Foundations for Functional Modeling of Technical Artefacts (Design Research Foundations) 3031459172, 9783031459177

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Design Research Foundations

Morten Lind

Foundations for Functional Modeling of Technical Artefacts

Design Research Foundations Series Editors Ilpo Koskinen, School of Design, University of New South Wales, Sydney, NSW, Australia Peter Gall Krogh, Department of Digital Design and Information Studies, School of Communication and Culture, Aarhus, Denmark Editorial Board Members Katja Battarbee, Mountain View, USA Lucienne Blessing, Singapore University of Technology and D, Singapore, Singapore Mieke Boon, Philosophy, University of Twente, Enschede, The Netherlands Amaresh Chakrabarti, IISc Quarters NE-305, Indian Institute of Science, Bangalore, Karnataka, India Lin-Lin Chen, Eindhoven University of Technology, Eindhoven, The Netherlands Gilbert Cockton, Northumbria University, Newcastle upon Tyne, UK Nathan Crilly, Department of Engineering, University of Cambridge, Cambridge, UK Kees Dorst, University of Technology Sydney, Sydney, Australia Claudia Eckert, Engineering and Innovation, The Open University, Milton Keynes, UK Per Galle , The Royal Danish Academy of Fine Arts, S, Birkeroed, Denmark Annie Gentes, Dépt SES, Telecom Paristech, Paris, France Armand Hatchuel, Mines ParisTech, Paris, France Paul Hekkert, Delft University of Technology, Delft, The Netherlands Caroline Hummels, Eindhoven University of Technology, Eindhoven, The Netherlands Giulio Jacucci, Dept of Computer Science, University of Helsinki, Helsinki, Finland Gesche Joost, Prozessgestaltung, Raum Ein 220, Univ der Künste Berlin, Inst Produkt, Berlin, Berlin, Germany Tobie Kerridge, Goldsmiths University of London, London, UK Anita Kocsis, Swinburne University of Technology, Melbourne, Australia Peter Gall Krogh, Engineering, Aarhus University, Aarhus, Denmark Jung-Joo Lee, Division of Industrial Design, National University of Singapore, Kent Ridge, Singapore Stefano Maffei, Department of Design, Politecnico di Milano, Milano, Italy Charles Lenay, COSTECH, University of Technology of Compiègne, COMPIEGNE CEDEX, France Tuuli Mattelmäki, Aalto University, Espoo, Finland Anthonie W.M. Meijers, Dept of Philosophy and Ethics, Eindhoven Univ of Technology, Eindhoven, Noord-Brabant, The Netherlands Kristina Niedderer, University of Wolverhampton, Wolverhampton, UK Panos Y. Papalambros, Dept.of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA Johan Redstrom, Umeå University, Umea, Sweden Yoram Reich, Wolfson - Engineering, 230, Tel Aviv University, Ramat Aviv, Israel Arne Scheuermann, Kommunikationsdesign, Hochschule der Künste Bern, Bern, Switzerland Kin Wai Michael Siu, School of Design, The Hong Kong Polytechnic Univ, Kowloon, Hong Kong Oscar Tomico, Eindhoven University of Technology, Eindhoven, The Netherlands Pieter E. Vermaas, Department of Philosophy, Delft University of Technology, Delft, The Netherlands John Zimmerman, Carnegie Mellon University, Pittsburgh, USA

Managing Editor Clementine Thurgood, Faculty of Health, Arts and Design, Swinburne University of Technology, Melbourne, VIC, Australia The goal of the series is to provide a platform for publishing state of the art research on foundational issues in design and its applications in industry and society. Suitable topics range from methodological issues in design research to philosophical reflections on the specificities of design rather than actual design work or empirical cases only. The definition of design behind the series is inclusive. In terms of disciplines, it ranges from engineering to architecture. In terms of design work, it ranges from conceptual issues in design through design experiments and prototypes to evaluative studies of design and its foundations. Proposals should include: • • • • •

A proposal form, as can be found on this page A short synopsis of the work or the introduction chapter The proposed Table of Contents The CV of the lead author(s) If available: one sample chapter

We aim to make a first decision within 1 month of submission. In case of a positive first decision the work will be provisionally contracted: the final decision about publication will depend upon the result of the anonymous peer review of the complete manuscript. The series editors aim to have the complete work peerreviewed within 3 months of submission. The series discourages the submission of manuscripts that contain reprints of previous published material and/or manuscripts that are below 150 pages / 75,000 words. For inquiries and submission of proposals authors can contact the series editors, Ilpo Koskinen via: [email protected] or Peter Gall Krogh via: [email protected]

Morten Lind

Foundations for Functional Modeling of Technical Artefacts

Morten Lind Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. Lyngby, Denmark

ISSN 2366-4622 ISSN 2366-4630 (electronic) Design Research Foundations ISBN 978-3-031-45917-7 ISBN 978-3-031-45918-4 (eBook) https://doi.org/10.1007/978-3-031-45918-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are reserved 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 Paper in this product is recyclable.

