Network 35(4), 129–135 (2021). https://doi.org/10.1109/MNET. 011.2000643 3. Delibasoglu, I.: UAV images dataset for moving object detection from moving cameras. arXiv preprint arXiv:2103.11460 (2021) 4. Yang, C., Huang, Z., Wang, N.: QueryDet: cascaded sparse query for accelerating highresolution small object detection. arXiv preprint arXiv:2103.09136 (2021)

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5. Messmer, M., Kiefer, B., Zell, A.: Gaining scale invariance in UAV bird’s eye view object detection by adaptive resizing. arXiv preprint, arXiv:2101.12694 (2021) 6. Mert, A.B., Sedat, O.: SyNet: an ensemble network for object detection in UAV images. arXiv preprint, arXiv:2012.12991 (2020) 7. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 6517–6525. IEEE (2017). https://doi.org/10.1109/CVPR.2017.690 8. Springer Link (2021). https://link.springer.com/chapter/10.1007%2F978-3-030-11021-5_27. Accessed 09 July 2021 9. COCO (2021). https://cocodataset.org/#home. Accessed 29 Sept 2021 10. Paperswithcode (2021). https://paperswithcode.com/dataset/uavdt. Accessed 29 Sept 2021 11. MATLAB (2021). https://www.mathworks.com/help/vision/ug/getting-started-with-yolo-v2. html. Accessed 29 Sept 2021 12. Towards data science (2021). https://towardsdatascience.com/adam-latest-trends-in-deeplearning-optimization-6be9a291375c. Accessed 29 Sept 2021 13. Kvasnov, A.V.: Method of classification of fixed ground objects by radar images with the use of artificial neural networks. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 608–616. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34983-7_60 14. Kvasnov, A.V.: Methodology of classification and recognition of the radar emission sources based on Bayesian programming. IET Radar Sonar Navig. 14(8), 1175–1182 (2020) 15. Boytsov, A., Gladilin, P.: Separating real-world photos from computer graphics: comparative study of classification algorithms. Procedia Comput. Sci. 178, 320–327 (2020) 16. Zingoni, A., Diani, M., Corsini, G.: Tutorial: dealing with rotation matrices and translation vectors in image-based applications: a tutorial. IEEE Aerosp. Electron. Syst. Mag. 34(2), 38– 53 (2019). https://doi.org/10.1109/MAES.2018.170099 17. Li, J., Yuan, P., Gu, D., Tian, Y.: Hierarchical deep cosegmentation of primary objects in aerial videos. IEEE Multimedia 26(3), 9–18 (2019). https://doi.org/10.1109/MMUL.2018. 2883136 18. Kvasnov, A.V., Shkodyrev, V.P.: A classification technique of civil objects by artificial neural networks using estimation of entropy on synthetic aperture radar images. J. Sens. Sens. Syst. 10(1), 127–134 (2021)

Deep Learning Applications in Industrial Grading System Mikhail A. Miae1(&) , Galina F. Malykhina1 and Dmirtii Manev2

,

1 Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya st. 29, 195251 St. Petersburg, Russia [email protected] 2 Neudesic LLC, Irvine, CA, USA

Abstract. Automation of the classification processes for diamond crystals is an actual challenge since the classification is a direct component of the sorting process. The collection of data necessary for the crystal’s class determination is carried out using a camera. Within the framework of this paper, we developed a software prototype which initial stages are the transformations of the previously obtained images. These transformations are aimed at reducing image noise and highlighting object boundaries in the image. Also, we have carried out an analysis of the popular pretrained artificial neural network (ANN). As a result of this analysis, the most suitable pretrained model for the task was selected. One of the main selection criteria was the ability to add new quality classes and retrain the pretrained network model. This software is based on a convolutional neural network that allows you to determine the characteristics of an object in an image to determine the pattern of the certain class of diamond crystals. A special neural network is used to make the final decision on a crystal quality class. It uses the metrics from each of the seven projections and summarizes the result. This solution allows you to reduce the size of the neural network. Keywords: Cyber-physical system
Machine learning Deep learning
Pretrained deep neural network


Diamond crystal


1 Introduction The cyber-physical system as an information technology concept is characterized by a high integration of computing resources into production systems and industrial robots that are capable of performing complex operations using computer vision. Deep system integration of computing and production systems allows accumulating large amounts of video data and making decisions in real time. Cloud computing makes it possible to create platforms for the collaboration of industrial automation systems based on the exchange of data between distributed systems and the use of more complex computational methods of machine learning. Automation of production processes in the mining industry is a modern challenge, for the solution of which it is necessary to develop modern algorithms and methods.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 431–441, 2022. https://doi.org/10.1007/978-3-030-98832-6_38

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This problem includes a special case of automating the diamond crystals classification by quality characteristics based on machine learning. Machine learning and neural networks can improve plant productivity and ensure consistent and high-quality sorting of raw materials. The classification of diamond crystals is carried out according to four main criteria: weight, color, clarity (transparency), and shape [1, 2]. The most valuable are transparent diamonds or diamonds with a slight tint. Such diamonds are used to create jewelry and their value is also based on their size [3]. An objective assessment of the chromaticity and transparency of diamond crystals is one of the most important problems that researchers and engineers face when developing hardware and software for the diamond classification [4]. The most popular solution for this problem is a system of several synchronized cameras that take pictures of several [3–9] nonorthogonal projections of the crystal being evaluated. Cameras that take photographs of falling crystals in seven projections are used to automatically classify crystals with high performance. The measurement scheme is shown in Fig. 1. The crystals are preliminarily supposed to be divided into three classes: “Boart”, “Near Gem” and “Gem”.

falling crystals cameras

Fig. 1. Simplified diagram of photographing falling crystals.

Examples of photographs of crystals are shown in Fig. 2. Crystals of the “Boart” class are intended for industrial applications, for example, for the manufacture of diamond blades, diamond core bits in equipment for the stone and construction industries. Crystals have an irregular shape and are not transparent almost everywhere. Crystals of the “Near Gem” class of almost gem-quality have a more regular shape and small areas of transparency. Crystals of “Gem” class are of gem quality, in photographs they have large areas of transparency, even with a break in the perimeter. At the same time, the corners of the “Gem” crystal remain dark, which makes it possible to complete the perimeter by means of preliminary image processing. For the study, 800 sets of photographs of seven crystal projections were obtained for each class of crystals. There were 2,400 images in total.

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The objective of this article is to develop a solution for an automated multiclass diamond crystals classification based on images of its projections using machine vision and neural networks.

a).

b).

c). Fig. 2. An example of images of crystal projections: a) crystals of the “Boart” class for use in industry; b) crystals of “Near Gem” class of almost gem quality; c) gem-quality crystals of the “Gem” class.

2 Materials and Methods 2.1

Restoring the Shape of Crystals with a Perimeter Break

To automate the diamond crystal’s shape determination from the image of its projection, we propose to use a mask image obtained during the processing of the initial image. The mask is a black and white image in which only the background and the location of the object itself are distinguishable. To improve the accuracy of the mask formation, we propose to use the Canny boundary detector, which makes it possible to improve the standard mask generation function. The main advantage of the Canny edge detection is the presence in the image preprocessing algorithm of the step with the usage of a low-pass Gaussian filter, which helps to smooth the image and get rid of noise [5, 6]. The filling of the internal area of the object in the image is carried out on the basis of recurrent conditional dilation procedure ðÞ [7]: Xk ¼ ðXk1  BÞ \ I C ; k ¼ 1; ::; K;

ð1Þ

where I C is a complementary image containing borders, Xk is a new image with filling in areas at a step k, B is a primitive. After the completion of the filling operation I ¼ XK . The transparency of diamond crystals can cause inaccuracies in the formation of the boundaries of the object, and therefore the mask. In such cases, it is assumed that the crystal has a convex shape, which allows the application of a closure operation involving multiple dilatation and erosion. As a result of this operation, a mask of the

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object in the image was obtained. It is reflecting the shape criterion used in automated classification. 2.2

Determination of the Chromaticity of a Diamond Crystal

The color of a diamond crystal is one of the most important quality characteristics of a product [8]. Since in this case images of crystal projections taken at different angles are used as input data, the transparency characteristic of the crystal can be obtained only from the chromaticity criterion. Thus, the problem of determining the chromaticity of a crystal in an image becomes even more important for the proposed automatic classification system [9–12]. To solve this problem, the object mask obtained by determining the shape of the crystal can be used. With its help, it is possible to get rid of the influence of the background on the determination of the color component of the image of crystal [13]. 2.3

Choosing a Pretrained Neural Network

To simplify and accelerate the training of a neural network, as it is the main component of the proposed software, it was decided to use a pre-trained ANN as a basis. In the course of the work, pretrained models from Visual Geometry Group (VGG), Inception (GoogLeNet), ResNet and EfficientNet families were considered. The models of each of these families have their own advantages and disadvantages, which are worth considering in more detail. VGG-16 is a member of the VGG family, consisting of 13 convolutional layers and 3 fully connected layers. Figure 3 shows the architecture of VGG-16 model.

Fig. 3. VGG-16 architecture.

The VGG-16 model is very popular in the case of solving classification problems, but it has two significant drawbacks: a very slow learning rate and a large weight of the network architecture [14–16]. ResNet50 is a member of the ResNet family, whose feature is the availability of shortcut connections. They were added to solve the problem that as the depth of the network increases, the accuracy first increases and then quickly degrades [17, 18]. Microsoft has introduced a deep “residual” learning structure [19]. Instead of hoping that every few stacked layers directly correspond to the desired base view, they explicitly allow those layers to correspond to the “residual”. The F(x) + x formulation can be implemented using neural networks with the shortcut connections. The shortcut

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connection of the ResNet model and the architecture of ResNet model are shown in Figs. 4 and 5.

Fig. 4. Shortcut connection in ResNet model.

Fig. 5. ResNet50 architecture.

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Inception_ResNet_v2 is a member of the Inception family of networks developed by Google. Like VGG-16, it is a convolutional network and is the fourth version of the proven deep convolutional network model. This model incorporates the speed of its predecessors and the precision of the ResNet algorithm using shortcut connections [19, 20]. EfficientNetB0 is a model from the EfficientNet family which was also developed by Google and has an impressively small number of parameters equal to 5.3 million [21, 22]. For comparison, Inception and ResNet use *25 million parameters, and VGG * 138 million. Unfortunately, the accuracy of this model with the original data set also turned out to be lower than that of competitors. The result of the comparison of parameters and accuracy of the considered models can be seen in Table 1. The specified classification accuracy only applies to the original training data. In our case, the model will be retrained on sample data with images of crystals. Table 1. Results of the research for the pre-trained models. Model VGG-16 ResNet-50 Inception_ResNet_v2 EfficientNetB0

Number of parameters 138 Million 25 Million 24 Million 5.3 Million

Accuracy 92.7% 94.2% 95.1% 93.5%

3 Results 3.1

Neural Network

To accomplish the task, a deep convolutional ANN was chosen, since it is the most suitable for solving the classification problem when working with images. When developing the program, the main criteria were the possibility of further training of the network, resistance to noise in the image, the ability to add new classes of diamond crystals and free crystal detection in the image, regardless of its location. To reduce the consumption of computing resources and the time required for model training without any loss in accuracy, it was decided to use a pre-trained ANN from Google called Inception_ResNet_v2, trained on the weights provided by the ‘imagenet’ image database [23]. This model uses shortcut connections in its architecture, which allows achieving high recognition and classification accuracy [24]. It also fits well with the initial size of the sample images so there is no need for another preprocessing stage.

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Based on the pre-trained network, a new convolutional ANN was compiled, which has been already trained on the images of diamond crystals with predetermined quality classes. Since the number of images was relatively small, it was decided to use an input data generator that changes the position and orientation of the object in the image. It also made it possible to solve the problem of recognizing the crystal in the image regardless of its location. During the development phase, the sample of images for training was divided into 3 main classes, namely good quality diamond crystals, poor quality diamond crystals, and acceptable quality diamond crystals suitable for industrial use. After that, the activation functions used in convolutional neural networks were considered [25]. The Heaviside step function allows only values above a certain threshold to be put through. This function is often used in convolutional neural networks, but it allows only binary classification, which is not suitable for our task. The linear function allows the multi-class classification but has a significant drawback in which the error propagates regardless of the input. The sigmoid function eliminates the disadvantages of the first two types of activation functions, but limits us in the scope of training, since at certain values the function stops or greatly slows down training. The hyperbolic tangent function is the corrected sigmoid function. This feature is very popular, although it has the same problems as the sigmoid. Used when a combination of ANN layers is needed. In this case, the ReLU function was chosen as the activation function, which is a linear rectifier described by the formula:
f ð xÞ ¼

0; x\0 x; x  0

ð2Þ

This activation function allows you to leave some of the neurons in a passive state, which helps to save resources when working with deep neural networks. During training, the ANN identified a number of features characteristic of the images of each of the classes of diamond crystals. These features can be extracted from the ANN model to simplify further training and add new crystal classes. From a photograph of one of the projections of a diamond crystal, it is impossible to reliably determine which quality class it belongs to. Since the analyzes individual projections of the crystal in turn, it was decided to add another analyzer that receives data from the output layer of the ANN and analyzes it. The final classification is made when 4 or more results coincide for the projections of one crystal. The task is also simplified by the fact that the snapshots of the projections of one crystal are in the same directory of the storage device. 3.2

Results of the Diamond Crystal Classification

The use of the input data generator made it possible to obtain 12600 images for training the ANN and 2100 images for checking the quality of training, belonging to 3 quality classes. The input data for the generator were images of 7 projections of 100 crystals of

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each class. As a result of the generator’s operation, for each image, 6 images were created with a crystal displacement in space and with a change in orientation in space. Before transferring the images to the input layer of the neural network, they underwent preprocessing. Canny’s edge-finding algorithm was applied to all images and a template was compiled to reduce noise and more accurately analyze chroma and transparency. This kind of preprocessing is applied to all images that came from the image generator described earlier. Figure 6 shows the image processing flow for the color and shape classification.

a)

b)

c)

d)

Fig. 6. Preprocessing of the diamond image. a). The original RGB image, b). The result of applying the Canny’s algorithm, c). Image template, d). Applying the template to the original image.

The ANN was trained for 20 epochs, which made it possible to achieve recognition and classification accuracy of 99.83%. Testing the classifier with an artificial noise overlay on the images made it possible to determine that when the image is noisy by 25%, the detection probability drops to 85%, which can still be considered as a good result. Since the ANN was coded in Python, the features allocated to it to describe each of the classes, like the network itself, can be saved on any device and can be restored or updated later without much effort on the part of the user.

4 Discussion An analysis of the concepts of systems theory shows that at present it is advisable to design production systems on the basis of adaptive methods that accumulate data from sensors, cameras, and video cameras. It is advisable to store the obtained data in corporate databases and use it to build and continuously improve the classification model. The use of shallow ANN in solving a simpler problem of separating two classes of crystals “Boart” and “Gem” allowed the authors to achieve a classification accuracy of

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only 0.98% (as shown in [26]). Application of deep convolutional ANN Inception_ResNet_v2 provided a higher classification accuracy of 99.83% for three types of crystals “Boart”, “Gem” and “Near Gem”. Image preprocessing for shallow ANN included morphological image processing to obtain a crystal template and the construction of histograms of crystal image colors. While for Inception_ResNet_v2 it is enough to get a crystal template. An important characteristic of the algorithm is the time it takes to classify each crystal using a trained ANN, which determines the productivity of the diamond sorting production process. The accumulation of data used to train the ANN makes it possible to improve the results of its application in the production process. Therefore, the more complex learning algorithm of the ANN Inception_ResNet_v2 does not lead to the disadvantages of the proposed algorithm for the classification of crystals in the industry.

5 Conclusion The main criteria for determining the quality class of diamond crystals and methods for determining their characteristics are considered. Common models of pretrained neural networks and activation functions used to solve classification problems are considered. The proposed method for automating the classification of diamond crystals by shape, color, and transparency, based on the use of a deep convolutional neural network, can significantly improve the quality of sorting of mining products. The developed software makes it possible to classify a diamond crystal based on photographs of its projections with an accuracy of 99.83%. The proposed solution is part of a hardware-software complex, which also includes 7 cameras and a product sorting system. Further development of this solution is closely related to obtaining a sufficient amount of input data in the form of images of diamond crystals of various quality classes. This solution can be implemented in an existing quality control system as a classification part of the software. As it is flexible for retraining it can be also used for the classification of other objects and not only for the mining industry. The most important criterion for training data is that there should be a lot of samples of previously classified images.

References 1. Gorbunova, E.V., Korotaev, V.V., Chertov, A.N.: Vozmozhnosti sortirovki almaznogo syr’ya optiko-elektronnymi metodami. [Possibilities of sorting rough diamonds by optoelectronic methods.] Nauchno-tekhnicheskij vestnik informacionnyh tekhnologij, mekhaniki i optiki [Sci. Tech. Bull. Inf. Technol. Mech. Opt.] 4(80), pp. 13–17 (2012). (In Russian) 2. Breeding, C.M., Shigley, J.E.: The “Type” classification system of diamonds and its importance in gemology. Gems Gemol. 45(2), 96–111 (2009)

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3. Zavyalova, M.A. Obidin, Yu.V.: Bystrodejstvuyushchij kolorimetricheskij metod dlya avtomaticheskoj klassifikacii almazov po cvetu. [Fast colorimetric method for the automatic classification of diamonds by color.] Vestnik Sibirskogo gosudarstvennogo universiteta geosistem i tekhnologij [Bull. SSGA] 2(13), 101–107 (2010). (In Russian) 4. Viktorov, M.A.: Sovremennye klassifikacii almaza: sravnitel’naya harakteristika. [Modern classification of diamonds: a comparative characteristic.] Vestnik Moskovskogo universiteta. Seriya 4. Geologiya [Bull. Moscow Univ. Ser. 4. Geol.] 5(5), 72–75 (2011). (In Russian) 5. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 4th edn. Pearson, London (2018) 6. Ma, X., Li, B., Zhang, Y., Yan, M.: The Canny edge detection and its improvement. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds.) AICI 2012. LNCS (LNAI), vol. 7530, pp. 50–58. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33478-8_7 7. Nguyen, L., Lin, D., Lin, Zh., Cao, J.: Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5. IEEE (2018) 8. Judd, D.B., Wyszecki, G.: Color in Business, Science and Industry. Wiley, New York (1973) 9. Rosin, P.L.: Edges: saliency measures and automatic thresholding. Mach. Vis. Appl. 9(4), 139–159 (1997) 10. Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Comput. Vis. 43, 7–27 (2001) 11. Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 117–156 (1998) 12. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000) 13. Lin, S.: Development of an Effective Algorithm for Automatic Diamond Clarity Grading. The Hong Kong University of Science and Technology Library, Hong Kong (2011) 14. Tammina, S.: Transfer learning using VGG-16 with deep convolutional neural network for classifying images. Int. J. Sci. Res. Publ. (IJSRP) 9(10), 9420 (2019) 15. Ding, Z., Fu, Y.: Robust transfer metric learning for image classification. IEEE Trans. Image Process. 26(2), 660–670 (2017) 16. Theckedath, D., Sedamkar, R.R.: Detecting affect states using VGG16, Resnet50 and SEResnet50 networks. SN Comput. Sci. 1(2), 1–7 (2020). https://doi.org/10.1007/s42979-0200114-9 17. Rajpal, S., et al.: Using handpicked features in conjunction with Resnet-50 for improved detection of COVID-19 from chest X-ray images. Chaos Solitons Fract. 145, 110749 (2021) 18. He, K., et al.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770–778. IEEE, New York (2016) 19. Mutegeki, R., Poulose, A., Han, D.S.: iSPLInception: an inception-ResNet deep learning architecture for human activity recognition. IEEE Access 9, 68985–69001 (2021) 20. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, vol. 17, pp. 4278–4284 (2017) 21 Duong, L.T., et al.: Automated fruit recognition using EfficientNet and MixNet. Comput. Electron. Agric. 171, 105326 (2020) 22. Pak, A., et al.: Comparative analysis of deep learning methods of detection of diabetic retinopathy. Cogent Eng. 7(1), 1805144 (2020) 23. Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE, New York (2009) 24. El Asnaoui, K., Chawki, Y.: Using X-Ray images and deep learning for automated detection of coronavirus disease. J. Biomol. Struct. Dyn. 39, 3615–3626 (2021)

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25. Feng, J, Lu, S.: Performance analysis of various activation functions in artificial neural networks. In: Journal of Physics: Conference Series, vol. 1237, p. 22030. IOP Publishing, Bristol (2019) 26. Malykhina, G.F., Miae, M.A.: Avtomaticheskaya klassifikaciya kristallov s ispol’zovaniem nejronnyh setej [Automatic crystal classification using neural networks]. In: XXII International Scientific and Technical Conference “Neuroinformatics-2020”: Collection of Scientific Papers, pp. 333–340. NRNU, MEPhI, Moscow (2020)

Models of Cyber-Physical Control Systems for Pollution Minimization Technologies Gennady I. Korshunov1,2(&) , Remir I. Solnitsev3 , Natalia A. Zhilnikova2 , and Sergey L. Polyakov2 1

3

Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya Street 29, 195251 St. Peterburg, Russia [email protected] 2 Saint-Petersburg State University of Airspace Instrumentation, 67 BolshayaMorskaya Street, 190000 St. Peterburg, Russia Saint Petersburg Electrotechnical University “LETI”, Professora Popova Street 5, 197376 St. Peterburg, Russia

Abstract. The article proposes an approach to the creation of models of cyberphysical systems to control pollution minimization. The transition from the equations of convection and diffusion known from mathematical physics to the “input-output” form is accompanied by the introduction of control provided by the best available technologies and technological innovations. On the basis of previously developed models to minimize emissions into the air, models of discharges into the aquatic environment are considered. Models of distribution of concentrations of biochemical oxygen consumption, which characterizes the degree of wastewater pollution, and a model of adsorption industrial wastewater treatment from pollutants are given as examples. The adsorption method is considered as one of the examples of the implementation of wastewater treatment technology within the cyber-physical control system for minimizing pollution to adapt technologies to their source. On the basis of the considered models of various types and their interrelationships, a configuration of a cyberphysical control system for minimizing wastewater pollution is proposed, which allows the control process to be adapted to specific regulated treatment parameters. The introduction of technological innovations in the field of adaptation of treatment technologies to the source of pollution demonstrates a promising direction in the development of cyber-physical systems. Keywords: Cyber-physical system
Pollution emissions
Pollution discharges
Best available technologies
Control
Convection
Diffusion equations

1 Introduction The relevance of the tasks of minimizing emissions and discharges of harmful substances is beyond doubt. Strategies in the problematic area of the environmental protection are represented by the creation of a circulation economy, the fight against global warming, and others. The processes for solving these issues are based on technologies to minimize emissions and discharges, including by converting them into secondary © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 442–450, 2022. https://doi.org/10.1007/978-3-030-98832-6_39

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raw materials and reducing the amount of final waste, as well as neutralizing emissions and discharges by converting them into harmless substances. At present, various methods have been developed or have already been tested, some of which have been brought to the level of technology. However, the introduction of technologies for minimizing emissions and discharges of harmful substances into real production processes is taking place at an insufficient pace in comparison with the growth of emerging and anticipated pollution. There are various economic and technical reasons for that. To analyze them and give the necessary recommendations in one article is not possible. At the same time, the concept, methods, and tools of a new class of systems, called cyberphysical, make it possible to distinguish the object and process of pollution as a physical subsystem, in a broad sense, and a cybernetic subsystem containing the achieved and implemented intellectual level of environmental management to minimize pollution. This process is accompanied by the gradual elimination of the “human factor”. This should allow structuring problems and applying mathematical models to accelerate technology adaptation and control pollution minimization processes. The purpose of the article is to develop an approach to modeling cyber-physical systems (CPS) for the management of technologies for minimizing pollution and adapting known technologies to sources of emissions and discharges.

2 Materials and Methods The technologies for minimizing pollution mentioned in the article are included in the guidance on the best available technologies (BAT), joining world experience in their development [1–3]. It should be noted that the degree of their elaboration is different, in some cases BAT do not contain the composition and values of technical characteristics, or they are represented by a minimum set of terms for the composition of contaminants and applied methods. The direct implementation of such BAT is difficult and requires structural and parametric identification to be included in the CPS control loop. Known models of the processes of generation and propagation of pollution in the form of emissions into the atmosphere and discharges into the water environment are represented by differential equations in partial derivatives. Such models are applicable for solving the problems of pollution inventory, but cannot be used directly to control their minimization. To do this, they must be supplemented with control components and brought to the “input-output” form. The mathematical expression for generating the BAT control signal can be derived in terms of the concentration of the contaminant or other parameters related to the concentration functionally. The reduction of such models of distributed systems to the “input-output” form for atmospheric pollution was published in [4]. Further development of the creation of models in the form of “input-output” received both for the atmosphere air and for the water environment.

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Models of Cyber-Physical Control Systems for Minimizing Pollution in the Atmosphere Air

In [5] mathematical models of control objects (CO) are given in the form of models with distributed parameters in the atmospheric air. The formation of CPSs to control the minimization of emissions into the atmosphere air requires bringing these models to the “input-output” form. First consider the distribution of pollution in the atmosphere air for a single source. Having selected a single source of pollution, we can write the equation of turbulent diffusion and convection for it in the form: @y @y @y @y @ @y @ @y þ Vn þ Vg þ Vf ¼ Kn þ Kg @t @n @g @f @n @n @g @g @ @y Kf þ K1
X  K2
y þ @f @f

ð1Þ

where Y(n, η, f, t) - concentration of contaminants, Vn , Vg , Vf - projection of velocity vector of transfer pollutants on axis n; η, f; Kn, Kη, Kf - the components of diffusion coefficient K. The diffusion coefficient is calculated by A. Fick’s formula J ¼ K


@Y @Ns

where J is the flow density of mass, passing through a surface area S, Ns is a normal to S, K1 is the coefficient “transformation” of source material X to pollution, K2 is the coefficient of pollution compensation due to operation of dust and gas traps (DGT) – K2DGT, dry and wet precipitation and chemical transformations, K2P ; K2 ¼ K2DGT þ K2P : Equation (1) becomes definite only after specifying the initial and boundary conditions. When creating a CPS for a micro-district, you can use the equation with known meteorological and other characteristics of the atmosphere air. For a one-dimensional process of pollutions propagation within a micro-district, it was determined in [5]: the convective flow is directed only along the n axis, which makes it possible to make approximate estimates, set boundary and initial conditions. After performing the transformations outlined in [5], the corresponding transfer function will have the form   ðp þ K2 ÞðLn0 Þ YðL; pÞ K1  Vn ¼
1e Xðn0 ; pÞ Vn

ð2Þ

Transfer function (2) allows you to find the “output parameters” Y (L, p) for various disturbances X(t). K2 characterizes the introduced control. Examples of existing and proposed technologies for minimizing emissions are given in [1–3, 5], the use of which in the composition of the CPS is determined by the degree of the model’s elaboration.

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Models of Cyber-Physical Control Systems for Minimizing Pollution in the Water Environment

Mathematical models of distribution for the inventory of pollution and a model of the spatial distribution of concentrations of biochemical oxygen demand (BOD), characterizing the degree of pollution of effluents, are given in [6, 7]. BOD is the most important indicator of water quality in water bodies and characterizes the value of easily oxidized organic substances contained in a unit volume of water. For distributed sources of BOD, the following are accepted as the boundary conditions: at the initial moment, the water did not contain non-conservative substances CBOD(x, 0) in the studied section of the river, and in the initial section of the BOD there was always zero CBOD(x, t). M(t) is the discharge flow, X is the river cross-section. The model describing this process is as follows [8–10]: @CBOD @CBOD M1 ðtÞ þm ¼ K1 CBOD þ X @t @x

ð3Þ

Equation (3) is solved using the Laplace transform. Omitting intermediate transformations, as a result of applying the Laplace transform to the functions CBOD(x, t) and M(t), taking into account that CBOD(x, 0) = 0 and integrating the equation, we obtain (4), where T(x) denotes the time required for an elementary particle to travel the distance x, i.e. CBOD ðx; PÞ K ¼ ð1  eKTðxÞTðxÞp Þ; MðPÞ 1 þ Tp

ð4Þ

Here K characterizes the pollution remedies. Equation (4) establishes the fact that a dynamic phenomenon, the cause of which is the “input” M(t), and the consequence is the “output”—the value of CBOD(x, t). This parameter can be used as input to a private plant-specific pollution minimization control system. Equation (4) establishes that the process of minimizing pollution in an aquatic environment, where the input is M(t), and the output is the value of CBOD(x, t), can be described using a transfer function. SPC for wastewater treatment is presented by the block diagram in Fig. 1.

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Fig. 1. Diagram of a closed-loop purification control system.

Figure 1 denotes: X, Y1, Y2, Y3,…, Y7 are the vectors from constituent discharges—X(t); U is a vector of control signals; L1(p) is a polynomial matrix of transfer functions Wi,j1(p) actuators, connects the input action X(t) with the result of cleaning from pollutants Y1(t); L2(p) is a transfer function matrix Wi,j2(p) the passage of liquid from the outlet pipe of the cleaning device (sorber) to sensors measuring the concentration Y2(t); L3(p) is a transfer function matrix Wi,j3(p) measuring devices; L4(p), L5(p), L6(p) are transfer functions matrices Wi,j4,5,6(p) transforming measuring devices; Y5*(t) is the vector of maximum permissible concentrations; Z(t) is the vector of error signals; L7(p), L8(p) are transfer functions matrices Wi,j7,8(p) amplifying-converting and control devices,i; j ¼ n
n. The most common methods for purifying wastewater from pollution, in particular, from heavy metal ions, are methods of physical and chemical purification, widely presented in articles [11–17] and in BAT [1–3]. In the process of adsorption, the pollutants dissolved in the liquid are selectively absorbed by a solid absorber (sorbent). This process is a transition of a substance from a liquid to a solid phase and includes three main stages: external diffusion, internal diffusion and sorption of the pollutant on the sorbing surface. The adsorption process can take place in both static and dynamic modes, the choice of which depends on the technology used and the properties of the sorbent selected for absorption pollutant. It should be noted that the adsorption process can take quite a long time; therefore, it is most efficient to carry out it in a dynamic mode with low flow rates. The works [18–24] present the results of the influence of sorbents on the considered pollutant - lead. Let us consider a one-component model of wastewater treatment from a specific pollutant lead by the adsorption method. The dynamic model of the adsorption process must be considered taking into account the main factors: material balance (5), kinetics (6), and equilibrium (7)

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@c @c @a þe þ ¼0 @x @t @t

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ð5Þ

@a ¼ bðc  c Þ @t

ð6Þ

c ¼ f 1 ðaÞ

ð7Þ

V – the rate of fluid flow through the sorbent bed; x – height of the sorbent layer in the column; e – sorbent bed porosity; b – volumetric mass transfer coefficient. The adsorption process usually takes place in a column with a vertical feed of waste water through a sorbent bed. One of the main parameters of the process is the adsorption time t, which determines the concentration of the pollutant in the liquid c and the concentration in the sorbent itself a. To bring the model to the “input-output” system, it is necessary to determine the effect of the sorbent (absorber) layer on the concentration of the pollutant c* is the lead in the liquid. This problem can be solved only for a specific case: a sorbent-pollutant, this is due to the selective influence of sorbents on specific pollutants. Based on Eqs. (5), (6) and (7), we obtain a simplified expression for the transfer function of the adsorption process (8): cðp; xÞ ¼1 c ðp; 0Þ

  p 1  be pþ

b e

ð8Þ

A model of a cyber-physical control system for minimizing harmful discharges based on adsorption technology can be obtained by including the control model in the “input-output” transfer function. The model provides for the direct formation of the control signal, which ensures the adaptation of the technology to the source.

3 Results The above-mentioned analysis of BAT has shown that their use in production cycles requires adaptation to sources of pollution, as well as the introduction of technological innovations. The currently developed approach to the digitalization of processes finds expression in the form of the creation of a CPS. The article shows that even a detailed modeling of the processes of distribution, monitoring and inventory of pollution, for example, for the atmosphere air and the water environment - respectively (2), (4), (8) is only a necessary condition for solving the problems of minimization contamination. In terms of CPS, such models represent a physical subsystem. For control problems as part of the CPS, such models should be converted to the “input-output” form and supplemented with a control component, as is done in the example (5). The choice of management can be carried out on the basis of BAT or other justified options in the form of technological innovations. The briefly presented examples of minimizing

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organic pollutants by the indirect indicator of CBOD and using an adsorption demonstrate an approach based on the use of CPS.

4 Discussion Obtained results given refer to single sources of pollution and should be extended to more general cases. Further development of models and methods for creating a CPS for controlling pollution minimization technologies will be carried out by adapting the models to the object and transforming them considering the BAT and in each case is carried out within the framework of the CPS concept [25–28]. Modeling the interaction of substances in an aqueous medium based on stoichiometric equations requires additional consideration. Of interest is the experimental development of CPS on water bodies—rivers, subject to industrial discharges. Further studies provide for the creation of a concept at the level of meta-models of CPS, including multicomponent models of the relationship between products and discharges.

5 Conclusions The article describes an approach to modeling cyber-physical systems, considering the chain links as distributed systems, presented in the “input-output” form based on the equations of mathematical physics to control pollution minimization technologies. Further development of such models is carried out using mathematical methods based on integral transformations and represents non-trivial problems. The introduction of the obtained models into the CPS in combination with various types of control leads to the construction of CPS metamodels. From the point of view of technologies for minimizing pollution, the proposed models can be applied in the corresponding problem areas.