Preface

Readers of this book could be academics interested in concepts for modelling and control of complex industrial artefacts, professional engineers involved in development of process and automation systems, managers of design and operations, and human factors specialists involved in research and development of human machine interfaces and decision support systems. Hopefully, the book will also be of relevance to readers interested in systems theory and the philosophy of engineering science. Finally, readers already familiar with or wanting to learn about Multilevel Flow Modelling (MFM) may use the book as a companion introducing its conceptual foundations. The contents of this book belong to the philosophy of engineering and technology of three reasons: • It is philosophical by dealing with conceptual issues in modelling technical artefacts. • It is about engineering because it is about design and operation of industrial processes. • It is about philosophy of technology because it is about the relation between humans and technology. The book is an outgrowth of the author’s research in concepts, methods, and tools for design and operation of safety critical industrial systems. The overall purpose of this work is to demonstrate the high relevance of concepts and theories of action and semiotics from the human and social sciences to the design and operation of industrial systems. It addresses challenges in the modelling of complex technical artefacts and their interaction with human beings. The modelling problem is of interdisciplinary nature and the questions it raises cannot be answered within a single existing academic discipline. Solutions to the challenges are urgently required considering the changes of the human technology relation caused by the industrial application of artificial intelligence and information technology in general. Although the research has been done mainly in an academic setting, it has been motivated by industrial needs rather than applying existing theories and methods from academic disciplines. These methods are mostly tuned towards the solution of v

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a particular class of problems and do not address the interdisciplinary challenges in modelling technical artefacts and their interactions with human beings. In addition to the orientation towards problems of industry, the research has also been aiming at development of theory and tools which can be applied in the solution. It should also be mentioned that the research has been explorative and is therefore by necessity incomplete. There is clearly more to add. Motivations for this choice of research focus can be found in the historical development of highly automated industrial processes, and the experiences gained in operating them during the last half century. The experiences show that safety, reliability, and efficiency of operations can be improved by better use and sharing of knowledge during design and operation of technical artefacts. However, the expected increase in performance and sustainability of technical artefacts requires deeper insights in the nature of design and operational knowledge. Here the concept of function plays a key role. The development of the conceptual foundations, which are necessary to put functional modelling on a firmer ground, has disclosed another relation to the past regarding the conceptual background of cybernetics. Usually the invention of this field is attributed to Norbert Wiener, but he followed lectures by John Dewey, who in his pragmatism and functional psychology proposed the reflex arc (i.e. feedback) and the use of means-end concepts, for the study of goal directed behaviour of intelligent organisms interacting with physical environments. The idea of feedback and Wiener’s mathematical theory of cybernetics seems accordingly to have foundations in means-end concepts and functional thinking. Bringing forward the conceptual foundations of cybernetics from the past is important for the future use of knowledge and reasoning in design and operation of industrial processes. An important message of the book is that functions are depending on context. In the end everything is so, of course, but functions are more than depending, they are about context. The particular functions ascribed to artefacts should be seen in contexts of goal-directed interaction between humans and technical artefacts. Modelling functions of artefacts reveal the contextual relations binding their subsystems together as means and ends. The ultimate dependence of functions on contexts of human intentions is relevant for the design of autonomous systems. Autonomy, in the light of functional modelling, therefore seems to have limits because any technical artefact is an embodiment of the designers’ intentions, and its functions when used are also dependent on human intentions. Hopefully functional modelling can be used as a tool to create an increased awareness of these contextual relations. Kgs. Lyngby, Denmark

Morten Lind

Acknowledgements

This book has only been possible through support and interaction with colleagues and students over the years. In particular I will mention the support of Prof. Sten Bay Jørgensen and Ass. Prof. Michael May who participated in many informal seminars over the years at DTU where we discussed functional modelling and its relations to process and automation design and human-machine interaction. I also appreciate the support and interest of Prof. Ole Ravn and the privilege of working with many bright PhD students at DTU over the years including Xinxin Zhang, Denis Kirchhübel, Jing Wu, and Mengchu Song in the development of Multilevel Flow Modelling and its applications. Their contributions are highly appreciated. The interactions internationally with Professors Akio Gofuku, Hidekazu Yoshikawa, Ming Yang, Björn Wahlström, and Leena Norros over the years have also been very stimulating for me. My acknowledgements also include members of the cognitive engineering group at the former Risø National Laboratories under the leadership of Prof. Jens Rasmussen. The cooperation in this period with Jim Easter and Dave Woods at Westinghouse, USA, offered a unique opportunity to test early MFM ideas in an industrial context. My participation in the Centre of Human Machine Interaction (CHMI) lead by Prof. Peter Bøgh Andersen and Dr. Annelise Mark Pejtersen and Center of Semiotics lead by Prof. Per Aage Brandt was also very valuable by introducing me to fields of knowledge which play a central role in this book. My recent interaction with the members of the Water Management project at the Danish Hydrocarbon Research and Technology Center (DHRTC) at DTU and KAIROS Technology in Norway is also acknowledged. A main objective of the project is to apply functional modelling (MFM) and reasoning on an industrial scale for risk assessment and operator decision support. Confronting theory with practical application has been a great learning experience and has confirmed that functional modelling has much to offer to industry and that firmer foundations are required to unleash its full potential. I also acknowledge the valuable support given by Prof. Sten Bay Jørgensen for reading, commenting, and giving valuable inputs to early versions of the book vii