References 1. Wastewater treatment in manufacture of products (goods), performance of works and provision of services in large enterprises. In: Information-Technical Handbook on the Best Available Technologies, ITH 8. Russian BAT Bureau, Moscow (2015). (In Russian) 2. Reduction emissions from storage. In: Information-Technical Handbook on the Best Available Technologies, ITH 46-2019. Russian BAT Bureau, Moscow (2019). (In Russian) 3. Communal wastewater treatment using urban centralized systems. In: Information-Technical Handbook on the Best Available Technologies, ITH 10-2019. Russian BAT Bureau, Moscow (2019). (In Russian) 4. Solnitsev, R.I.: Voprosy postroeniya zamknutoj sistemy upravleniya “Priroda–Tekhnogenika” [Questions of building a closed control system “Priroda-Tekhnogenika”]. Izvestiya SPbGETU “LETI” 7, 23–32 (2009). (In Russian) 5. Solnitsev, R.I., Korshunov, G.I.: Sistemy upravleniya “Priroda-Tekhnogenika” [Control systems “Nature-technogenics”]. Politekhnika, St. Petersburg (2013). (In Russian) 6. Zhilnikova, N.A.: Geoinformation modelling system of natural technical complexes for simulation modelling and optimization of load distribution. In: IOP Conference Series: Materials Science and Engineering, vol. 450, p. 062010 (2018)

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7. Zhilnikova, N.A.: Geoinformation modelling of quotas distribution of technogenic load for water users. In: IOP Conference Series: Journal of Physics: Conference Series (JPCS), vol. 1515, p. 052034 (2020) 8. Shishkin, A.I., Antonov, I.V., Yepifanov, A.V.: Geoinformacionnaya modeliruyushchaya sistema normirovaniya dopustimyh sbrosov dlya cellyulozno-bumazhnyh kompleksov [Geoinformation modelling system for norm setting of permissible discharges for pulpand-paper mills.] Vodnoe hozyajstvo Rossii: problemy, tekhnologii, upravlenie [Water Sector Russia] 1, 66–80 (2011). (In Russian) 9. Shishkin, A.I., Antonov, I.V., Yepifanov, A.V.: Normirovaniye sbrosa stochnykh vod pri proizvodstve tsellyulozy i produktov yeye pererabotki s primeneniyem GIS tekhnologiy [Normirovaniye of dumping of sewage by production of cellulose and products of its processing with application of GIS of technologies] Tsellyuloza. Bumaga. Karton. 1, 66–74 (2012). (In Russian) 10. Bolshakov, N.: Use of math models for optimization of biogenic elements removal process and modernization of wastewater treatment plants. Water Ecol. 4(44), 36–44 (2010) 11. Semenov, A.F., Lieberman, E.Y., Kolesnikov, V.A.: Obzor sovremennykh metodov ochistki stochnykh vod galvanicheskikh proizvodstv ot ionov tyazhelykh metallov [Review modern methods of wastewater treatment of electroplating plants from heavy metal ions]. Uspekhi v khimii i khimicheskoy tekhnologii 4(34), 83–85 (2020) 12. Kolesnikov, V.A., Il’in, V.I., Kolesnikov, A.V.: Electroflotation in wastewater treatment from oil products, dyes, surfactants, ligands, and biological pollutants: a review. Theor. Found. Chem. Eng. 53(2), 251–273 (2019). https://doi.org/10.1134/S0040579519010093 13. Kolesnikov, A.V., Milyutina, A.D., Ladygina, Y.S., Kolesnikov, V.A.: Development of electroflotation technology for extraction of anionic surfactants and nonferrous metal ions from wastewater produced at electroplating industries. Russ. J. Appl. Chem. 91(7), 1081– 1089 (2018) 14. Lin, J.-Y., Mahasti, N.N.N., Huang, Y.-H.: Recent advances in adsorption and coagulation for boron removal from wastewater: a comprehensive review. J. Hazard. Mater. 407, 124401 (2021) 15. Tegladza, I.D., Xu, Q., Xu, K., Lv, G., Lu, J.: Electrocoagulation processes: a general review about role of electro-generated flocs in pollutant removal. Process Saf. Environ. Protect. Trans. Inst. Chem. Eng. Part B 146, 169–189 (2021) 16. Sahu, O., Mazumdar, B., Chaudhari, P.K.: Treatment of wastewater by electrocoagulation: a review. Environ. Sci. Pollut. Res. 21(4), 2397–2413 (2013). https://doi.org/10.1007/s11356013-2208-6 17. Linares-Hernández, I., Barrera-Díaz, C., Roa-Morales, G., Bilyeu, B., Ureña-Núñez, F.: A combined electrocoagulation-sorption process applied to mixed industrial wastewater. J. Hazard. Mater. 144(1–2), 240–248 (2007) 18. Kostin, A.V., Mostalygina, L.V., Bukhtoyarov, O.I.: Izucheniye mekhanizma sorbtsii ionov medi i svintsa na bentonitovoy gline [Study of the mechanism of copper and lead ions sorption on bentonite clay]. Sorbtsionnyye i khromatograficheskiye protsessy 6(12), 949– 957 (2012). (In Russian) 19. Telkhozhayeva, M.S., Seilkhanova, G.A., Rakhym, A.B., Imangaliyeva, A.N., Akbayeva, D. N.: Sorption of lead and cadmium ions from aqueous solutions using modified zeolite AlFarabi. Chem. Bull. Kazakh Univ. 4(91), 16–22 (2018) 20. Terek, S.V., Grabelnykh, V.A., Levanova, E.P., Russavskaya, N.V.: Novaya adsorbatsionnaya tekhnologiya ochistki stochnykh vod ot soyedineniy svintsa [New adsorption technology for wastewater treatment from lead compounds]. Vestnik IrGTU 7(102), 121– 126 (2015). (In Russian)

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21. Tamarkina, Yu.V., Shendrik, T.G.: Burougol’nye adsorbenty dlya ulavlivaniya ekotoksikantov iz vodnyh i vozdushnyh sred [Lignite adsorbents for capturing ecotoxicants from water and air environments]. Ekologiya i promyshlennost’ 4, 45–48 (2010). (In Russian) 22. Simonova, V.V., Isaeva, L.N., Tamarkina, Yu.V., Shendrik, T.G., Kucherenko, V.A.: Adsorbciya svinca na burom ugle, aktivirovannom gidroksidom kaliya. HTT 2, 47–49 (2010). (In Russian) 23. Klimov, E.S., Buzaeva, M.V.: Prirodnye sorbenty i kompleksony v ochistke stochnyh vod [Natural sorbents and complexones in wastewater treatment]. UlGTU, Ul’yanovsk (2011). (In Russian) 24. Redinova, A.V., Grabel’nyh, V.A., Levanova, E.P., Korchevin, N.A.: Izvlechenie ionov tyazhelyh metallov iz vodnyh rastvorov serosoderzha-shchimi polimernymi sorbentami [Extraction of heavy metal ions from aqueous solutions by sulfur-containing polymeric sorbents]. Vestnik IrGTU 1(72), 113–116 (2013). (In Russian) 25. Solnitsev, R.I., Korshunov, G.I., Petrushevskaya, A.A., Paranichev, A.V.: Kiberfizicheskie sistemy v ekologicheskoi bezopasnosti i geomonitoringe avtotransporta [Cyberphysical Systems in Environmental Safety and Geomonitoring of Motor Vehicles]. SPbGETU “LETI”, St. Petersburg (2019). (In Russian) 26. Solnitsev, R.I., Korshunov, G.I.: Formalization of target and assessment of quality of the cyber-physical control systems for emissions reduction technologies. In: E3S Web of Conferences, vol. 220, p. 01018 (2020) 27. Korshunov, G.I., Solnitsev, R.I., Polyakov, S.L., Frolova, E.A.: Purposeful ensuring energy efficiency and environmental friendliness of waste and review of used local conservation laws. In: E3S Web of Conferences, vol. 220, p. 01023 (2020) 28. Korshunov, G.I., Polyakov, S.L.: Information and thermodynamic fundamentals of cyber physical systems modeling. In: Journal of Physics: Conference Series, vol. 1515, no. 2, p. 022065 (2020)

Robotics Systems Monitoring and Correction by Means of Automatic and Software Control Tatyana S. Katermina(&)

and Maksim V. Sliva

Nizhnevartovsk State University, 56 Lenina Street, Khanty-Mansiysk Autonomous Area – Yugra, Nizhnevartovsk 628605, Russia [email protected]

Abstract. The article describes the problems of robotics systems monitoring and correction in real time. The possibilities of automatic control and correction by adding redundancy to the source systems and the possibilities of a software approach in the remote control mode within the IoT (Internet of Things) are considered. The article discusses both approaches, reveals their advantages and disadvantages, and provides an analysis of situations in which it is recommended to use one or another approach. Autonomous computing systems should be able to automatically monitor and adjust their work in case of various external actions. The control and correction of automatic control systems involve the introduction of redundancy at a certain level, and the method of redundant variables not only introduces redundancy to correct errors under various disturbances, but also manage the system parameters to minimize the amount of error, such as the speed of replay of the trajectory. It also describes the possibilities of software solution of a number of problems arising at simultaneous user access to devices, problems with synchronization of control of the device, and in the temporary absence of connection with the device. Keywords: Management
Control
Correction
Method of redundant variables
IoT
Software control
Robotics systems

1 Introduction During the operation of any robotics system, failures are possible due to internal factors and environmental factors. Internal factors include, for example, inaccuracy of calculations, errors made during programming. External factors have a diverse nature by the type of their occurrence. For example, an external factor for a robotics system can be an untimely receipt of a control signal if the system is controlled remotely. Other external factors of influence can include failures, interference, surface irregularities, material compaction during cutting, etc. The ability to control and manage robotic systems has been at the forefront of science for decades. New science and industrial areas are constantly emerging that make robotic use necessary. It is worth mentioning the national scientists who take an active part in developments in this field of science. This article reflects the method of redundant variables for the control and correction of information systems created by M.B. Ignatiev [5–7, 15], © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 451–460, 2022. https://doi.org/10.1007/978-3-030-98832-6_40

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V.I. Kiyaev, O.N. Granichev [3], V. Gorodetsky [4], as well as I. Kozhemyakin et al. [8] have shown in their works the possibility of using internal redundancy of a system constructed by means of a multi-agency approach. V. Zaborovsky et al. [14, 16] are developing a network-centric approach within the framework of cyber-physical systems. The problems of safety, reliability, and control of robotic systems come to the fore in the papers [1, 13]. The principles of device interaction within the Internet of Things systems are also often considered [2, 9, 10]. If the system is controlled remotely, it means that such control depends on many conditions. Such as, for example, the stability and speed of communication channels, the ability to control from several devices at once, which may also have different characteristics. Autonomous computing systems should be able to automatically monitor and adjust their work in case of various external actions. In this case, the system must have hardware or software redundancy.

2 Software Correction Approach in IoT Systems Today Internet of Things (IoT) actively enters the lives of many people. If one looks at a schematic representation of such a system (see Fig. 1) [12], three parts can usually be distinguished: – hardware part of control devices (Arduino, sensor); – software server part (server, Raspberry Pi); – software client part (Android client, web client).

Fig. 1. IoT system scheme.

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In general, it is possible to construct the following sequence of data transmission from the user to a specific device: – the user changes the parameter using the application or site; – the data is sent to the main server of the “smart room”; – according to internal data, the necessary intermediate hub is located, to which the desired device is connected; – the data received from the user is sent to the found intermediate hub; – in the intermediate hub, data is sent to the proper device. Conversely, from device to user, the following sequence may be used when transmitting data: – – – –

data is sent from the device to an intermediate hub; data is sent from the intermediate hub to the main server of the “smart room”; the server internally finds all interested users and sends them data from the device; in the application or on the user’s webpage, the data is converted into visual or textual information.

This sequence of actions shows that several users can receive information from the same device. Ideally, each user should be able to configure the workspace of a “smart room” in the design mode, indicating the specific devices that need to be controlled or information from which you need to receive. Therefore, it is necessary to define the functionality of the system being created in order to consider the separation of the actions of an ordinary user and the system administrator (see Fig. 2).

Fig. 2. Use case diagram of system users’ actions

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As you can see from the diagram, an ordinary user can go to the system’s main page, log in and go to his page, where he selects a device and controls it (or monitors its status). At the same time, the system must remember which devices the user has worked with in order to show them at the next session. The system administrator, in addition to the actions of an ordinary user, can also add a device to the system and configure its characteristics. It is also up to the administrator to add new system users. Based on the above, we can identify the following problems to be solved: – multiple users try to control one device simultaneously; – the user attempts to control a device that ceases to be available and then becomes available again, but the completed actions of the user are ignored; – the user controls the device with feedback, and there is a synchronization problem. The simplest way to solve these problems is through software. To solve the first problem, it is easier to organize a queue with a check of the level of access to the released device. The second problem solving requires verification of the performed actions: the actions done by the user are recorded (on the server side), and the actions performed by the device on behalf of this user are recorded too, synchronization occurs with each connection, and, if there are inconsistencies, missed actions are performed. When solving the third problem, it should be considered that the user can perform some operations with the device, and, at the same time, the device itself can transmit data about its current state. Therefore, by analogy with the solution of the previous problem, you can organize a bidirectional check, depending on the current activity: the user or the device.

3 Control and Correction of the Robotics Device in Case of Redundancy Redundancy - hardware, software, or time - is often used to control and correct robotic systems. Depending on the nature of the purposes for which a robotic system may be designed, redundancy can be introduced and used in it in different ways. If the system is designed for a single purpose, redundancy is most often artificially introduced into the system. When the system is designed for more than one purpose, a natural redundancy can be used to improve the accuracy and reliability of the system [5–7]. The method of redundant variables involves the injection of redundancy into the original problem and the construction of equivalent equations in which certain restrictions are imposed on the redundant information. The structure of equivalent equations of systems with structured uncertainty contains arbitrary coefficients that can be used to adapt the system to various changes in order to increase the accuracy and reliability of the functioning of systems, their survivability in the flow of changes. As a simple example, consider a system with argument correction for a generator whose variables satisfy the circle equation.

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x2 þ y2 ¼ R2 :

ð1Þ

The equations with arbitrary coefficients are derived from the method of redundancy variables and have the following form


dx dt ¼ U1 y dy dt ¼ U2 x

ð2Þ

Arbitrary factor U1 can be used to correct generator 1, as shown in Fig. 3, where there are two servo mechanisms, and where f1 and f2 are interference, x and y are servo mechanism errors. The “correction signal” block calculates the correction signal D ¼ c2  Dx2  Dy2 ;

ð3Þ

where c is the norm, the specified accuracy, and the error signal D should tend to zero. The transfer functions of tracking systems are of the first order, they can have different time constants. In this way, the speed of generating control signals for servos is regulated, which reduces the error of reproducing the circle. The system in Fig. 3 simulates a moving robotic system. There are interferences f1 and f2 in Fig. 3, acting on the drives, including interferences due to surface irregularities. Figure 4 shows the model scheme in MatLab. Scheme designations: – – – – –

Error waveform—error signal detection unit; Generator—a unit that generates setting actions for servomechanisms; g—given accuracy; Signal Builder1/Signal Builder2—error signals; Servosystem1/Servosystem2—tracking system units.

Figure 5 shows the oscillograms of the operation of the system with an argument correction for identical servo mechanisms for different speeds (U1) in a ratio of 1:2:3:4:5. For multi-dimensional dynamic systems, the requirement to change the speed of the driving action can be mathematically formulated as a requirement to have the minimum possible difference between the tolerable value of the error vector module c and its actual value at each time, i. e. D ! 0. It can be concluded that in an argument-corrected system, the speed of reproduction of the reference effect coefficient can be adjusted and used in such a way that the error value decreases, and the system stabilizes. Thus, the problem of providing a given geometric shape regardless of the noise and dynamic properties of the actuating systems is solved. An argument correction can be used both to ensure maximum reproduction accuracy of a given form and to ensure maximum speed. Argument control can be used to give some dynamic characteristics to the output signals of the programming device. The argument may be adjusted either inside the programming device, on the basis of a priori information about the required dynamic characteristics of the signals, or on the basis of feedback signals received from an object controlled by a programming device.

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Fig. 3. Scheme of the system operation with correction by argument

Fig. 4. A MatLab model of a system with correction by argument

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Fig. 5. Oscillograms 1–5. The servomechanisms have the same characteristics, the value of the time constant of the tracking system is T = 80.

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4 Results The article shows the possibility of applying the method of redundant variables for the control and correction of robotic systems. The results of the experiments are as follows: • The ability to control the speed and direction of the robot movement by introducing redundancy and imposing restrictions on the built extended system. • The ability to control and report system errors by means of restrictions. • The ability of using the received error signal for the system correction via feedback. • The ability of correcting the access and user actions by means of software restrictions using software redundancy.

5 Discussion The paper [11] describes the addition of integral control schemes constructed by genetic algorithms to robot control systems. This technique can achieve optimum resource-efficient results, since genetic algorithms, as an optimization tool, have long proven to be an excellent choice. Artificial redundancy is thus introduced. The paper [3] shows the use of multi-agent technologies in swarm robotics. In this case, it is possible to use both internal redundancy inherent in the system itself and IoT technologies. The multi-agent approach is also described in [4]. The network-centric approach to the distribution and exchange of information within a robotic system is considered in [16]. This approach is very close to the IoT ideology. The methodologies discussed above use some form of natural or artificially added redundancy for control and correction. In the future the authors of this paper plan the possibility of combining the considered methods.

6 Conclusion The article considers the methods of control and correction of robotics systems of automatic and software control. The control and correction of automatic control systems involve the introduction of redundancy at a certain level, and the method of redundant variables not only introduces redundancy to correct errors under various disturbances, but also manage the system parameters to minimize the amount of error, such as the speed of replay of the trajectory. It also describes the possibilities of software solution of a number of problems arising at simultaneous user access to devices, problems with synchronization of control of the device, and in the temporary absence of connection with the device.

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References 1. Bolgov, A.A., Ermakov, S.A., Parinova, L.V. (eds.): Internet of things networks predictive risk assessment method and security management. In: IOP Conference Series: Materials Science and Engineering. Krasnoyarsk Institute of Physics and IOP Publishing Limited, Krasnoyarsk (2020). https://doi.org/10.1088/1757-899X/862/5/052035 2. Dmitriev, A.S.: Direct chaotic communications and active RFID tags for Internet of things and Internet of robotic things. In: Radioelectronics. Nanosystems. Information Technologies, pp. 313–322 (2018). https://doi.org/10.17725/rensit.2018.10.313 3. Erofeeva, V., Granichin, O., Kiyaev, V.: Multi-agent based adaptive swarm robotics control in dynamically changingand noisy environments. In: Superkomp’juternye dni v Rossii Trudy mezhdunarodnoj konferencii [Supercomputer days in Russia Proceedings of the international conference], pp. 808–813 (2016) 4. Gorodetsky, V.: Multi-agent autonomous group control in collective robotics-based assembly. In: CEUR Workshop Proceedings, Moscow, pp. 95–110 (2020) 5. Ignat’ev, M.B., Katermina, T.S.: Chaos control and uncertainty. In: XIX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 449–452. Institute of Electrical and Electronics Engineers Inc. Publ., St. Petersburg (2016). https://doi.org/10. 1109/SCM.2016.7519810 6. Ignat’ev, M.B., Korshunov, A.V., Pyatishev, E.N.: Micromechanical robotic systems problems. In: Proceedings of SPIE — the International Society for Optical Engineering. Proceedings of the 1999 Indo-Russian Workshop on Micromechanical Systems, pp. 59–76. SPIE – International Society for Optical Engineering Publ., New Delhi (1999) 7. Ignat’ev, M.B.: The study of the adaptational phenomenon in complex systems. In: Computing Anticipatory Systems — CASYS’05: Seventh International Conference on Computing Anticipatory Systems, Liege, pp. 322–330 (2006) 8. Kozhemyakin, I., Semenov, N., Ryzhov, V., Chemodanov, M.: Multi-agent control system of a robot group: virtual and physical experiments. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2019. LNCS (LNAI), vol. 11659, pp. 40–52. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26118-4_5 9. Mentsiev, A.U., Gatina F.F.: Internet of Things and data analysis in agriculture. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing Ltd, Krasnoyarsk (2020). https://doi.org/10.1088/1755-1315/677/3/032099 10. Petrenko, S.: Cyber resilient platform for Internet of things (IIOT/IOT)ed systems: survey of architecture patterns. In: Voprosy Kiberbezopasnosti, 2(42), 81–91 (2021). https://doi.org/ 10.21681/2311-3456-2021-2-81-91 11. Rashad, S.A., Sallam, M., Bassiuny, A.M. (eds.): Control of master slave robotics system using optimal control schemes. In: IOP Conference Series: Materials Science and Engineering. Kobry Elkobbah, Cairo (2019). https://doi.org/10.1088/1757-899X/610/1/ 012056 12. Sliva, M.V.: Using Node.js as a Smartroom Platform. In: Tradicii i Innovacii v Obrazovatel’nom Prostranstve Rossii: Materialy VII Vserossijskoj Nauchno-Prakticheskoj Konferencii, pp. 49–52. Izdatel’stvo Nizhnevartovskogo Gosudarstvennogo Universiteta Publ., Nizhnevartovsk (2018). (In Russian) 13. Tahsien, S.M., Karimipour, H., Spachos, P.: Machine learning based solutions for security of Internet of Things (IoT): a survey. J. Netw. Comput. Appl. 161, 102630 (2020). https://doi. org/10.1016/j.jnca.2020.102630

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14. Muliukha, V., Ilyashenko, A., Zaborovsky, V. (eds.): Cyber-physical approach to the network-centric robotics control task. In: AIP Conference Proceedings. American Institute of Physics Inc., Pizzo, Calabro (2016). https://doi.org/10.1063/1.4965420 15. Vorobev, G.M., Ignatev, M.B., Katermina, T.S.: Retention of plasma column in tokamak. In: International Conference “Stability and Control Processes” in Memory of V.I. Zubov (SCP), pp. 216–218. (2015). https://doi.org/10.1109/SCP.2015.7342098 16. Zaborovsky, V., Guk, M., Muliukha, V., Ilyashenko, A.: Cyber-physical approach to the network-centric robot control problems. In: Balandin, S., Andreev, S., Koucheryavy, Y. (eds.) NEW2AN 2014. LNCS, vol. 8638, pp. 619–629. Springer, Cham (2014). https://doi. org/10.1007/978-3-319-10353-2_57

Using Loginom Low-Code Platform for the Modeling of LTV Site Subscriber Nikolay B. Paklin1 , Igor A. Katsko2(&) and Elena V. Kremyanskaya2

,

1

2

G.V. Plekhanov Russian University of Economics, Stremyanny Lane 36, 117997 Moscow, Russia [email protected] Kuban State Agrarian University named after I. T. Trubilin, 13 Kalinina Street, 350044 Krasnodar, Russia [email protected]

Abstract. The article focuses on the development of a methodology for assessing the long-term value of a customer (LTV—Lifetime value; CLV— Customer Lifetime Value) subscribed to a thematic web resource. About 90% of the time spent on building the model, from data collection to data usage, is taken by the ETL (Extraction, Transformation, Loading) process. To simplify the ETL process, it is proposed to use the low-code approach implemented in the Loginom analytical platform. Currently, when considering algorithmic marketing, an increased interest has arisen (returned) in basic (parametric) statistical models, which are based on intuitive assumptions. In our case, survival models are used to predict the LTV subscriber, which allows us to obtain the most accurate estimates. The Cox regression model is used as a base model to predict the likelihood of remaining an active subscriber. The proposed approach can become the basis for developing automated content distribution strategies that optimize available resources and are aimed at maximizing targeted actions. Keywords: LTV
CLV
ETL
Loginom
Long-term customer value Mailings automation
Cox regression
Survival analysis




1 Introduction The article addresses the personalized marketing challenge—developing a methodology for assessing the long-term value of a client subscribed to a thematic web resource in order to form optimal strategies for working with the existing subscriber base, which is much cheaper than attracting new customers [2, 24]. To simplify the modeling process, it is proposed to use a low-code approach (which does not require coding), implemented in the Loginom analytical platform, which includes such object-oriented and functional modeling capabilities as inheritance and sub-models [17, 19]. Analysis of events developing in time is carried out in many areas of human activity. Data of this kind are studied by medicine, economics, engineering, biology, demography, insurance, and industry [3, 4, 11]. In recent years, there has been an interest increase in similar research in electronic marketing [12]. Large amounts of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 461–472, 2022. https://doi.org/10.1007/978-3-030-98832-6_41

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information on the websites of various marketplaces (e-commerce platforms) today have led to understanding the possibility of using data to increase profits, to advertise to a target audience by selecting that satisfies certain mechanisms (targeting). Attracting new customers can cost 5–10 times more than retaining existing ones. In addition, the probability of selling your goods and services to new clients is several times lower than selling them to existing ones [6].

2 Review of Existing Solutions Numerous practical experiments have proven that an effective way to manage a customer base at a strategic level is to focus on the LTV metric—long-term customer value [12]. There are several classes of LTV models [17]. The most effective ones are based on two indicators that need to be evaluated by statistical methods—the probability of a client leaving pk and an estimate of the expected income mk at the time tk . The forecasting horizon in the general case is infinite, but as a rule, not less than a year. In fact, it is possible to formulate a multi-criteria problem of maximizing profit when changing the influence of indicators that form a client’s LTV. Analysis of the customer base for identifying customer churn and predicting customer behavior involves considering the hidden depletion of their aggregate or marketing role assessment [6, 21]. Typically, churn models (customer depletion) do not reflect the impact of the role of marketing, and conversely, marketing exposure models do not take customer churn into account. Incorporating time-dependent covariates into the structure of the attrition model allows the aggregation of the client population attrition model and the marketing role model to be combined into a single schema, which, among other things, allows marketing to influence the ‘lifetime’ of customers. The application of the LTV forecasting methodology in retail, food sector, services and e-marketing provides different profit margins and creates the need to use different indicators. Thus, to calculate the site subscriber LTV, you need to coin the following terms and concepts: • subscriber—a user of a specific site, identified by PID; • stream—a stream of data generated in relation to a subscriber (sending letters), as well as a stream of data generated by the actions of the subscriber himself (opening a letter, clicking, etc.); • date of relevance in relation to the stream—the date until which (not inclusive) the entire data stream of subscribers is considered; • non-clicker—a subscriber who has not made a single click on the date of relevance • clicker—a subscriber who made at least one click on the date of relevance.

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3 The Developed Technique For the task of automating mailings, we have a stream that is generated, on the one hand, in relation to the subscriber, and on the other hand, by the actions of the subscriber himself. LTV was taken as the expected number of clicks that a subscriber will make during its entire life cycle. Taking into account the introduced concepts, the predicted total value of the i-th subscriber LTVti at time t depends on the expected number of clicks Click it and the probability of remaining an active subscriber Pit by the period t: LTVti ¼ Clickti
Pit :

ð1Þ

The total value of the i-th subscriber (within one web resource) is determined as the sum over all periods: LTV i ¼

X Ti t¼1

LTVti ;

ð2Þ

where Ti is the expected ‘lifetime’ of the subscriber. The Clickti , Pit , Ti values are evaluated on historical data. With the described approach, a two-dimensional vector of possible errors in LTV estimation is formed: E ¼ ðe1 ; e2 Þ;

ð3Þ

where e1 is an error associated with predicting the probability of remaining an active subscriber until period t  Pt , e2 is an error related to predicting the number of clicks in period t. Client Capital CC is the total of all future cumulative values of all subscribers to a website: CC ¼

Xn i¼1

LTV i :

ð4Þ

Subscribers who have unsubscribed, as well as subscribers who have not been streaming for a long time, should probably be deducted from the CC client capital. The impact of errors diminishes at the aggregate level of the customer equity estimate and increases at the granular level of an individual subscriber’s LTV estimate. The predicted LTV values, which are, in fact, the subscriber’s potential, can only be realized if they are influenced by sending emails. It is assumed that the intensity of the impact (frequency of sending and the number of letters) will remain approximately the same. The technique implies the construction and subsequent use of separate models that calculate the predicted LTV for the ‘clicker’ and ‘non-clicker’ segments. For any web resource, you need to build your own models. They need to be updated (rebuilt) in each period, in the light of new data. LTV for the clicker segment is estimated k months ahead, for non-clickers only 1 period of time ahead.

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Let’s take a closer look at the algorithm for building a model for the ‘clicker’ segment. The calculation consists of two steps: directly building a model and obtaining forecasts. To form the sample, actions on subscribers are used from the moment of start to the date of relevance 1 (see Fig. 1). Next comes the observation of the event for 12 months, and the month in which the event occurs is recorded. Types of events that were investigated are as follows: unsubscribing; the last click (best result); opening a letter; the last click or opening the letter.

Fig. 1. Sampling scheme.

Precleaning of the training sample. The training sample is divided into 6 segments (see Fig. 2). Segment s1 ‘Abnormal behavior’ includes subscribers: – with an extremely high number of clicks (deviation 5-sigma); – with the number of clicks exceeding the number of sent emails; – with the ratio of email openings to clicks of more than 100 percent. Segment s5 included subscribers with a long ‘tail’ of actions after the first unsubscribe. They were recognized as reactivated and required additional preprocessing. Each record of the training set represents a subscriber profile as of the date of relevance. This is a list of characteristics calculated according to certain rules. The profile can be expanded to include additional features in the model. The minimum required list of profile characteristics includes such indicators as total actions; unique actions; domain group; channel; CTR, number of clicks; number of letters; number of openings; days from the last click; days from the first click; days from the last opening; days from the last action; average intensity of clicks per day; average intensity of letters per day; age in days. Algorithm for preprocessing the fields ‘Domain group’, ‘Channel’, ‘Days from last opening’. For the ‘clicker’ segment, the Days Since Last Opened field is the only continuous field that can contain gaps. This happens if the openings in the stream were not recorded for the subscriber. Therefore, for the correct handling of missing values, it is necessary to convert the field to a string, highlighting the gaps in a separate Missing category. It is possible to split the range of field values into several equal intervals with open boundaries, but the recommended method is optimal binning. It bins the output variable based on WoE maximization and encodes the field with WoE index values. In Python, these algorithms are available in the optbinning library (http://gnpalencia.org/ optbinning).

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Fig. 2. Segmentation for ‘clickers’.

For categorical fields Domain group (domain_group) and Channel (acq_chan), optimal quantization is combining similar values in terms of influence on the event. Further, the following sequence of actions is assumed: • form a training sample from the profiles of subscribers of the main segment and the corresponding event markup; • preprocess the Domain group, Channel, Days since last opening fields; • build a survival-model on the training sample and calculate the quality metric. Figure 3 shows a data preprocessing script that implements the above sequence of actions (ETL process) in the Loginom analytic platform. Figures 4 and 5 show the ‘Merge’ and ‘Sampling’ scenario sub-models which implement the ideology of objectoriented modeling [19]. As a result of the preprocessing script execution, the subscriber profile is formed. Next, a model was built for estimating the expected lifetime of a subscriber. As mentioned above, the analysis of events developing in time has a long history, primarily in medicine, and was developed to assess the life expectancy, including those censored on the right (when there is no way to trace the life expectancy of subjects outside the dimension). In 1958, Kaplan and Meyer showed that it was possible to give a nonparametric estimate of the survivor function (the probability that the event time is greater than t). If there are no censored data (for which it is not known whether an event has occurred or not), the Kaplan-Meier (KM) estimate is simply the fraction of observations in the sample with event times greater than t. If there are censored data among the observations, there can be considered such time intervals t1 \. . .\tj \. . .\tk where at each boundary tj the subjects did not experience the event. Let their number be equal to dj from nj. Then the estimate of the KM survival function will be:

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Fig. 3. A scenario of preprocessing source files.

Fig. 4. Sub-model ‘Merge’.

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Fig. 5. Sub-model ‘Sampling’.

  dj SðtÞ ¼ PðT [ tÞ ¼ Pj:tj \t 1  nj

ð5Þ

Thus, the statistical ideology of survival analysis allows us to estimate its duration both with and without censorship. In the general case, in the analysis of survival, a function of reliability (survival) is introduced [3]: SðtÞ ¼ PðT [ tÞ ¼ expðH ðtÞÞ;

ð6Þ 0

where H ðtÞ ¼  R t0 hðzÞdz is the aggregate risk function, hðtÞ ¼  SSððttÞÞ is the intensity (failure) function. The logarithm of the risk function, considered as a linear function of the covariates (independent variables affecting the outcome) and the logarithm of the baseline risk, depending on time, is called the Cox proportional hazards model (Cox regression): lnðhðt=xÞÞ ¼ lnðb0 ðtÞÞ þ

n X

bi ðxi  xi Þ:

ð7Þ

i¼1

For the ‘clicker’ segment, a class of statistical survival models is used—Cox regression with a set of independent covariates [17]. The quality of the model (the possibility to use it for forecasting) is usually assessed using the Fisher concordance coefficient, which shows the consistency of the opinions of m experts (in our case, the obtained forecast on the model and the available data) [9, 13]: W¼

S : m2 ðn3  nÞ=12

ð8Þ

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It represents the ratio of the actual sum of squared deviations of m sequences of ranks (S) to the maximum possible sum of squared deviations under consideration, that is, in fact, the ratio of variances, therefore, W 2 ½0; 1. The value W = 0.5 corresponds to the random guessing model.

4 Numerical Experiment on Real Data Data Characteristics. The technique was developed and tested on the data of two thematic resources from March 2018 to February 2021. The total volume of stream data is more than 1.4 billion lines. Data analysis showed that only 15% of subscribers are active after a year, that is why it is enough to take 12 months as a forecast period for LTV. Median estimates from past behavior were taken as the expected number of clicks, and they showed the best quality in retro tests. As a result of applying the proposed technique to the available data, a multiplier estimate of the Kaplan-Meier survival rate (see Fig. 6) and Cox regression (see Fig. 7) were obtained. The value of the concordance coefficient 0.76 allows us to assert that the model can be used for forecasting and is of above average quality. Algorithms from the lifelines Python library (https://lifelines.readthedocs.io/en/latest) and from the Loginom analytical platform (https://loginom.ru) were used.

Fig. 6. Kaplan-Meier survival curve.

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Fig. 7. Cox regression coefficients and confidence intervals.

Retro tests showed that the relative error in the overall LTV forecast was 2% for the first web resource and 7% for the second. In 71% of cases, the deviation of the predicted and actual lifetimes did not exceed 3 months, and in 29% of cases, this forecast coincided with the fact.

5 Results of the Developed Method Application Application of the developed method for predicting customer behavior on real data made it possible to obtain the following recommendations for practical application. 1. In order to form mailing strategies, it is necessary to combine forecasts into value matrices with measurements of Days from last action—LTV predicted (frequency matrix, Table 1) and historical LTV—LTV predicted (contingency matrix, Table 2). Table 1. The number of days from the last action—LTV predicted (clickers). LTV prediction Days from last action 0 From 0 to 2 From 2 Total: To 4 5 665 15 794 19 324 40 783 From 4 to 334 30 970 6 461 5 226 42 657 From 334 32 580 4 658 4 582 41 820 Total: 69 215 26 913 29 132

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N. B. Paklin et al. Table 2. Contingency matrix LTV historical—LTV predicted (clickers). LTV historical

> ð1Þ ð1Þ P ð1Þ ð1Þ > > ¼ Hu ; u ðtÞ  pn ; 0  u ðtÞ  e; uj_ x > > j js js js > > > > s¼1 j¼1 s¼1 > > > > > > ð1Þ ð1Þ ð1Þ > = < for t 2 ðt0 ; tf  ¼ T; xj ðt0 Þ ¼ 0; xj ðtf Þ ¼ aj ; > " # ¼     v > > ð2Þ > P uð1Þ P að1Þ  xð1Þ ðtÞ þ P að2Þ > > ¼ 0; > g  xg ðtÞ > > js b b > > > > g23 s¼1 b22 > > > > > > ð1Þ ; : ujs 2 f0; 1g; j ¼ 1; . . .; m; s ¼ 1; . . .; v;

ð1Þ

Z J1 ¼

ds; t0

ð2Þ

492

Z. Valerii m X v Z X

tf

J2 ¼

j¼1 s¼1

J3 ¼

ð1Þ

ð1Þ

cjs ðsÞ
ujs ðsÞds;

ð3Þ

t0

m h i2 1X ð1Þ ð1Þ aj  xj ðtf Þ ; 2 j¼1 ð1Þ

ð4Þ

where H is the time matrix function; xj is a variable that characterizes the state of the BP at time t.; u is control input, which takes the value “1” if the BP (Dj (s = j = 1,…, m)) is performed using the Bs (s = 1,…, v) information service, and takes the value “0” in opposite case; the Eq. (1) indicates that the control action belongs to the program control model of the BP (M(1)); p, e are the values that describe the capabilities of the information service to provide simultaneous support for multiple BP, as well as to specify the need for a particular BP to use multiple services; n is th vector functions that determine the minimum and maximum values of the disturbing effects; t0, tf are the start and end time points; ai is the specified volume of BP operations; T is the time interval at which the process of functioning and modernization of the CTO and the EIS is considered; b 2 Г1 are the set of operation numbers immediately preceding and technologically related to the BP operation using the logical operations “AND”; η 2 Г2 are the set of service numbers providing information support of BP. In other words, the BP cannot start until all the operations included in the technological management cycle are completed, and the information service is provided at full. Thus, during the modernization of the EIS services, the start of the next BP operation is excluded. Abovementioned logical interlevel constraints allow to classify presented models as logical-dynamic. ð1Þ djs is the function that allows us to evaluate the overall operation quality of providing the information service to the BP at the stage of parallel operation and ð1Þ modernization of the CTO and the EIS; cjs is the cost function of time describing the costs associated with the implementation of the BP at the stage of the EIS modernization. Indicator of the type (2) is designed to maximize the speed of the BP at the stage of EIS modernization. Indicator of the type (3) is designed to estimate the total capital costs associated with the processes of operation and modernization of the EIS at the BP level. It is the part of total cost ownership indicator. Indicator (4) is introduced to maximize the accuracy of BP implementation. The hierarchical multi-model description of the integrated processes of the CTO functioning and the EIS modernization is based on logical-dynamic models, the principles of service-oriented and functional-cost approaches, structural dynamics control. It allows to take into account the variability of all main constraints associated with the EIS modernization stage. Additionally, there is possibility to introduce and optimize the total cost ownership indicator (see Eq. (3)). Figure 3 shows an interconnection of models. It presents models, variables and control inputs. For more information see [10].

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Fig. 3. Interconnection of logical-dynamic program control models of CTO and EIS.

4 Combined Algorithm Due to the limitations of the paper size, we cannot present combined algorithms in detail. For more information see [16]. Step 1. For a given state of the environment and given constant input data the additional data are received through the models of the CTO functioning. To receive data necessary for planning the numerous computation experiments are performed with the models of the CTO and the EIS functioning. The following information should be obtained: the possible variants of BP execution intensity and possible rates of data transmission and processing in the EIS. This information is entered into the models via H and n. Step 2. In accordance with the computation resources the schedule of modeling is worked out. Step 3. The optimal plan is being chosen via the interaction with decision-makers. We propose to using fuzzy probabilistic approach for assessing received plans 11, 19]: ð1Þ Jres ¼ k0 þ

m X i¼1

ki J 1 þ

m m X X i¼1

kij J1 J2 þ ::: þ k123 J1 J2 J3 þ e;

ð5Þ

j¼1 j 6¼ i

where k is a dynamic multi-criteria priority that takes into account the contextual information that was obtained through the expert survey [21, 24]; e is a miscalculation.

494

Z. Valerii

Step 4. The transformation of the Hamilton function, the conjugate system of equations, and the transversality condition into the necessary form are required for the correct solution of optimal control problems with mixed constraints [25, 27, 29]. Step 5. The existence of a solution for the planning and scheduling tasks is analyzed. Here the end conditions in the planning problem are verified through examination of the previously constructed attainability set [15]. Step 6. If the solution exists, the allowable plans and schedules of BP and the EIS functioning are worked out in an automatic mode or through the interaction with decision-maker. Step 7. The stability of the plans and schedules obtained at the step 6 is verified via the simulation models of BP, the EIS functioning, its modernization and goal directed applications under conditions of environmental impact. The input data for the simulation models can have different form: deterministic, stochastic, fuzzy, and interval form. The principles of reflexive control are implemented in the CTO simulation models. This is the main distinctive feature if the proposed models. Step 8. The parametric and structure adaptation of the plans to the perturbation inputs is performed [16].

5 Results The conducted studies have shown that CTO functioning and EIS modernization should be based on integrated planning not only of the sequence of replacement of elements and processes, but also optimization of the BP and information processes. The proposed approach attracts and widely uses the fundamental scientific results obtained so far in the modern theory of control of complex dynamic systems with a changeable structure in the course of managing the modernization of the CTO and its EIS: 1. It allows to significantly reduce the dimensionality of the tasks of managing modern digital transformation (or the ongoing modernization), which are solved at each moment of time (due to the recurrent description of models and logical constraints). 2. It possible to synthesize sequences of solved tasks and operations (to synthesize control technology) and to find reasonably choose compromise solutions in the presence of several options for CTO functioning and EIS modernization [17, 25]. Additionally, we note that the efficiency of the CTO functioning is largely determined not so much by the availability of powerful computing facilities, switching equipment and communication channels, as by the level of study of the issues of formal description and algorithmizing of the main control functions at various stages of the CTO and EIS life cycle. Our approach allows us distribute operations across heterogeneous resources at the BP level, at the EIS functioning level, at the EIS modernization level optimally. We can suppose that the effectiveness of the functioning CTO at the EIS modernization stage primarily depends on the quality of its special software, mathematical and information support created and implemented.

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6 Conclusion The control of I4.0 objects functioning involve complexity, which mainly stems from the high dimensionality which is determined by practical conditions. There are still many challenges and issues that need to be resolved for digital transformation to become more applicable. However, the development of advanced methodologies, especially formal methods and systems approaches, has to be synced with rapid information technologies integration and CTO functioning [23]. In this paper the new combined algorithm of integrated planning and scheduling of CTO functioning and EIS modernization is considered. New model-algorithmic support is focused on EIS and BP integrated control. Combined algorithm is useful in many areas. Specific examples and results of its usage are presented in [16]. In our paper we have tried to touch upon the main ideas that we used for synthesizing integrated plans and schedules of CTO functioning and EIS modernization. The developed special model-algorithmic software is based on the concepts of service, BP, operation, resource, structure, etc. The accepted concepts are related to operations at all levels of the considered processes. They are available for private (subject) interpretation and usage in many areas of human economic activities such as composable and virtual enterprises, financial organizations (broker companies) and spacecraft industry. Acknowledgements. The research described in this paper is partially supported by the Russian Foundation for Basic Research (grants 20-08-01046), state research FFZF-2022-0004.

References 1. Anichkin, A.S., Semenov, V.A.: A survey of emerging models and methods of scheduling. In: Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS), vol. 26, no. 3, pp. 5–50. (2014). (in Russian). https://doi.org/10.15514/ISPRAS-201426(3)-1 2. Ashimov, A.A., Geida, A.S., Lysenko, I.V., Yusupov, R.M.: System functioning efficiency and other system operational properties: research problems, evaluation method. SPIIRAS Proc. 5(60), 241–270 (2018) 3. Athans, M.S., Falb, P.L.: Optimal Control, McGraw Hill,Inc. (1966) 4. Costa, F.S., Nassar, S.M., Gusmeroli, S.: FASTEN IIoT: an open real-time platform for vertical, horizontal and end-to-end integration. Sensors 20(19), 54–99 (2020) 5. Csalodi, R., Süle, Z., Jaskó, S., Holczinger, T., Abonyi, J.: Industry 4.0-driven development of optimization algorithms: a systematic overview. Complexity, 1–22 (2021) 6. Dolgui, A., Ivanov, D., Sethi, S.P., Sokolov, B.: Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications Int. J. Prod. Res. 57(2), 411–432 (2019) 7. Gnidenko, A., Sobolevsky, V., Potriasaev, S., Sokolov, B.: Methodology and integrated modeling technologies for synthesis of cyber-physical production systems modernization programs and plans. IFAC-PapersOnLine 52(13), 642–647 (2019) 8. Humar, J.: Dynamics of Structures, 3rd edn. CRC Press, Balkema (2012)

496

Z. Valerii

9. Ivanov, D., Sokolov B., Werner F., Dolgui, A.: Proactive scheduling and reactive real-time control in Industry 4.0. Sched. Ind. 4.0 Cloud Manufact. 289, 11–37 (2020). 10. Ivanov, D., Sokolov, B., Chen, W., Dolgui, A., Werner, F., Potryasaev, S.: A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints. IISE Trans. 53(1), 21–38 (2021) 11. Ivanov, D., Tang, C., Dolgui, A., Battini, D.A., Das, A.: Researchers’ perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management. Int. J. Prod. Res. 59(7), 2055–2078 (2021) 12. Ivanov, D., Pavlov, A., Sokolov, B.: Optimal distribution (re)planning in a centralized multistage supply network under conditions of the ripple effect and structure dynamics. Eur. J. Oper. Res. 237(2), 758–770 (2014) 13. Ivanov, D., Sokolov, B., Dolgui, A.: Applicability of optimal control theory to adaptive supply chain planning and scheduling. Annu. Rev. Control 36, 73–84 (2012) 14. Ivanov, D., Sokolov, B., Evelio, A.D.R.: Integrated dynamic scheduling of material flows and distributed information services in collaborative cyber-physical supply networks. Int. J. Syst. Sci.: Oper. Logist. 1(1), 18–26 (2014). https://doi.org/10.1080/00207721.2013. 879226 15. Kofnov, O., Sokolov, B., Ushakov, V.: The synthesis of the control function in optimal tasks as a N-dimensional area using parallel projection on 2D plane. In: 32nd European Modeling and Simulation Symposium, EMSS 2020, pp. 262–269 (2020) 16. Laboratory of Information Technologies in System Analysis and Modeling. http://www. litsam.ru. Accessed 07 Sept 2021 17. Xu, L.D., Xu, E.L., Li, L.: Industry 4.0: state of the art and future trends. Int. J. Prod. Res. 8 (56), 2941–2962 (2018). 18. Mikoni, S.B., Sokolov, B.V., Yusupov, R.M.: Qualimetry of Models and. Polymodel Complexes. Nauka, Moscow (2018) 19. Moshe, F.: A Mathematical programming model for optimal scheduling of buses’ departures under deterministic conditions. Transp. Res. 2(10), 83–90 (1976) 20. Pavlov, A., Ivanov, D., Dolgui, A., Sokolov, B.: Hybrid fuzzy-probabilistic approach to supply chain resilience assessment. IEEE Trans. Eng. Manag. 65(2), 303–315 (2018) 21. Pavlov, A.: Integrated modelling of the structural and functional reconfiguration of complex objects. SPIIRAS Proc. 5, 143–168 (2013). https://doi.org/10.15622/sp.28.5 22. Pavlov, A.N., Pavlov, D.A., Zakharov, V.V.: Technology resolution criterion of uncertainty in intelligent distributed decision support systems. Stud. Comput. Intell. 868, 365–373 (2020) 23. Radanliev, P., De Roure, D., Van Kleek, M., et al.: Artificial intelligence in cyber physical systems. AI & Soc. 36, 783–796 (2020) 24. Slowinski, R.: Preemptive scheduling of independent jobs on parallel machines subject to financial constraints. Eur. J. Oper. Res. 15, 366–373 (1984) 25. Sokolov, B., Pavlov, A., Potriasaev, S., Zakharov, V.: Methodology and technologies of the complex objects proactive intellectual situational management and control in emergencies. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds.) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry”(IITI 2019). IITI 2019. Advances in Intelligent Systems and Computing, vol. 1156, pp. 234–243. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50097-9_24 26. Sokolov, B.V., Kalinin, V.N.: Autom. Remote Control 5, 106–114 (1985) 27. Sokolov, B.V., Zelentsov, V.A., Brovkina, O., Mochalov, V.F., Potryasaev, S.A.: Models adaptation of complex objects structure dynamics control. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds.) Intelligent Systems in Cybernetics and Automation Theory. CSOC 2015. Advances in Intelligent Systems and Computing, vol. 348. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18503-3_3

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28. Sokolov, B., Zakharov, V., Kofnov, O., Vladimir, S.: Integrated dynamic planning and scheduling of enterprise information system modernization. Paper presented at the 32nd European Modeling and Simulation Symposium, EMSS 2020, pp. 270–276 (2020) 29. Tabak, D., Kuo, B.C.: Application of mathematical programming in the design of optimal control systems. Int. J. Control 10(5), 545–552 (1969) 30. Zimin, I.N., Ivanilov, Y.: Solution of network planning problems by reducing them to optimal control problems. USSR Comput. Math. Math. Phys. 11(3), 632–641 (1971)

Digital Interactive-Documentary Model in the Framework of Subject Ontology System Analysis Vasily V. Ponomarev1(&)

and Vladimir E. Tumanov2

NPP “Rumb”, 142400 Noginsk, MO, Russia [email protected] Moscow Regional Medical College 3, 142410 Noginsk, MO, Russia 1

2

Abstract. The system analysis during the modern documents developing at the level of regional and municipal structures had been. The structure of a temporal interactive document in the framework of the document life cycle model and interactive-documentaries models had been considered. The model of a temporal interactive document (DITD) as five objects set in an electronic environment had been proposed, that let to interact with DITD in the framework of space and time, as well as in the framework of the logical or associative sequence of speech or non-speech signs. The composition of program modules, knowledge bases and databases that is related to the temporal interactive document had been briefly presented. The purpose of our publication is to propose the detailed description of DITD model that had been designed for conflict situations resolving during the digital communications’ implementation by the narrative in the virtual DITD space representation, where are all stakeholders in the decisionmaking process has been participated. The processing tools of the document fragments structures synaptic framework in accordance with the document providing temporary chain of additional content sources support dynamics had been described in the “initiator of the N-link of the interactive”\“N-link trigger interactive” subtext resume format. This is the result of the preprocessor using for the text corpus of primary sources processing by the following content n-units distribution that are pertinent to the context of ontological inventories over the following slots with the defining of properties of the reality discourse implicature execution control of the problem area events sequence. Keywords: Subject ontology Synaptic framework


Digital interactive temporal document model


1 Introduction The interactive document is understood as “the created indefinite software media document in the narrow (technical) sense.” The software environment offers the set of tools, but does not usually concern the whole spectrum of the document semantic meaning and of the document purpose. The interactive document is understood as “a form of representation of hypertext, and special material structure The interactive document permits the data storing and the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 498–511, 2022. https://doi.org/10.1007/978-3-030-98832-6_44

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transmitting processes in spacetime, that is adapted for reading the text (as a logical or associative sequence of speech or non-speech signs) in the broad (linguistic) sense (code, program, existing in an electronic environment)” [14, p. 168].

2 The Task Set 2.1

The Description of the Subject Area

The digital interactive document is an integral part of the information system of local self-government bodies that is the system for administration decisions-making and supporting the wide range of issues. The intellectualization of such systems should be based according to the meaning presentation and sense analysis in the framework of the interactive document maintenance like its life cycle (intelligent processing) [15, pp. 143–150]. We are talking about describing the formal model at the conceptual level aspect: “Trends in the creation of modern documents”: {; ; ; ; ; ; ; ; }. Thus, the purpose of our publication is to propose the model of the temporal interactive document that is designed to resolve conflict situations in the process of digital communications. 2.2

Problem Formulation

The social electronic communication between the citizen of Russian Federation and the local self-government bodies involves a dialogue in the virtual space between the stakeholder and administration representatives (The request, The resolution required conflict situation). The temporal interactive document is defined by the events time chain and each of that corresponds to the complete coordinated hypertext fragment. As can be seen from Table 2, for each event, the stages are repeated for each fragment. Thus, the temporal interactive document is the narrative in the virtual space representation, and all stakeholders participate in the decision-making process. By the mechanism of its creation and usage, such a document is close to an interactive movie [16], The structure of such documents is shown in the follows formula: {; ; ; ; ; }. The task is to create a model of a temporal interactive document and determine the software requirements for its processing.

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3 The System Analysis Methods Used Taking into account the representative and authoritative scientific research in modeling (system analysis) of the structural representations of the problem taxonomy named in the topic of this publication, it should be noted that if we talk about such a formal apparatus as “structural representations” of the subject areas discussed below, then in this case, in the aspects of basic conceptualizations, a number of “specific features” are naturally inherent, which are most fully characterized by the tools that were actually revealed in a number of authoritative works [17, p. 46], especially in the part of “systems theory and systems analysis”, as in the notations of the terminological apparatus of “decompositions”, so in the context of the terms “multilevel hierarchical structures” [17, p. 49]. With the above-mentioned constructions, “a set of successively solved problems is determined so that the solution of the above-lying model determines the constraints (permissible degree of simplification) when modeling at the lower level”, as well as obtaining the expected result of system-analytical and system-technical transformations of the considered system configuration under consideration up to the acquisition of a certain optimal status, that is indicated by the tautologically sounding, but existentially inevitable meta-terminological formula, as a “sequence of control actions at the controlled process” [17, p. 54]. And it is the subject area of analytical and synthetic constructions of this publication from the branches of “System Analysis” in this review part of this article, point of view, and it will concern such system models (cases) as: 1]. The modern system of State Youth Policy (GMP) in terms of the initial elements of the infrastructure of mass sports (physical culture) supporting and applied experience in designing the subject ontology for the web service for the linguistic support of GMP at the municipal and educational sectors [URL: http://ism-m.net]. 2]. A modern system for the Innovative Breakthrough Providing and the applied experience in designing a subject ontology for a web service for linguistic support of state youth policy in the municipal and educational sectors [URL: http://kmtz. info]. 3]. The modern system of health care institutions optimization in the field and applied experience in the designing the subject ontology for the linguistic support web service in the field of public health policy in the framework of one municipal scale case [URL: https://youtube.com]. 4]. The modern system for regional and municipal resource-saving timely solutions cases and the applied experience in the web service subject ontology design for linguistic support design of environmental monitoring synchronously with the reclamation of the natural environment [URL: http://spldess.ru].

4 Problem Solution Thus, we define a temporal interactive document as the five objects [16] (Fig. 1):

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Fig. 1. Temporal interactive document.

FðtÞ ¼ fF1 ðtÞ; F2 ðtÞ; . . .F4 ðtÞg is a set of completed document fragments (hypertext), where are Fn ðtÞ  ½\FðtÞi1 ; FðtÞ1j ; . . .FðtÞz1 [ ; \FðtÞi2 ; FðtÞ2j [ ; . . .FðtÞz2 [ ; \FðtÞi3 ; FðtÞ3j ; . . . FðtÞz3 [ ; \FðtÞi4 ; FðtÞ4j ; . . .FðtÞz4 [  . CðtÞ  fC1 ðtÞ; C2 ðtÞ; . . .C4 ðtÞg is the content (text, graphic, audio content, video content) that is linked to a fragment of a document, in the follows set: Cn ðtÞ  ½\CðtÞi1 ; CðtÞ1j ; . . .CðtÞz1 [ ; \CðtÞi2 ; CðtÞ2j ; . . .CðtÞz2 [ ; \CðtÞi3 ; CðtÞ3j ; . . .CðtÞz3 [ ; 
\CðtÞi4 ; CðtÞ4j ; . . .CðtÞz4 [ : SðtÞ ¼ fS1 ðtÞ; S2 ðtÞ; . . .S4 ðtÞg is the dynamic structure of the document. The path of the processing document fragments, taking into account branching and attracting additional sources of information had been described by the graph, in the follows set:  Sn ðtÞ  \SðtÞi1 ; SðtÞ1j ; . . .SðtÞz1 [ ; \SðtÞi2 ; SðtÞ2j ; . . .SðtÞz2 [ ; \SðtÞi3 ; SðtÞ3j ; . . .SðtÞz3 [
\SðtÞi4 ; SðtÞ4j ; . . .SðtÞz4 [ : An example of a typical dynamic document structure is shown below. TðeÞ ¼ fT1 ðeÞ; T2 ðeÞ; . . .T4 ðeÞg is a temporary chain of events, where are  Tn ðeÞ  \TðeÞi1 ; TðeÞ1j ; . . .TðeÞz1 [ ; TðeÞi2 ; TðeÞ2j ; . . .TðeÞz2 [ ; TðeÞi3 ; TðeÞ3j ; . . .TðeÞz3 [ ;
TðeÞi4 ; TðeÞ4j ; . . .TðeÞz4 [ : GðeÞ ¼ fG1 ðeÞ; G2 ðeÞ; . . .G4 ðeÞg are the document software modules: ontologies, thesaurus, neural network, knowledge base, links to external databases, in the follows set:  Gn ðeÞ  \GðtÞi1 ; GðtÞ1j ; . . .GðtÞz1 [ ; \GðtÞi2 ; GðtÞ2j ; . . .CðtÞz2 [ ; \GðtÞi3 ; GðtÞ3j ; . . .GðtÞz3 [ ;
\GðtÞi4 ; GðtÞ4j ; . . .GðtÞz4 [ :

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The process itself has been implemented by the software of the temporal interactive document within the framework of the problem setting in terms of the above and below-stated model of the digital interactive document in the system analysis of the subject ontology with the subsequent development of the corresponding composition and main functions of the document software modules for subsequent implementation into the integrated complex of the previously mentioned type of monitoring (Table 1).

Table 1. Document software module structure. The module for the initial fragment of the document input The parsing module Built-in expert system: semantic analysis of the text, subject areas’ definition, connection of the ontologies, the definition of the processing paths, list of the executers definition, search profiles definition The module for final version creating: the interactive promotion through the fragment’s structure, the extracting information from the external sources, citations obtaining, links saving, hypertext forming Document fragment approval module The updated document for reading to stakeholders’ presentation module The completion formation of the document decision module

Table 2. The above-named cases 1–4 control matrix in the terms of temporal interactive documents object model by FlorentMaurin [16] in the framework of the multilayer hierarchy of Mesarovich's decision-making notation [18].

n\n 1. Input evaluation function G: M x Y ! V*G: M x Y ! V*

I. The illustrative case of one gap in the framework of children’s and youth sports schools municipal infrastructure

II. The illustrative case of one gap at the junction of GMP and higher education industries at the regional level

(1)

(2)

III. The illustrative case of one gap at the junction of branches of medical infrastructure reforms and support of the electorate in the framework of municipality (3)

IV. The illustrative case of one gap at the junction of environmental monitoring with the recultivation of the natural environment at local levels (4)

(continued)

Digital Interactive-Documentary Model in the Framework Table 2. I. The illustrative case of one gap in the framework of children’s and youth sports schools municipal infrastructure

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(continued)

II. The illustrative case of one gap at the junction of GMP and higher education industries at the regional level

III. The illustrative case of one gap at the junction of branches of medical infrastructure reforms and support of the electorate in the framework of municipality

IV. The illustrative case of one gap at the junction of environmental monitoring with the recultivation of the natural environment at local levels

P: M x U** ! Y

alternative actions set n\n (1) (2) (3) (4)

set that adequately reflects the lack of knowledge about the relationship between the action of m and the output of Y

possible output results (or “outputs”) * -V: The choice of actions {V-component of G function} is based on the G\P estimate application—values set that can be associated with the characteristics of the system functioning quality. **- If the set U consists of the single element or it is the empty set, i. e. there is no uncertainty about the output result for the given action m, the choice can be based on the optimization: to find such m’ in M that the value v’ = G(m’, P(m’)) is less than v = G(m, P(m)) for any other action m 2 M.

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5 Description of the Author’s Innovation Some elements of the proposed model of the temporal interactive document were used in the processing of citizens ‘requests to the administration of several municipal structures in terms of 1991–2021s. The experiment on digitalization of communications between citizens and the administration had shown the effectiveness of using elements of applied artificial intelligence. The results of the experiment were studied by the methods of system analysis [17, p. 106–109], which allowed us to propose a model of the temporal interactive document and the peculiarities of its processing (see Fig. 2): The proposed model meets the requirements of definition in terms of the Belous model [14]:

{< Temporal interactive document>;< Defenition: TID = {F, C, S, T, G}>; < Timeline T={t1, t2, …}>; ;;;} Fig. 2. Model of a temporal interactive document.

The essence of the author’s applied design experience by means of special software from the class of packages of so-called ontological editors with decomposition according to the corresponding subject ontologies and with subsequent integration of dynamically updated content resources is into the corresponding corporate neural network web service, or into the set of the software and linguistic support tools for the data processing center of the information kiosks-public access terminals network. It is updated by the total monitoring of the document flow of all stakeholders in each case of the above-mentioned geographically and municipally isolated problem situations arrangement by synchronously forming the corresponding text bodies of content (including interactive) resources in the project notation described below: Stage I: The successive explication of logically interrelated formalized representations is the structural specifications of the corresponding subject areas for each inputoutput pair. Such explication should be revealed by the NP-VP-AP encoding with the significant (for contemporaries-speakers of the local individual dialect of dialogues, that has been recorded by generated and hypertext available multimedia means) references by the authors of the considered verbatim interactive acts. Stage II: The timekeeping output implications that have been generated by the responsibility of the authors of each of the stenographed replicas the resultant vectors of the existing business administration procedures and the dominant motivations of the authors (or their contextual successors). Such output implications should give the interactive under consideration acts of the development and its hypertext should be recorded by available multimedia means in the volume of semantic ranges from the keyword (descriptors) to the synaptic framework cluster.

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Stage III: Ontological reconstructions of the evolution of concepts priority problems dramaturgy should be understandable to contemporaries-carriers of the local individual dialect, and it has been presented according to the matrix formalization above in paragraphs 1].–4]. The presented ontological model of each subject area reflects several hundred subclasses, as well as the relationships between classes and instances of classes. The ontologies were developed using the Protege ontology design tool and will be used in intelligent systems.

6 Research Methodology 6.1

About the Reasons for the Non-esoteric Approach Demand

The relevance in the last decade of the experience of chronoscopy of reformist achievements artifacts [1, 2] and “empty sounds” are loyal to the dominant practices of the entire spectrum of the national tradition of management successors in the named in the topic of this publication subject areas. But even this relevance cannot allow hiding the present situation from colleagues, who are interested in the factual solutions of applied artificial intelligence problems for scientific purposes. 6.2

Corporate Restrictions of the Methodological Background

Taking into account the participation of the authors of the material presented below, equally in federal, municipal, and academic projects on this subject, as well as in connection with the recent receipt of official evidence [3] of the relative success of our original approach in each of the taxonomic categories stated above, we consider the possibility to present this message in the format of a poster report abstracts about the considered versions of linguistic support for ontologies current researches and the development of some applied intelligent systems [12, 13]. The fundamental monograph of the first of the present publication coauthors with the preface by the second coauthor of this report was devoted to the linguistic solutions of the expert superstructure design model over the public access of information resources network (the support of its updated content was funded at one time by the regulations of the relevant budgets) in the framework of GMP subjects in the municipal and educational sectors). In addition to joint participation in a number of examinations of the “Electronic Government” line during 1995–2007s [4, 5], we hope that the collegial community will be able to perceive this work together with our developments on the topics [6–8], throughout the 1990–2010s, as a missing link that allows researchers to be outside of cognitive dissonance. However, the joint efforts to generate the synaptic framework of the fundamental materials science ontology received the greatest impetus after the preparation and work under the agreement between the Russian State University for the Humanities (Department of Information Technologies of the Information Systems and Security

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Technologies Institute) and the Institute of Chemical Physics Problems by Russian Academy of Sciences in Chernogolovka in 2012-2017s. Coauthors participated in student of the above-mentioned and other universities externship co-guidance at the subject of ontologies linguistic platform design for the Free radicals and thermochemistry materials science [19]. Nevertheless, the subject usage of the author’s tools in information support at the relatively independent task of one item from the resolution of the “Chernogolovka City District” Municipality Head (in the area of the same name Scientific Center by Russian Academy of Sciences localization) implementation, allowed us to get a practical result in 5 years, which the intricacies of the phenomena of municipal problems, that did not allow to achieve the previous 35 years. 6.3

The Content of the Research Methodology

Taking into account the concepts of methodological paradigms analysis for the implementation of certain ontological structures pluralism, it seems optimal to the authors to implement the following solution for positioning our model in the Protege ontology design tool [7, 11]. Thus, in addition to the methodology, that was reflected by one of the co-authors in the publication [10], this paper for the first time manifests the conceptual innovation in the generation of class definitions and class hierarchies, the definition of class properties, the definition of class property constraints and the creation of instances, that are based at the list of concepts and terms with the data of the system knowledge repository coverage. It allows you to allocate slots of such nonisomorphic structures, that are necessary to be involved in the representative sample of the texts content primary sources corpus, such as the non-alligator-complete of tuples formalism: . At the same time, the emphasis on the obvious productivity of their practical application is indisputable due to the fundraising significance of the resources resonance emergence in the public consciousness, that are relevant to the interests of the World Bank Group according to the index http://kmtz.info, despite the modest thematic constraints of the last informational occasion of this publication.

7 Results 7.1

Class Hierarchy

We represent the following decomposition of classes, starting from the top level in the proposed subject ontology: the “Political Science” class with the “Municipal Administration” subclass, then, the decomposition of subclasses for other relevant reasons is presented as follows due to a number of specifics of the cognitive configuration of our experimental interactive text material (Table 3).

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Table 3. Decomposition of classes from the top level. 1. Political Science 1. K. 1. Municipal administration 1. K. 1. 1. Executive bodies of local self-government 1. K. 1. 0. Virtual bodies of operational municipal management 1. K. 1. 2. Representative bodies of local self-government 1. K. 1. 0. 1. 1. Interactive geolocators of thematic updating 1. K. 1. 0. 1. 2. Interactive hierarchical socioidentifiers of thematic updating 1. K. 1. 0. 1. 3. Decomposition of the conditions for the dominance of interactive information needs 1. K. 1. 0. 1. 4. Multi-level intelligent information needs 1. K. 1. 0. 1. 5. Typologization of the considered connections of the rhetorical opposition

Further, due to the number of specifics of the cognitive configuration of our experimental interactive text material, the decomposition of the subclass “Typologization of the considered connections of rhetorical opposition” is presented by us as follows (see Table 4). Table 4. Hierarchy of the subclass “Typologization of the considered connections of rhetorical opposition”. (i). Class definitions (ii). Class hierarchy (iii). Defining class properties (iv). Defining class property constraints (v). Creating instances (vi). List of concepts (vii). Knowledge of the system

7.2

Hypercontext Decomposition

Further, due to a number of specifics of the cognitive configuration of our experimental interactive text material, the decomposition of the subclass according to the bases (ivii) is presented by us as follows: (i). Class definitions; (ii). Class hierarchy; (iii). Defining class properties; (iv). Defining class property constraints; (v). Instantiation; (vi). List of concepts; (vii). System knowledge. 7.3

The Inventories Representation

The minimum-fixed level of the actual corpus aggregation of the processed linguistic and extralinguistic units of the corresponding links of the interaction of the material of the core of the development of the concept of the involved cognitive model has the

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following chronologically implicit structure (synaptic framework of subtexts with inventory markers vs. N-links interactive). 7.4

The Instrumentation Representation

The several bases (groups) of classification are distinguished in the classification of classes by the type of the connections in the problem area under consideration: #1. There is an inventory of antagonistic processes (−A. +) Between the actants of sets of tuples (−B. +). #2. At the same time, adequate use of the author’s Toolkit (−C. +) For the concept {D} leads to the identification of tactical and strategic means (−E. +) And conditions (−F. +) As counteraction (−G. +), and absorption by one (−H. +) antagonistic process of others (another) (−I. +). #3. It seems that in this case, the following case has the optimal (at the same time universal, but also the most visual) explanatory power, which is described by the following tuple of the above-mentioned inventories: .*—the term denoting the interested parties of the considered communication processes**—verification control (A.) consists (B.) in the formulation (C.), substantiation (D.), implementation (D.) of requests (E.) for production (J.) \ transformation (C.) of the primary information (I.). 7.5

Case Invariance

The subtext of the first link decoding (I.).–(II.) by the considered interactive is based at the cluster of the relevant ontological structure (1).–(2). in the synaptic framework of ontology No. 1—the generator of the implication of the intension “Protocol of the question-answer reaction (Iteration No. 1.)” in reconstruction: h 1). \FðtÞa1 ; FðtÞb1 ; . . .FðtÞx1 [ ; \CðtÞa1 ; CðtÞb1 ; . . .CðtÞx1 [ ; i \SðtÞa1 ; SðtÞb1 ; . . .SðtÞx1 [ : \TðeÞa1 ; TðeÞb1 ; . . .TðeÞx [ h 2). \CðtÞa1 ; CðtÞb1 ; . . .CðtÞx1 [ : \SðtÞa1 ; SðtÞb1 ; . . .SðtÞx1 [ ; i TðeÞa1 ; TðeÞb1 ; . . .TðeÞx1 [ : \FðtÞa1 ; FðtÞb1 ; . . .FðtÞx1 [

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8 Conclusion A brief analysis of the modern documents creation trends has been presented in the present paper. The temporal interactive document structure with the base at the document life cycle model and interactive cinema model had been considered. The temporal interactive document model as five objects in an electronic environment has been proposed. The presented here solution allows the processing of the digital document in time-space, as the logical or associative sequence of speech or non-speech signs. The composition of program modules, knowledge bases, and databases that is related to the temporal interactive document also had been briefly presented here at the background of the procedures for generating socio-psycho-linguistic-determination-adaptationverification cases [10]. The distribution of levels of terminological competence is in the docflow model of various structural divisions at mainly regional, municipal, and public organizations, that do not overlap in a functional way and complement each other with the reflection only those of them that are meaningfully relevant to the issues, that has been raised in the recorded multimedia and official correspondence in the aspect of introducing new ways of developing the politically correct and resource-saving palliative consensus to eligible stakeholders [9] at the present moment. Acknowledgement. This publication was carried out with partial support of the RFBR grant 1507-08645.

References 1. Ponomarev, V.: Multimedia web-service design as part of a pictogram annotating concept for multilanguage public access. In: Grout, V., Picking, R. (eds.) Proceedings of the Fourth International Conference on Internet Technologies and Applications (ITA11), Glyndwr University, Wrexham, North Wales, UK, 6–9 September, pp. 599–600 (2011) 2. Cap, C.: Content neutrality for Wiki systems: from Neutral point of view (NPOV) to Every point of view (EPOV). In: Grout, V., Picking, R.: (eds.) Proceedings of the Fourth International Conference on Internet Technologies and Applications (ITA11), 6–9 September, Glyndwr University, Wrexham, North Wales, UK, pp. 9–22 (2011) 3. NPPR Homepage, Decision No. 5-77/343 by Municipality of Chernogolovka City District Council of Deputies “Chernogolovskaya newspaper” № 51 dated 21 December 2017, p. 17. http://rumbnog.info/512017.jpg. Accessed 12 Aug 2021 4. Ponomarev, V.V.: Konceptual’naya model’ kompleksa sredstv lingvisticheskogo i programmnogo obespecheniya ekspertno-poiskovoj sistemy s elementami sociopsiho lingvisticheskoj determinacii [Conceptual model of the linguistic software complex of the expert search system with elements of the socio-psyho linguistic determination], 176 p. DialogMIFI, Moscow (2004). (In Russian)

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5. Ponomarev, V.: Vnedrenie novogo podhoda k avtomatizacii organizacii ucheta otchetnosti terminal’nyh stancij informacionnoj sistema Merii “Molodezh’” v ramkah programmy “Elektronnaya Moskva” [The new approach across the City Hall “Youth” terminal stations information system accounting adoption automation in the framework of the “Electronic Moscow” program]. In: Bykov, D., Azarov, V. (eds.) “Novye informacionnye tekhnologii”, mezhdunarodnaya studencheskaya shkola-seminar [New information technologies “XIII International Student school-seminar”] reports abstracts, pp. 331–337. MGIEM, Moscow (2005). (In Russian) 6. Ponomarev, V.: Istoriya i perspektivy proekta Komiteta po delam sem’i i molodezhi goroda Moskvy — set’ terminal’nyh stancij informacionnoj sistemy merii “Molodezh’”. [History and prospects of the “Youth” information system by Committee for family and youth affairs by Moscow City Hall program. In: Bykov, D., Azarov, V. (eds.) “Novye informacionnye tekhnologii”, mezhdunarodnaya studencheskaya shkola-seminar [New information technologies “XII International Student school-seminar”] reports abstracts, pp. 44–51. MGIEM, Moscow (2004). (In Russian) 7. A free, open-source ontology editor and framework for building intelligent systems/Stanford University Homepage. https://www.springer.com/series/15179. Accessed 12 Aug 2021 8. Kaufman, V., Natchetoi, Y., Ponomarev, V.: On-demand mobile CRM applications for social marketing. In: Coelhas, H., Coelho, V. (eds.) International Joint Conference on E-Business and Tele-Communications (ICETE 2008), Porto, Portugal. Book of Abstracts Proceedings, pp.70–71 (2008) 9. Ponomarev, V. Natchetoi, Y.: Semantic content engine for e-business and e-government with mobile web support. In: Cunningham, S., Grout, V. (eds.) Proceedings of the Third International Conference on Internet Technologies and Applications (ITA-09), Glyndwr University, Wrexham, North Wales, UK, pp. 702–710 (2009) 10. Ponomarev, V.: Socio-Psycho-Linguistic Determined Expert-Search System (SPLDESS) development with multimedia illustration elements. In: Kim, T.-H., Vasilakos, T., Sakurai, K., Xiao, Y., Zhao, G., Ślęzak, D. (eds.) FGCN 2010. CCIS, vol. 120, pp. 130–137. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17604-3_14 11. Amosova, E., Tumanov, V.: Presentation of chemical reactions, reagents and their thermochemical properties in an intelligent system for the physical chemistry of radical reactions in the liquid phase using an ontological model of the subject area. Butler’s Messages 39(7), 39–46 (2014) 12. Amosova, E., Berzigiyarov, P.: Development of the representation of an ontological model for the physical chemistry of radical reactions by relational database relations and its implementation in a subject-oriented system of scientific orientation. Butler’s Messages 45(1), 152–158 (2016) 13. Prokhorov, A., Varlamov, D., Amosova, E., Berzigiyarov, P., Tumanov, V.: Vnedrenie predmetnyh ontologij v sistemu nauchnoj analitiki po fizicheskoj himii radikal’nyh reakcij. [Introduction of subject ontologies into the system of scientific analytics on the physical chemistry of radical reactions]. In: Nauchnyj servis v seti Internet [Scientific service on the Internet]: Proceedings of the XVIII All-Russian Scientific Conference, 19–24 September 2016, Novorossiysk, pp. 298–302. IPM im. M.V. Keldysha RAN (Institut prikladnoj matematiki im. M.V. Keldysha RAN), Moscow (2016). (In Russian) 14. Belous, E.S.: Interaktivnye dokumenty: yazykovye osobennosti i dokumentnyj status. [Interactive documents: linguistic features and documentary status.] Vestnik Volgogradskogo gosudarstvennogo universiteta [Sci. J. VolSU (Volgograd State Univ.)]. Seriya 2: Yazykoznanie [Series 2, Linguistics] 20(1), 168–180 (2021). (In Russian). https://doi.org/10. 15688/jvolsu2.2021.1.14

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15. Rubashkin, V.: Predstavlenie i analiz smysla v intellektual’nyh informacionnyh sistemah. [Representation and analysis of meaning in intellectual and information systems], Nauka, Moscow, 189 p. (1989). (In Russian) 16. Narrative structures in interactive documentaries Florent Maurin Homepage. https://prezi. com/ilzwxzjz2t5p/narrative-structures-in-interactive-documentaries. Accessed 12 Aug 2021 17. Volkova, V., Denisov, A.: Teoriya sistem i sistemnyj analiz: uchebnik [Theory of Systems and System Analysis: Textbook], 2nd edn. Yurayt Publishing House, Moscow (2014). (In Russian) 18. Mesarovich, M., Takahara, I.: General Theory of Systems: Mathematical Foundations. Mir, Moscow (1978). (In Russian) 19. NPPR Homepage. http://rumbnog.info/index.html. Accessed 21 Aug 2021

Application of a Non-invasive Interface “Brain-Computer” for Classification of Imaginary Movements Anzelika Zuravska(&)

and Lev A. Stankevich

Peter the Great St.Petersburg Polytechnic University, Polytechnicheskaya Street 29, 195251 St. Petersburg, Russia [email protected]

Abstract. This paper discusses the problem of classifying human lower-limb movements based on brain activity signals perceived and decoded using the brain-computer interface (BCI). The structure of a non-invasive BCI is presented, focused on the recognition of imaginary motor commands by decoding electroencephalographic (EEG) signals. An analytical review of scientific works related to the classification of arm and leg movements has been carried out, and as the result, the most effective type of classifier based on Riemannian geometry has been determined. A mathematical description of the method for classifying multichannel EEG signals using Riemannian geometry is given. Initial experiments have been carried out based on the use of BCIs of this type for recognizing imaginary commands for movements of the feet. The accuracy of recognition of feet movements in real time was about 65%. It is proposed to develop these studies with the aim of increasing the accuracy and speed of the recognition. That will allow using the proposed method in systems of neurorehabilitation and control of lower limb exoskeletons. Keywords: Brain-computer interface
Electroencephalography
Lower-limb movements
Imaginary
Classification
Neurorehabilitation
System
BCI system
Control

1 Introduction In recent years, interest in the development of the brain-computer interface (BCI) technologies, which allow interaction between the brain and the outside world in the form of a new channel, has increased [1, 2]. The development of invasive and non-invasive BCIs has led to the creation of technologies that include a wide variety of devices that record signals of brain activity, as well as software for processing them with the ultimate goal of classifying brain states or imaginary commands that can be used for neurorehabilitation or neurocontrol. BCIbased neurorehabilitation systems have given hope to patients with movement disorders [3–5]. Several research groups have developed BCI-based neurocontrol technologies, for example, to control wheelchairs [6], robotic arms [2], exoskeletons [3, 7, 8], etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 512–521, 2022. https://doi.org/10.1007/978-3-030-98832-6_45

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There are many approaches to the use of non-invasive BCIs for neurocontrol. One of the most commonly used approaches is the motor imagery [9]. Limb movements imagining (for example, movements of the right/left arm or foot) creates characteristic patterns in the motor cortex that can be detected by processing multichannel electroencephalograms (EEG). Another approach is related to event-related potential (ERP). The most famous ERP type is P300, i. e. positive potential evoked by a visual stimulus across 300 ms after its appearance. However, most often this method is used to create communication BCIs for typing texts [10]. But it can also be used for control if the visual stimuli are not letters, but, for example, arrows that determine the direction of movement of an external device. Machine learning methods are used to extract characteristic features and classify EEG patterns of imaginary movements. Until about 2007, there were no highly specialized machine learning methods that would take into account the characteristics of BCIs based on EEG [11, 12]. At this, the main problems that complicate the classification of imaginary movements in BCIs, were: low signal-to-noise ratio and variability of EEG signals, both for one user and for a group of users. Even for a single user, EEG signals change between or even within the same session. In addition, there is a limited amount of training data that are usually available for the calibration of classifiers, and the overall low reliability and performance of current BCIs [12]. It was shown that classification methods based on Riemannian geometry can increase the speed and reliability of EEG pattern classification, since these methods use correlation matrices, and only on their basis the classification is made [12]. The purpose of the article is to investigate the possibility of using a non-invasive BCI in the neurorehabilitation system, in particular, the restoration of impaired leg movements. Current tasks—a review of the work on BCI for imaginary movements of the lower limbs of a person, the search for an effective classifier of EEG patterns of imaginary leg movements. Further, Sect. 2 of the article highlights the current state of the art in the online classification of motor imaginary leg movements, Sect. 3 discusses classifiers based on Riemannian geometry, and Sect. 4 describes an experiment on the classification of motor imaginary leg movements.