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and to Hanne Søndergaard who helped with the final proofreading. Finally, I also acknowledge the patience and support given by my family through the years. I take full responsibility for errors and omissions.

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 The Problems Addressed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Complexity of Design and Operation of SPCS . . . . . . . . . . 1.2 The Aim of Functional Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Modelling Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 The Need for Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Theory or Data Driven Modelling . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 From Theory to Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Contextual Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Frameworks of Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 A Readers Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.1 Organization of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.2 Reader Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 2 3 3 5 6 6 6 7 8 8 10 10

Part I Background 2

Technical Artefacts and Humans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 What Is a Technical Artefact? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Socio-Cyber-Physical Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 The Physical Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Control and Instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 The Operation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Human-Artefact Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Technical Artefacts Are Parts of Action Systems . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 15 17 19 20 20 21 23 24

3

Functions in Design and Operation of SCPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Functions in Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Design of Socio-Cyber-Physical Systems . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Design Requirements and Constraints . . . . . . . . . . . . . . . . . . . 3.2.2 Automation of SCPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Engineering Documents Used by the Industry. . . . . . . . . . .

25 25 26 28 29 31 ix

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Contents

3.3

Approaches to SCPS Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 The Traditional Linear Design Approach . . . . . . . . . . . . . . . . 3.3.2 System Centered Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Design for Reliability and Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Reliability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Defense in Depth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Existing Methods for Modelling Safety Functions . . . . . . 3.4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Quantitative Models Used by Industry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Summary of Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34 35 36 39 39 40 41 43 43 44 45 50

Existing Frameworks for Artefact Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 A Framework for Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Two Types of Decomposition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Two Types of Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.3 Reasoning with the Artefact Model . . . . . . . . . . . . . . . . . . . . . . 4.1.4 Problems with Rosenman’s Artefact Model . . . . . . . . . . . . . 4.2 A Framework for Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 A Model of Decision Making in Supervisory Control . . 4.2.2 The Abstraction Hierarchy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Problems with the Abstraction Hierarchy . . . . . . . . . . . . . . . . 4.3 Overview of Methods for Functional Modelling . . . . . . . . . . . . . . . . . . 4.3.1 Knowledge Representation and Reasoning . . . . . . . . . . . . . . 4.3.2 Mechanical Engineering and Manufacturing . . . . . . . . . . . . 4.3.3 Process Control, Safety and Autonomous Systems. . . . . . 4.3.4 Autonomous Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 53 55 56 57 58 59 59 61 63 64 65 67 71 77 78

Part II Preparatory Foundations 5

Modelling as a Cognitive Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.1 The Model Relation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.1.1 Encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.2 Frameworks of Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.2.1 Goffman’s Frame Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.2.2 The Natural Framework of Interpretation . . . . . . . . . . . . . . . . 94 5.2.3 The Social Framework of Interpretation . . . . . . . . . . . . . . . . . 95 5.2.4 Interpretation of Time and Space . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.3 Modelling Stances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.4 The Hermeneutics of Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

6

Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 6.1 Concepts of Causality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

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104 105 105 106 107 109 111 111 112 112 114 117

Defining Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Changing or Becoming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Contiguity in Time and Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Causality as a Dyadic Relation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Dispositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 General and Singular Causation . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Causal Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 Causation by Exchange of Mass and Energy . . . . . . . . . . . . 6.4 Causality as a Triadic Relation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Signs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Modes of Signifying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Three Notions of Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 The Three Senses and the Frameworks of Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2 Non-causal Connections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 The Means-End Relation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.1 Teleological and Causal Aspects . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.2 The Means-End Relation as a Conceptual Schema . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

119 119 120 120 122 122

Part III The Concept of Function 7

Aspects of Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 What Are Functions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 A General Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Natural Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 The Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Intentions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Decomposition of Functions and Objectives. . . . . . . . . . . . . 7.2.4 Dispositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.5 Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.6 Function and Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.7 Device- and Environment Centric Functions . . . . . . . . . . . . 7.2.8 Roles and Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.9 Latent and Manifest Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Abstraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Two Types of Abstraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Levels of Abstraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Functions, Wholes and Parts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.4 Function and Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Validation of Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

127 127 128 130 131 132 133 134 135 136 137 137 140 142 143 143 145 147 148 149 150