2 Non-invasive BCIs and Imaginary Leg Movements For the purposes of rehabilitation and control, non-invasive BCIs are most often used, since they do not injure the brain and do not require expensive equipment and a lot of time to master and start working with them. In general, the BCI-based control system includes the following parts (see Fig. 1): – a device for recording the bioelectrical activity of the brain; – a data preprocessing unit designed to filter the signal from noise and remove various kinds of artifacts (oculomotor, muscle, etc.); – a block for calculating characteristic features of EEG signals; – a classifier, which, by the value of features, determines the input signal to a particular class;

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– a block for post-processing and generation of commands to an external technical device, based on the data received from the classifier; – feedback, for example, visualization of the command execution by the device, tactile stimulation, etc.

Fig. 1. Structure of a control system based on BCI.

Many methods are used to classify imaginary leg movements online. Linear classifiers are especially popular, for example, Linear Discriminant Analysis (LDA) [12]. There are many others methods to classify imaginary leg movements. For example, an auto-calibrating two-class online BCI was proposed for the classification of movements of the left and right arms and both legs [13]. Event-related desynchronization (ERD) is used that refers to a decrease in the EEG signal associated with an event. Algorithm testing was performed on twelve healthy subjects over three sessions. By the end of the third session, all subjects achieved an average classification accuracy of 80.2 ± 11.3%. Two-class BCI was used to control the exoskeleton [3]. Motor imagery (MI) of the movement of the subjects’ right leg was used to decipher their intention to gait and trigger the movements of the exoskeleton. Among healthy subjects, the average classification accuracy reached 84.44 ± 14.56%. The work shows that BCI-based control of the lower extremity exoskeleton is possible for patients with a good prognosis for rehabilitation.

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A portable BCI device for two classes is described: flexion of the right leg, relaxation [14]. The device is tested for three healthy subjects with minimal experience in BCI. The average classification accuracy was 97.5%. A linear Bayesian classifier (LBC) was used for classification [4, 5]. On the other hand, support vector machines (SVM) were used for classification in the works [7, 15]. BCI is presented for controlling an exoskeleton using imaginary commands “walk” and “sit” [7]. To switch between operating modes, triple blinking was used. Ten healthy subjects demonstrated more than 80% classification accuracy. A nonlinear classifier based on ensembles of decision trees (Random Forests—RF) was tested on two classes of imaginary movements of the right arm and leg [16]. The authors compared the RF algorithm with sLDA (shrinkage regularized LDA). The average peak accuracy for sLDA is 82.7% versus 85.2% for RF. The average median accuracy is 73.2% for sLDA compared to 76.1% for RF. To compensate for the low signal-to-noise ratio, researchers have explored new signal processing and classification methods that combine feature extraction and selection in a single step. This became possible by using matrix methods based on Riemannian geometry classifiers [12]. Classification methods based on Riemannian geometry can increase the speed and reliability of the classification. These methods use correlation matrices formed instead of the feature vector, and only on their basis, the classification is performed. By the way, the classifier based on Riemannian geometry took first place in the competition in offline classification of EEG data for BCIs in 2014–2016, bypassing other methods in terms of classification accuracy [17]. Methods of applying Riemannian geometry for the classification of MI, ERPs, SSEP (Steady-State Evoked Potentials) are also described. The high accuracy of classification by methods based on Riemannian geometry can be seen in the works [18, 19]. In [18], the average classification accuracy reached 90.2 ± 6.6%. The work [19] demonstrated the average classification accuracy of 83.95%, and for the motor imaginary movement of the tongue in three out of nine subjects the accuracy was more than 90%. Based on the above analysis of the literature, we can conclude that the use of classifiers based on Riemannian geometry is preferable to classify imaginary leg movements online. Therefore, further, we will consider in more detail the mathematical foundations laid down in classifiers based on Riemannian geometry.

3 Classifiers Based on Riemannian Geometry The task of recognizing the user’s mental commands by recording an EEG is reduced to establishing the belonging of a multidimensional time series, in the form of which the EEG is stored in a computer, to one of several known classes [20]. Using the approaches of Riemannian geometry, we use covariance matrices that are in a metric space—a Riemannian manifold. It is possible to obtain the covariance matrix from the EEG signal based on the following expression:

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Ci ¼

1
ðXi  E ½Xi Þ
ðXi  E ½Xi ÞT ; T 1

ð1Þ

where fX1 ; X2 ; . . .; XN g is the set of N EEG signal samples, for each of which a class yi 2 f1; . . .; Kg is known, K is the total number of classes (recognizable user commands). Each sample is a matrix of size E  T, where E is the number of electrodes, T ¼ Dt
fs is the number of time counts for the sample length Dt and sampling frequency fs . To determine the proximity of samples, the metric of the distance between the covariance matrices of these samples is introduced, determined by expression (1): hXN i1=2 
 2 dR ðC1 ; C2 Þ ¼ log C11 C2 F ¼ log k ; i i¼1

ð2Þ

where ki ; i ¼ 1; 2; . . .; N is a matrix eigenvalues C11 C2 . Expression (2) is the basis of the Minimum Riemannian Distance to Mean (MRDM) algorithm [21]. This algorithm is a generalization of the k-nearest neighbor’s algorithm, which is one of the basic classification methods. It is based on the assumption that close objects must belong to the same class. The MRDM algorithm includes the following steps: 1. Using expression (1), calculate the covariance matrices of the samples from the training sample. 2. For the sample, the class of which is to be determined, calculate the covariance matrix. 3. Calculate the geometric mean of the covariance matrices of the samples C1k ; C2k ; . . .; CNk k , corresponding to this class for all classes k ¼ 1; 2; . . .;:   XN
 Ck C1k ; C2k ; . . .; CNk k ¼ argC min d2 C; Cik : i¼1 R

ð3Þ

4. Calculate the distance from the matrix of the sample, the class of which is unknown, to the average matrices of each of the classes, and assign the class to the new sample, the distance to the matrix of which turned out to be the smallest:
 k ¼ argk mindR Ct ; Cik : Many well-known machine learning methods used in BCI (SVM, LDA, neural networks and their modifications) cannot be used in the introduced metric space (Riemannian manifold), but this does not mean that it is impossible to combine the approaches of Riemannian geometry and linear classifiers. Covariance matrices are symmetric and positive definite, the Riemannian space is locally similar, that is, it can be mapped onto the Tangent Euclidean Space, which allows the use of linear methods on the Tangent Space (TS) [22].

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When constructing the TS at any point of the Riemannian manifold, the vectors on the hyperplane corresponding to the covariance matrices will be determined by the following expression:   Si ¼ LogCðCi Þ ¼ C1=2 log C1=2 Ci C 1=2 C 1=2 ;

ð4Þ

where C is the point (covariance matrix) at which the TS is constructed; log is the matrix logarithm. Figure 2 shows a schematic representation of the construction of a TS on a Riemannian manifold. Thus, the algorithm for constructing the tangent space can be represented as follows: 1. According to expression (1), calculate the covariance matrices of the samples from the training sample. 2. Using expression (3), calculate the Riemannian mean of the covariance matrices of all samples. 3. Using expression (4), project the covariance matrices of all samples onto the tangent space at the point of the Riemannian mean. 4. Then you can use MRDM and apply linear classification methods such as LDA or SVM for the obtained projections, since these projections are in Euclidean space.

Fig. 2. Schematic representation of the construction of a tangent space on a Riemannian manifold in cross section. The dR ðC1 ; C2 Þ metric is Riemannian distance.

Manipulating with covariance matrices in a Riemannian manifold is described in more detail in [23].

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As shown in [24], classifiers based on Riemannian geometry, in particular, the MRDM algorithm, are simpler and include fewer stages than more classical approaches (they allow to exclude preprocessing and feature vector construction steps). In addition, Riemannian classifiers are equally well applicable to most BCI paradigms, with the only difference being the way of mapping data points in the variety of covariance matrices [17]. Unlike most classification methods, the MRDM approach does not contain a vector of characteristic features (instead, covariance matrices), that is, it does not require feature selection, for example, by cross-validation. Consequently, Riemannian geometry provides new tools for building simpler, more reliable, and accurate prediction models. The following is an experiment on the use of MRDM to classify imaginary leg movements in offline and online modes.

4 Experiment to Classify Imaginary Leg Movements For the experiment, we used an EEG device “SmartBCI” from Mitsar. The recording was made on 24 electrodes, which were located according to the international 10–20 system. EEG signals with a duration of 1200 ms were recorded in the frequency band 0.53 Hz–30 Hz. The sampling rate was 250 Hz. Before the classification of the recordings, oculomotor artifacts were excluded, such as blinking, slow and fast waves, fragments of EEG signals with an amplitude of more than 100 lV. The next 18 electrodes were selected for EEG studies Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, C4, T4, T5, P3, Pz, P4, T6, O1, and O2. In the experiment on recording and recognizing imaginary leg movements, 3 healthy participants aged 25 to 27 years old, right-handed, took part. The subjects took part in the research voluntarily, in accordance with the rules and ethical standards for conducting research with the participation of volunteers (Declaration of Helsinki 1964 with subsequent amendments and additions). In total, 5 sessions of recording motor imaginary leg movements were conducted. Before the start of EEG recording, the subjects were asked to alternately raise and lower their feet several times, focusing on kinesthetic sensations. Subjects were also asked to reduce the number of blinking or not blinking while recording motor imaginary movements. When recording EEG data, visual cues were used; each subject sat relaxed on a chair in front of a laptop screen, where pictures of the right or left leg appeared during the session, which served as a signal to start real or imagined movements. Recording of the actual movements of raising and lowering the foot to a rhythmic sound signal began. After the subject made real movements, he made the same imaginary movement in the same rhythm. The cycles of real and imaginary movements alternated. For each movement, a segment of 100 ms was allocated, after recording, we got 84 samples of EEG data with real movements for one leg and 114 with imaginary ones. Only imaginative samples were used for analysis. One session lasted 12 min. The classification results can be seen in Table 1. Additionally, the classifier was set to work online. To investigate the accuracy of his work, subject 3 was invited. There were no changes in the protocol scenario,

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the subject worked according to the same paradigm, at the end of the session a sign appeared informing the classification accuracy. The subject has shown an accuracy classification of 65%, which exceeds the random threshold of 50%, but is insufficient for use for neurocontrol, for example, with an exoskeleton. Table 1. Results of offline classification of imaginary leg movements for three subjects in five trials. Subject (#) Session 1 Session 2 Session 3 Session 4 Session 5 Mean 1 68% 70% 82% 73% 75% 74% 2 65% 63% 65% 71% 72% 67% 3 67% 72% 74% 69% 85% 73%

Analysis of the results of the experiment made it possible to identify the reasons for this level of classification: (1) the involvement of the subject; (2) a large number of artifacts; (3) a large number of channels for recording EEG data; (4) poor customization of the classifier. With low involvement, the classification results may be lower, a large number of oculomotor artifacts that are in the sample at the moment of imagination affect the final result. A large number of channels for recording EEG data leads to an increase in the covariance matrix, the accuracy becomes worse, and the computation speed increases. As the dimension of the covariance matrix grows, more samples are required to construct non-singular/non-degenerate covariance matrices. When nearly singular/degenerate covariance matrices are created, they cannot be efficiently handled with Riemannian geometry. In this case, methods based on Euclidean geometry will surpass Riemannian geometry. In the development of this experiment, it is planned to increase the accuracy of the classifier due to a more thorough adjustment of its parameters. If the results of the online classification remain at the level of the first experiment, it is proposed to reduce the number of electrodes from 18 to 9 by selecting the electrodes located on the sensorimotor area of the cerebral cortex: F3, Fz, F4, C3, Cz, C4, P3, Pz, P4.

5 Conclusion In this paper, BCIs based on motor imagination are considered. The emphasis is placed on the recognition of imaginary movements of the lower limbs, since in the continuation of this study it is planned to refine the current classifier for its possible use for controlling exoskeletons. For the classification, the algorithm based on Riemannian geometry was chosen, which allows calculating the covariance matrices for each EEG sample and calculating the distance between them in Riemannian space. Since covariance matrices contain spatial information (24 EEG channels cover the entire scalp of the subject), this approach allows combining spatial filtering and classification into one unique step.

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Offline testing of the Riemannian distance classifier gave an average accuracy of 71%, but values in the 80–90% range were expected. Online testing showed an accuracy of 65%. These results show that it is necessary to refine the current version of the classifier and test it on a larger number of subjects using different editing and setting of the classifier.

References 1. Son’kin, K.M., Stankevich, L.A., Khomenko, J.G., Nagornova, Z.V., Shemyakina, N.V.: Classification of electroencephalographic patterns of real and imaginary one-hand finger movements using the support vector method. Pac. Med. J. 56(2), 30–35 (2014) 2. Athanasiou, A., et al.: Towards rehabilitation robotics: off-the-shelf BCI control of anthropomorphic robotic arms. BioMed Res. Int. 2017, 1–17 (2017). https://doi.org/10.1155/ 2017/5708937 3. López-Larraz, E., et al.: Control of an ambulatory exoskeleton with a brain-machine interface for spinal cord injury gait rehabilitation. Front. Neurosci. 10, 359 (2016). https:// doi.org/10.3389/fnins.2016.00359 4. Do, A.H., Wang, P.T., King, C.E., Schombs, A., Cramer, S.C., Nenadic, Z.: Brain-computer interface controlled functional electrical stimulation device for foot drop due to stroke. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2012, pp. 6414–6417 (2012). https://doi.org/10.1109/EMBC.2012.6347462 5. King, C.E., Wang, P.T., Chui, L.A., Do, A.H., Nenadic, Z.: Operation of a brain-computer interface walking simulator for individuals with spinal cord injury. J. Neuroeng. Rehabil. 10 (1), 1–14 (2013). https://doi.org/10.1186/1743-0003-10-77 6. Lopes, A.C., Pires, G., Nunes, U.: Assisted navigation for a brain-actuated intelligent wheelchair. Robot. Auton. Syst. 61(3), 245–258 (2013). https://doi.org/10.1016/j.robot. 2012.11.002 7. Choi, J., Kim, K.T., Jeong, J.H., Kim, L., Lee, S.J., Kim, H.: Developing a motor imagerybased real-time asynchronous hybrid BCI controller for a lower-limb exoskeleton. Sensors 20(24), 7309 (2020). https://doi.org/10.3390/s20247309 8. Lee, K., Liu, D., Perroud, L., Chavarriaga, R., Millán, J.D.R.: A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers. Robot. Auton. Syst. 90, 15–23 (2017). https://doi.org/10.1016/j.robot.2016.10.005 9. Echtioui, A., Zouch, W., Ghorbel, M., Mhiri, C., Hamam, H.: A novel ensemble learning approach for classification of EEG motor imagery signals. In: International Wireless Communications and Mobile Computing (IWCMC), pp. 1648–1653 (2021). https://doi.org/ 10.1109/IWCMC51323.2021.9498833 10. Ganin, I.P., Kaplan, A.Y.: A brain–computer interface based on the P300 wave: presentation of complex ‘illumination + movement’ stimuli. J. High. Nerv. Act. Named I.P. Pavlov 64, 32 (2014) 11. Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), R1 (2007). https:// doi.org/10.1088/1741-2560/4/2/R01 12. Lotte, F., et al.: A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J. Neural Eng. 15(3), 031005 (2018). https://doi.org/10.1088/ 1741-2552/aab2f2

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13. Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., Scherer, R.: Autocalibration and recurrent adaptation: towards a plug and play online ERD-BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 20(3), 313–319 (2012). https://doi.org/10.1109/TNSRE.2012.2189584 14. McCrimmon, C.M., et al.: A small, portable, battery-powered brain-computer interface system for motor rehabilitation. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2776–2779 (2016). https://doi.org/10. 1109/EMBC.2016.7591306 15. Wang, H., Li, T., Bezerianos, A., Huang, H., He, Y., Chen, P.: The control of a virtual automatic car based on multiple patterns of motor imagery BCI. Med. Biol. Eng. Comput. 57 (1), 299–309 (2018). https://doi.org/10.1007/s11517-018-1883-3 16. Steyrl, D., Scherer, R., Faller, J., Müller-Putz, G.R.: Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier. Biomed. Eng./Biomedizinische Technik 61(1), 77–86 (2016). https://doi.org/10. 1515/bmt-2014-0117 17. Congedo, M., Barachant, A., Bhatia, R.: Riemannian geometry for EEG-based braincomputer interfaces: a primer and a review. Brain-Comput. Interf. 4(3), 155–174 (2017). https://doi.org/10.1080/2326263X.2017.1297192 18. Kapralov, N.V., Nagornova, Zh.V., Shemyakina, N.V.: Methods for the classification of EEG patterns of imaginary movements. Inform. Autom. 20, 94–132 (2021). https://doi.org/ 10.15622/ia.2021.20.1.4 19. Guan, S., Zhao, K., Yang, S.: Motor imagery EEG classification based on decision tree framework and Riemannian geometry. Comput. Intell. Neurosci. 2019, Article ID 5627156, 13 pp. (2019). https://doi.org/10.1155/2019/5627156 20. Trofimov, A.G., Skrugin, V.I.: Brain-computer interfaces. review. Inf. Technol. 2, 2–11 (2011) 21. Barachant, A., Bonnet, S., Congedo, M., Jutten, C.: Multiclass brain-computer interface classification by Riemannian geometry. IEEE Trans. Biomed. Eng. 59(4), 920–928 (2012). https://doi.org/10.1109/TBME.2011.2172210 22. Barachant, A., Bonnet, S., Congedo, M., Jutten, C.: Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing 112, 172–178 (2013). https://doi.org/10.1016/j.neucom.2012.12.039 23. Congedo, M., Barachant, A.: A special form of SPD covariance matrix for interpretation and visualization of data manipulated with Riemannian geometry. Am. Inst. Phys. Conf. Proc. 1641(1), 495–503 (2015). https://doi.org/10.1063/1.4906015 24. Yger, F., Berar, M., Lotte, F.: Riemannian approaches in brain-computer interfaces: a review. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1753–1762 (2017). https://doi.org/ 10.1109/TNSRE.2016.2627016

System Analyses in the Educational Process and Higher Education Management

Systemic Risks of Government Control Over Large-Scale Projects in the Development of the Russian Higher School Vladimir G. Khalin(&) , Galina V. Chernova , Alexander V. Yurkov , and Mikhail V. Zaboev Saint Petersburg State University, 7/9 Universitetskaya Emb., 199034 St. Petersburg, Russia [email protected]

Abstract. One of the directions of state management of personnel training within the framework of the national higher school is the development of a number of educational projects. The Project 5–100 is aimed at ensuring the global competitiveness of leading Russian universities – their entry into the authoritative world university rankings. Decisions taken within the framework of public administration, although aimed at achieving the goals of the Project, are not always accompanied by the expected positive results. It is proposed to describe the uncertain possibility of negative consequences of the adoption and implementation of a specific management decision through the risk of state management of this Project. In view of the fact that the consequences of the implementation of these risks carry the threat of destruction of the Russian higher education system itself, they must be considered as systemic. The high significance of the Project for the entire Russian higher school determined the need to identify systemic risks of public administration primarily at the federal level. To identify a particular systemic risk, the authors have developed a special procedure. The analysis showed that the implementation of most of the risks of managing on this Project did not allow achieving the goals set for it, that negatively affected the development of the entire system of Russian higher education. Keywords: Russian higher education
State educational policy
Government control
Systemic risks
Competitiveness
Rankings
Large-scale projects

1 Introduction The success in higher education has largely determined and still determines the competitiveness of not only national higher education, but also the country’s place in the entire world community. A significant contribution to the formations of national higher education systems become the establishment of world-class universities, the problems of which were considered primarily by foreign authors [3, 27, 28]. Later, this scientific research led to the analysis and classification of the forms of organization of higher education [6, 25], as well as to the study of the theory and practice of the evaluating of world rankings of universities [7, 35]. The development of Russian © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 525–537, 2022. https://doi.org/10.1007/978-3-030-98832-6_46

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higher education had a number of stages characterized by different levels of its competitiveness in the world labor market [20, 22, 31, 34]. The current stage of Russian higher education is characterized by the study of world experience in the functioning of competitive systems of higher education [11, 29], as well as reforms aimed at increasing its competitiveness [5, 17, 26, 32]. Large-scale all-Russia projects play a significant role in achieving the strategic development goals of the national higher education [10, 16, 23]. The development and implementation of such programs and projects are determined to a great extent by the quality of managerial decisions taken with respect thereto at multiple levels. For the Russian higher school, such levels of the development and adoption of managerial decisions for projects and programs which promote effective development of the national higher education system are the federal level, the industry level, and the level of higher education establishments. The aggregate of managerial decisions generated on the aforesaid three levels is the system of management of the national higher school. The modern stage of the development of the higher school of Russia is characterized by its essential renewal, including the development and adoption of large-scale projects and programs focused on the development of the Russian higher education system. That is why the objective of assessment of the quality of managerial decisions related to the development and implementation of appropriate projects and programs becomes relevant [4, 8, 10, 14, 18]. In the article, the objective and problems of evaluation of the quality of management of the Russian higher school are illustrated by the example of Project 5–100. The start of the generation and implementation of the project was Decree no. 599 “On measures for implementation of the state policy in the sphere of education and science” signed by the President of Russia Putin on the day of his inauguration May 7, 2012. In the Decree, the Russian Government (below the Government) was assigned to ensure “inclusion of at least five Russian universities in the world top 100 leading universities according to the world rankings of universities”. The objective set in the Decree determined the introduction of such a term as a globally competitive university. A university will be determined as globally competitive if it is one of the top 100 in at least one of the world rankings [13]. Later the action plan for addressing the objective set in Decree no. 599 was called the Project 5–100. It can be ranked as one of the most important projects for the renewal of the Russian higher school which should result in the existence of at least 5 globally competitive universities in Russia by 2020. Note that there had been only one globally competitive university in Russia—Lomonosov Moscow State University—by the time when Decree No. 599 was signed, i.e. in 2012. This subject of research was chosen on the basis of the fact that the comparison of the purposes and objectives initially set for the project with the actual results obtained on completion thereof gives a sufficiently full idea of the problems which exist in the course of development of any project or program related to the development of the Russian higher school. It is known that not all managerial decisions taken give an expected positive result. Part of them may cause undesirable adverse consequences. That is why it is important to be able to evaluate a decision from the very beginning from the point of view of possible risks associated with such a decision as an undetermined possibility of emergence of adverse consequences of the adoption and implementation of the

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managerial decision being assessed. Risks which are especially important in the course of reforming the Russian higher school are so-called systemic risks peculiar for the elements of the Russian higher education system and/or for the education system in general the realization of which may have adverse results for the Russian higher school system itself and eventually may cause the destruction of the same [12, 15, 33]. The Project 5–100 was focused on the effective development of the Russian higher education, so the risks associated with its adoption and implementation actually became systemic risks for the whole Russian higher school for the following reason. Theoretically speaking, their possible realization could cause such material damage to the higher education system in general that would pose a threat of destruction to the higher education system itself. That very fact gives reason to consider the risks related to the Project 5–100 as systemic risks of the Russian higher school. The purpose of this article is to evaluate the Project 5–100 as a tool to improve the global competitive position of a number of leading Russian universities in the context of realization of systemic state management risks and the consequences of the same for the development of the Russian higher school.

2 Materials and Methods: Managerial Decisions on Project 5–100 and Their Evaluation The Project 5–100 was initiated by the President of Russia Putin who entrusted the Russian Government to procure “inclusion of at least five Russian universities should in the top 100 of the world-leading universities according to the world university rankings” by his Decree of 07.05.2012 No. 599. Execution of the Project implied the development and implementation of interrelated managerial decisions (hereinafter, MD) on the level of the President, federal executive branches, and specific universities—members of the Project. It is known that the analysis of the quality of any MD provides for the audit of compliance with the requirements of a number of criteria, including, for example, such as “the MD has a clear purpose and clearly described problem-based situation”, “the MD is targeted and understandable as to in whose interests it is taken”, “the MD is well-reasoned and feasible”, “the MD is well resourced” etc. [9, 15]. Compliance with the requirements of all criteria of quality of the managerial decision being assessed will mean its conformity to the purpose for the achievement of which it was taken, i.e. it will mean its high quality. If the requirements of the relevant criteria for the quality of such MD are not complied with, adverse results related thereto may occur, i.e. results that contradict the purpose of the MD taken. The possibility of occurrence of an adverse consequence related to a certain MD can be described through risk with the two prime characteristics: the risk realization probability and the damage driven by such realization.

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We will approach the assessment of Project 5–100 from these very positions: • consider the most important Project-related MD taken on the level of President of Russia and federal executive branches; • assess the MD in terms of compliance with the requirements for the MD quality; • determine possible adverse consequences for each MD; • describe such possible adverse consequences through risks; • see whether such risks have been realized in fact and if yes, how this affected the resolution of the Project objectives. The most important MDs taken under the Project 5–100 were adopted on the following three levels: on the level of President of Russia in the form of Decree of 07.05.2012 No. 599, on the level of federal executive bodies—the Government, the Ministry of Education and Science and the Project 5–100 Board in the form of the aggregate of the Resolutions and orders the most essential of which is the Resolution of the Government of 16.03.2013 No. 211 “On measures of government support for leading Russian universities to increase their competitive position among the world’s leading research and education centers” (hereinafter, Resolution No. 211), on the university level in the form of the “Program of development of a certain university up to 2020” approved by the relevant order of the Russian Government. By the Resolution No. 211, it was planned to conduct an open competitive selection of universities for the right to receive a government grant to improve their competitive position among the world leading research and education centers. The total expenditures of the Russian federal budget for the Project 5–100 from 2012 to 2020 amounted to about $ 1.43 billion [1]. Results of Selection of Project Participants. In view of the special status (Federal Law of 10.11.2009 No. 259-FZ “On MSU and SPbU”) and the high positions in the world rankings, the two leading classical universities MSU and SPbU were granted relief from the competitive selection. All the other leading Russian universities which desired to take part in the project to improve their global competitive position were to comply with the requirements established by the Government and undergo the competitive examination. Following the results of the first competition held in 2013, 15 Russian universities were the winners: FEFU, KFU, MIPT, NUST MISiS, HSE University, MEPhI, Lobachevsky University, NSU, Samara University, SPbPU, ETU LETI, ITMO University, TSU, Tomsk Polytechnic University, UrFU. Following the results of the second competition in 2015, the following six universities have been announced as the winners: IKBFU, Sechenov University, RUDN University, Siberian Federal University (SibFU), University of Tyumen, SUSU [1, 14]. Thus the Government selected 23 leading universities of Russia (MSU, SPbU, and 21 competition winners) which received additional special governmental grants for the improvement of their competitive positions and were to ensure compliance with the Decree of President of Russia by 2020.

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3 Results: The Consequences of Studying the Project 5–100 Positions of Russian universities in world rankings of 2020 have shown [2, 24, 30] that the objective stated in the Decree of the President of Russia of 07.05.2012 No. 599 on the inclusion of at least Russian universities in the top 100 of the world-leading universities according to the world ranking of universities has not been achieved and this managerial decision has not been implemented, in particular: • in 2020–2021 only one Russian university—Lomonosov Moscow State University (MSU)—was among Top 100 both in ARWU and QS rankings; • no other Russian university was not only among Top 100 in 2020 but even among Top 200 of any authoritative common ranking of world universities; • universities—participants of the Project 5–100—Immanuel Kant Baltic Federal University (IKBFU), I.M. Sechenov First Moscow State Medical University (Sechenov University), and Tyumen State University (University of Tyumen), were not among Top 1000 of either of ARWU, QS, or THE; For reference: for the same period from 2012 to 2020 Top 100 of ARWU ranking included 6 universities from China [2, 15]. This clearly demonstrates that even with significant state financial support, large managerial projects in higher education may not achieve the targets without an economic justification for the attainability of the desired results. This fact seems to be demonstrative not only for Russia—see the Discussion below. Assessment of the Decree of the Russian President of 07.05.2012 No. 599 from the Perspective of Risks. Considering the signing of the Decree by the Russian President as a managerial decision (MD) taken on the President’s level and applying the decision theory to the Decree text itself, one may note that this MD cannot be assessed as an MD of satisfactory quality due to a number of systemic errors made in the course of the development and adoption of the same. For example, the text of the Decree does not include a clearly defined problematic situation; it is not clear who is interested in the adoption of such Decree, the sources and volumes of resourcing including financial support are not specified, and the MD is not substantiated, timely, and feasible. The low quality of the Decree text content caused the possibility of occurrence of adverse consequences for the Russian higher school which can be described, for example, by the following inherent risks: the risk of incapability of achievement of the Project purposes; the risk of incapability of ensuring high quality management of the Project 5–100 as an implementation of the state policy in the field of science and education; the risk of incapability of resourcing the Project 5–100; the risk of incapability of selecting the most important and high priority growing points for the development of the Russian higher school; the risk of incapability of creating a favorable management system in Russian universities, etc. It is known that risk is an undetermined possibility of realization of adverse consequences, therefore to give an ultimate answer to the question of the quality of the adopted Decree—the quality of an MD adopted on the level of the President, one should see whether any of the aforesaid risks have been realized in fact. Pitifully, some of those risks have been realized in practice. For example, realization of the risk of

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incapability of achieving the Project purposes is confirmed by the aforementioned data on the positions of the Russian universities in world rankings in 2020–2021. In support of such conclusions it is appropriate to remind that as early as 2014 on the basis of information from international and Russian databases (InCites, Scopus, Web of Science, etc.), data on key performance indicators of leading Russian universities and application of data mining methods to the analysis of such indicators (clustering, neural networks, Kohonen maps, etc.) it has been proved that the Project 5–100 was neither soundly based nor feasible [13, 15, 19]. An essential systemic error made in the course of the development and adoption of this MD was also the fact that the President of Russia only relied on the opinion and recommendations of his experts and assistants without in-depth discussion of such MD with the university and scientific community. Competent Russian scientists and experts in the field of Russian higher education such as, for example, academicians V. Sadovnichiy, Zh. Alferov and L. Verbitskaya and others were not involved in the adoption of such an important and budget cost-intensive MD [15]. Risk assessment of managerial Decisions Taken Under the Project 5–100 by the Government and the Ministry of Education and Science. As the analysis has shown, the Resolution No. 211 as an MD adopted on the federal level of the executive branch also cannot be deemed to be satisfactory because a number of systemic errors were made in the course of the development and adoption of the same. In particular, the Government failed to ensure: • material increase in public expenditure for the development of the Russian higher education system in view of the adoption of the decision on the commencement of implementation of the large-scale and cost-intensive Project 5–100 by the President of Russia; • maintenance and stabilization of public expenditure for the sustainable development of the higher education system in Russia. Moreover, the Government allowed reduction of the share of state education expenditure. The share of expenditure of the Russia consolidated budget and the budgets of public non-budgetary funds for education in GDP reduced from 4.3% from the Project start time fell to 3.7% in 2020 [21]; • procurement of additional targeted financing from non-budgetary sources for successful implementation of the Project 5–100. Moreover, the Russia Government, in order to re-distributed state budgetary funds within the Russian higher school among the leading universities—participants of the Project and other state universities of the country, initiated adoption of a number of regulatory documents which materially worsened resourcing of state universities which were not involved in the Project and the conditions of work of the faculty and academic staff in such universities. The reduction down to 40% of universities and 80% of branches plus the increase in the number of students per university professor up to 12:1 by 2018 in fact is the reduction of the academic staff headcount in the Russian universities by at least 30% [14];

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• involvement and participation of competent Russian scientists and experts in the field of Russian higher education in expert evaluation, preparation, and adoption of managerial decisions on implementation of the Project; • high quality of government control at implementation and performance of Project 5–100 on the part of the Russian Government, the Ministry of Education and Science, and the Board of Project 5–100. For example, for the Project implementation period from 2012 till 2020 the composition of the Board approved by the Resolution of the Government of 06.04.2013 No. 529-r changed 5 times by the following Resolutions of the Russian Government: of 10.02.2015 No. 193-r, of 07.12.2016 No. 2609-r, of 24.01.2018 No. 69-r, of 20.10.2018 No. 2269-r. 9 members were excluded and 8 new members were introduced. Analysis of professional competences of the chairman, deputy chairmen, and members of the Board shows that pitifully neither of them is a competent expert in the field of management and development of the Russian higher education system [1]; • high quality of regulatory support of the Project implementation. For example, the text of the Resolution No. 211 changed 10 times according to the Resolutions of the Government of 30.12.2013 No. 1311, of 26.12.2014 No. 1519, of 22.05.2015 No. 491, of 31.10.2015 No. 1176, of 09.04.2016 No. 287, of 10.02.2017 No. 171, of 15.11.2017 No. 1382, of 14.07.2018 No. 822, of 05.10.2018 No. 1202, of 30.12.2020 No. 2372 [1]; • objective selection of candidate universities for participation in Project 5–100 and substantiated distribution of additional government grants among the universities— participants of the Project. This is confirmed in particular by the fact that some Russian universities which were not involved in the Project, e.g. Bauman MGTU and MGIMO, were ranked higher in world rankings than a number of universities— participants of Project 5–100 [24, 30]; • creation of a favorable management system in Russian state universities which makes it possible to involve and keep the best professors, researchers, and students and ensure real participation of academic personnel in the solution of key matters of university management, including procedures of competitive selection of faculty staff and academic researchers. For example, the new Regulation on the procedure of filling the vacancies of academic and teaching staff which was approved by the order of the Ministry of Education and Science of 04.12.2014 No. 1536 does not mention the role of the department, the faculty board of studies, and the university board of studies in the conduct of competitive selection for filling the vacancies of academic and teaching staff and researchers. At the same time, the document provides for the substantial increase in the role of the university administration in the matters of recruitment, dismissal, and redundancy of academic and teaching staff of universities [14]. The analysis of published sources shows that the number of state-funded students and professors of state universities has drastically reduced, and the standing and stature of academic profession and higher education in general has gone down a lot. Thus, the number of state universities reduced from 653 to 497 (by 24%); the number of state-funded students dropped down from 2619 thousand to 1905 thousand people (by 27.3%); the number of academic and teaching staff in Russian universities reduced from 356.8 thousand to 223.1 thousand people (by 37.5%) [21]. The number of postgraduate students reduced by 40% to 93.5

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thousand people, and the share of postgraduates who successfully defended their theses on time fell down to 12% [32]; • improvement of financial position and social status of academic staff of Russian state universities. For example, average salary of some full-time professors who perform the training activities to the full extent even in the leading Russian universities may be below 200% of the average gross payroll in the respective region of Russia. Academic staff of universities is practically excluded from preparation and adoption of important managerial decisions on all levels [14, 15]. Thus, managerial decisions on the Project 5–100 has resulted in manifestation of a wide range of adverse consequences which are the realization of risks systemic for the higher education in Russia. They include: the risk of incapability of implementation of the Project purposes; the risk of incapability of ensuring sustainable development of the Russian higher school under the conditions of implementation; the risk of incapability of resourcing; the risk of incapability of improving the financial position and social status of academic staff of Russian state universities. Assessment of Management Decisions Taken Under the Project 5–100 at the Level of Participating Universities in Their Implementation Context of Their Roadmaps and Development Programs Until 2020. Data on the target indicators of the roadmaps and development programs of the universities participating in the Project presented by their positions in world rankings in the Table 1 below. Table 1. Target indicators 2020 and actual positions in world rankings. No University 1 2 3 4 5 6 7 8 9 10 11 12 13

MSU SPbU MIPT NSU MEPhI UrFU HSE TSU ITMO KFU NUST MISiS SPbPU Tomsk Polytechnic 14 FEFU 15 Samara University 16 Lobachevsky University

Target indicators ARWU QS

THE

Actual positions ARWU QS

THE

Indicators reached ARWU QS THE

50 100 151–200 300–400 – – – – – – – – 401/500

50 100 50–100 90–100 51–101 100 51–100 51–100 171 99 – 50–101 51–101

50 100 75–100 150–200 121–170 250 151–200 151–200 251–300 115 100 150–200 151–200

93 301–400 401–500 501–600 701–800 701–800 801–900 801–900 901–1000 901–1000 901–1000 – –

74 225 281 228 314 331 298 250 360 370 428 401 401

174 601–800 201–250 601–800 401–500 1001 251–300 501–600 501–600 601–800 601–800 301–350 801–1000

Yes No No No – – – – – – – – No

Yes No No No No No No No No No – No No

No No No No No No No No No No No No No

– –

200 950 251–300 -

– –

– –

No No

No –

–

1–100

–

493 1000+ 591– 1000+ 600 601– 1000+ 650

–

No

No

251- 300

(continued)

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Table 1. (continued) No University

Target indicators ARWU QS

THE

Actual positions ARWU QS

17 ETU LETI

–

51–100

51–101

–

18 Sechenov University 19 RUDN University 20 IKBFU 21 SibFU

–

301–350 351–400

–

701– 1000+ 750 – –

–

301–351 501–600

–

326

– 100 – 401–500 201–250 301–350

– –

– No

No No

– No

22 University of Tyumen 23 SUSU

–

401–450 –

–

– 1000+ 1001 1000+ + – –

–

No

–

–

251–300 401–500

–

801– 1001+ 1000

–

No

No

THE

Indicators reached ARWU QS THE –

No

No

–

No

No

801–1000 –

Yes No

For MSU, SPbU and MEPhI the table shows the highest position (up to 50 places) on the leading world rankings. It should be noted that, according to the programs for the development of these universities, the indicator is considered fulfilled if the target value is achieved in at least one of the three ratings. For other universities their positions in the overall ranking (up to 50 places) are shown. An analysis of the roadmaps and development programs up to 2020 of the universities participating in the 5 in 100 Project showed that the target indicators indicating the position of the university in 2020 in the authoritative world rankings were fulfilled as follows: • only MSU fulfilled its obligations on the value of the indicator of entry into world rankings. • RUDN University fulfilled its obligations under this indicator for the QS ranking (326th place), but did not fulfill them according to the ranking THE; • The remaining 21 out of 23 universities participating in the Project 5–100 in 2020 did not fulfill their obligations under this indicator for any of the world's leading rankings of the general list; • The values of the universities target indicators in 2020 world rankings (general list), for 8 out of 23 universities participating in the Project 5–100 (FEFU, Sechenov University, RUDN University, Samara University, SibFU, University of Tyumen, ITMO University, SUSU) were initially below the 150th place. Consequently, within the framework of the Project 5–100, the management quality only at MSU can be considered satisfactory. For all the other 22 participating universities, it cannot be considered satisfactory, since these universities have not reached the mandatory values of indicators on their roadmaps and development programs in 2020. Such a situation for these universities confirms the realization of the risks of nonachievement by indicators in the development programs until 2020.

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4 Discussion Comparing and assessing the competitiveness of leading universities using authoritative world rankings has a number of obvious advantages, such as: simplicity, clarity, regularity and availability. However, ratings as a tool to assess the performance of leading universities has a number of significant drawbacks. For example, high positions of a university in one rating do not guarantee it high places in other ratings; subjective choice of criteria and ranking methods. New mathematical methods based on artificial neural networks [13, 15, 19] make it possible to obtain more accurate estimates of the universities positions in authoritative world rankings (see Fig. 1).

Fig. 1. Some universities’ global competitiveness: clustering with Kohonen map.

Therefore, it is advisable to use these methods to prepare, substantiate and assess the quality of managerial decisions to improve the universities competitiveness. For example, the cluster analysis of significant parameters of global competitiveness following the results of the EACEA Erasmus + project [36] carried out by 11 European universities clearly proved that LUT University (Finland) outperforms many of the world’s leading universities in key quantitative indicators. Based on our research on a large project to increase the global competitiveness of leading Russian universities as the Project 5–100 is, we seem to have been able to understand the reasons for the failure of the project plans despite significant financial state support.

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Since large projects in the field of higher education are currently being implemented in EU at the interstate level [37], it seems the results of our research may be useful not only for Russia but also for Europe and the New World as well.

5 Conclusion The analysis of managerial decisions in relation to the development and implementation of Project 5–100 which is described in the article has shown that, unfortunally, in Russian higher school some systemic risks have been realized. Assessment of the performance of the Project conducted on the basis of comparison of the purposes and objectives set for the Project and the results obtained upon the implementation of the same has confirmed its non-sustainability: only one Russian university—MSU—was globally competitive in 2000, like it was prior to implementation of the Project. The reason was the fact that managerial decisions developed, adopted, and implemented in the framework of the Project proved to be of extremely low quality even on the federal level. It determined the actual realization of most of the risks related to the Project. The Project 5–100 was a large-scale one, the managerial decisions taken under the Project covered the whole higher education system of Russia, therefore the negative impact of risks seemingly related to this Project only proved to be so essential for the whole Russian higher education system that they shall be viewed as systemic risks. Based on quantitative information on the activities of leading universities from authoritative databases such as InCites, Scopus, and Web of Science, and new mathematical methods for analysis and forecasting of university indicators in world rankings using artificial neural networks the authors managed to obtain [13, 15, 19]: • an objective assessment of the global competitiveness of leading Russian universities in 2020; • a quantitative assessment of the targets for the performance indicators of Russian universities upon reaching which they might get the Top 100 in selected authoritative world rankings in 2020; • an objectively assess of the systemic risks and quality of public administration of the Project 5–100 at all its levels.

References 1. 5–100 Russian Academic Excellence Project. Documents. https://5top100.ru/documents/ regulations/. Accessed 12 Oct 2021 2. Academic Ranking of World Universities (ARWU), http://www.shanghairanking.com/. Accessed 12 Oct 2021 3. Altbach, P.G., Salmi, J.: The Road to Academic Exellence. The World Bank, Washington, D.C. (2011)

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4. Balashov, A.I., Khusainova, V.M.: Proekt “5–100”: pogonja za global’noj konkurentosposobnost’ju ili instrument perezagruzki nacional’noj sistemy vysshego obrazovanija? (Project “5–100”: The Pursuit of Global Competitiveness or a Tool to Reload the National System of Higher Education?). Èkonomika i upravlenieenie. Econ. Manag. 10(132), 79–86 (2016). (in Russian) 5. Balykhin, G.A.: Sil'nomu gosudarstvu – sil'noe konkurentnoe obrazovanie (Strong state strong competitive education). Vestnik obrazovaniya Rossii. Bull. Educ. Russ. 14 (2006). (in Russian) 6. Clark, B.: Creating Entrepreneurial Universities: Organizational Pathways of Transformation. IAU Press, Oxford, Pergamon (1998) 7. Douglass, J.: The New Flagship University: Changing The Paradigm from Global Ranking to National Relevancy, 1st edn. Palgrave Macmillan, London (2016) 8. Drugova, E.A., Pleshkevich, I.B., Klimova, T.V.: Transformation of the personnel policy of Russian universities participating in project 5–100: the case of National Research Nuclear University MEPhI. Vysshee obrazovanie v Rossii. High. Educ. Russ. 30(6), 9–26 (2021). https://doi.org/10.31992/0869-3617-2021-30-6-9-26 9. Fatkhutdinov, R.A.: Upravlencheskie reshenija: Uchebnik. (Management Decisions: Textbook. INFRA-M Publisher, Moscow (2009). (in Russian) 10. Froumin, I. Lisyutkin, M.: The state as the driver of competitiveness in Russian higher education: the case of project 5–100. In: International Status Anxiety and Higher Education: Soviet legacy in China and Russia. CERC-Springer, Hong Kong (2018) 11. Issledovatel'skie universitety SShA: mekhanizm integratsii nauki i obrazovaniya (Research universities in the United States: a mechanism for integrating science and education). In: Supyan, V.B. (ed.) Magister Publication, Moscow (2012). (in Russian) 12. Khalin, V.G., et al.: Riski upravleniya pri formirovanii blagopriyatnoi sistemy upravleniya v vedushchikh universitetakh Rossii (Risks of management in forming a favorable management system in the leading university of Russia). Upravlenie riskom. Risk Manag. 82(2), 53– 56 (2017). (in Russian) 13. Khalin, V. (ed.): Global Competitiveness of Leading Universities: Models and Methods for Its Evaluation and Forecasting. Prospect Publishing, Moscow (2018) 14. Khalin, V. (ed.): Rossiiskie universitety v usloviyakh tsifrovizatsii: matematicheskie i instrumental’nye metody otsenki kachestva upravleniya (Russian Universities in the Context of Digitalization: Mathematical and Instrumental Methods for Assessing the Quality of Management). Prospect Publishing, Moscow (2019). (in Russian) 15. Khalin, V.G., Chernova, G.V.: Proekt 5–100: sistemnye riski gosudarstvennogo upravlenija i ih realizacija (Project 5–100: system risks of public administration and their implementation) Upravlenie riskom. Risk Manag. 98(2), 3–15 (2021). (in Russian) 16. Yudkevich, M.M. (ed.): Kontrakty v akademicheskom mire (Contracts in the Academic World). HSE University Publication, Moscow (2011).(in Russian) 17. Kurbatova, M.V., Levin, S.N.: Effektivnyi kontrakt v sisteme vysshego obrazovaniya RF: teoreticheskie podkhody i osobennosti institutsional’nogo proektirovaniya (Effective contract in the higher education system of the Russian Federation: theoretical approaches and features of institutional design). Zhurnal institutsional’nykh issledovanii. J. Inst. Stud. 5(1), 55–80 (2013). (in Russian) 18. Longden, B.: Ranking indicators and weights. In: Shin, J.Ch., Toutkoushian, R.K., Teichler, U. (eds.) University Rankings: Theoretical Basis, Methodology and Impacts on Global Higher Education. Springer, Dordrecht, pp. 73–104 (2011). https://doi.org/10.1007/978-94007-1116-7_5

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19. Meleshkin, M.I.: On prospects of Russia's higher education institutions’ entering the top 100 world leading universities according to the Times Higher Education ranking. Econ. Anal.: Theory Pract. 19(370), 56–62 (2014) 20. Obrazovanie i obshchestvo: gotova li Rossiya investirovat’ v svoe budushchee? Doklad Obshchestvennoi palaty Rossiiskoi Federatsii (Education and Society: is Russia Ready to Invest in Its Future? Report of the Public Chamber of the Russian Federation0. HSE University Publication, Moscow (2007). (in Russian) 21. Obrazovanie v tsifrakh: 2020: kratkii statisticheskii sbornik (Education by the numbers: 2020: a concise compilation of statistics). In: Gokhberg, L.M., Ozerova, O.K., Sautina, E.V., Shugal, N.B. (eds.) HSE University Publication, Moscow (2020). https://www.hse.ru/mirror/ pubs/share/404878648.pdf. (in Russian) 22. Obrazovanie, kotoroe my mozhem poteryat’. Sbornik. (Education that we can lose. Collection.) In: Sadovnichij, V.A. (ed.) MGU Publications, Moscow (2002). (in Russian) 23. Paleari, S., Donina, D., Meoli, M.: The role of the university in twenty-first century European society. J. Technol. Transf. 40(3), 369–379 (2014). https://doi.org/10.1007/ s10961-014-9348-9 24. QS World University Rankings. https://www.topuniversities.com/university-rankings/worlduniversity-rankings/. Accessed 12 Oct 2021 25. Rozovsky, G.: Issledovatel’skie universitety: amerikanskaya isklyuchitel’nost’? (Research Universities: American Exceptionalism?). Voprosy obrazovaniya. Educ. Stud. Moscow 2, 8– 19 (2014). (in Russian) 26. Rubin, Yu., Emelyanov, A.: Standartizatsiya kak faktor konkurentosposobnosti vysshego obrazovaniya (Standardization as a factor in the competitiveness of higher education). Vysshee Obrazovanie v Rossii. High. Educ. Russ. 11, 28–41 (2005). (in Russian) 27. Salmi, J.: The Challenge of Establiching World-Class Universities. World Bank, Washington, DC (2009) 28. Salmi, J., Froumin, I.: Excellence initiatives to establish worldclass universities: evaluation of recent experiences. J. Educ. Stud. 1, 25–69 (2013) 29. Tambovtsev, V.L., Rozhdestvenskaya, I.A.: Reforma vysshego obrazovaniya v Rossii: mezhdunarodnyi opyt i ekonomicheskaya teoriya (Reform of higher education in Russia: international experience and economic theory). Vopr. Ekon. 5, 97–108 (2014). (in Russian) 30. THE’s rankings, https://www.timeshighereducation.com/world-university-rankings. Accessed 12 Oct 2021 31. Timoshenko, S.P.: Inzhenernoe obrazovanie v Rossii (Engineering Education in Russia). PIK VINITI Publications, Lyubertsy (1997).(in Russian) 32. Torkunov, A.V.: University as a part of national economy. Voprosy Ekonomiki. Econ. Issues 12, 111–122 (2019). https://doi.org/10.32609/0042-8736-2019-12-111-122. (in Russian) 33. Upravlenie finansovymi riskami vysshego professional'nogo obrazovaniya Rossii v usloviyakh ego modernizatsii: v 2 kn. (Financial risk management of higher professional education in Russia in the context of its modernization) In: Grishina, V.I., Khominich, I. P. (eds.) Moscow, Plekhanov Russian University of Economics (2014), vol. 2. (in Russian) 34. Volkov, A., Livanov, D., Fursenko, A.: Vysshee obrazovanie: povestka 2008–2016 (Higher education: 2008–2016 agenda). http://expert.ru/expert/2007/32/vysshee_obrazovanie_2008/. Accessed 12 Oct 2021. (in Russian) 35. Yudkevich, M., Altbach, P.G., Rumbley, L.E. (eds.): The Global Academic Rankings Game: Changing Institutional Policy, Practice, and Academic Life. Routledge (2016) 36. PWs@PhD Project. http://fase.it.lut.fi/. Accessed 04 Dec 2021 37. European Education and Culture Executive Agency (EACEA) EU's programme https:// www.eacea.ec.europa.eu/grants/2021-2027/erasmus_en. Accessed 04 Dec 2021

A System Approach for Cognitive Learning in Digital Transformation of Education Alexander V. Rechinskiy , Liudmila V. Chernenkaya(&) and Vladimir E. Mager

,

Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya Street 29, 195251 St. Petersburg, Russia [email protected]

Abstract. This work is devoted to exploring the optimal resource (functional) state (ORS) of university students to improve the education quality and to evaluate the possibility of managing their psycho-emotional state by themselves in conditions of stress and cognitive load. Digitalization of education is characterized by technological breakthroughs, the processing of big data, the implementation of artificial intelligence, cloud technologies, machine learning development, the global educational landscape changing. It leads to a cognitive load increase, and in this regard, the development and experimental study of a model for monitoring and evaluating the ORS index of university students are relevant and strategically important. Algorithms for studying students’ ORS under stress and high cognitive load are considered. A model of ORS index monitoring has been developed based on the system approach and decomposition of psycho-emotional state, and tools for ORS studying have been proposed. A layout of a software model for the evaluation ORS index has been developed. The developed polyeffector model has no direct analogs. The hypothesis of the study has been experimentally confirmed: the assimilation of educational materials is by persons who are in an ORS compared to persons who are in a non-optimal resource (functional) state. Keywords: Decomposition of training quality system
Optimal resource (functional) state
Psycho-emotional state
Polyeffector model

1 Introduction The digital economy is rapidly developing and penetrates all spheres of activity, including educational activities. Digitalization of education activates system technological breakthroughs, the development of new ways of information processing and assimilation, which inevitably leads to a change in the educational system and the development of the human potential in conditions of required digital skills and abilities deficit. If the previous stage of the educational system development was characterized by computerization and digitization of various objects and processes, now the digitalization stage is characterized by the processing of big data, implementation of artificial intelligence, cloud technologies, machine learning technologies development, as well as the global educational landscape changing. All changes in the modern © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 538–547, 2022. https://doi.org/10.1007/978-3-030-98832-6_47

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educational system lead to a rapid increase in the cognitive load on all participants of the educational process. In this regard, the development and experimental study of a model for monitoring and evaluating the optimal resource (functional) state index of a university’s students and the teaching staff is relevant and strategically important [1]. The goal of this work is to explore the optimal resource (functional) state of university students to improve the education quality and to evaluate the possibility of managing by themselves their psycho-emotional state in conditions of stress and cognitive load. In this study, a comprehensive psycho-physiological approach is used, which provides an increase in the objectivity of evaluation of the optimal resource (functional) state of a person in the process of work based on modern, perspective trends in the field of psychophysiology and neural technology. Among them: methods of functional biocontrol, analyzing and monitoring technologies of a person’s neural-biological and psychological status, and methods for determination of a person’s psycho-emotional states.

2 Justification of the Research Method The optimal resource (functional) state is characterized by a high level of efficiency with the full compensation of body losses. During the learning process, the student's functional state is changed. If the deviations of the functional state go beyond the zone that is optimal for this type of activity, then such deviations can cause a decrease in the educational process quality, on the one hand, and a decrease in the assimilation quality of new knowledge and skills, on the other hand. To study the optimal resource (functional) state of students, we will use data of the following psycho-emotional states: 1) 2) 3) 4)

stress; cognitive load; passion; concentration.

It is possible to ensure the students’ cognitive abilities quality only if conditions are created for simultaneous maximization (improvement) of all these four states, which are multifunctional systems. To understand how improvements can be affected, it is necessary to determine which factors and parameters of states should be operated on. To do this, we bring brief characteristics of each of the listed states: Stress. This is the corresponding nervous system state. It is described by a set of nonspecific adaptive (normal) reactions of the organism to impacts of various adverse factors-stressors (physical or psychological) that violate its homeostasis — the ability of an individual to coordinate various reactions, allowing to maintain balance, adapt correctly to environmental changes and continue to develop. Cognitive Load. In general, this is the total amount of mental effort used in a person’s memory. There are distinguished: the external cognitive load (determined by the educational materials assimilation quality), relevant cognitive load, and the internal

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cognitive load (determined by the level of complexity associated with a specific educational topic). Passion. This is a positive, satisfying, and study-related phenomenon that is characterized by energy, enthusiasm, and absorption. Perhaps this is the most difficult state to achieve with distance learning. Instead of receiving verbal approval from the teacher during face-to-face communication, which is the highest form of motivation, students are forced to confirm their knowledge by completing an increasing number of assignments and tests. In addition, an initiative student may not only disagree with the proposed answers to the test questions but also disagree with the phrasing of the question itself. However, he is practically deprived of the opportunity to argue and prove his point of view, which leads to disappointment. The Concentration of Attention. It depends on the interaction of several multilevel systems of the student’s psychics: arousal, vigilance, orientation, and managing control. The posterior associative attention system covers the parietal lobe of the cerebral cortex, the thalamus, and midbrain areas associated with eye movement, and reflects the processes of visual-spatial attention. Managing control is one of the attention systems responsible for the selection of information, coordination and execution of relevant processes, and suppression of irrelevant ones. Mainly it is activated during conflict resolutions. It is obvious that all of the above factors are interrelated and intertwined in the consciousness and person psychics, and it is not seem to identify the most significant among them. Therefore, it is necessary to find and select direct or indirect methods that will allow to “digitize” (measure, evaluate) these factors and parameters, and strive to improve them to achieve the optimal resource (functional) state of students. The ability to sustain the optimal level of the nervous system resource (functional) state for as long as possible without significant deviations from this level is an essential factor in the stability of a person’s work in any activity. Therefore, the results of this study could be used in other fields of activity, not limited only by educational activities. The study uses a comprehensive psycho-physiological approach that will assure the objectivity of the person’s optimal resource (functional) state evaluation in the process of work based on perspective directions in the field of psychophysiology and neural technologies: • Methods of functional bio-control, which represent an interdisciplinary integration of medicine, biology, and technology. Currently, this successfully developing area of science and practice has already received international recognition. • Analyzing and monitoring technologies of a person neural biological and psychological status and methods for objective determination of person psycho-emotional states using the construction of polyeffector models. These methods are currently being actively developed due to the emergence of wireless and contactless sensors that make available measurements of human biometric data to a large number of research groups. A polyeffector approach that allows simultaneous registration of an organism several reactions that occur as a response to the irritant action. The use of a polyeffector approach can significantly increase the reliability and the authenticity of a person’s condition diagnosis, since the study of only one physiological indicator, as a rule,

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cannot give an unambiguous answer about the testee condition. The principle of polyeffector registration consists of simultaneous recording and subsequent analysis of a complex of reactions (indicators). The developed polyeffector model is based on the EEG data collection and processing; it defines a person’s psycho-emotional states: stress, passion, cognitive load, and concentration, and reflects the user’s index of the ORS. An integrated psycho-physiological approach to the analysis and objective evaluation of a person’s functional state in the course of work is currently in demand; research in this area is carried out by groups of scientists in Switzerland, Japan, Greece, and Canada. This determines the prospects and timeliness of the conducted research. During the research it is necessary to solve the following tasks: • to develop a multi-effect model for monitoring and evaluating the index of students optimal resource (functional) state; • to develop a software for experimental research, monitoring, and evaluation of the index model of the students’ optimal resource (functional) state; • to conduct an experimental study of the developed model for monitoring and evaluation of the index of students’ optimal resource (functional) condition. As a method for the brain electrical activity assessment within the framework of the developed model, the registration of an electroencephalogram (EEG) from sensors located on a person’s forehead is used. Currently, this signal can be recorded using portable devices — neural interfaces, which make it possible to use the model outside the conditions of the laboratory EEG registration. Stress is characterized by a set of adaptive reactions of an individual to an external event, the impact of which is associated with the formation of certain physiological and behavioral reactions [2], while the acute stress (the stage of anxiety due to Selya) and chronic (the stage of exhaustion) forms various reactions of the organism. Moderate physiological stress (the resistance stage), caused, for example, by physical exercise, can be registered using EEG [3]. By the Yerkes-Dodson law, for the effective implementation of activities, it is necessary to support an optimal level of activation (optimum), which, along with cognitive characteristics and changes in the functional state, is an important factor in the productivity of activity. Stress acts as a variable affecting the support of an optimal activation: with excessive short-term stress, activation can take on an excessive and chaotic character, destroying the course of activity; with the prolonged (chronic) stress, activation may decrease due to the reduction of motivational components of activity. Thus, it is important to support an optimal stress level. Stress is a complex biological reaction of the organism in the response to environmental changes. The stress response is associated with various physiological manifestations and is carried out due to neuro-humoral regulation, including the sequential activation of a cascade of hormones, such as adrenaline and cortisol [4]. In studies involving both animals and humans, it has been shown that early stress experienced in childhood can affect individual response to stress [5], affecting also the cortisol level [6], and such changes can persist throughout life. The influence of genetic and environmental factors associated with stress can lead not only to functional, but also to structural changes in the brain [7]; changes occur as well at the cellular level [8], and also at the level of communications of the brain neural networks [9]. Considering

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stress as a factor of professional activity, it should be considered that it is associated with the realization of cognitive functions, thereby affecting the effectiveness of tasks. Stress affects the performance of tasks with constant attention [10, 11]. Stressful states can be quantified by recording direct indicators of brain electrical activity (EEG). Studies have shown that activation of the prefrontal cortex of the right hemisphere is associated with increased cortisol levels [12, 13]. The increase in frontal asymmetry is a sufficiently stable indicator, it is registered already at early stages of ontogenesis (in six-month-old infants) during the formation of emotions of fear and sadness, and is accompanied by an increase in cortisol levels [14]. The data are replicated in other studies; the stability of the effect is demonstrated [15]. The Schore review [15] also reports that violations of affection, mainly associated with the right hemisphere, lead to a violation of its development in ontogenesis. Some studies have also shown the involvement of the right frontal region in the realization of emotional reactions with negative coloring. Tomarken with colleagues [16] showed similar results in videos that cause negative emotions, in particular, fear. Wittling [17] supposed that the right hemisphere is endowed with a unique response system that prepares the body to effectively solve problems coming from the external environment. Presumably, important adaptive systems of the body — hypothalamic-pituitaryadrenocortical, as well as sympathetic adrenocortical-medullary — are under the right hemisphere control. These data are consistent with Davidson’s motivation model, according to which frontal activation of the left hemisphere is associated with the desire for a stimulus, while frontal activation of the right hemisphere reflects the avoidance of a stimulus. In this model, a certain consensus is reached on the relationship between negative emotions and stress, which is the justification for using the frontal asymmetry indicator as an indicator of stress reactions. Table 1 presents a list of input data for calculating the state of stress. Table 1. List of input data for calculating the stress state. Name of the registered variable Sensor AF7

Designation

Unit of measurement

Description

DAF7

mV

Sensor AF8

DAF8

mV

Signal flow from the electrode at the point AF7 via the “MCN” system Signal flow from the electrode at the point AF8 via the “MCN” system

Thus, the initial data for calculating the state of stress will be obtained by registering an EEG from two sensors: AF7, AF8.

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3 Description of the Monitoring and Evaluation Model Algorithm The registration of indicators for stress evaluation is carried out by two methods, depending on the specifics of indicators: 1. Collecting data on the user activity: entering data using the keyboard and mouse, starting and ending of processes, operations in the software development environment and other professional actions, physical actions of the user, etc. 2. Registration of a person’s electrophysiological parameters, allowing the evaluation of his psycho-physiological state, using an electroencephalogram (EEG). For the flow of the EEG signal from the electrodes AF7, AF8, recorded in the “raw” form, the following sequential processing takes place: • registration of electrical voltage fluctuations under electrodes; • filtering of artifacts (eye movements, facial muscle tension, jaw, high under electrode resistance); • passing of the signal through a band-barrage filter and Fourier transformation; • estimation of the spectral power density in the alpha range under each electrode, highlighting average values, standard deviations during the current analysis period; • saving data on average values and standard deviations of a particular user in a database (DB). h Recorded data represent a time-varying vector of data flows D ¼ D1 ðtÞ; ::: ; i Dn ðtÞ , based on which special indicators – indexes characterizing various aspects of the human condition – are calculated. The model for the index of a person’s resource (functional) state monitoring and evaluating uses data on personalized values of cognitive load, concentration, enthusiasm, and stress indexes at the input. The optimal resource (functional) state is determined by: • determination of the stress state correlate based on highlighted left-hemisphere and right-hemisphere alpha rhythms; • determination of the fascination or monotony state based on the highlighted lefthemisphere theta rhythms, right-hemisphere alpha and theta rhythms; • determination of concentration based on highlighted left-hemisphere beta rhythms, normalization of each parameter values; • determination of the cognitive load state correlate based on left-hemisphere theta activity; • determination of the resource state intermediate value, normalization of the resource state intermediate value, determination of the resource state. After determining the resource state, a check is carried out for compliance with the boundary conditions of the optimal and non-optimal state, and a corresponding note in the database is made about the user’s state, which can then be used to determine the sections of educational content that cause the optimal or non-optimal state of listeners.

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4 Description of Software for Conducting an Experimental Study of the Model To conduct an experimental study of the monitoring and evaluation model for determining the optimal resource (functional) state index of university students, a layout of a special software model for monitoring and evaluation of the optimal resource state index (hereinafter referred to as the layout of the SSM ORSI) was developed. The layout of the SSM ORSI is destined for biometric data registration. The SPO IORS layout functionality includes: • user authentication; • device customization and connecting a neural interface; • registration of a person’s biometric data from a personal portable device: the EEG signal is recorded from the under electrode points AF7 and AF8 of the neural interface and loaded into the database for data accumulation and storage on the server; • processing of recorded signals based on algorithms necessary for the implementation of the functional state models; • visualization of the user’s current functional state: displaying pictograms of stress (emotional load), cognitive load, enthusiasm, and concentration; • accumulation and storage of data in a database, which is a centralized repository of information designed to store personal and group events of users within the framework of the SSM ORSI layout. The SSM ORSI layout provides connection and simultaneous operation of at least 20 users, and consists of the following components: 1) a component that registers a person’s biometric data from a personal portable device; 2) a component that supports the recorded signal processing and implements the algorithms needed for the SSM ORSI model implementation; 3) a component that realizes the visualization of results of the developed SSM ORSI model; 4) database for the accumulation and storage of data. The SPO IORS layout is realized as a mobile application for the Android operating system version 6.0. The component that registers a person’s biometric data from a personal portable device using the Bluetooth-connected Muse neural interface registers the EEG signal from the AF7 and AF8 under electrode points and then uploads them via the Internet to the data accumulation and storage database (PostgreSQL) located on a server that supports the processing of recorded signals and implements algorithms needed for the SPO IORS model realization. During the online program operation in the process of students’ psycho-emotional state determination users can follow up their biometric data on screens of their portable devices. For a pity, since the pilot version of the SPO IORS layout is designed in domestic language, all inscriptions on screens are reflected in Russian. That’s why, we

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avoid demonstrating screenshots in this paper, but we’ll try to explain what is displayed on screens. In general, all screens include similar blocks of information: • • • • • •

current time, day, status of the system (available or not), status of the task (percentage of completing), name (title) of the indicator under monitoring, the simple and visual graph in two axes: time flow on the axis X and values of the indicator under monitoring on the axis Y (these values are gradated from “zero” to “ten”), and • hints at the bottom of the screen, which are different for various indicators, but help the user to understand his results. Here we quote 2 examples of screens inspirations:

1) upper inscription (title of the indicator) is translated as “Keep track how your concentration changes depending on the lesson”, and the lower one (a hint) explains “High values (7–10) are well suited for focused work”; 2) upper inscription (title of the indicator) is translated as “Engagement shows how interested you are in what you are doing now”, and the lower one (a hint) explains “High values (7–10) show that a person is completely absorbed in the current task”. Using this system, all 20 students can monitor their indicators by themselves. The obtained results of the study allow formulating the following conclusion: the developed SPO IORS model reflects the presence of an optimal/non-optimal state of a person involved in the educational activity, which testifies the efficiency of the model.

5 Discussion The system approach was used by authors to make the decomposition of the integrated index of students’ optimal resource (functional) condition into 4 separate indicators, which are: stress, cognitive load, passion, concentration. The contents of parameters that characterize each of these indicators and ways of their measuring are described in many articles, mentioned in the first section of the paper. Based on these parameters, a model and algorithm for monitoring and evaluation of the optimal resource (functional) state index of a university’s students were developed. The polyeffector model based on the EEG data collection and processing was developed, that has no direct analogs. The model has been realized as a mobile application for the Android operating system version 6.0 simple software, available in student’s portable devices, which allows visualization of the user’s current functional state online: displaying pictograms of stress (emotional load), cognitive load, enthusiasm, and concentration. The experiment was conducted with the involvement of 20 students. The obtained results allow identifying the presence of an optimal/non-optimal person’s state, which indicates the efficiency of the model. It’s evident that the confidence probability of estimates could not be very high because of differences in the psychology and mentality of testees. It

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needs a special study, based on the big representative sets of results. That might be the perspective research aimed at the improvement of the model.

6 Conclusion The developed polyeffector model is based on the EEG data collection and processing; it provides an objective definition of a person’s psycho-emotional states: stress, passion, cognitive load, and concentration, and reflects the user’s index of the optimal resource (functional) state. The hypothesis of the study has been experimentally confirmed: the assimilation of educational materials is better achieved by persons who are in an optimal resource state compared to persons who are in a non-optimal resource state.

References 1. Rechinskiy, A., Stankevich, L., Chernenkaya, L.: Biometricheskie metody identifikacii lichnosti: Spravochnik. (Biometric methods for Identification of personality: Handbook.) Polytech-Press, St. Petersburg (2020). (In Russian) 2. McEwen, B.: The neurobiology of stress: from serendipity to clinical relevance. Brain Res. 866(1–2), 172–189 (2000) 3. Bonnet, M., Arand, D.: Heart rate variability in insomniacs and matched normal sleepers. Psychosom. Med. 60(5), 610–615 (1998) 4. Dantzer, R., O’Connor, J., Freund, G., Johnson, R., Kelley, K.: From inflammation to sickness and depression: when the immune system subjugates the brain. Nat. Rev. Neurosci. 9(1), 46–56 (2008) 5. Ichise, M., et al.: Effects of early life stress on [11C] DASB positron emission tomography imaging of serotonin transporters in adolescent peer-and mother-reared rhesus monkeys. J. Neurosci. 26(17), 4638–4643 (2006) 6. Koenen, K., Amstadter, A., Nugent, N.: Gene-environment interaction in posttraumatic stress disorder: an update. J. Trauma. Stress 22(5), 416–426 (2009) 7. Ahmed-Leitao, F., Rosenstein, D., Marx, M., Young, S., Korte, K., Seedat, S.: Posttraumatic stress disorder, social anxiety disorder and childhood trauma: differences in hippocampal subfield volume. Psychiatry Res. Neuroimaging 284, 45–52 (2019) 8. Czéh, B., Lucassen, P.: What causes the hippocampal volume decrease in depression? Eur. Arch. Psychiatry Clin. Neurosci. 257(5), 250–260 (2007) 9. Bluhm, R., et al.: Alterations in default network connectivity in posttraumatic stress disorder related to early-life trauma. J. Psychiatry Neurosci. 34(3), 187–194 (2009) 10. Ellenbogen, M., Schwartzman, A., Stewart, J., Walker, C.-D.: Stress and selective attention: the interplay of mood, cortisol levels, and emotional information processing. Psychophysiology 39(6), 723–732 (2002) 11. Preston, S., Buchanan, T., Stansfield, R., Bechara, A.: Effects of anticipatory stress on decision making in a gambling task. Behav. Neurosci. 121(2), 257–263 (2007) 12. Kalin, N., Larson, C., Shelton, S., Davidson, R.: Asymmetric frontal brain activity, cortisol, and behavior associated with fearful temperament in rhesus monkeys. Behav. Neurosci. 112(2), 286–292 (1998)

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13. Buss, K., Schumacher, J., Dolski, I., Kalin, N., Goldsmith, H., Davidson, R.: Right frontal brain activity, cortisol, and withdrawal behavior in 6-month-old infants. Behav. Neurosci. 117(1), 11–20 (2003) 14. Davidson, R., Fox, N.: Frontal brain asymmetry predicts infants’ response to maternal separation. J. Abnorm. Psychol. 98(2), 127–131 (1989) 15. Schore, A.: Dysregulation of the right brain: a fundamental mechanism of traumatic attachment and the psychopathogenesis of posttraumatic stress disorder. Aust. N. Z. J. Psychiatry 36(1), 9–30 (2002) 16. Tomarken, A., Davidson, R., Wheeler, R., Doss, R.: Individual differences in anterior brain asymmetry and fundamental dimensions of emotion. J. Pers. Soc. Psychol. 62(4), 676–687 (1992) 17. Sweller, J., Van Merrienboer, J., Paas, F.: Cognitive architecture and instructional design. Educ. Psychol. Rev. 10(3), 251–296 (1998)

Modeling of the Educational Process Based on Smart Technologies Sergey Yablochnikov1 , Mikhail Kuptsov2(&) Kirill Bukhensky2 , and Ivan Kuptsov3

,

1

2

Moscow Technical University of Communications and Informatics, Aviamotornaya Street, 8a, 111024 Moscow, Russia Ryazan State Radio Engineering University named after V.F. Utkin, Gagarin Street, 59/1, 390005 Ryazan, Russia [email protected], [email protected] 3 Instamart Technologies LLC, The Territory of the Skolkovo Innovation Center, Bolshoy Boulevard, 42/1, 121205 Moscow, Russia

Abstract. The relevance of the article is caused by the rapidly growing importance of qualitative higher education. Higher levels of scientific and technological processes lead to the ever-growing demand for highly-qualified specialists where low-skilled workers can lose their jobs without any stop in the trend to change the usual style of life. Specialists should be able to possess a very important skill, i.e. the skill to adapt to high-tech changes in the working environment. Changing requirements to workers’ professional competencies cause changing the direction of educational systems development forcing them to be modified. One of the new fundamental directions in this modification is the emergence and improvement of educational smart technologies. However, due to the specific character of smart education, the task to assess its quality becomes more complicated creating difficulties primarily for employers. The article offers a new approach to assess the quality of modern education based on the mathematical models proposed by the authors. Methods to build and analyze differential equations as well as methods of the mathematical theory of fuzzy sets are used allowing us to transfer qualitative educational criteria into quantitative ones and further perform their analysis. The model combines key advantages of existing methods to assess the quality of education. The model offered is flexible as it can be adapted to any number of various criteria and factors and can be used as the basis for the system of education quality indicators applicable both for job seekers and employers. Keywords: Mathematical modeling
Educational process
Smart technologies

1 Introduction Scientific and technological progress has completely changed the conditions for the existence of man and humanity as a whole. Thanks to this progress mankind have risen to a completely different stage of development, modernizing production, creating new approaches to management which led to changes in lifestyle including work, leisure, and interpersonal communication. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 548–560, 2022. https://doi.org/10.1007/978-3-030-98832-6_48

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However, the scale of all past industrial revolutions is completely incomparable with what is happening now. Current industrial development has been called the “Fourth industrial revolution” the essence of which being “the transition to completely automated digital production being controlled by intellectual systems and the formation of a global industrial network of goods and services” [1]. Therefore, the fourth industrial revolution — is an unprecedented reorganization of production as well as the whole system of its management that is currently taking place at an incredible rate. One of the most obvious consequences of the fourth industrial revolution is changing requirements to the level of knowledge and skills for workers in all areas of activity. The fourth industrial revolution will lead to the disappearance of many “human” working specialties, in which humans will be replaced by automata and robots. First, unskilled labor being easily automated will disappear. Therefore, the requirements to knowledge and skills of job seekers will become more stringent imposing additional requirements to educational systems of all countries in the world. Various indicators and ratings are used to determine the level of education [2–7]. These include complex composite indices such as “Education Index” and “Global Index of Cognitive Skills and Educational Attainment”. At the same time, individual simple indicators are also used, e.g., the number of winners in international subject Olympiads, or the employment opportunity, or the income of specialists with a certain level of education. We should note here that correlation coefficients between “Education Index” [5], “Global Index of Cognitive Skills and Educational Attainment” [2], and GDP per capita at purchasing power parity (for 2019) turned out to be statistically significant (significance levels less than 0.001). This fact emphasizes the dominance of the economic approach to determine the quality of education in the world. In many cases, the use of such indicators is quite justified. However, in a dynamically changing environment, the main purpose of education is to form the state of being ready for independent effective actions and the ability to constantly adapt. Nowadays we need to develop such an adaptation mechanism, such an intellectual potential, which will allow forming completely new competencies in the future that will help us to adapt to continuously changing technologies. In other words, it is necessary to learn to receive “education throughout life” [8]. All factors mentioned above lead to decreasing value of “classical” education for practical activity and forces education itself to be changed. Therefore, we witness the revolution in education that affects all its sides. One of the main innovations in modern education is smart technologies. Smart educational technologies have gained particular importance in connection with the COVID-19 pandemic, the growing importance of “network” education, and the abundance of information for decision-making [9]. Smart educational technologies help to create individual educational programs, select individual training schedules, contribute to the formation of the ability to independently make decisions and act in situations of uncertainty [10]. Smart educational technologies are used in managing the educational process [11], as intelligent learning systems [12], to assess and analyze subject knowledge [13], also as adaptive personalized systems [14]. Smart educational technologies are the basis to create special environments in higher education (e.g., https://www.microsoft.com/ru-ru/education/ higher-education/campus-solutions, https://e.huawei.com/ru/solutions/industries/smartcampus, http://smaartcampus.com). A smart learning environment can support various

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teaching methods and combine them, create conditions where emergence, testing, and dissemination of new teaching methods and techniques are possible, include functions that promote engagement, efficiency, and effectiveness, develop cooperation, motivation, support educational, organizational, educational innovations [15, 16]. The effectiveness of smart educational technologies is usually higher than conventional pedagogical methods [17], except for tutoring [18]. However, even despite the use of smart educational technologies and an increasing number of people with higher education as well as the expanding content of higher education including more and more information, skills, and knowledge, the lack of qualified specialists for economy and industry can be observed [8, 19]. Specialists leaving the university are not always ready to immediately get involved in work; they do not always have all the necessary competencies. Therefore, employers with job seekers are forced to conduct interviews and internships. Scientific sources offer a large number of different models of pedagogical (educational) processes. The general structure of a mathematical model in a pedagogical process can be considered as follows. The result of the educational process being implemented is represented as п of interacting elements gi ðtÞ, depending on time t, being combined in a scalar variable gðtÞ ¼ Fðg1 ðtÞ; g2 ðtÞ; :::; gn ðtÞÞ, or conditionally independent elements combined into vector variable gðtÞ ¼ colonðg1 ðtÞ; g2 ðtÞ; :::; gn ðtÞÞ, each component being able to represent some function. The result of the implementation of the educational process gðtÞ in a mathematical model is usually assessed using some criterion of the level (quality) of the education received. Such a criterion usually turns out to be associated with those “measurements” of education quality that we have considered above. Further, we shall consider gðtÞ as “success in mastering the content of educational program” that is assessed with the help of a certain criterion of education. These criteria can be represented by ratings, indices, and indicators mentioned above as well as other (temporal, probabilistic, information, etc.) criteria introduced by the authors of corresponding models. All mathematical models of the educational process can be divided into several classes. The first class includes deterministic models based on the construction and study of differential or difference equations [20]. In such models, the same initial data lead to the same results. Most deterministic models proceed from the fact that gðtÞ either satisfies ordinary differential equations of the form g_ ¼ kð1  gÞ;

ð1Þ

or differential equations of the form: g_ ¼ kgð1  gÞ;

ð2Þ

where parameter k characterizes the average intensity of successful mastering the content of the educational program. Here we should note that integral curves of Eqs. (1) and (2) are either exponential or logistic, and the equations themselves can be n-dimensional n 2 N.