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Contents

8

Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Functions as Doings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 An Example from Process Control . . . . . . . . . . . . . . . . . . . . . . . 8.2 Functions as Variable Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

153 153 155 157 157 158 160

9

Modelling Perspectives and Human-Artefact Relations . . . . . . . . . . . . . . . 9.1 Two Contexts of Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Human-Artefact Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 The Designer-Artefact Relation . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 The Operator-Artefact Relation . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.3 Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Modelling Perspectives in SCPS Design . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 The Process Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.2 The Control and Operation Perspective . . . . . . . . . . . . . . . . . . 9.3.3 The Work Domain Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

161 161 162 162 162 166 167 170 171 173 177 178

Part IV Concepts of Action 10

Action Aspects and Types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 The Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.1 The Actors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.2 The Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.3 The Modality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.4 The Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.5 The Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 The Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 Some Distinctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

181 181 182 182 183 183 183 186 186 188 190

11

Dyadic Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Von Wright’s Theory of Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Situations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Elementary Changes and Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.1 Situations, Propositions and Elementary Changes. . . . . . . 11.4 Intervening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4.1 Passive Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5 Letting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6 Doing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.1 Descriptions of Elementary Actions. . . . . . . . . . . . . . . . . . . . . . 11.6.2 Description of Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

191 191 194 195 195 197 198 200 201 202 203

Contents

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11.7

Forbearing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.7.1 Representing Because-of Motives . . . . . . . . . . . . . . . . . . . . . . . . 11.8 Bringing About . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.8.1 Bringing About and Composite Actions . . . . . . . . . . . . . . . . . 11.9 Elementary Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.10 Elementary Successes and Failures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

207 208 209 209 210 210 212

12

Role Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 The Concept of Role . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.1 Fillmore’s Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Greimas’ Actant Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.1 Sharing and Arbitration of Actors . . . . . . . . . . . . . . . . . . . . . . . . 12.3 Using Greimas’ Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 Interpretation of Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.1 Causal Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.2 Interpretation by Intentions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

213 213 214 214 218 219 220 221 222 223 224

13

Triadic Transformations and Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1 Action and Triadic Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Experiencing, Evaluating and Intervening . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.1 Stages of the Act . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.2 Cognitive Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 Models from Cognitive Psychology and Engineering. . . . . . . . . . . . . 13.3.1 Norman’s Action Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.2 Rasmussen’s Decision Ladder . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.3 The BDI Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

225 225 226 226 227 229 229 230 232 232

14

Action Phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.1 A Logic of Narratives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.1.1 The Narrative Atom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.1.2 The Enclave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Extending the Phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.1 Possibility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.2 Actualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3 Phases and Dyadic Causal Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3.1 An Agent in the Foreground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3.2 An Object in the Foreground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4 Action States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5 Action Phases and Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5.1 Design Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5.2 Operation and Control Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

233 233 234 234 235 237 238 239 240 241 241 242 242 243 243 245

xiv

Contents

15

Action Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.1 The Concept of Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 The Practice Schema. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3 Extending Greimas’ Schema and the Semiotic Triangle . . . . . . . . . . 15.3.1 Causal Schemas and Reasoning About Failure . . . . . . . . . . 15.4 Embedding Forms for Physical Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4.1 Chains of Accomplishment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4.2 Chains of Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5 Embedding Forms and Chains of Cognitive Actions. . . . . . . . . . . . . . 15.6 Hybrid Embedding Forms and Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.6.1 Forms with Accomplishments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.6.2 Forms with Avoidances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.7 Application of Embedding Forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.7.1 The Principle of Reciprocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.7.2 Levels of Abstraction and Context of Use . . . . . . . . . . . . . . . 15.8 The Designer and Artefact as an Action System . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

247 248 248 249 252 253 254 259 263 263 263 266 266 268 268 269 271

16

Control Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.1 Control as a Relation Between Two Objects . . . . . . . . . . . . . . . . . . . . . . 16.1.1 The Nature of Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 The Control Relation is Bi-directional . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2.1 Three View Points on the Control Relation . . . . . . . . . . . . . . 16.2.2 The Process Control View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2.3 The Decision Making View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2.4 The Representation View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3 Challenges in Representing Control Functions . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

273 273 274 276 276 278 280 285 285 286

Part V Means and Ends 17

The Means-End Relation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.1 The Relation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2 Means-End Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.1 Chains of Means and Ends. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.2 Aggregation and Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.3 Many-to-Many Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.4 Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.5 Loops of Means and Ends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.6 Hierarchies and Heterarchies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.3 Countermeasures and Hazards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.4 Means and Ends in Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

289 289 290 290 291 292 293 294 296 297 297 299

18

Ends, Means and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 18.1 Types of Ends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301

Contents

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18.1.1 Heckhausen’s Types of Objectives . . . . . . . . . . . . . . . . . . . . . . . 18.1.2 Describing Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2 Types of Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2.1 Achinstein’s Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2.2 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