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Besides, Eqs. (1) and (2) can have different forms but integral curves under interpretation tend to remain either exponential or logistic or piecewise smooth consisting of different exponential and logistic curves for different time intervals. The Next (second and, apparently, most widespread) group of models includes probabilistic models [20–22]. Probabilistic models can be also represented by the ones being described by the equations of forms (1) and (2), but gðtÞ here is either the probability of complete mastering the content of the educational program or mathematical expectation of successful mastering the content of the educational program (e.g., refer to [20]). However, in principle, such models differ little from those discussed above. Probabilistic models can also be constructed based on Kolmogorov differential equations [20]. Besides, probabilistic models of educational processes based on different variations of Rasch models can be considered [21, 22]. Such models do not explicitly contain differential equations; however, functional dependences of probabilities on the training time obtained as a result of modeling are still the varieties or the combinations of exponential, exponential integral, and logistic functions. Thus, these models can be reduced to some of the previously considered types of differential equations. The third class of models includes the models being constructed with the help of the theory of fuzzy sets [23, 24]. These models are also rather algorithmic and provide principles, schemes, and rules to measure some “educational” characteristics that include, in particular, the levels for the formation of competencies and their changes during training in higher educational institutions. Thus, the basis for the models with fuzzy sets is the task to assess the quality of education. In other words, these models just make attempts to solve the question of how to measure successful mastering of an educational program gðtÞ considered in deterministic and probabilistic models. On the other hand, all models can be divided into groups by assessing the quality of education being received. The basis of most models to assess the quality of education is the internal criteria of the university compared with certain standards of knowledge [22]. These standards are naturally different for different universities. These models are called “subject-oriented” models. The other group is the models that consider both internal criteria and external ones (international, public, and state criteria) [25]. As an example, certain educational ratings, indices as well as those indicators that are used when conducting state accreditation or state monitoring of universities are offered to be considered. These are “ratingoriented” models. Finally, we have models that take into account some of the requirements offered by employers alongside the criteria mentioned above [21]. Such models will further be called “economically oriented” ones. Consequently, the analysis of existing ways and methods to assess the quality of education allows us to conclude an excessive number of its “economic” components and the relative scarcity of the indicators connected with graduates’ adaptability being one of the most important indicators of education in the modern world of fast-growing technologies. One common disadvantage of all the models of educational processes under consideration is a low degree of formalization for the education quality concept. Criteria

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for successful mastering of educational programs gðtÞ are often vague and highly subjective. One of the few exceptions to this is the models of “iterative learning” for simple actions, such as the actions performed by a rat in a maze. In such simplest (one might say physiological) models, the criteria for the success of “education” are quite objective and sufficiently formalized. Moreover, none of the models of an educational process known to us to assess the success of educational programs developed gðtÞ in any way uses the degree of satisfaction with their education by the graduates of educational institutions. At the same time, a high level of satisfaction with education can be observed even when other criteria have low values. Therefore, we believe that satisfaction with education should be included in the overall assessment of the level of education. This work aims to receive a flexible system of educational quality indicators based on the mathematical models proposed by the authors including external and internal assessments as well as the assessments of employers and the degree of satisfaction with education. The results of the article given follow and generalize the results received by the authors in [23, 26].

2 Materials and Methods As a mathematical model for successful mastering of the content of educational program the value ni ðtÞ ¼ li
gðtÞ [27, p. 285] is used, where i ¼ 1; 5, g ¼ gðtÞ - is the indicator of compliance with the master plan of the educational program. Components of the vector l ¼ colonðl1 ; l2 ; l3 ; l4 ; l5 Þ are calculated based on an economically oriented approach to assessing the quality of education including internal and external assessments together with employer assessments as well as the degree of satisfaction with education. As g ¼ gðtÞ different measures can be chosen but we have used one of the simplest ones: the ratio of classroom hours taught by the moment of time t to the total number of classroom hours according to a curriculum. Here t ‒ is a period equal to one day. As seen in the introduction most models of learning processes are based on differential Eqs. (1) or (2). Therefore, we shall consider g ¼ gðtÞ satisfying one of these equations (or both but in different periods). To illustrate the algorithms for constructing such a mathematical model we shall take bachelor curriculum for “Taxes and Taxation” specialization (3772 h in 208 weeks) and calculate the share of a daily number of classroom hours mastered by students over four years of study. Further, we make use of SPSS and approximate the shares received with the help of the “Regression” tool (“Nonlinear regressions” tab). To do this, we have set the following types of functions: ^ gðtÞ ¼ 1  C
expðktÞ or ~gðtÞ ¼ 1=ð1 þ C
expðktÞÞ with unknown parameters C and k, corresponding to general solutions of Eqs. (1) or (2). For the example considered to solve ^ gðtÞ we 2 received C  1:12, k  0.0015 with determination coefficient R ¼ 0:949. To solve ~gðtÞ we received C  10.83, k  0.0037 with determination coefficient R2 ¼ 0:992. Therefore, solutions ~gðtÞ better approximate real data than solutions ^ gðtÞ. Here k 

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0.0037 is well interpreted as the average share of classroom hours studied by students over four years. The procedure described can be carried out for any curriculum, any specialty, and any university. In addition, the measure of “getting an education” is quite objective (the proportion of hours “studied”). However, the share of “studied” hours gives little in terms of assessing the quality of the education received. It can only be used to estimate how many of the hours planned by the curriculum the student “has listened to”, in other words, in what course the student is currently studying (or what course he has graduated from). Therefore, to measure the quality of the education received we combined the described approach with the theory of fuzzy sets [27, 28] since it is the theory that has been developed to formalize linguistic, qualitative forms without unambiguous interpretations. Here the criteria for the quality of education are rather vague and subjective (see the introduction), and therefore, to objectify and formalize them, one should use measurement methods based on the mathematical theory of fuzzy sets. Let us consider, as an example, the application of fuzzy set theory to assess the quality of education provided by the universities in Russia. To do this, we shall introduce a linguistic variable X — “level of university education quality” with the terms: “high”, “above average”, “average”, “below average”, “low”. To determine the value of a variable, we shall choose four criteria: percentage of unemployed among graduates (refers to employers assessments), the average salary of graduates (refers to employers assessments), university rank in The World University Rankings [7] for Russia (refers to external assessments), level of graduates satisfaction with their education (found by questioning). For each criterion to assess the degree of belonging to the terms of linguistic variable X, we were given five experts with the experience to assess the level of education. Since the quality of education of a whole university but not of a person is considered, it is not necessary to take into account internal (subjectoriented) assessments of a university. According to the criterion “average salary of graduates”, we get the terms: “high salary level”, “salary level above average”, “average salary level”, “salary level below average”, “low salary level”. To construct “normalized” membership functions ~1i ðx1 Þ ¼ sinða1i ða2i
x1 þ a3i ÞÞ for these terms statistical data from [3] on the average l salary of university graduates will be used. Selection of parameters aji for membership functions is made based on expert estimates making use of SPSS tool “Nonlinear regression” according to the similar scheme as for functions ^ gð t Þ ~ gðtÞ and. Since the values of the variable x1 (average salary of a graduate) change continuously, ~1i ðx1 Þ must also be continuous. This fact explains the choice the membership function l ~
1i ðx1 Þ excluof the SPSS tool “Nonlinear regression” and the inability to construct l ~ji xj are consively based on expert assessments. Similarly, membership functions l structed for other criteria. Here i ¼ 1; 5j ¼ 1; 4; value j ¼ 2 corresponds to the criterion “percent of unemployed among graduates”, j ¼ 3 — “university rank in The World University Rankings”, j ¼ 4— “level of graduates satisfaction with their education”; value i ¼ 1 corresponds to term “high”, i ¼ 2 — “above average”, i ¼ 3 — “average”, i ¼ 4 — “below average”, i ¼ 5 — “low” (Fig. 1).

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µ%1

µ%12 µ%13

µ%14 µ%15

~1i ðx1 Þ. Fig. 1. Graphs of “normalized” membership functions l

Since the data from the questionnaire survey of university graduates are used to ~4i ðx4 Þ we shall describe the questionnaire procedure in detail. construct the functions l The questionnaire has 6 questions. 1. How much does your higher education help you in your work (including job search)? 2. How much does your higher education help you to achieve desired social status? 3. How much does your higher education help you to achieve your personal goals (self-development, hobby, etc. neglecting work and social status)? 4. What higher education do you have (indicate which university and faculty you graduated from)? 5. Are you employed? 6. In case you are employed, indicate your average income. The first three questions were asked to be answered in points from − 2 to 2 (−2, − 1, 0, 1, 2). Here “ − 2” was interpreted as “my education provides sufficient problems to me”, “ − 1” — my education provides some problems to me, “0” — neutral, “1” — my education helps me, “2” — my education strongly helps me. The survey was carried out with the help of Google Forms tools, and the link to the questionnaire was distributed among closed groups of students and alumni of ten Russian universities in social networks “VKontakte” and “Telegram”. A complete list of these ten universities is shown in Table 3. A total of 185 respondents took part in the survey. Questions 5 and 6 of the questionnaire turned out to be not interpretable, as they were often ignored by respondents. We should note that the measurements used in the survey refer to ordinal scales. Medians are quantities distribution centers measured in ordinal scales. This is the reason why, in our calculations, we often use survey data medians. However, in our opinion, average values (mathematical expectations) can also be used to increase assessment details. Mathematical expectation estimates for the degree of university graduates’ satisfaction with the quality of education: 1.130 (work), 1.103 (achieving the desired social status), 0.595 (achieving personal goals (self-education, hobbies, etc.)).

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Further let us construct a vector l ¼ colonðl1 ; l2 ; l3 ; l4 ; l5 Þ, each component characterizing the possibility of membership for a university graduate to the terms of a linguistic variable X. Accordingly, l1 shows the value of membership function to the term “high level of university education quality”, l2 shows the value of membership function to the term “above average level of university education quality”, and the same way up to l5 - “low level of university education quality”. Values li can be found with the help of T-norm   ~i ð xÞ ¼ min l ~i ðx1 Þ;~ ~i ðx3 Þ; l ~li ðx4 Þ ; l li ðx2 Þ; l

ð3Þ

where i ¼ 1; 5, l1 ¼ 1, l2 ¼ 1, l3 ¼ 2, l4 ¼ 3, l5 ¼ 3, x ¼ colonðx1 ; x2 ; x3 ; x4 Þ. Instead of T-norm (1), the following T-norm can also be used. ~ i ð x1 Þ
l ~ i ð x2 Þ
l ~i ðx3 Þ
l ~li ðx4 Þ; ~ i ð xÞ ¼ l l

ð4Þ


 ~ji xj can be replaced with the values of “subnormal” and “normalized” functions l  5 


 P
 ~ji xj ~jk xj . l membership functions lji xj lji xj ¼ l k¼1

Vector ð1; 0; 0; 0; 0Þ is sure to characterize the highest assessment of education quality as each of the components can take the values from 0 to 1. As an example, we believe that vector ð1; 0:2; 0; 0; 0Þ shows a lower assessment of education quality as it gives the possibility of membership to the term “above average level of education quality” (equals 0.2). The technique offered to calculate l can include any finite number of the criteria necessary for a researcher. It can be adapted to calculate the level of education for a separate person. To do this we need to add internal criteria of the universities to the four criteria discussed above. This can be e.g.., GPA of a higher education diploma and the level of the diploma (bachelor, specialist, master, etc.). In addition, criteria can be excluded, replaced, or added. In particular, when assessing the level of higher education in a state, university criteria should no longer be used, but general ones instead, related to higher education in general. To determine the quality of incomplete education we use the values gðtÞ and ni ðtÞ ¼ li
gðtÞ.

3 Results Further, the application of offered techniques to measure the education quality of three Russian universities: Lomonosov Moscow State University (MSU), HSE University (HSE), and Moscow Institute of Physics and Technology (MIPT) are considered. To compare them we calculate l by two T-norms (3) and (4) as well as for subnormal and normalized membership functions. When calculating, we use statistical data from [3, 7] and the results of the author’s survey among university graduates and students. The calculation results are shown in Tables 1 and 2. The following designations are used in Tables 1 and 2: 1.1 means that calculations are carried out based on T-norm (3) and

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normalized membership functions, 1.2 — T-norms (3) and subnormal membership functions, 2.1 — T-norms (4) and normalized membership functions, 2.2 - T-norms (4) and subnormal membership functions. In Table 1 to assess the level of satisfaction medians are used, in Table 2 we use the sample mean. Table 2 unlike Table 1 has no similar values of vector allowing us to choose the best university even among the universities being so close in terms of education quality. On the other hand, such detailing arose only because MIPT graduates are the most critical on average to their education (average value of satisfaction with their education is 0.86), and HSE graduates are the least critical (average value is 1.05). Here the medians of a sample questionnaire coincide for all three universities (equal to 1). Therefore, it is possible that such detailing is not needed (one indicator becomes too important). At the same time, the fourth line in both tables has changed little (at least the hierarchy has not changed at all). In other words, it seems to us that measurements based on T-norm (4) and subnormal membership functions are more resistant to random influences and suit better to measure the quality of education. However, the practical implementation of the models proposed is rather laborious. Therefore, for practical calculations, we offer to use one of the modifications of the antifragility index of education [23, 26]. Let us consider a university antifragility index IAU ¼ c1


r3 þ c2
R1 þ c3
R2 þ c4
U: r2

Here r3 is the average monthly income of a graduate of the university given (thousand rubles), U is a mathematical expectation estimate for the degree of university graduate satisfaction with the quality of his education, R1 are points given for the university according to “QS World University Rankings” [7], R2 are points given for the university according to “100 best Russian universities according to Forbes” [4], ci — coefficients, r2 — average monthly income of the population in a country (or region). Points Ri are calculated according to the following ratings: for the first place, 1 point is assigned decreasing for each subsequent place by 0.01 points (for the second place 0.99, for the third 0.98, etc.). Values r3 were determined according to [3]. Weight coefficient values were determined by experts c1  0:15; c2  2:57; c3  0:55; c4  0:1. Table 1. Values of vector l (medians are used). Calculation technique MSU 1.1 ð1; 0; 0; 0; 0Þ 1.2 ð0:476; 0; 0; 0; 0Þ 2.1 ð1; 0; 0; 0; 0Þ 2.2 ð0:181; 0; 0; 0; 0Þ

MIPT ð1; 0; 0; 0; 0Þ ð0:476; 0; 0; 0; 0Þ ð1; 0; 0; 0; 0Þ ð0:216; 0; 0; 0; 0Þ

HSE ð1; 0; 0; 0; 0Þ ð0:389; 0; 0; 0; 0Þ ð1; 0; 0; 0; 0Þ ð0:078; 0; 0; 0; 0Þ

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Table 2. Values of vector l (sample mean is used). Calculation technique MSU 1.1 ð0:999; 0; 0; 0; 0Þ 1.2 ð0:464; 0; 0; 0; 0Þ 2.1 ð0:999; 0; 0; 0; 0Þ 2.2 ð0:176; 0; 0; 0; 0Þ

MIPT ð0:996; 0; 0; 0; 0Þ ð0:446; 0; 0; 0; 0Þ ð0:996; 0; 0; 0; 0Þ ð0:202; 0; 0; 0; 0Þ

HSE ð1; 0; 0; 0; 0Þ ð0:389; 0; 0; 0; 0Þ ð1; 0; 0; 0; 0Þ ð0:08; 0; 0; 0; 0Þ

Table 3. Education quality indicators. University MIPT MSU HSE

Bauman MSTU 0.196 0.177 0.080 0.063 3.718 3.682 3.616 3.571

MEPhI ITMO SPBU MISIS NSU MPEU

l1 IAU

0.053 3.547

0.045 0.013 0.009 3.535 3.411 3.343

0.008 0 3.337 3.279

Table 3 contains the values of the first component l1 for vector l and index IAU for ten Russian universities. The technique to calculate the components of the vector l almost always coincides with the calculations in Table 2, line 2.2. However, instead of using the percentage of unemployed graduates, in this case, rating [4] was used as the result of the survey has shown that the percentage of unemployed graduates turned out to be 0 for all universities. For the values and the authors’ calculated determination coefficient: (significance level less than 0.01). This means that the values of the indicators used in a mathematical model of the educational process and the values of the university antifragility index are consistent.

4 Discussion Thanks to the introduction of smart educational technologies, the effectiveness of the educational process increases, but smart technologies also require constant feedback from graduates and employers. To achieve its different methods to measure education quality need to be introduced into smart technologies. The analysis of existing ways and methods to measure the quality of education allows us to conclude an excessive number of their “economic” components simultaneously with a relative deficit of indicators connected with graduates’ adaptability. At the same time, adaptability is one of the most important indicators of modern education. Such one-sided measurement of education quality can be overcome by adding the results of the questionnaire made by university graduates as well as by taking into consideration the requirements of employers. Further advancement of ideas, methods, and algorithms expressed in the research can significantly change the existing practice of hiring. The proposed system of education antifragility indices (being especially true for university and personal [26] indices) can significantly save time spent by employers in search of qualified personnel, and by job seekers in search of work. In our opinion, the indices proposed to allow us

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to take into account many aspects of education quality received by an applicant and, accordingly, can help to skip at least part of the interviews when applying for a job. This requires a long way to go. First of all, we need to create smart educational technologies with feedback that will record graduates’ satisfaction with their university education not only during studying but in a year or two or more, after graduates gain experience in applying their education into practice. In addition, we need to popularize the approach to pedagogical measurements formulated in this article to increase the number of universities and employers interested in developing the methods proposed.

5 Conclusions The mathematical model proposed in the article is based on a universal approach to assessing the quality of education. The model combines two mathematical technologies: the use of differential equations and the theory of fuzzy sets. These technologies allowed us to transform “vague” qualitative educational criteria into quantitative ones. The method proposed combines key advantages of existing methods to assess education quality as well as to take into account a set of different groups of parameters: internal subject-oriented assessments; external rating-based assessments; economically oriented assessments by an employer, personal assessments of graduates’ satisfaction with the quality of their education (based on questionnaire results). To construct the last group of indicators, a comprehensive study was carried out in the form of a questionnaire survey of senior students and university graduates where the degree of graduates satisfaction was determined according to the following guidelines: how much education helps them to achieve the desired levels and success in three categories: work, self-development, and hobbies, achieving desired social status. The model we have proposed is flexible since it can be adapted to any number of different criteria and factors, their number can be increased or decreased, and the criteria themselves can be modified. With its help, adding or removing different parameters as well as using averaging allows comparing the level of education for individual graduates and students as well as universities within one country and at the national level. This approach is also of practical importance. Accordingly, it can be used by employers when assessing the level of candidates for vacant positions or by an applicant choosing a university for admission.

References 1. Gutorovich, O.V.: The fourth industrial revolution and its possible consequences. Discourse 4(6), 11–17 (2018). https://doi.org/10.32603/2412-8562-2018-4-6-11-17 2. Education for people and planet: creating sustainable future for all. Global education monitoring report 2016, UNESCO Publishing, Paris (2016). https://www.eda.admin.ch/dam/ deza/en/documents/aktuell/news/20160923-weltbildungsbericht_EN.pdf. Accessed 12 Mar 2021 3. Rating of Russian universities (2021). https://students.superjob.ru/reiting-vuzov/it/. Accessed 8 Mar 2021

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4. Universities for the future elite: 100 best Russian universities according to Forbes (2020). https://www.forbes.ru/obshchestvo/403369-universitety-dlya-budushchey-elity-100luchshih-rossiyskih-vuzov-po-versii-forbes. Accessed 9 Mar 2021 5. Education Index 2020: Human development reports (2020). http://hdr.undp.org/en/ indicators/103706. Accessed 10 Mar 2021 6. Education at a Glance 2020: OECD Indicators, OECD Publishing, Paris (2020). https://doi. org/10.1787/69096873-en, Accessed 12 Mar 2021 7. Ranking of the best universities in the world for 2021 according to QS World University Rankings (2021). https://www.timeshighereducation.com/world-university-rankings/2021/ world-ranking#!/page/0/length/25/locations/RU/sort_by/rank/sort_order/asc/cols/stats. Accessed 11 Mar 2021 8. Results of the London conference of ministers of education of European countries. Voprosy Obrazovaniya, vol. 2, pp. 5–19 (2007) 9. Aladro Vico, E.: Comunicación sostenible y sociedad 2.0: particularidades en una relación de tres décadas. SEECI (53), 37–51 (2020). https://doi.org/10.15198/seeci.2020.53.37-51 10. Zawacki-Richter, O., Marín, V.I., Bond, M., Gouverneur, F.: Systematic review of research on artificial intelligence applications in higher education – where are the educators? Int. J. Educ. Technol. High. Educ. 16(1), 1–27 (2019). https://doi.org/10.1186/s41239-0190171-0 11. Kardan, A.A., Sadeghi, H.: A decision support system for course offering in online higher education institutes. Int. J. Comput. Intell. Syst. 6(5), 928–942 (2013). https://doi.org/10. 1080/18756891.2013.808428 12. Howard, C., Jordan, P., Di Eugenio, B., Katz, S.: Shifting the load: a peer dialogue agent that encourages its human collaborator to contribute more to problem solving. Int. J. Artif. Intell. Educ. 27(1), 101–129 (2015). https://doi.org/10.1007/s40593-015-0071-y 13. Duzhin, F., Gustafsson, A.: Machine learning-based app for self-evaluation of teacherspecific instructional style and tools. Educ. Sci. 8(1), 7 (2018). https://doi.org/10.3390/ educsci8010007 14. Lo, J.J., Chan, Y.C., Yeh, S.W.: Designing an adaptive web-based learning system based on students’ cognitive styles identified online. Comput. Educ. 58(1), 209–222 (2012). https:// doi.org/10.1016/j.compedu.2011.08.018 15. Gros, B.: The design of smart educational environments. Smart Learn. Environ. 3(1), 1–11 (2016). https://doi.org/10.1186/s40561-016-0039-x 16. Pons, P., Catala, A., Jaen, J.: Customizing smart environments: a tabletop approach. J. Ambient Intell. Smart Environ. 7(4), 511–533 (2015) 17. Miwa, K., Terai, H., Kanzaki, N., Nakaike, R.: An intelligent tutoring system with variable levels of instructional support for instructing natural deduction. Trans. Jpn. Soc. Artif. Intell. 29(1), 148–156 (2014). https://doi.org/10.1527/tjsai.29.148 18. Steenbergen-Hu, S., Cooper, H.: A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. J. Educ. Psychol. 106(2), 331–347 (2014). https://doi.org/10.1037/a0034752 19. Schuster, J.H., Finkelstein, M.J.: The American Faculty: The Restructuring of Academic Work and Careers. The Johns Hopkins University Press, Baltimore (2006) 20. Novikov, D.A.: Zakonomernosti iterativnogo naucheniya. (Regularities of iterative learning) Institut problem upravleniya RAN [V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences], p. 77, Moscow (1998). (In Russian), http://www.methodolog.ru/ books/file_37.pdf. Accessed 11 Mar 2021

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21. Kayukova, I.V.: Development of mathematical methods and models for analyzing and predicting the quality of education in higher education on the basis of a competence-based approach: dissertation of the Candidate of ec. n. Volgograd State Technical University, Volgograd (2014). (In Russian), https://search.rsl.ru/ru/record/01007530833. Accessed 11 Mar 2021 22. Tsvetkov, V.Y., Voinova, E.V.: Modification of Rasch model for free testing assessment. Vestnik of Ryazan State Radio Eng. Univ. 63, 90–94 (2018). https://doi.org/10.21667/19954565-2018-63-1-90-94 23. Yablochnikov, S., Kuptsov, M., Yablochnikova, I.: Innovative approach for the education quality assessment. In: Proceedings of the 26th Interdisciplinary Information Management Talks «IDIMT-2018: Strategic Modeling in Management, Economy and Society», pp. 497– 505. TRAUNER Druck GmbH & Co KG, Linz (2018) 24. Bolshakov, A.A., Veshneva, I.V., Melnikov, L.A., Perova, L.G.: Application of the theory of fuzzy sets to the problems of assessment and management of the formation of competencies: description of the problem and an approach to its solution. Vestnik AGTU. Ser. Manage. Comput. Eng. Comput. Sci. 2, 174–181 (2012). http://www.mathnet.ru/links/ 136ee64bcd944eac4e8b4b49c503d0a4/vagtu81.pdf. Accessed 11 Mar 2021 25. Kruglov, V.I., Solov'ev, V.P., Kochetov, A.I., Pronichkin, S.V.: Razrabotka kriterial’noj modeli dlya nezavisimoj ocenki deyatel’nosti vuza karegorii “Nacional’nyj issledovatel’skij universitet”. (Development of criterion models for independent evaluation activities of the university category “National Research University”.) Higher Education Today 7, 6–16 (2010). (In Russian), https://www.elibrary.ru/item.asp?id=15185262. Accessed 11 Mar 2021 26. Yablochnikov, S.L., Yablochnikova, I.O., Kuptsov, M.I., Kuptsov, I.M., Yablochnikova, M. S.: To the question of synchronization of the processes of functioning of the components of the socio-economic sphere. In: Conference Proceedings: 2019 Systems of Signal Synchronization, Generating and Processing in Telecommunications, pp. 1–11. IEEE (2019) 27. Osowski, S.: Sieci neuronowe do przetwarzania informacji. Warszawa (2013) 28. Dubois, D., Prade, H.: Theorie des Possibilities. Masson, Paris, Milan, Barcelone, Mexico (1988)

Quantitative Analysis of Informational Significance of SWEBOK Knowledge Areas in IEEE/ACM Curriculum Guidelines Alain Abran1 , Alexander V. Yurkov2(&) Vladimir G. Khalin2 , and Olga Shilova3

,

1

Ecole de Technologie Superieure (ETS), Software Engineering and Information Technology, Notre-Dame Street West, Montréal, QC, Canada 2 Information Systems in Economics, Saint Petersburg State University, 7/9 Universitetskaya Emb., 199034 St. Petersburg, Russia [email protected] 3 Saint-Petersburg Academy of Postgraduate Pedagogical Education, Pedagogy and Andragogy, 11 Lomonosova Street, 191001 St. Petersburg, Russia

Abstract. This paper reports on a quantitative analysis of the informational significance of SWEBOK knowledge areas in the curriculum guidelines developed as IEEE/ACM recommendations for educational programs in software engineering. The analysis uses a representation of the hierarchical structure of educational content in the form of an oriented bipartite hypergraph. The outdegrees of the vertices of the graph transitive closure are selected as the quantitative characteristics of the studied topics, reflecting their mutual influence within educational programs. A feature of the computational algorithm is the representation of the transitive closure matrix in the form of a sparse matrix of a block structure, the nonzero blocks of which have a significantly lower dimension. The graph approach to quantitative analysis of SWEBOK knowledge areas, is a novel approach to curricular analytics: it is not limited to the software engineering example presented and may be useful for the development of an evidence-based educational policy. Keywords: Software engineering
Curriculum
Hypergraphs
Computations on matrices
Numerical algorithms

1 Introduction Given the competitive educational market and the challenges induced by the increasing digitalization in all spheres of human activity, universities must respond promptly by adapting the design of their educational programs [1]. This paper concerns the academic analytics that is data mining methods applied to the information that appears in the activities of educational organizations and aims to improve the management and distribution of resources affecting the innovative development capabilities of an educational organization. The research reported here explores the possibilities of academic analytics for curricula design at a university level using quantitative methods, with an illustration of an application to the design of software engineering programs. This © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 561–573, 2022. https://doi.org/10.1007/978-3-030-98832-6_49

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choice seems to be actual, since it is software engineers who are the specialists creating tools for the digital economy of future [2]. The design of curricula aimed at the competencies of specialists and aligned with this body of knowledge is not an easy task. The Software Engineering Body of Knowledge (SWEBOK) establishes standards for software engineering education. SWEBOK covers a wide range of knowledge from mathematical foundations and computing essentials to software modeling and professional practice, including dozens of specialized topics [3, 4]. The educational design in software engineering proposed by the IEEE/ACM curriculum guidelines for SEEK (Software Engineering Education Knowledge) is based on SWEBOK [5]. SEEK describes the two-level structure of SWEBOK knowledge areas (KA) and the corresponding knowledge units (KU) required for study together with the recommended number of teaching hours. Of course, these SEEK recommendations are subjective but come from a consensus developed by highly qualified specialists in software engineering education and used in curricula design at universities (for example, see [5], Appendix A). But the experts did not take explicitly into account the KA mutual influence within educational programs designed in accordance with SEEK. This paper proposes an approach to evaluate KA informational significance by considering the interrelationships among the studied topics. This approach uses the algorithm developed to calculate the semantic significances of the elements in a course thesaurus [6]. Calculations provide quantitative criteria to determine which topics have to be mastered, which ones have to be understood, and which may be included in specialized educational programs. Teachers or curriculum course designers can then use this grouping for planning purposes. Mathematics describing the algorithm uses graph theory and matrix algebra in minimal amounts and seems to be acceptable even for practitioners. Links to the clarifying sources are made, where necessary. The research in this paper was initiated by an international collaboration within the project Joint Programs and Framework for Doctoral Education in Software Engineering [7]. The project was carried out in the framework of the EU Erasmus+ Program Capacity building in higher education [8]. The scientific workshops held by representatives of 11 European universities led to the publication of a number of results related to the training of highly qualified personnel in software engineering [9–14].

2 Related Work A number of publications have described the structure of knowledge needed by software engineers [14, 15]. Some researchers in software engineering education have compared the educational programs of different universities with the IEEE/ACM recommendations and studied the compliance of their own programs with guidelines for software engineering curricula [16–18]. However, there are few studies devoted to the quantitative analysis of the informational significance of the knowledge areas included in educational programs. For instance, the SCOPUS scientometric database www. scopus.com identified only 325 relevant links on the request:

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TITLE-ABS-KEY('software engineering') AND TITLE-ABS-KEY('knowledge areas') AND TITLE-ABS-KEY(curriculum)

At the same time, the query with the additional keyword quantitative analysis TITLE-ABS-KEY('software engineering') AND TITLE-ABS-KEY('knowledge areas') AND TITLE-ABS-KEY(curriculum) AND TITLE-ABS-KEY('quantitative analysis')

identified a single link, which is only indirectly related to the analysis of educational programs in software engineering. Moreover, queries with the additional keyword informational significance did not provide relevant links in the SCOPUS database, Web of Science Core Collection, or in the bibliographic database Google Scholar. All the bases were accessed on 10.06.2021 through the proxy server of Saint Petersburg State University. Several related studies have been identified that addressed the quantitative analysis of educational programs in software engineering. For example, there are various models that represent knowledge. Depending on the generality of a concept to be described, various representation models can be used: predicate logic, frames, semantic networks, patterns, and graphs. These models are discussed in [6], where it is shown that the hierarchical structures of knowledge can be adequately described by graph theory, namely hypergraph models. Information on graph theory can be found in [19]. The hypergraph model allows for a quantitative evaluation of informational significances/rankings of the elements of the knowledge thesaurus. When designing an academic course, priority should be given to the most significant elements on the proper understanding of which the teachers’ activities should be focused. The algorithm in [6] to calculate the semantic significance of the elements of a course thesaurus has been successfully used “as is” in other studies [20, 21]. This algorithm is based on building a transitive closure of the adjacency matrix of an oriented bipartite graph describing the relationships between the elements of the thesaurus. A feature of the algorithm is the representation of the transitive closure matrix in the form of a sparse matrix of a block structure, the nonzero blocks of which have a significantly lower dimension. This allows the researcher to overcome the problem of computational complexity, which is typical for sparse matrices of large dimensions [22, 23]. The authors of this paper are experienced in calculations detailing academic disciplines for more than 200 elements of a thesaurus [21]. The first results of the study were published in the proceedings of scientific conferences [24, 25].

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3 Research Approach In the research approach presented in our paper, the term thesaurus is understood as a hierarchical taxonomy of concepts, which is the structure of KA/KU, and not in a narrow linguistic sense as in [6]. Since the elements of the thesaurus in our study are not the concepts and topics of a specific academic course, but the knowledge areas and corresponding knowledge units, according to the algorithm, the informational significances of the SWEBOK KA within the SEEK recommendations are evaluated, but not the semantic significances of the thematic thesaurus elements in [6]. The essence of the method that makes it possible to obtain the desired ratings is as follows: Let the SWEBOK knowledge areas included in SEEK correspond to the basic elements of the thesaurus of an academic course and their relationships. To describe this structure, an (n, m)-hypergraph HðV; R; FÞ, is introduced where: V ¼ fv1 ; :::; vn g — graph vertices, R ¼ fr1 ; :::; rm g — graph edges, F — incidence mapping that sets to each edge ri a subset Fðri Þ ¼ fvj1 ; vj2 ; :::; vjki gV, ki ¼ jFðri Þj. It should be noted that a hypergraph is a generalization of an ordinary graph and its edges can be any number of incidences of vertices (e.g., it is not limited to two incidences — see Fig. 1): The vertices are interpreted as the primary elements of the thesaurus: concepts, categories or facts: the SEEK knowledge units (KU) in the example below. The edges are semantic subgroups in which these concepts are logically connected: the SWEBOK knowledge areas (KA) included in SEEK. Edges are also named by themes and identified by aliases (headers) T1 ; T2 ; :::; Tm .

Fig. 1. A sample bipartite hypergraph (left) and its incidence representation (right).

The main goal is to trace the influence of the concepts included in the thesaurus on each other to clarify, in this way, the informational significance of the elements of the thesaurus. When the logical dependencies between concepts are represented in the form of an oriented graph, a measure of the informational significance of a concept can be associated with the number of oriented routes outgoing from a vertex corresponding to a given concept, that is, the out-degree of a vertex of the graph.

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As mentioned above, our study analyzes the two-level structure SEEK KA/KU. Therefore, the starting point of the sequence of steps to evaluate the KA/KU dependences is the König’s representation of the graph H in the form of a simple oriented bipartite graph: KðHÞ ¼ ðV [ R; AÞ A ¼ fðv; rÞ : ½ðv; rÞ 2 V  R & ½v 2 rg; where: • The out-degrees of vertices vi of the graph KðHÞ characterize the “frequency” of using the thesaurus elements: here, the KU hours in our KA/KU sample, and • The out-degrees of vertices rj —— the “saturation” with primary concepts of the corresponding thesaurus theme: here, the total KA hours in the sample. To take into account the didactic features of the thesaurus elements intended for mastering by students, one should consider another oriented graph G with vertices T ¼ fT1 ; T2 ; :::; Tm g and edges A0 ¼ fa1 ; a2 ; :::; as g, describing the logical sequence of studying topics with aliases T1 ; T2 ; :::; Tm . Then, the informational significances (semantic significances in [6]) of the primary elements of the thesaurus are characterized by the out-degrees of the vertices of the multigraph G, which is a transitive closure of the union of graphs KðHÞ and G: G¼

[

ðKðHÞ [ GÞk

k

. The study of the structure of the considered graphs was implemented using matrix representations. The result is the adjacency matrix S of the multigraph G, which can be obtained using the following formula: SðGÞ ¼

m X

Sk ðKðHÞ [ GÞ

k¼1

Because in our case, the graphs KðHÞ and G are disjoint (do not have common edges), then: SðKðHÞ [ GÞ ¼ SðKðHÞÞ  SðGÞ . Hence, SðGÞ ¼

m X k¼1

.