303 304 305 305 307 307

19

A Functional Modelling Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1 From Actors and Doings to Values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1.1 Physical and Cognitive Actions . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2 Embedded Actions in a Means-End Perspective . . . . . . . . . . . . . . . . . . 19.2.1 Action Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.2 Means-End Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.3 Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.4 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

309 309 310 311 312 313 313 316 317

A

Dyadic Transformation Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 A.1 A.2

Change Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Transformation Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

List of Abbreviations

AH

BDI CNS CSE FM GPS

MFM P&ID

PBB PFS

SCPS

SOP TMI

Abstraction Hierarchy. A modelling framework used in CSE for representing a complex system on several interconnected levels of means-end and part-whole abstraction. Belief Desire Intention. The BDI model is used by AI researchers for design of reasoning architectures for software agents. Canonical Narrative Scheme. An extension of Greimas Actant Scheme. Cognitive System Engineering. An approach to design of human-centred automation. Functional Modelling. A type of modelling representing systems teleology. General Problem Solver (GPS) is a computer program created by Herbert A. Simon, J. C. Shaw, and Allen Newell intended to work as a universal problem solver machine using means-ends analysis Multilevel Flow Modelling. A language for functional modelling of material and energy processing plants. Piping and Instrumentation Diagram. A type of document used by industry representing connections between process equipment and instrumentation and control systems. Used by the automation designer. Phenomena Based Building blocks. Used in chemical engineering. Process Flow Sheet. A type of document used by industry representing material and energy flows in a process. Used mainly by the process designer. Socio-Cyber-Physical System. A term used to characterize a complex system comprising interacting social, cybernetic, and physical subsystems. Typical to most industrial supply, production, and delivery systems. Standard Operating Procedures. Documents used by operators during startup and shut-down. Three Mile Island. Mainly known for the nuclear power plant accident which demonstrated consequences of inadequate human-machine interaction design.

xvii

Chapter 1

Introduction

The work presented in this book is the result of the author’s long-term research in human supervisory control and automation of industrial plants, commonly called socio-cyber-physical systems (SCPS) to indicate their interdisciplinary nature. The overall purpose of the research has been to develop concepts and methods of functional modelling which can be applied in control and operation of SCPS in safety critical industries, but the results obtained are believed to have applications for technical artefacts in general. The function of something describes how it works in a particular context i.e. as seen from without, and functional modelling is accordingly focused on the whole artefact rather than its parts. The purpose of such a holistic approach to modelling is to capture aspects of the artefact which are important for its efficient and safe design and operation, but which cannot be explained by the behavior of the parts. One of the aims of functional modelling research is to develop unified concepts of function which can be applied on many whole-part levels and across several technologies. Main challenges in achieving this aim are to cope with the context dependency of functions, and to address the multiple dimensions of context which are involved. The book presents an array of contexts involved in the modelling of technical artefacts, which should be considered in the formalization of concepts and to a methodology for functional modelling.

1.1 The Problems Addressed Industrial process operations have through the years experienced several accidents caused by problems in the interaction between human operators and machines, which have their origin in the principles used for system design and operation. The accidents have shown that highly automated systems are vulnerable to failure in high risk situations because the automation and the human-machine interface are © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_1

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not designed so that the human operator can respond appropriately. A well know example is the Three Mile Island (TMI) accident in 1979 where a nuclear power plant in Pennsylvania USA suffered from a partial core meltdown partly due to problems in the human-machine interface (see Cummings [1]). Industrial experience also show that attempts to reduce the influence of operators decisions by increasing the level of automation, can lead to system failure in situations not predicted by the designers of the automated systems (see Bainbridge [2] and Baxter et al. [3]). The problem is that human operators loose their skills if they are not actively engaged in the operation. The lesson learned is accordingly that the automation and the human-machine interface should be designed so that operators are actively involved in the operations. This is of particular importance in situations where the automated system fail and operator intervention may be necessary in order to prevent an accident. Usually automated protective systems are provided for this purpose, however even in such cases the human operator need to monitor the response of the automation to ensure safe operation. In the end, it is a question about sharing the responsibility for the operation between the designers and the human operators in situations of uncertainty, and whether the benefits of involving human beings in the operation can outweigh the costs involved. Problems in human-machine interaction which can cause failure of operation in critical operational situations can be divided into two: • the information about system states presented in the human-machine interface are not compatible with the operators task, and requires interpretation which can be cognitively demanding • the interface does not facilitate intervention in the system on a level compatible with the operators intentions when they require interpretation and translation into executable plans of action

1.1.1 Complexity of Design and Operation of SPCS Complexity is a significant aspect of nearly all industrial processes and technical infrastructures. It influences decisions made by system designers and it may affect the vulnerability of systems to disturbances, the efficiency and safety of their operations, and their maintainability. A main challenges in both design and operations is to identify, evaluate and develop alternative means of realising intentions into action. Automation systems, the human machine interface, and the operators play key roles in handling the operational complexity of industrial processes and infrastructures. Automation can reduce complexity of the operators task by relieving him from the responsibility of responding to events and situations predicted by the control engineers. But automation can also increase the complexity of the operators supervisory task when the automation system is failing in performing its function. In such situations the operator must understand the causes and consequences of automation failure and take responsibility to gain control of the situation. There is

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therefore a need for system models which can reveal aspects of complexity which are relevant for system design and operation. Modelling methods used in current approaches to system engineering are traditionally influenced by the natural sciences and have therefore a strong focus on the system parts and their physical interactions and are not capable of capturing features of the whole. In design and operation of technical artefacts modelling concepts and methods are needed to understand the whole as a system of actions and associated functions directed at the achievement of human purposes and goals. Engineering professions have developed semi-formal (often graphical) methods for representation of system functions to compensate for this lack (see Chap. 3). However, the informal nature of these diagrams make them unsuitable for formal reasoning.

1.2 The Aim of Functional Modelling The aim of functional modelling (FM) is to provide concepts and tools to represent and reason about system functions and to support decision making in design and operation. There is accordingly a need for creating a firm conceptual basis for FM of technical artefacts that systematically allows both representation of functions as well as their relations to physical and chemical features of technical artefacts. The main features of FM are the following: • FM allows decomposition of a design or operational problem into smaller manageable sub-problems • FM supports decision making among design or operational alternatives on various levels of means-end abstraction • FM provides criteria for detection and diagnosis of deviations from planned operations and planning of remedial actions • FM provides the basis for allocation of tasks and cooperation between the automated systems and the operator • FM allows sharing of design information between design and operational teams

1.3 Modelling Challenges The concept of function has a long history in philosophy going back to the teleology of the Greek philosopher Aristotle [4], who also introduced the term functionalism. The idea of teleology is to explain phenomena by the purpose or the end they serve rather than by their causes. The concept is part of common language and used widely in engineering. It has traditionally been investigated within design of mechanical- and manufacturing systems (see e.g. Hubka et al. [5] and Pahl et al. [6]) and to a lesser extent in other engineering domains. The importance of functional

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concepts in handling complexity in engineering design are addressed by Suh [7] and ElMaraghy et al. [8]. The use of functional concepts for modelling and reasoning about technical artefacts is a more recent research topic (see e.g. the reviews by Umeda et al. [9] and by Erden et al. [10]). However, extending these insights from mechanical engineering to technical artefacts like SCPS which comprises both mechanical, chemical, control and information technologies is met with challenges. Control Systems Technical artefacts like SCPS which include control systems cannot be represented fully with concepts taken from mechanics alone. Control systems share certain properties with mechanical devices but are also distinctly different, especially when considering the higher-level cognitive functions realized by the use of advanced information technology. The development of intelligent machines such as autonomous robots therefore makes functional modelling of the interactions between control systems, mechanical devices, and the environment an urgent challenge. The increased complexity of such systems makes it difficult to identify causes of operational failures and to predict their consequences for users and the environment only on the basis of the behaviour of the parts. Modelling tools are accordingly required in order to analyze the functional relations between the whole and the parts. Chemical Processes Other challenges occur in functional modelling of chemical processes when considering the interaction between the process equipment, the chemical reactions, and biochemical processes. Here there is a need for considering functions of the materials processed in addition to the functions of the devices which are in focus in mechanical engineering. Furthermore, the functions of man-made technical devices and the functions of biological cells, are defined in relation to design and evolutionary processes which are two different contexts for explaining functions. Modelling and reasoning about the functional relations between technical artefacts and biological processes in biochemical production requires accordingly a combination of two different interpretations of the concept of function. Safety Systems Technical Artefacts like SCPS have functions which is provided by the design to prevent undesirable consequences of the operation for the environment or the system to happen. Compared to functions which are serving goals of production i.e. making products, the functions of safety systems are not meant to have any impact on normal operation but only be active when abnormal conditions occur. Their purpose is to avoid situations rather than accomplishments. The distinction between accomplishments and avoidances are important for functional modelling of SCPS since equipment can support both types of function and be interdependent. The distinction has no particular focus in existing approaches. Engineering Practice Another challenge is to introduce new modelling techniques in engineering practices. Here it is important that model development and use is

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integrated into the existing workflows of engineering and operation. The acquisition of knowledge required for modelling should build on existing sources of engineering knowledge. A common approach to accommodate this need has been to develop taxonomies or functional ontologies by consensus among practitioners within a field, and in this way build on existing concepts and cope with the dependency of functions on the specific engineering disciplines. The advantage of a consensus approach is that it has an immediate value for engineering by supporting standardization of existing practices within the fields. However, the disadvantage is the difficulty of generalizing across disciplines because context is often tacitly known and therefore difficult to acquire or formulate using natural language or other means of expression. The tacit nature of context is a challenge to both generalization and formalization and therefore to the development of theoretical foundations for functional modelling. I think that the problems mentioned above in creating a unified approach to functional modeling explain why it has not gained the popularity it deserves. There are still problems to be solved concerning the context dependency of functions. This is a key theme of the book.