ðSðKðHÞÞ  SðGÞÞk

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This SðGÞ expression allows us to calculate the out-degrees of the graph vertices and reduce the required computing resources. The latter makes it possible to quantitatively analyze the informational significance of thematic thesaurus elements. The transition from the mathematical description of the research approach to the practical ranking of the SWEBOK KAs within the SEEK recommendations is described below.

4 Application to SEEK To design educational programs in accordance with the SEEK concept curriculum designers should include the number of hours required for the study and the relationship of SWEBOK KAs in the SEEK recommendations. Some of the 37 KA/KU and the corresponding hours required for the study of their knowledge units (KU) as recommended by the SEEK guidelines are listed in Table 1 below (see [5, p. 28]).

Table 1. KA and KU in the seek guidelines [24]. KA/KU CMP CMP.cf CMP.ct CMP.tl FND FND.mf FND.ef FND.ec PRF … MAA … REQ … DES … V&V … PRO … QUA … SEC

Title Computing essentials Computing science foundations Construction technologies Construction tools Mathematical and engineering fundamentals Mathematical foundations Engineering foundations for software Engineering economics for software Professional practice … Software modeling and analysis … Requirements analysis and specification … Software design … Software verification and validation … Software process … Software quality … Security

Hours 152 120 20 12 80 50 22 8 29 … 28 … 30 … 48 … 37 … 33 … 10 … 20

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The total number of KA is 10 and the KU is 37. This research uses the KA dependencies as described in [5], p. 53 and reproduced in Fig. 2 below with the abbreviations from Table 1. It should be noted that Fig. 2 presents only nine KA, the SEC being dealt with separately in the SEEK recommendations. The transitive closure matrix of the bipartite graph describing the KA/KU dependencies is as follows [20]:  S¼

 9 X 0 S12 ðE þ S22 Þ Sk22 ; S22 ¼ 0 S22 k¼1

ð1Þ

where: E is the identity matrix of dimension 10 * 10; S12 is the 37 * 10 adjacency matrix KA/KU; S22 is the 10 * 10 adjacency matrix describing the KA logical dependencies (see Fig. 2); S22 is the transitive closure of the matrix S22 , calculated by the formula (1) (degrees greater than 9 of the nilpotent matrix 10 * 10 are zero matrices).

Fig. 2. KA dependencies between SEEK [5].

For calculation purposes it is assumed that the study of SEEK topics is acyclic, which is natural for well-developed curricula. This entails the nilpotency of the adjacency matrix of the subgraph that models the mutual influence of topics and, consequently, the ability to calculate the transitive closure of the matrix in a finite number of steps that does not exceed the number of topics.

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In this KA/KU example, the dimension of the adjacency matrix is 10 + 37 = 47, and calculations are easily performed using MATLAB on a standard personal computer. Studies have shown that an effective calculation according to the algorithm can be carried out for matrix dimensions an order larger [21].

5 Computation The out-degrees of the vertices of the digraph corresponding to matrix (1) can be chosen as the KA significances. These values are the sum of the elements of matrix S rows. Matlab, an application specialized in matrix processing, can be used to calculate matrix (1). It is convenient to prepare the initial matrices in an MS Excel spreadsheet, which makes it possible to automate the input of block matrices with a sparse structure S12 and S22 (see Figs. 3 and 4).

Fig. 3. Matrix S12 in Matlab.

A fragment of the matrix describing, in accordance with Table 1, the structure of the SEEK topics is presented in Fig. 3, and the matrix calculated on the basis of the dependencies shown in Fig. 2 is presented in Fig. 4. Note: the matrix S22 shown by Fig. 4 is the transitive closure of the matrix S22 corresponding to the graph G shown in Fig. 2.

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Fig. 4. Matrix S22 in Matlab.

Table 2. Results for KA informational significances. KA Significances (hours) Relative significances Share of hours in SEEK CMP 2888 6,18 0,33 FND 1280 2,74 0,17 PRF 290 0,62 0,06 MAA 224 0,48 0,06 REQ 120 0,26 0,06 DES 144 0,31 0,10 V&V 37 0,08 0,08 PRO 33 0,07 0,07 QUA 10 0,02 0,02 SEC 20 0,04 0,04

Thus, it contains not only digits 0 and 1, as S22 but also their linear combinations according to the graph G structure. Table 2 shows the calculated cumulative significances for KA in accordance with the SEEK recommendations. The second column of Table 2 presents the recalculation of the values in the Column 1 into proportions of the total hours of the curricula.

6 Results The analysis of the calculation results (see Table 2) allows us to classify all topics into three groups based on their informational significance levels: 1. Basic topics, which have a great informational significance when the criterion is a value in ones (here: CMP and FND) 2. Important topics, which have a medium informational significance, the criterion is a value in tenths (here: PRF, MAA, REQ and DES), and 3. Special topics that have a small informational significance, the criterion is a value in the hundredths (here: VAV, PRO, QUA and SEC).

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These groupings can assist the teacher or curriculum course designer to identify topics have to be mastered (basic topics); topics have to be understood (important topics); and topics provide a transition to the probable future relevant for this field of professional activity (special topics) [24]. It should be noted that the rating of special topics does not indicate their insignificance for professional training, but rather a tenuous relation to the basic topics in the framework of the discussed recommendations. These topics represent the possible response of education to the current needs of the economy and have a significant innovation potential. The last column of Table 2 shows the initial KA shares in the total hours of the curricula, illustrating the synergistic effect introduced by the interrelationships of the studied topics.

7 Discussion The algorithm has been applied to SEEK, illustrating its applicability when used as inputs, published professional group consensus, and recommended teaching hours within a bachelor level curriculum in software engineering. Applicability is, of course, the first level of empirical validation. The next level of usefulness validation is the algorithm outcomes in terms of three groups of topics for a curriculum or a specific course (topics to be mastered, understood, and specialized professional tasks). At a higher level, this research work validates the appropriateness of using quantitative methods in decision-making in education. Other words, it is a validation of analytics in higher education — an emerging new scientific discipline in modern pedagogies.

8 Conclusion The research reported in this article has explored the possibilities of academic analytics for the design of curricula at a university level by using quantitative methods, illustrated with its application to the design of software engineering programs. The educational design in software engineering proposed by the IEEE/ACM curriculum guidelines for SEEK describes the two-level structure of SWEBOK knowledge areas (KA) and the corresponding knowledge units (KU) required for study, together with the recommended number of teaching hours. The number of teaching hours recommended in SEEK is based on the opinions of SEEK contributors and can be considered an initial quantification of the “significance” of the KU across the SWEBOK KA. The research proposed in this paper represents an objective approach to quantify the related informational significance of the knowledge embedded within SEEK. The proposed approach evaluates KA informational significances by considering their relationships in the SEEK recommendations using an algorithm developed previously to calculate the semantic significances of the elements of a course thesaurus. This algorithm uses the representation of the hierarchical structure of the KA/KU educational content in the form of an oriented bipartite hypergraph, where the outdegrees of the vertices of the graph transitive closure are selected as the KA

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quantitative characteristics — that is informational significances, reflecting their mutual influence within educational programs. These objectively quantified informational significances allowed us to classify the SEEK topics into topics to be mastered, topics to be understood, and topics relevant to the variable components of specialized educational programs. The practical implications of the these objectively quantified informational significances results (e.g., significances in hours and relative significances) are in terms of three groups of topics for a curriculum or a specific course (topics to be mastered, to be understood and for specialized educational programs): teachers or curriculum course designers can use this grouping for planning educational process based on interrelationships of the studied topics. The graph approach to quantitative analysis of SWEBOK knowledge areas is not limited to software engineering: it is applicable to the analysis and design of academic curricula for different subject areas. Thus, this paper demonstrates the mathematical methods of graph theory are viable quantitative methods that can be applied to the design of educational programs and the educational analytics as a response to the challenges of the modern economy becomes a real tool for educational programs design, including that for software engineering curricula. The tool for the implementation of the necessary calculations is provided by mathematical packages that eliminate the problem of time-consuming calculations. Matlab, the calculation tool used here, successfully coped with the processing of matrices of significant dimensions. A trend of the current curricula research focuses on competency in today’s world through a three-dimensional entity with skills and behaviors combined with knowledge. Our study has only addressed one of those dimensions (knowledge) and further work could look into both skills and behaviors. Quantitative methods to support decision-making on the strategic development of educational programs for breakthrough digital technologies, such as software engineering, create the basis for the competitive advantage of leading universities. In addition, the results of applying the method described herein may be useful for the development of an evidence-based educational policy. Acknowledgment. The authors acknowledge with gratitude the international project Joint Programs and Framework for Doctoral Education in Software Engineering (PWs@PhD Project 2015–2018) [7] conducted within the EU Erasmus+ Program Capacity building in higher education. It was cooperation within the project that contributed to the emergence of our scientific contacts and research, the results of which are published in this paper.

References 1. Shilova, O., Yurkov, A.: ICT and the education system – key factors in the competitiveness of worldclass universities. J. Appl. Inform. 12(6(72)), 50–57 (2017) 2. Terekhov, A., Khalin, V., Yurkov, A.: Do candidates and doctors of science in software engineering need to modernize and technological development of the Russian economy? J. Appl. Inform. 13(4(76)), 42–52 (2018)

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3. Software engineering body of knowledge (2014). https://www.computer.org/education/ bodies-of-knowledge/software-engineering. Accessed 20 Nov 2021 4. Bourque, P., Fairley, R.E. (eds.): Guide to the Software Engineering Body of Knowledge, Version 3.0. IEEE Computer Society (2014). https://dl.acm.org/doi/book/10.5555/2616205. Accessed 20 Nov 2021 5. Curriculum Guidelines for Undergraduate Degree Programs in Software Engineering. IEEE Computer Society (2015). https://www.acm.org/binaries/content/assets/education/se2014. pdf. Accessed 20 Nov 2021 6. Monakhova, L.Yu.: Adaptatsiya informatsionnykh tekhnologii k formirovaniyu tezaurusa u studentov tekhnicheskikh vuzov [Adaptation of information technologies to the formation of a thesaurus among students of technical universities]. Dissertacija ... kandidata pedagogicheskih nauk [Dissertation for the degree of Candidate of Pedagogy]. Saint Petersburg (1997). (In Russian), https://dlib.rsl.ru/viewer/01000324734#?page=1. Accessed 20 Nov 2021 7. PWs@PhD Project. http://fase.it.lut.fi/. Accessed 20 Nov 2021 8. European Commission|Education, Audiovisual and Culture Executive Agency (EACEA)| ERASMUS+|Capacity building in the field of higher education projects (2015). https://www. eacea.ec.europa.eu/grants/2014-2020/erasmus/capacity-building-field-higher-education2015_en. Accessed 20 Nov 2021 9. Gadasina, L., Voitenko, S., Yurkov, A.: Research of student prospects on developing international PhD programs in software engineering. CEUR Workshop Proc. 1729, 56–65 (2016). http://ceur-ws.org/Vol-1729/paper-08.pdf. Accessed 20 Nov 2021 10. Khalin, V., Terekhov, A., Tkachenko, S., Yurkov, A.: Software engineering education for ensuring Russia’s priorities in the digital economy. CEUR Workshop Proc. 2256, 1–6 (2018). http://ceur-ws.org/Vol-2256/SWEPHD18_paper_01.pdf. Accessed 20 Nov 2021 11. Khalin, V.G., Voitenko, S.S., Yurkov, A.V., Kosov, Y.V.: Training of PhDs in software engineering in Russia: a proposal for new specialty. CEUR Workshop Proc. 1991, 20–25 (2017). http://ceur-ws.org/Vol-1991/paper-02.pdf. Accessed 20 Nov 2021 12. Khalin, V.G., Yurkov, A.V., Kosov, Y.V.: Challenges of the digital economy in the context of globalization: training of PhDs in software engineering in Russia. In: Alexandrov, D.A., et al. (eds.) DTGS 2017. CCIS, vol. 745, pp. 120–129. Springer, Cham (2017). https://doi. org/10.1007/978-3-319-69784-0_10. Accessed 2021/11/20 13. Voitenko, S., Gadasina, L., Sørensen, L.: The need for soft skills for Ph.D.’s in software engineering. CEUR Workshop Proc. 2256, 7 (2018). https://vbn.aau.dk/ws/files/307055227/ SWEPHD18_paper_03.pdf. Accessed 20 Nov 2021 14. Voitenko, S.S., Gadasina, L.V.: Soft skills of developers in software engineering: view from the PhD students’ side. CEUR Workshop Proc. 1991, 1–19 (2017). http://ceur-ws.org/Vol1991/paper-01.pdf. Accessed 20 Nov 2021 15. Quezada-Sarmiento, P.A., Elorriaga, J.A., Arruarte, A., Washizaki, H.: Open BOK on software engineering educational context: a systematic literature review. Sustainability 12 (17), 6858 (2020). https://doi.org/10.3390/SU12176858, https://www.mdpi.com/2071-1050/ 12/17/6858/pdf. Accessed 20 Nov 2021 16. Alarifi, A., Zarour, M., Alomar, N., Alshaikh, Z., Alsaleh, M.: SECDEP: software engineering curricula development and evaluation process using SWEBOK. Inf. Softw. Technol. 74, 114–126 (2016). https://doi.org/10.1016/j.infsof.2016.01.013. Accessed 20 Nov 2021 17. Garousi, V., Giray, G., Tuzun, E.: Understanding the knowledge gaps of software engineers: an empirical analysis based on SWEBOK. ACM Trans. Comput. Educ. 20(1), 3 (2019). https://dl.acm.org/doi/10.1145/3360497. Accessed 20 Nov 2021

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18. Vitols, G., Arhipova, I., Paura, L.: Engineering study program compliance evaluation to guidelines for software engineering curriculum. Eng. Rural Dev. 18, 1909–1914 (2019). https://doi.org/10.22616/ERDev2019.18.N328. http://tf.llu.lv/conference/proceedings2019/ Papers/N328.pdf. Accessed 20 Nov 2021 19. Lowell, W.B., Wilson, R.J., Cameron, P.J. (eds.): Topics in Algebraic Graph Theory’. Encyclopedia of Mathematics and its Applications, vol. 102. Cambridge University Press, Cambridge (2004). https://doi.org/10.1017/S0963548306008054. Accessed 20 Nov 2021 20. Shilova, O.: Teoreticheskie osnovy stanovleniya informatsionno-pedagogicheskogo tezaurusa studentov v sisteme vysshego pedagogicheskogo obrazovaniya [Theoretical foundations of the formation of information and pedagogical thesaurus of students in the system of higher pedagogical education]. Dissertacija ... doktora pedagogicheskih nauk [Dissertation for the degree of Doctor of Pedagogy], St. Petersburg (2001). (In Russian), https://search.rsl. ru/en/record/01000325114. Accessed 20 Nov 2021 21. Dautova, O.B., Torkhova, A.V. (eds.): Sistematika terminologicheskogo apparata sovremennoi paradigmy obrazovaniya kak metodologiya otbora soderzhaniya pedagogicheskogo obrazovaniya [Systematics of the terminological apparatus of the modern paradigm of education as a methodology for selecting the content of pedagogical education]. Publishing house “Bukval’no”, St. Petersburg (2019). (In Russian) 22. Pissanetzky, S.: Sparse Matrix Technology. Academic Press Inc, London (1984) 23. Duff, I.S., Erisman, A.M., Reid, J.K.: Direct Methods for Sparse Matrices. Numerical Mathematics and Scientific Computation, 2nd edn. Oxford University Press, New York (2017) 24. Shilova, O.N., Yurkov, A.V.: Matematicheskie metody teorii grafovkak instrument sistemnogo analiza v obra-zovanii na primere dizajna obrazovatel’nyh programmv oblasti programmnoj inzhenerii [Mathematical Methods of Graph Theory as a Tool of System Analysis in Education on the Example of the Design of Educational Programs in Software Engineering]. V sb.: Sistemnyj analiz v proektirovanii i upravlenii, vol. 3. Politeh-Press publ, Saint Petersburg, pp. 385–391 (2019). (In Russian), https://www.elibrary.ru/download/ elibrary_38582558_79857613.pdf. Accessed 29 Nov 2021 25. Shilova, O.N., Yurkov A.V.: Ispol'zovanie teorii grafovkak instrumenta analitiki v obrazovanii na primere dizajna obrazovatel'nyh programmv oblasti programmnoj inzhenerii [The use of graph theory as a tool in education analytics on the example of the design of educational programs in software engineering]. V sb.: Jenergetika, informatika, innovacii – 2018, vol. 3, pp. 167–171. Universum Publications, Smolensk (2018). (In Russian), https:// sbmpei.ru/files/uplfiles/f5be3fe96cafbbTom-3.pdf. Accessed 20 Nov 2021

Contemporary Aspects of Online Teaching Mathematics in Technical Universities Ekaterina A. Blagoveshchenskaya1(&) , Viktor V. Garbaruk1 Nina V. Popova2 , and Lutz Strüngmann3 1

,

Emperor Alexander I St. Petersburg State Transport University, 9 Moskovskiy Avenue, 190031 St. Petersburg, Russia [email protected] 2 Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya Street 29, 195251 St. Petersburg, Russia 3 Institut Für Mathematische Biologie, Fakultät Für Informatik Hochschule Mannheim, 10, Paul-Wittsack-Str., 68163 Mannheim, Germany

Abstract. The article is devoted to the pedagogical problems’ analysis of distance learning of mathematics in a modern technical university. Study materials have specific features in distance learning. It is necessary that a possibility of independent material mastering and control of the studied sections in course books be provided. For the development of interdisciplinary links, students must solve the applied problems, which should help mastering the disciplines studied in the senior years. An example of a test for the first-year students who entered the university, is given. The results of school mathematical training of junior students have been studied, the measures to eliminate possible gaps in their knowledge have been proposed, the possibility of predicting the results of an examination finals period by groups, faculties and the university as a whole has been shown. The examples of term course projects that are of great practical importance for building students’ skills in applying the knowledge gained in the process of studying higher mathematics to solving technical problems have been presented. Keywords: Distance learning technologies
Tests in mathematics Engineering problems
Course projects
Reliability




1 Introduction Constant reduction in the hours allocated to the study of higher mathematics forces to change the way it is taught. The need to adapt the traditional system of teaching mathematics in a technical university to modern realities is also due to the objective trend of introducing various forms of distance learning, which has become explosive due to the pandemic [1–3]. Currently, when remotely checking the students’ work progress, the main attention is paid to basic concepts and skills, which is quite true and natural. One of the negative consequences of the above factors is the inevitable formalization of the study and control of a discipline mastering (testing of various levels),

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 574–584, 2022. https://doi.org/10.1007/978-3-030-98832-6_50

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which inevitably reduces the possibility of developing students’ independent work skills, expanding their creative potential [4–7]. An introduction to the course of applied examples of the mathematical methods’ application turns out to be insufficient during test check. Therefore, it turned out to be natural to include in a typical calculation in mathematics a task involving the study of a real technical object’s mathematical model. To deepen the concept of the higher mathematics studied sections’ applied meaning, the textbook “Mathematical modeling of electromagnetic processes” was written. It has the following sections “Operational calculus”, “Fourier series”, “Theory of stability”. The topics of the assignments fit into the broadening of the future specialists’ horizons and give the students an opportunity to evaluate the mathematical methods’ importance and their place in the disciplines studied in senior courses: the theory of automatic control systems, signal transmission theory, stability theory. To stimulate the development of creativity during distance learning periods, it is possible to recommend writing essays and creating presentations on the topics of the mathematics course. The work on essays is voluntary, but it is taken into account when passing the exam. Topics for essays can be selected both from the list of the proposed topics that are consistent with the material being studied, but not sufficiently detailed in the classroom, or independently. First-year students were offered the following topics: “Curves of the second order in the plane”, “Surfaces of the second order”, “Double vector product”, “Comparison of methods for the approximate finding of the function roots”, “Transformation of coordinates in plane and space”, etc. Some students independently suggested interesting and informative topics: “Mathematics and public key ciphers”, “The use of Fibonacci numbers in the search for an extremum”, “Game theory”, “Mathematical sections of experiment planning”. Second-year students were offered the topics corresponding to the branches of mathematics they had studied: “Special solutions of differential equations”, “Resonance. Benefit or harm”, “Approximation of a function in the mean by a trigonometric polynomial”, “Spectra of periodic functions”, “Fourier integral. Fourier transformation”, etc. The second-year students independently proposed the following topics “Universalism of the method of indefinite coefficients”, “What was proved by Grigory Perelman”, “Geometry branches”, etc. Choosing a topic by the learners themselves is an element of creative work that should be encouraged. The presented essays differ in complexity, level of the topic study, implementation independence, but they allow us to judge about the students’ interest and their creative capabilities. This is how the skills of logical thinking, the ability for self-education and independent work are consolidated.

2 Applied Problems and Essays 2.1

Educational Materials for Distance Learning

Within the framework of teaching mathematics at a technical university, one of these areas is the development and use of teaching aids adapted to the existing conditions. The most important task is to provide students with study materials on the course of

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higher mathematics, specifically focused on distance work. Study manuals for distance learning have their own characteristics that have not yet been fully formulated. The main requirements for such manuals are the completeness and consistency of the material presentation, a sufficient minimum of theoretical foundations and exemplariness, the ability to independently master the material and control the progress. Universities must have the required number of textbooks suitable for distance learning. The choice of a book as a medium of information allows to some extent to overcome the “Internet addiction” of a student, formed by means of communication and mass media. The medical component of the long-term use of electronic devices by students should be also taken into account [8]. The practical implementation of this direction was the publication of the textbook “Solving problems in higher mathematics. Intensive course for students of technical universities”, containing basic concepts, definitions, theorems and formulas of the studied material. The manual contains tests for each section, allowing students to independently assess the work progress degree in the section, which allows them to better prepare for testing. The book contains the detailed solutions to a large number of problems. This allows students to independently learn problem solving by considering the examples provided in the manual. Students’ demand for this manual should be noted. 2.2

Mathematical Training of Applicants

Another problem of teaching mathematics at a university is the insufficient mathematical training of the applicants who have become students. Therefore, the task of checking the residual knowledge of school programs at the beginning of training with newly admitted students is urgent. In technical universities of St. Petersburg starting testing in mathematics has been conducted since 2003. Over the past years, a task bank has been created, the structure and content of the test for express-check of mathematical knowledge have been developed and optimized. 30 different versions of the evidencebased test, which takes 45 min to complete, have been created. Requirements for the students’ skills and knowledge are formulated for each example. This information enables the lecturer to choose both the presentation speed of various mathematics sections and to correct their content. In the proposed tests there are no tasks in which the student must choose one of the proposed options for possible answers. This forces the student to solve the problem and eliminates the problem of “guessing the answer”. In the proposed starting test, the answers to all problems are integers, which makes it theoretically possible to automate the test. The test also additionally includes a more difficult task for students who have perfectly mastered the course of school mathematics. This equalizes the duration of solving the problems of the starting test by students with different levels of training. The basic concepts of a mathematics course - a vector, a differential of a function, an integral, are presented at the university in great detail, starting with their foundations. Therefore, these sections are missing in the start-up testing tasks. Analysis of the test results made it possible to identify the sections that were insufficiently mastered by former school students.

Contemporary Aspects of Online Teaching Mathematics Table 1. Test example.

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3 Results 3.1

The Test Results

Comparison of the test results and the first student finals period shows that they are related. Table 1 shows the exam grades in mathematics received by students of one of the groups of the Faculty of Automation and Intelligent Technologies at the first exam of the winter finals period of the 2018–2019 academic year. Table 2. Exam results The tasks of the starting test solved 5 4 3 Referred to Less than 25% 0 0 2 2 From 25% to 50% 0 3 1 1 From 50% to 75% 1 5 0 2 Over 75% 3 2 0 0

Initial testing results allow us to predict the success of further training, since there is a noticeable statistical relationship between them. The exam scores given in Table 2 show that in order to successfully study at a technical university, a student must solve more than half of the tasks in the starting test. An equally important consequence of the initial testing is students’ adaptation to the perception of different mathematics sections. Initial testing allows us to identify students who will have problems in mastering the lecture material during distance learning. At Emperor Alexander I St. Petersburg State Transport University, the problem of insufficient mathematical preparation of students is solved, for example, by conducting additional classes in elementary and principles of higher mathematics. Teachers show how to solve problems in different sections, and students consolidate the knowledge gained by solving similar examples at home. Such classes are provided with a specially prepared manual “Solving problems in mathematics”. The material of the book is structured in accordance with the programs of studying mathematics in the upper secondary school and in secondary special educational institutions. In the book, the course of school mathematics is considered from the point of view of a university professor. In the manual, the student can learn the problems that he should be able to solve after entering the university. The student can be advised to study a specific chapter to fill in the gaps in a specific section without referring to school manuals. 3.2

Course Projects

In the previous curricula for a number of specialties of the faculty of “Automation and Intelligent Technologies” in the third semester, a course project was provided for the course of higher mathematics. Completing coursework, students not only gain skills in the practical application of various branches of mathematics in solving technical problems, but begin to better understand the meaning of abstract mathematical terms

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and formulations. Confident solution to higher mathematics problems, obtained during the coursework, will help students to solve real technical problems arising both in further education and practical work [9–11]. The course work, as it follows from this example, can be divided into 4 main stages. 1. Building a mathematical model. Transient processes and steady state operation RC circuit or RL circuit (Fig. 1).

uc i uin

i2

i1

R1 R2

Uout(t)

Fig. 1. RC circuit

1:a. Drawing up the differential equations for uout after the key is closed and the key output. 1:b. Calculation of transients when a step input signal is turned on uin . 1:c. Determination of the voltage at the output of the link when the input signal is disconnected. 1:d. Determination signal at the output, when a harmonic signal is applied to the input uin ¼ u0
sin xt. 1:e. Studying voltage uout ðtÞ and current iðtÞ changes. 2. Description of the link work in the temporary t and frequency x areas. ðpÞ : 2:a. Finding the transfer function of the dynamic link WðpÞ ¼ UUout in ðpÞ 2:b. Definition of impulse wðtÞ link characteristics, where wðtÞ is an original image wðtÞ. 2:c. Finding the transitional hðtÞ link characteristics, where hðtÞ is an original image WðpÞ p . 2:d. Finding the amplitude-frequency characteristic of the link AðxÞ ¼ jWðixÞj. 2:e. Finding the phase-frequency characteristic of the link UðxÞ ¼ arg WðixÞ. 2:f. Plotting wðtÞ; hðtÞ; AðxÞ; UðxÞ. 3. Investigation of converting a periodic input signal of a given shape by a link by the operational method. 3:a. Representation of an input signal by a Fourier series in complex and real forms. 3:b. Calculation of the amplitude jCn j and phase arg Cn spectra of the input signal. 3:c. Representation of the output signal by the Fourier series in complex and real forms.

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3:d. Calculation of the amplitude and phase spectra of the output signal. 4. Solutions of the obtained differential equations using the Laplace transform. 4:a. Formulation of operator equations relating the voltage at the output and at the input of the two-pole network. 4:b. Solution of a differential equation using the Laplace transform when a rectangular pulse voltage of a given shape is applied to the input. For the specialties of the faculty “Transport and energy systems” the section “Reliability of technical systems” is included in the disciplines “Probability Theory” and “Mathematical Statistics”. For a number of specialties, coursework is provided for this section of mathematics. To complete course work with full understanding, students must know the basic concepts of reliability theory, probability theory and mathematical statistics. The necessary information is given in the manual “Investigation of the reliability of technical systems”, compiled by professors of the Department of Higher Mathematics. Coursework can be divided into two main stages. 1. Calculation of numerical parameters that determine the reliability of the system (Fig. 2)

Fig. 2. A structural diagram of reliability

1:a. Assuming that the random variables (the moment of failure of one of the system elements) are independent, a formula is created for calculating the probability of the system’s working capacity as a whole. 1:b. The probabilities of the moment of system failure are calculated for various periods of time T. 1:c. Cтpoитcя тaблицa фyнкции pacпpeдeлeния вepoятнocтeй QðtÞ ¼ Pðt\TÞ нa зaдaннoм интepвaлe дo дocтижeния вepoятнocти oткaзa 0,99. 1:d. According to the equation QðtÞ ¼ c, the c system percentage resource tc is determined for different values of probabilities c. 1:e. Expectation and variance of a random variable operating time of the system before failure are calculated. 2. Assessment of system reliability by statistics methods. 2:a. A formula for calculating the system working time depending on the random value distribution functions of the failure moments of the system elements. 2:b. The operating hours before the element failure are generated using a random number generator according to the given laws of their failure-free operation

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duration probability distribution. As a result of n-fold repetition, a sample of system operating time values before failure is created. 2:c. Creation of a variational series of the system efficiency as a whole, construction of a histogram and a graph of the empirical distribution function. 2:d. Point and interval estimates of the numerical sample characteristics are calculated. 2:e. A hypothesis is put forward on the normal random value distribution of the system failure moments, which is checked according to two criteria: those of Pearson and Kolmogorov.

4 Discussion After completing coursework, students will be able to assess the potential of the higher mathematics methods, the boundaries of the practical application of its sections. Generally speaking, there should be a place in the teaching system for university students to prepare for independent scientific work. Therefore, teaching mathematics it is necessary that we disclose the general laws of science development. Research directions emerging in fundamental science continue to be filled with new remarkable results. At the same time, discoveries arise that are of independent importance for various fields. The highlighting and analysis of these components are intended to capture and analyze the achievements known to date in their interconnection. For example, in fundamental algebra this applies to the theory of Abelian groups, as well as to the theory of rings and modules. In teaching special sections of algebra, one should disclose the properties of algebraic structures in stages, using illustrations and considering illustrative examples [12]. It is important to formulate both parallel connections of algebraic structuring manifestations in various disciplines, including the language of graph theory, and to reveal the “points of growth” of science, analyzing its development in chronological order and moving from classical results to modern ones. At the same time, the repetition of results on a new level of understanding should be considered as parallelism inherent in any spiral, including that in the philosophical sense. Thus, in the educational process, both parallelism without complication (revealing the penetration of a certain theory into various areas) and parallelism with complication (discovering the general properties of structures of varying complexity) do arise. For example, the properties of Abelian groups X are reflected in their endomorphism rings End (X). For the latter, the question is posed of the possibility of describing their multiplicative automorphism Aut (End (X)) group. This example is methodologically related to many disciplines, whose logical component plays an important role in their structuring. Distance learning of mathematics at a technical university, as we have shown, allows us to build meaningful interdisciplinary links between higher mathematics and technical disciplines of graduating departments. Organizationally, an important prerequisite for the actualization of interdisciplinary links in the educational process is the teaching of higher mathematics at the same time as certain professionally oriented

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technical disciplines. Due to the fact that mathematical aspects are studied in the same semester with general technical disciplines, for which course project is provided, students have the opportunity to apply their knowledge of mathematics in practice, adapting it to solving engineering problems. In order to prevent possible problems in the synthesis of mathematical and special knowledge that may arise in poorly prepared students, teachers of graduating departments and departments of higher mathematics can coordinate the pace and logics of studying their disciplines based on the existing working curricula. Ideally, the mathematical aspects should be at least slightly ahead of the technical aspects, so that the student can optimally apply mathematics to solving professional problems. Thus, the course projects we have described are the recommended format for updating horizontal interdisciplinary links in the university educational process. As for the actualization of vertical interdisciplinary links, when the related disciplines are spaced in time and taught in different courses of study, this is also possible. To write term projects in senior years, students need to restore in their memory the residual mathematical knowledge acquired in 1–2 years of the training program. This is quite laborious, but it provides a holistic understanding of higher mathematics, which will contribute to the competent writing of coursework in special disciplines. In general, the analysis and comparison of the coursework results allows students to form a holistic picture of the possibilities and boundaries of the methods application, an understanding of the relationship between sections of the mathematics course (for example, probability theory and mathematical statistics), the relationship between theoretical knowledge and its practical application [13]. At the same time, mainly the students’ abilities for self-education and the skills of logical thinking are consolidated, which are important components of the universal competencies of a modern student that we develop.

5 Conclusions Teaching higher mathematics to university students contributes to preparing them for independent scientific work, and applying mathematical skills to professionallyoriented course projects provides the necessary interdisciplinary links for holistic understanding of higher mathematics and the specialized disciplines. To date, the development of information technology has allowed distance learning to take shape as a separate form of education. It is necessary, while preserving the already existing teaching technologies, to propose new methods that use the mass “digitalization” of contemporary students. Realizing the importance of numerous social, psychological, medical and other consequences of the distance learning, we presently focus on the knowledge transfer process from teacher to student. The main factor determining this transfer is essentially technical means transformation from an assistant into an intermediary between teacher and student. The time for the general assessment of the distance learning impact on the quality of education has not come yet, but some negative consequences can be noted now. The inevitable formalization of the knowledge transfer process, monitoring the material assimilation by students, results evaluation has already led to a change in teaching

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priorities. The methods aiming at building the solving problem skills on the basis of the obtained theoretical knowledge is replaced by the formation of a set of correspondences “task-solution” and algorithm memorization [13]. The development of students’ creative abilities, logical thinking skills is largely excluded. The grounds for the development of such an approach has been prepared by the secondary school, which was the first to have perceived the negative consequences of education reform. There is no general recipe for solving the problem yet, but it is necessary we form the directions for its solution [14, 15]. Education has never been a service. The teacher not only teaches a future specialist, but also contributes to the formation of a citizen, for whom a diploma of higher education is an opportunity to benefit the country. This learning function cannot be entrusted to technical means. Acknowledgements. The work was carried out within the framework of the Priority-2030 program under the strategic project “Intelligent ecosystem of digital priorities for transport and logistics”. The work is supported by the Russian Foundation for Basic Research (project 20-0100610).

References 1. Adnan, M., Anwar, K.: Online learning amid the COVID-19 pandemic: students’ perspectives. J. Pedagogical Sociol. Psychol. 1, 45–51 (2020) 2. Toquero, C.M.: Challenges and opportunities for higher education amid the COVID-19 pandemic: the Philippine context. Pedagogical Res. 4, 162–175 (2020) 3. Wu, Z.: How a top Chinese university is responding to coronavirus. Retrieved from World Economic Forum. https://www.weforum.org/agenda/2020/03/coronavirus-china-thechallenges-ofonline-learning-for-universities. Accessed 21 Nov 2021 4. Malkova, T.V., Baranov, A.: Some organizational problems of distance learning. Mod. Sci. 4, 278–280 (2020) 5. Romanov, E.V., Drozdova, T.V.: Distance learning: necessary and sufficient conditions for effective implementation. Mod. Educ. 1, 172–195 (2017) 6. Soltogulova, M.U.: Topical problems of learning in the system of distance education. Izvestiya vuzov Kyrgyzstan 5, 69–70 (2016) 7. Orlova, E.R., Koshkina, E.N.: Problems of the development of distance learning in Russia. Priorities Russia 23, 12–20 (2013) 8. Fadeev, E.V.: Organizational and psychological problems of distance learning. World Sci. Cult. Educ. 3, 308–310 (2017) 9. Benin, A., Guzijan-Dilber, M., Diachenko, L., Semenov, A.: Finite element simulation of a motorway bridge collapse using the concrete damage plasticity model. In: E3S Web of Conferences, vol. 157, p. 06018 (2020) 10. Benin, A., Semenov, S., Semenov, A., Bogdanova, G.: Parameter identification for coupled elasto-plasto-damage model for overheated concrete. In: MATEC Web of Conferences, vol. 107, p. 00042 (2017) 11. Benin, A.V., Semenov, A.S., Semenov, S.G., Beliaev, M.O., Modestov, V.S.: Methods of identification of concrete elastic-plastic-damage models. Mag. Civil Eng. 76(8), 279–297 (2017)

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12. Blagoveshchenskaya, E.A., Filimonov, A.V., Trifonov, A.E.: Near isomorphism for countable-rank Torsion-free Abelian groups. J. Math. Sci. 259(4), 394–402 (2021). https://doi.org/10.1007/s10958-021-05628-4 13. Chvanova, M.S., Kiseleva, I.A.: Problems of distance learning on the Internet. Bull. Tambov Univ. Ser. Nat. Tech. Sci. 5, 1200–1203 (2017) 14. Bolshakov, M.A., Molodkin, I.A., Pugachev, S.V.: Comparative analysis of machine learning methods to assess the quality of IT services. In: Models and Methods for Researching Information Systems in Transport, St. Petersburg, Russia, pp. 142–149. http:// ceur-ws.org/Vol-2803/paper20.pdf/. Accessed 21 Nov 2021 15. Adadurov, S., Fomenko, Y., Khomonenko, A., Krasnovidov, A.: Integration of the MATLAB system and the object-oriented programming system C# based on the Microsoft COM interface for solving computational and graphic tasks. In: Silhavy, R. (ed.) CSOC 2020. AISC, vol. 1224, pp. 581–589. Springer, Cham (2020). https://doi.org/10. 1007/978-3-030-51965-0_51

Flipped Learning and Education System: Key Activities and Indicators Inna A. Seledtsova1(&) 1

, Sergey G. Redko1

, and Iulia Shnai2

Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya Street 29, 195251 St. Petersburg, Russia {seledtsova_ia,redko_sg}@spbstu.ru 2 Lappeenranta-Lahti University of Technology, Lappeenranta, Finland [email protected]

Abstract. Educational systems have been undergoing a period of restructuring in the past to years. New technical and methodological tools and approaches are coming to the educational process. Many of them have long established themselves, but in the last two years they have been gaining popularity due to the need to introduce blended learning formats. Flipped learning is a well-known approach that has become increasingly popular in recent years. Basically, flipped learning is described as “online theory and offline practice”. Actually, there is the concept of individual and group spaces related to Bloom’s Taxonomy. This concept determines the flipped learning process. This paper aims to show the key principles of individual and group space building for using flipped learning approach in higher education. Two-year experience of teaching in a flipped way is on the basis of this work. The results and conclusions of using flipped learning approach are demonstrated. It is suggested the structure of an educational process and the model for the student onboarding to the flipped learning. Keywords: Flipped learning Education system


Blended learning
Higher education


1 Introduction Over the past year, the popularity of blended learning formats has grown significantly. New, post-covid realities require new formats for organizing educational processes. Flipped learning is one of the blended learning formats. The concept of flipped learning was born in 2007. American chemistry teachers - Jonathan Bergman and Aaron Sams decided to help those students who missed the classroom. They have recorded their lectures and posted them online for students who missed lessons. They decided to give notes and videolectures as homework and set aside the free class time for interactive forms of education. Typically the flipped learning concept is this: that which is traditionally done in class is now done at home, and that which is traditionally done as homework is now completed in class [1]. In practice, a more serious approach to the organization of the educational process is behind this way of education [2]. The validity of the use of flipped learning approach in higher education is noted by a number of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 585–595, 2022. https://doi.org/10.1007/978-3-030-98832-6_51

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teachers [3–6]. An example is the experiment of a group of scientists who for 7 years conducted a comparative observation of students (Computer Engineering department). During the experiment, there were always two groups of students. One group studied exclusively in a traditional format: lectures were held in face-to-face format, the use of digital technologies was minimal. Another group studied in a blended format according to the flipped classroom model: theoretical material was presented to students in an online format (video lectures, notes), and the face-to-face lesson was devoted to practical work with a teacher and classmates. Scientists were prompted to such an experiment by a drop in the level of involvement in the educational process, a low level of reference to educational materials, and a low level and results of practical homework. As a result of a seven-year experiment, it was found that groups of students who studied in a flipped classroom format showed much higher academic results than groups that studied in a purely traditional format [7]. This paper aims to provide an experience of using flipped learning in the 2019/2020 and 2020/2021 academic years. We use flipped learning to teach “Team management” for the fourth-year students. More than 80 students have been taught over the past two years. The need to use the flipped learning format was due to several factors: • the majority of students work starting from the 4th year, therefore they attend theoretical classes less and less; • discipline materials imply a large amount of individual and group practice work; • the need to reduce the personal contact of students over the past year due to the pandemic. Flipped learning allows students to learn preliminary materials outside of the class and apply what they learn in the classroom while working and discussing and getting immediate feedback from the teacher [8]. “Flipped Learning is a pedagogical approach in which direct instruction moves from the group learning space to the individual learning space, and the resulting group space is transformed into a dynamic, interactive learning environment where the educator guides students as they apply concepts and engage creatively in the subject matter” [9]. This definition highlights two fundamental terms of flipped learning: individual and group learning space. Individual space implies that the student is alone with educational content: independently studies it, independently performs simple tasks, independently reflects the studied material and the obtained educational experience. Group space implies active in-class interactions with the teacher in various forms: performing practical tasks, individual and group consultations, etc. According to Bloom’s Taxonomy, there are six levels of educational objectives: remembering, understanding, applying, analyzing, evaluating, creating [10]. Flipped learning ensures the achievement of the lower levels of educational objectives (remembering, understanding, applying) in the individual space and the upper levels (analyzing, evaluating, creating) in the group space. The principles of organizing a group and individual space are considered further.

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2 Materials and Methods The course “Team management” is the obligatory discipline for the fourth-year students at Peter the Great Saint Petersburg Polytechnic University. The length of the course is 14 weeks. Students have one class of this course per week. The course was transited into the flipped format in the 2019/2020 academic year. Previously it was taught in a traditional way. The translation of the course into the flipped format was started along with the participation of a group of SPbPU teachers in the international project CEPHEI. CEPHEI aims to increase the digitalization, internationalization and visibility of Industrial Innovation Education in the world scope with blended learning approaches [11]. The structure and content of the course were developed in the frame of the backward design model [9, 12]. The backward design assumes three stages algorithm: • defining objectives, • defining the key question, • defining the content. The content for the individual space of the course is placed on the Moodle platform. Group space assumes in-class offline activities or online synchronous activities with the teacher. All activities of the students for individual and group spaces are measured by the metrics that are represented in Tables 1 and 2. Data source for the metrics in individual space is the platform (Moodle). Data source for the metrics in group space is the measurements made by the teacher manually. Table 1. Metrics for the individual space. Metrics

Description

Video Completion Rate (VCR) Notes Completion Rate (NCR) Assigments Scores (AS) Time co complete (T)

Ratio of the number of students who watched the video to the end to the total number of students

Unit of measurement %

Ratio of the number of students who read the note to the end to the total number of students

%

Average results of the obligatory online assignments

Points (1–100) Minutes

Average time it takes a student to complete a module (plat-form data and student's own sensations)

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Metrics

Description

Preparation rate (RP)

Ratio of the number of students who is actively involved to the process of knowledge alignment Average time it takes to complete the knowledge alignment process

Time of the knowledge alignment (TA) Practice Scores (PS)

Average results of the obligatory practice assignments

Unit of measurement % Minutes

Points (1–100)

In addition, at the end of each educational module, an anonymous survey of students about their satisfaction with the organization of the educational process was conducted. It is important to note that for all students this format of study was new. The criteria proposed in [13] were taken to assess satisfaction: • • • •

System Convenience Satisfaction (FSCS). Interaction Satisfaction (FIS). Assistance Service Satisfaction (FASS). Learning Satisfaction (FLS).

Historical data characterizing the educational process until 2019/2020 was not found, however, the existing problems (low involvement, high percentage of working students) suggested that the flipped classroom would be the most effective model for organizing the educational process. During the observation in 2019/2020 and 2020/2021, it was necessary to check a number of questions: • students’ engagement in the educational process grows during the course; • the higher the involvement of students in the individual space, the less time it takes in the classroom format to align knowledge and more time can be spent on practical work; • the higher the involvement of students in the educational process, the higher the academic results.

3 Results As noted earlier, it is common to refer to the individual and group space of the student in flipped learning. The structure of the “Teams Management” course also involves individual and group spaces. It consists of 13 online (individual space) and face-to-face (group space) modules.

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589

Individual Space Structure

Traditionally, an individual space consists of video lectures, audio lectures, notes, and simple tasks. The typical structure of the online module for the individual space in the “Teams management” course is represented in Table 3.

Table 3. Structure of the individual space. Activities Video lecture Notes Online assignment Questions

Description Author’s video lectures or video lectures from other resources with the key theory parts Author’s reading notes or notes from other resources with the expanded theory parts Quizzes, drag and drop tasks with automatic feedback Questions that students ask after studying the material. Three questions must be asked. There are two types of questions: – what was not clear in the material or what would you like to know more? – what would you ask your classmate to verify that he or she had studied the preparatory material?

All materials are placed on the Moodle platform. During the experiment over the past two academic years, it was established that the duration of video lectures should be no more than 7 min. The same goes for audio lectures. The average time to read the notes must be less than 10 min. Tasks must be with immediate feedback or automatic review. As a rule, the teacher does not participate in reviewing tasks performed in an individual space. It was detected how to increase involvement in viewing preliminary videos or reading notes. For some videos and notes, the tasks might be embedded inside the materials. In our case, the tasks were embedded in several video lectures (modules № 4, 5, 7), the answer to which had to be given in the form under the video. Students could only find out the tasks if they watched the video. Table 4 shows the dependence of the built-in question inside the video lecture and the percentage of students who examined the video lecture until the end (VCR). Noteworthy are also values that show that the higher the VCR, the higher the average score for online assignments (AS) at the end of the online part (see Fig. 1).

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Table 4. VCR rates, %. Modules Module 1 Module 2 Module 3 Module 4 Module 5 Module 6 Module 7 Module 8 Module 9 Module 10 Module 11 Module 12 Module 13

2019/2020 21 29 32 32 39 43 50 57 61 68 75 79 86

2020/2021 51 55 60 78 82 67 84 71 71 78 85 87 93

100 95 90 85 80 75 70 65 60 55 50

VCR, %

AS (1-100)

Fig. 1. Dependence of VCR and AS (y = 0,5235x + 42,03)

3.2

Group Space Structure

The group space structure consists of four types of activities. All of them are held in face-to-face mode with students. The descriptions of each element of the face-to-face lesson structure are given in Table 5.

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Table 5. Structure of the group space. Activities Warm-up activities, knowledge alignment Questions and answers

Practice Reflection

Time taken 15– 45 min 15 min

30– 45 min 15 min

Description Quick tasks and activities that allow determining the level of preparation, active and inactive students Answers to questions asked by students after studying online materials, as well as questions that appeared in the class Cases, project and laboratory works Quick reflection of each student: What did you do? What didn’t work out? How did the online preparatory materials help?

Figure 2 shows the dependence of the number of students who watched the preparatory video to the end (VCR) and the preparation rate (RP), time of the knowledge alignment (TA), and practice scores (PS). An analysis of the results shows that the higher the percentage of students prepared for the face-to-face class, the higher the results of students and the less time the teacher needs to spend on knowledge alignment. 3.3

Additional Results

At the beginning of the transition to flipped learning a number of hypotheses were formulated: 1. Students’ engagement in the educational process grows during the course. 2. The higher the involvement of students in the individual space, the less time it takes in the classroom format to align knowledge and more time can be spent on practical work. 3. The higher the involvement of students in the educational process, the higher the academic results. The second hypothesis is currently proved according to the results (Fig. 2). The third hypothesis should be more rigorously tested during further work since so far there is not enough data to formulate unambiguous conclusions. The conclusion about the impact of engagement on academic performance so far can only be made regarding engagement in the individual space. As for the first hypothesis, it is worth paying attention to Table 6. The values presented in the table for 2019/2020 and 2020/2021 academic years show that student involvement in the educational process in an inverted format is growing with each module. This conclusion can be made based on the VCR and NCR values.

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It is worth noting that involvement in the process in 2020/2021 is higher than in the previous year. This is due to the fact that there was an organizational innovation at the beginning of the course. All students at the beginning of the course undergo onboarding [14–16], in the framework of which they are told: • What is an inverted learning format? • How is it necessary to learn in an inverted format? • How is it necessary to work with each material type in the individual and group space? • Why was the format of the inverted class chosen? Onboarding was made as a short introductory online lecture. As the results show, onboarding of the students in the educational process significantly increases their involvement from the very beginning of the course. It may be one of the reasons for the higher rates of student satisfaction with the educational process in the 2020/2021 academic year (Table 7).

100 90 80 70 60 50 40 30 20 10 0

VCR, %

RP, %

TA, min

PS (1-100)

Fig. 2. Dependence of the number of students who watched the preparatory video to the end (VCR) and preparation rate (RP, y = 1,1105x − 21,18), time of the knowledge alignment (TA, y = −0,5595x + 65,63), and practice scores (PS, y = 0,8525x − 1,62).

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Table 6. VCR and NCR rates. Modules Module Module Module Module Module Module Module Module Module Module Module Module Module

1 2 3 4 5 6 7 8 9 10 11 12 13

VCR, % 2019/2020 21 29 32 32 39 43 50 57 61 68 75 79 86

2020/2021 51 55 60 78 82 67 84 71 71 78 85 87 93

NCR, % 2019/2020 18 25 36 32 43 43 54 61 57 71 79 79 82

2020/2021 45 49 56 62 65 67 71 73 73 76 82 84 89

Table 7. Average satisfaction of the students. Metrics System Convenience Satisfaction (FSCS), 1–100 points Interaction Satisfaction (FIS), 1–100 points Assistance Service Satisfaction (FASS), 1–100 points Learning Satisfaction (FLS), 1–100 points

2019/2020 62 84 92 96

2020/2021 73 90 95 97

4 Discussion The key point of flipped learning is the concept of individual and group spaces. This concept was described in this paper as the basis of suggested structure of educational process. The obtained results confirm the effectiveness of flipped learning for students with a high level of involvement in the individual space: the higher the level of preparation in the individual space, the higher the academic and practical results obtained in the group space. This is achieved because less time is spent on knowledge alignment in the group space. Classroom time is mainly spent on practical assignments.

5 Conclusions Flipped learning is one of the formats for the effective organization of the educational process, which allows you to use classroom time more productively. Theoretical information is presented to the student, to a greater extent, in an online format, in an individual educational space. While classes with a teacher in a group environment

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consist mainly of practical tasks, which require active feedback and the involvement of the teacher. The group space covers the upper levels of Bloom’s taxonomy. The paper presents the intermediate results of two years of experience in translating the educational process in higher education into an inverted format. The experience gained is currently the basis for the development of a new master's program for SPbPU. In this program, 70% of the disciplines will be presented in an inverted format. Acknowledgement. The research is funded by the Ministry of Science and Higher Education of the Russian Federation (contract No. 075-03-2021-050 dated 29.12.2020).

References 1. Basal A.: The implementation of a flipped classroom in foreign language teaching. Turk. Online J. Distance Educ. – TOJDE (2015). https://files.eric.ed.gov/fulltext/EJ1092800.pdf. Accessed 21 Oct 2021 2. Roehling, P.V.: Introduction to Flipped Learning. Flipping the College Classroom. Palgrave Pivot, Cham (2019). https://doi.org/10.1007/978-3-319-69392-7_1. Accessed 11 Oct 2021 3. Warter-Perez, N.J., Dong, J.: Flipping the Classroom: How to Embed Inquiry and Design Projects into a Digital Engineering Lecture (2012) 4. El Miedany, Y.: Flipped learning. In: Rheumatology Teaching. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98213-7_15. Accessed 11 Oct 2021 5. Bates S., Galloway R.: The inverted classroom in a large enrolment introductory physics course: a case study. High. Educ. Acad. (2012) 6. Findlay-Thompson, S., Mombourquette, P.: Evaluation of a flipped classroom in an undergraduate business course. Bus. Educ. Accred. 6(1), 63–71 (2014) 7. Jones, K.A., Sharma, R.S.: Higher Education 4.0: The Digital Transformation of Classroom Lectures to Blended Learning. Springer, Singapore (2021). https://doi.org/10.1007/978-98133-6683-1 8. Chao, C.-Y., Chen, Y.-T., Chuang, K.-Y.: Exploring students’ learning attitude and achievement in flipped learning supported computer aided design curriculum: a study in high school engineering education. Comput. Appl. Eng. Educ. (2015). https://www.researchgate. net/publication/273181858_Exploring_students'_learning_attitude_and_achievement_in_ flipped_learning_supported_computer_aided_design_curriculum_A_study_in_high_school_ engineering_education. Accessed 12 Dec 2021 9. Flipped Learning Network (FLN). The Four Pillars of F-L-I-PTM (2014). http://www. flippedlearning.org/definition. Accessed 11 Oct 2021 10. Schaffer, H.E., Young, K.R., Ligon, E.W., Chapman, D.D.: Automating individualized formative feedback in large classes based on a directed concept graph. Front. Psychol. (2017). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5329031/. Accessed 11 Oct 2021 11. Cooperative E-learning Platform for Industrial Innovation. https://www.cephei.eu/en/aboutus/. Accessed 11 Oct 2021 12. Kim, K.Y., Kim, Y.: What are learning satisfaction factors in flipped learning? In: Park, J.J. (H., Pan, Yi., Yi, G., Loia, V. (eds.) CSA/CUTE/UCAWSN -2016. LNEE, vol. 421, pp. 750–755. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-3023-9_115 13. Ryan, M., Ryan, M.: Sustainable pedagogical change for embedding reflective learning across higher education programs. In: Ryan, M.E. (ed.) Teaching Reflective Learning in Higher Education, pp. 213–227. Springer, Cham (2015). https://doi.org/10.1007/978-3-31909271-3_15

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14. Ransburg, D., Sage-Hayward, W., Schuman, A.M.: Onboarding. In: Human Resources in the Family Business. A Family Business Publication. Palgrave Macmillan, New York (2016). https://doi.org/10.1057/9781137444271_6. Accessed 11 Oct 2021 15. Banyan, M., Demers, N.E., Romine, J.: Planning the sustainable university: from aspiration to implementation. In: Leone, K., Komisar, S., Everham III, E.M. (eds.) Making the Sustainable University. ES, pp. 25–44. Springer, Singapore (2021). https://doi.org/10.1007/ 978-981-33-4477-8_3 16. Bauer, T.N., Erdogan, B.: Organizational socialization: the effective onboarding of new employees. In: Zedeck, S. (ed.) APA Handbooks in Psychology. APA Handbook of Industrial and Organizational Psychology, Maintaining, Expanding, and Contracting the Organization, pp. 51–64 (2012). https://doi.org/10.1037/12171-002. Accessed 11 Oct 2021

Clustering and Analysis of the Participants’ Results and Completed Test Tasks on Massive Open Online Course Sergey Nesterov(&)

, Victoria Sazhnova

, and Elena Smolina

Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya Street 29, 195251 St. Petersburg, Russia [email protected]

Abstract. The paper presents the results of the analysis of the data about the learning outcomes of participants on MOOC “Data Management” on the Russian open education platform openedu.ru. The course was developed in Peter the Great St. Petersburg Polytechnic University at 2016 and for three years this course has been conducted twice a year: autumn session from September till January and spring session from February till June. But for the 2019–2020 academic year it was provided in a new format, as one long session for the academic year. Clustering of students has been done before and after changing the course format. It was shown that the modifications in the course format did not lead to significant changes in the characteristic groups of students. Clustering of test tasks has also been performed and it helped to make segmentation by the difficulty level. Those results will be taken into account for changing the assessment scale for these tasks. Keywords: E-learning


MOOC
Educational data mining
Clustering

1 Introduction Nowadays education is being transformed by the introduction of information technologies and massive open online courses (MOOC). The pandemic and self-isolation have led to a real boom in the field of online education. However, despite the convenience of this training format, it also has some significant problems. For example, it is known that the percentage of MOOC participants who complete courses is quite small [1]. Data mining technologies could be used to understand the problems with passing the course. Analysis of the participants’ results on the course helps to determine complex topics, assess the correctness of test tasks, etc. There is a special direction of data mining, named data mining in education, which is solving such problems [2–5]. The results of the analysis can help in understanding the educational process and increase the effectiveness of learning in future sessions of the distance course. In this work, we analyzed the reports about students’ results on the MOOC “Data Management” on the Russian platform of open education openedu.ru [6]. This course first time was launched in the autumn of 2016 and was conducted six times in a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 596–605, 2022. https://doi.org/10.1007/978-3-030-98832-6_52

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semester format, closely corresponding to the format of such course at Peter the Great St. Petersburg Polytechnic University. The results of the analysis of these sessions of the course were published in papers [7–9]. In autumn 2019 the course format was changed — it was launched in September for the entire academic year, until the end of June. So participants could start their studies at any time from September to mid-April, and all deadlines for the course tests were set for the end of June. The reports about the learning outcomes of participants of this session of the course were analyzed. As a result of clustering, the characteristic groups of the course participants were identified. After that the most difficult tasks for each of these groups were identified, and the reasons for those difficulties were analyzed.

2 Methods: Preliminary Data Analysis The course “Data management” lasts for 16 weeks, each week presenting a new topic [8]. The course content includes video lectures, lecture notes, workshops, and weekly tests (marked as homework in reports). The students have a midterm exam after the 8th week and a final exam after the 16th week. The final grade consists of a combination of average homework results (with weighting factor 0.2), the midterm (with weighting factor 0.2), and final (with weighting factor 0.6) exam grades. As a software tool for our analytics, we used the programming language R which has a large number of libraries (packages) for visualization, data analysis, and ma-chine learning [10, 11]. Files with grade reports were downloaded from the openedu.ru web portal and imported into R as data frames. After it, column names were made shorter and the absent grade data were replaced by zero. Table 1 summarizes data about student activity on the course during different sessions. In the 2019–2020 academic year about 6000 students enrolled in the course, but only about 20% started to complete the tests. For comparison, Table 1 also presents data about previous sessions of 2016– 2018 years. It can be seen that the number of students enrolled in the 2019–2020 academic year is more than twice the number of students enrolled in previous (semester) sessions of the course. At the same time, the percentage of students who enrolled but did not complete any test task in the 2019–2020 academic year roughly corresponds to semester sessions. Thus, it is fair to conclude that the topic of the course (database systems and data management) is very relevant at the moment and is of interest to study. However, learning the course material requires self-discipline and time from the course participant. The diagram in Fig. 1 shows the number of students completing the tasks during the 2019–2020 academic years. 1153 students completed the assignment for the first week, but a quarter of them did not perform the test of the second week. After the third week, the number of students who take tests has stabilized, with a slight increase in the midterm exam.

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S. Nesterov et al. Table 1. Students’ activity during different course sessions.

The session

Parameter

2019–2020

Number % Number % Number % Number % Number % Number %

2018 (autumn) 2018 (spring) 2017 (autumn) 2017 (spring) 2016 (autumn)

of of of of of of

Enrolled in the course 5937 100 2346 100 1504 100 1823 100 1572 100 2547 100

Did not perform any task 4717 79.45 1817 77.45 1225 81.45 1396 76.58 1073 68.26 1749 68.67

Started passing tests 1220 20.55 529 22.55 279 18.55 427 23.42 499 31.74 798 31.33

There are about 600 participants who were regularly completing assignments on the course. Such dynamics of passing the course can be associated with several reasons: • • • • •

lack of time to study the course material and do homework; the material turned out to be difficult to study; the participant lose interest in the course; the course is too long; missed deadlines for completing tasks.

Fig. 1. Number of students completing the tasks (session 2019–2020).

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Figure 2 shows the results of previous sessions in the 2017–2018 years [7, 8]. The graphs illustrate the number of students (in percentage) who completed the test of the first week (100%) and the number of students who continued further study and were completing assignments. It can be seen that the dependences shown in Fig. 1 for the 2019–2020 year session and in Fig. 2 are nearly similar. But in Fig. 1 the number of students who complete assignments decreases more smoothly. This may indicate that increasing the duration of the course, as well as shifting the deadlines to the end of the course, helps students to manage their time and choose their own pace of learning.

Fig. 2. Percentage of students who passed the test as compared to the first test (sessions of 2017–2018 years).

3 Clustering of Course Participants The aim of clustering is to divide the set of elements (here − all students, who enrolled in the online course) into groups (clusters) that have common features. Let denote the set of participants as I: I ¼ fi1 ; i2 ; :::; ij ; :::; in g;

ð1Þ

where each participants ij , possesses a set of attributes: ij ¼ fx1 ; x2 ; :::; xh ; :::; xm g:

ð2Þ

600

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We need to form a set of clusters: C ¼ fc1 ; c2 ; :::; ck ; :::; cg g;

ð3Þ

where each cluster ck includes similar objects from I: ck ¼ fij ; ip jij 2 I; ip 2 I u dðij ; ip Þ\rg:

ð4Þ


 Here d ij ; ip is the distance between the objects, and r − is a boundary value of distance to include objects in one cluster. In our case, the Euclidean distance will be used: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi m X dðij ; ip Þ ¼ ðxjt  xpt Þ2 :

ð5Þ

t¼1

To select the optimal number of clusters the analysis of the dependency of withincluster sum of squares on the number of clusters, called the “elbow method” [12, 13], was used. The “elbow” point on the plot corresponds to the optimal number of clusters. Figure 3 shows such a plot. It was built using the function fviz_nbclust() from the package factoextra which is designed to visualize the results of cluster analysis based on the ggplot2 graphics system in R. The bend of the graph shows that it will be better to use 4 clusters.

Fig. 3. The dependency of the within-cluster sum of squares on the number of clusters.

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601

The k-means method was used for the clustering. As a result, the set of students was divided into 4 characteristic groups (Fig. 4): • • • •

Cluster Cluster Cluster Cluster

1: 2: 3: 4:

125 students who passed only some of the tests; 504 students who regularly completed all tests; 427 students who stopped completing assignments after the first weeks; 4881 students who did not perform tasks.

Comparing these results with the cluster analysis performed for the previous sessions of the course [7–9], it can be mentioned that modifications in the course format did not lead to the changes in the characteristic groups of students.

Fig. 4. Percentage of students in each cluster who passed the tests.

4 Results and Discussion: Assessment of the Difficulty of Test Tasks To assess the complexity of the tasks, the data from problem grade reports were used. The openedu.ru web portal is based on the Open edX software platform [14] and the problem grade report is one of the standard Open edX reports, which contains details on how students completed test tasks for each of the tests. In the midterm and final exams test tasks are selected randomly from task libraries, so we analyzed only tasks from weekly tests, which are the same for all course students. There are 93 such tasks:

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S. Nesterov et al.

• tests of the 1st, 5th, 6th, 7th, and 8th weeks contains 5 tasks each; • tests of the 2nd, 4th, 9th, 10th, 11th, 12th, 13th, 14th, 15th, and 16th weeks contains 6 tasks each; • test of the 3rd week contains 8 tasks. To assess the difficulty of the tasks we divided the number of students who completed the task by the number of students, who have a result better than 0 for this weekly test. Diagrams in Figs. 5 and 6 show the results. Labels like 2.1 on the diagram mean that it is the first task in the test of the second week.

Fig. 5. The results of performing tasks of 1–8 weeks.

Fig. 6. The results of performing tasks of 9–16 weeks.

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The topics of the weeks of the online course are listed below. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

Introduction. Database system architecture. Steps of database design. Overview of the basic data models. The relational data model: basic structures and constraints. Relational algebra. Normalization: first, second, and third normal forms. Boyce-Codd normal form and senior normal forms. Entity-Relationship model, ER diagrams, IDEF1 notation. IDEF1 and IE notations, the transformation from logical to the physical model. SQL: history, data types, some functions, basic DDL statements. SQL: DML statements. SQL. SELECT statement: simple queries, selecting data from several tables. SQL. SELECT statement: subqueries. Views. Transactions. Programming in database: variables, operators, temporary tables. Programming in database: stored procedures, functions, cursors, triggers.

From Figs. 5 and 6, it could be mentioned that on average tests of 7th and 8th weeks were easier for students and on the contrary, the assignments of week 12th caused the most difficulties. This will be taken into account when the course will be modified. Especially interesting for the instructor could be information about tasks that were simple or difficult for certain groups of students. To get it, data from the two mentioned above reports was combined with the results of clustering students. For each cluster, the percentage of students who completed each task of each weekly test was calculated. Tables 2, 3, and 4 contain the obtained data about the first three weeks of the course. The task number is placed in the header of each table. Table 2. The percent of students who completed the tasks of the first week. Cluster Cluster Cluster Cluster Cluster

name 1 2 3 4

1.1 64.0 98.41 95.75 1.97

1.2 63.2 97.42 91.10 0.59

1.3 61.6 98.21 93.91 0.61

1.4 44.0 88.29 70.26 0.14

1.5 52.0 94.05 75.88 0.20

Table 3. The percent of students who completed the tasks of the second week. Cluster Cluster Cluster Cluster Cluster

name 1 2 3 4

2.1 60.0 96.63 38.88 0.25

2.2 66.4 99.01 41.22 0.33

2.3 58.4 94.84 34.89 0.20

2.4 56.0 95.04 35.83 0.14

2.5 56.8 95.24 31.85 0.16

2.6 29.6 79.56 13.82 0.02

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S. Nesterov et al. Table 4. The percent of students who completed the tasks of the third week. Cluster Cluster Cluster Cluster Cluster

name 1 2 3 4

3.1 62.4 98.61 15.46 0.14

3.2 55.2 97.22 13.11 0.23

3.3 63.2 98.61 18.03 0.16

3.4 36.0 90.67 7.96 0.04

3.5 68.0 97.42 14.26 0.14

3.6 42.4 92.26 11.01 0.08

3.7 26.4 82.14 4.68 0.06

3.8 28.8 87.10 5.39 0.04

Based on the data presented in the tables, it is possible to distinguish the tasks that were most difficult for each group of students: • • • •

for for for for

students students students students

from from from from

the the the the

first cluster it was: 1.4, 2.6, 3.4 и 3.7; second cluster it was: 1.4, 2.6; third cluster it was: 1.4, 2.6, 3.7 и 3.8; fourth cluster it was: 1.4, 2.6, 3.4 и 3.8.

It can be seen that for all groups of students, tasks numbered 1.4, 2.6 turned out to be problematic. Both those questions are multiple-answer with multiple choice. But revising them showed that one of them could be difficult because it is a practical task, and another one was formulated a little confusing. So in the first case, it could be given a higher score for the right answer, and in the second case, it was recommended to change the question.

5 Conclusion Nowadays, online education becomes extremely popular, but the percentage of participants who enroll and complete all tasks of the massive open online courses is rather small. Applying data mining methods to educational data can help to understand why students drop out and prevent it by making changes in timing or course materials. In our work, we used clustering to identify four permanent groups of participants of MOOC “Data management” on the openedu.ru platform. The characteristics of these groups did not change much after changing the duration of the course session. These groups are not exactly the same as what is commonly called in classifications of MOOC participants [15]; however, the difference can be explained by differences in the analyzed data [8]. For each group of students, the most difficult tasks were identified and some assumptions about the reasons for those difficulties have been made. In some cases, such tasks need to be modified, in others, it is enough to give higher grades for the right answers. The knowledge gained about the students of the online course will help to make teaching more effective and increase the percentage of students who complete the course. In future work, it is planned to combine these results with the analysis of event logs [16, 17] which contain information about the time spent by students to work with different types of course resources. The results and methods which are presented in this paper can be used to identify groups of students and difficult tasks on other online courses.

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References 1. Gelman, B., Revelle, M., Domeniconi, C., Johri, A., Veeramachaneni, K.: Acting the same differently: a cross-course comparison of user behavior in MOOCs. In: Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016, pp. 376–381 (2016). http://www.educationaldatamining.org/EDM2016/proceedings/paper_136.pdf. Accessed 28 June 2021 2. Villanueva, A., Moreno, L.G., Salinas, M.J.: Data mining techniques applied in educational environments: literature review. In: Digital Education Review, vol. 33, pp. 235–266 (2018) 3. Algarni, A.: Data mining in education. Int. J. Adv. Comput. Sci. Appl. 7(6) (2016). https:// doi.org/10.14569/IJACSA.2016.070659. 4. Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 40(6), 601–618 (2010) 5. Crues, R.W., et al.: How do gender, learning goals, and forum participation predict persistence in a computer science MOOC? ACM Trans. Comput. Educ. 18(4), 18 (2018). https://doi.org/10.1145/3152892 6. Nesterov, S.A., Andreeva, N.V.: Data management (Massive open online course). https:// openedu.ru/course/spbstu/DATAM/ (in Russian). Accessed 28 June 2021 7. Nesterov, S.A., Smolina, E.M.: Some results of the analysis of 3 years of teaching of a massive open online course. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 651–657. Springer, Cham (2020). https://doi.org/ 10.1007/978-3-030-34983-7_64 8. Nesterov, S.A., Smolina, E.M.: The assessment of the results of a massive open online course using data mining methods. Comput. Telecommun. Control 13(1), 65–78 (2020). https://doi.org/10.18721/JCSTCS.13106 9. Nesterov, S., Smolina, E., Egiazarov, T.: Predicting students’ performance on MOOC using data mining algorithms. In: Voinov, N., Schreck, T., Khan, S. (eds.) Proceedings of International Scientific Conference on Telecommunications, Computing and Control. SIST, vol. 220, pp. 285–292. Springer, Singapore (2021). https://doi.org/10.1007/978-981-336632-9_25 10. Grolemund, G., Wickham, H.: R for Data Science. https://r4ds.had.co.nz. Accessed 28 June 2021 11. Lantz, B.: Machine Learning with R. 2nd edn. Packt Publishing (2015). ISBN:9781784393908 12. Bruce, A., Bruce, P.: Practical Statistics for Data Scientists. O’Reilly Media (2017). ISBN:9781491952955 13. Grus, J.: Data Science from Scratch: First Principles with Python. O’Reilly Media (2015). ISBN:9781491901427 14. Open edX. https://open.edx.org. Accessed 28 June 2021 15. Hill, P.: Emerging student patterns in MOOCs: a (Revised) graphical view. https://eliterate. us/emerging-student-patterns-in-moocs-a-revised-graphical-view. Accessed 28 June 2021 16. Bylieva, D., Lobatyuk, V., Nam, T., Tolpygin, S.: Possible applications of analytics data in online courses. In: 19th PCSF 2019. Professional Culture of the Specialist of the Future, pp. 630–639 (2019). https://doi.org/10.15405/epsbs.2019.12.67 17. Barsukov, N.D., Sysoev, I.M., Pereskokova, A.A., Nikiforov, I.V., Posmetnijs, D.: Analysis of student activity on the e-learning course based on «OpenEdu» platform logs. Trudy ISP RAN/Proc. ISP RAS 32(3), 91–100 (2020). https://doi.org/10.15514/ISPRAS-2020-32(3)-8

Author Index

A Abran, Alain, 561 Afanaseva, Olga V., 187 Altangerel, Erdenebaatar, 367 Arefiev, Igor B., 187 B Batotsyrenov, Eduard, 367 Berestneva, Olga, 367 Blagoveshchenskaya, Ekaterina A., 574 Bober, Zhanna, 121 Bogdanov, Victor, 367 Bogomolov, Alexander I., 146 Bolsunovskaya, Marina V., 262, 392 Boykov, Alexey V., 392 Bukhensky, Kirill, 548 Burlutskaya, Zhanna V., 262 C Chernenkaya, Liudmila V., 538 Chernorutsky, Igor, 322 Chernova, Galina V., 525 Chernyy, Yu. Yu., 311 Chudesova, Galina P., 335 D Danchul, Alexander N., 347 Dashdorj, Zolzaya, 367 Diachenko, Natalia V., 382 Drogobytskiy, Ivan N., 250

E Efimenko, Sergei, 322 Efremov, Artem A., 177 Evseeva, Lidiya, 153 F Fedorin, Stanislav, 166 G Gapanovich, Stanislav, 166 Garanin, Dmitrii, 322 Garbaruk, Viktor V., 574 Gintciak, Aleksei M., 262 Gorelova, Galina V., 212 I Iakovleva, Daria D., 473 Iakovleva, Elena A., 198, 225 Istomina, Elena, 367 K Karakchieva, Vera V., 177 Karlik, Aleksandr E., 198, 225 Kasatkina, Ekaterina V., 288 Katermina, Tatyana S., 451 Katsko, Igor A., 461 Kazarin, Stanislav V., 382 Khalin, Vladimir G., 525, 561 Kleiner, George, 43 Kobylkin, Dmitriy, 367

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Y. S. Vasiliev et al. (Eds.): SAEC 2021, LNNS 442, pp. 607–609, 2022. https://doi.org/10.1007/978-3-030-98832-6

608 Kobylko, Alexander, 43 Korshunov, Gennady I., 442 Kozlov, Vladimir N., 3, 177 Krasnoshtanova, Natalia, 367 Kremyanskaya, Elena V., 461 Kudriavtceva, Arina, 401 Kuklina, Maria, 367 Kuklina, Vera, 367 Kukor, Boris L., 198 Kulsariyeva, Aktolkyn, 153 Kultin, Nikita B., 413 Kuptsov, Ivan, 548 Kuptsov, Mikhail, 548 Kvasnov, Anton V., 424 L Leonova, Alla E., 311 Loginova, Aleksandra V., 311 Lyabakh, Nikolay N., 29 M Mager, Vladimir E., 538 Makarenya, Tatiana A., 358 Malihina, Irina, 322 Malinetskiy, Georgy G., 60, 109 Malykhina, Galina F., 431 Manev, Dmirtii, 431 Miae, Mikhail A., 431 Mikoni, Stanislav, 238 Mobus, George, 16 Mokiy, Michael S., 89 N Nesterov, Sergey, 596 Nevezhin, Victor P., 146 Nikitin, Nikolai A., 424

Author Index Potapova, Anastasiya V., 382 Pozdeeva, Elena, 153 R Rechinskiy, Alexander V., 538 Redko, Sergey G., 585 Romanenko, Inna B., 166 Romanenko, Yuriy, 166 S Savastiyanov, Volodymyr, 74 Sazhnova, Victoria, 596 Seledtsova, Inna A., 585 Semenov, Konstantin, 300 Sharich, Ermin E., 473 Shatalova, Olga M., 288 Shilkin, Vladimir, 322 Shilova, Olga, 561 Shipunova, Olga, 100, 153 Shirokova, Svetlana V., 311 Shkodyrev, Vyacheslav P., 424 Shnai, Iulia, 585 Sliva, Maksim V., 451 Smetankin, Anatolii, 322 Smolin, Vladimir, 60 Smolina, Elena, 596 Sokolov, Boris V., 52 Solnitsev, Remir I., 442 Stankevich, Lev A., 512 Strüngmann, Lutz, 574 T Tibilova, Galina S., 382 Tikhomirov, Alexei, 367 Trufanov, Andrey, 367 Tselishcheva, Anastasiia, 300 Tumanov, Vladimir E., 498 Tunda, Elena A., 133 Tunda, Vladimir A., 133

O Obaidi, Ahmed Ibrahim Hussein, 358 Ovcharenko, Andrey V., 382

U Uspensky, Michail B., 392

P Paklin, Nikolay B., 461 Pankratova, Nataliya, 74 Petryaeva, Alexandra A., 262 Platonov, Vladimir V., 225 Polyakov, Sergey L., 442 Ponomarev, Vasily V., 498 Popova, Nina V., 574

V Valerii, Zakharov, 487 Vasiliev, Yuriy S., 3 Vinogradov, Andrei N., 473 Voitsekhovich, Viacheslav E., 109 Volkova, Violetta N., 3 Volnov, Ilia N., 109 Voronova, Natalia S., 473

Author Index Y Yablochnikov, Sergey, 548 Yurkov, Alexander V., 525, 561 Yusupov, Rafael M., 52

609 Z Zaboev, Mikhail V., 525 Zhilnikova, Natalia A., 442 Zhukovskaya, Lidiya V., 273 Zuravska, Anzelika, 512