1.4 The Need for Foundations The lack of theoretical foundations is especially a problem in areas where knowledge about functions needs to be formalized and used for logical reasoning about design or operational problems. Such needs occur in areas where new information technologies are used beyond existing practices for design or operation of SCPS. This is the case, for example, when the new opportunities for knowledge representation and reasoning offered by AI technology are used in the design of decision support systems for operators engaged in supervisory control. The operator’s tasks include diagnosis of abnormal situations where knowledge about complex relations between failure consequences on the level of the whole plant, and failure causes originating at the level of the parts plays an important role in decision making. Concepts and methods of functional modelling which can be applied for reasoning on many whole-part levels of the industrial plant and across different technologies are therefore required for the design of decision support systems. I believe that the foundations addressing these needs which are presented in the book, have applications for a wider range of problems in design and operation, also including technical artefacts with high levels of automation such as autonomous robots. To address these needs, the book develops conceptual schemas which capture dimensions of context relevant for functional modelling of SCPS. These schemas are conceptual tools for framing the modelling problem and comprise a foundation for functional modelling.

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1.4.1 Theory or Data Driven Modelling Functional modelling as presented here is theory driven and should be contrasted with data driven methods based on learning from examples or observations. Data driven modelling, which is promoted by AI research in machine learning, is also challenged by problems of context because the black box nature of neural networks makes it difficult to account for underlying assumptions in the learning algorithms, and for the context of observations. The author believes that the insights presented in the book regarding the context problems in functional modelling are also relevant for addressing the context problems in data driven modelling. The insights presented demonstrate that it is mandatory to address context when modelling complex technical artefacts like SCPS, and that it should be done on a theoretical basis to be able to systematically distinguish the many dimensions of context involved. The unification across disciplines aimed for in functional modelling is accordingly obtained through interpretation within each discipline of a common theoretic foundation rather than through consensus among practitioners.

1.4.2 From Theory to Practice The theoretical foundations for functional modelling presented in the book offer an alternative to consensus-based approaches but are not sufficient in themselves for solving the more practical problems which occur in model building for industrial applications. Firstly, the contextual dimensions identified in the book need to be interpreted within the different fields of engineering, for example through the development of domain specific languages and libraries of design patterns for modelling. Secondly, the acquisition of knowledge and subsequent building of functional models should be integrated into the existing workflow. These important aspects of using the foundations in practice are considered outside the scope of the book.

1.5 Contextual Dimensions Dealing with context is a challenge to all types of modelling. It is a thorny issue because there is potentially an infinite number of dimensions of context to explore. Sharfstein [11] defines context as follows: Context is what environs the object of our interest and helps by its relevance to explain it. The environing may be temporal, geographical, cultural, cognitive, emotional - of any sort at all. Synonyms for context, each with its own associations, are words such as environment, milieu, setting and background. A context is by definition relevant to whatever it is that one wants to explain and excludes everything, no matter how close in some way that lacks the required explanatory power. If

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one thinks of it as background, one sees that it is contrasted and paired with a foreground, and that the two are reversible.

There is accordingly a close connection to the idea of abstraction, by the emphasis on what should be explained and the exclusion of everything which is less relevant for that purpose. Context is also related to the concepts of framing and frames (and frameworks), which determine the strategies of attention of a problem solver and set the directions in which a situation is perceived and changed (see e.g. Goffman [12] and Schön [13], p. 309). Making abstractions can be seen as the purpose of framing, and contextual dimensions can be explored by a set of frames or conceptual schemas. Awareness of the framings involved is of particular importance for functional modelling of technical artefacts like SPCS because of the many contextual dimensions and types of framing involved, and because multiple levels of abstraction are required to capture the complexity of a SCPS. The question to be addressed in this book is therefore which dimensions are relevant to explore to cope with the contextual nature of functions? Of the five dimensions mentioned by Sharfstein (op. cit.), the cultural and the emotional context seems less relevant to functional modelling of technical artefacts. Locations in time and space are obviously relevant for modelling SCPS but is included in the cognitive dimension as features to be associated with functions.

1.5.1 Frameworks of Interpretation A significant challenge in functional modelling of technical artefacts is to separate concepts that in everyday language are often seen as synonymous, have overlapping meanings, or are vaguely defined. Thus, functions are often confused with concepts such as purpose, goal, objective, action, and behavior. This is not necessarily a problem in daily communication where the theme (yet another word for context) of the conversation often helps to clarify the meaning. But the ambiguities make it difficult to use language directly to set up actual models of artifacts’ function, which have the desired precision and expressive power. Furthermore, validation of functional models, which is a requirement in engineering, requires unambiguous concepts and is hindered by lack of clarity. The meanings of the concepts of purpose, goal, objective, action, function, means, and ends are entangled because functions cannot be defined without reference to intentions and causality, and are therefore related to objectives and means of action. The concepts are accordingly connected into a web of meanings within a common theme of purposeful goal-oriented action which, following Goffman (op. cit.), is the basis of a social framework of interpretation, which should be distinguished from a natural framework of interpretation where phenomena are described as being unguided and without purpose and intentions i.e., purely physical. Adoption of the social framework of interpretation when modelling a technical artefact means that it is perceived as a system of actions and not as a purely

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physical phenomenon. In addition, the actions and associated functions involved in technical artefacts are not only social by addressing human needs, they are also practical and productive by using physical means to transform raw materials into useful products (physical actions) and to transform physical events into meaning relevant for cognitive actions such as decision making and control. The focus on relations between the whole and the parts in functional modelling means that assignment of functions to a part of the SCPS depends on the functions of other parts and the purpose of the whole SCPS. This so-called principle of reciprocity is not valid within the natural framework of interpretation where the behaviour of the parts is considered independent of the whole. The productive utilization of biological processes in SCPS also require inclusion of a biological framework of interpretation where functions are seen in an evolutionary context where concepts of intention and action are not relevant for explaining behaviour. The elements of a SCPS require accordingly different interpretations of the concept of function, which must be used in combination in the modelling so that reasoning can be implemented across technologies. It is therefore important to define the concept of function so that this combination is possible, and the differences between technologies can be expressed and exploited in the analysis.

1.6 A Readers Guide The development of the foundations in the book rely on many background sources of knowledge of which some obviously are taken from the domains of mechanical, chemical and control engineering. However, a significant part is taken from sources outside engineering in order to bring in knowledge from other areas which are relevant for understanding goal oriented action and concepts of function. This includes philosophy of science, logic, sociology and biology, cognitive science (psychology and semiotics) and artificial intelligence. The amount of literature in these fields relevant for functional modelling is potentially vast, and the references included have therefore by necessity been selected based on their direct relevance for the foundations of functional modelling of technical artefacts described in the book. The sources can be investigated by the interested reader through the references given in the different chapters where they are relevant.

1.6.1 Organization of the Book Writing a book about the foundations of functional modelling of technical artefacts has challenges of its own, and a few additional comments may be appropriate for the reader regarding the overall organization of the book. Part I (Background) presents a general overview of key concepts including technical artefacts, human-artefact relations, the purpose and architecture of a SCPS,

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and the main propositions of the book - to see a technical artefact as an action system. The current uses of functional concepts in engineering disciplines relevant for SCPS are also included together with an overview of existing artefact modelling and applications of functional concepts in engineering. Part II (Preparatory Foundations) introduces topics which are considered preparatory to Parts III, IV, and V. This includes a presentation of modelling as a cognitive process, a general introduction to causality including a distinction between dyadic and triadic causality which required in functional modelling of technical artefacts. Parts III (Concepts of Function), IV (Concepts of Action), and V (Means and Ends) have two purposes. The first is to present detailed conceptual analyses of the concepts of function, action and means-end relations in order to clarify their meanings. The second purpose of the three parts (in particular Parts IV and V) is to explore formalizations of the concepts and to develop the concept of an action system which provides a formal representation of functional relations between physical, cyber and social aspects of SCPS. A main challenge in presenting the contents of the three parts has been to cope with the entanglement of the concepts of function, action, and the meansend relation. The interdependences are a challenge when presenting them in a linear medium like a book. Since the overall topic of is functional modelling, the concept of function is presented first, followed by a presentation of the concept of action including a highlighting of the differences and similarities between the two concepts. Finally it will be explained how the means-end relation is related to actions and thereby to functions. The relations between functions, actions, and the means-end relation discussed in Parts III (Concepts of Function), and IV can be seen as a contextual layering as shown in Fig. 1.1. This means that functions of something should be seen in the context of an action, which again should be seen in the context of the means used and the end to be achieved. Finally, the choice of means and ends depends ultimately on human values. Fig. 1.1 Contextual layering of functions, actions, means and ends, and values

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The layering is used to separate conceptual relations and patterns belonging to each individual layer, such as the structure of functional statements, the logic structure of actions, and the relations between means, ends, and values. The organization of the layers also indicates that the concept of action is the “glue” which binds the means and the ends together through the functions. The linear structure of presentation from the centre to the outer layers, is supplemented by reflections connecting back to the inner layers (the dotted arrows in Fig. 1.1). Thus, the concept of function introduced in the inner layer is further elaborated when the concept of action and means-end relations have been discussed etc.

1.6.2 Reader Profiles The book may have an interest for readers with different backgrounds and interests: Introductory. As a companion to an interdisciplinary course in philosophy of the engineering sciences for university students and practitioners interested in the conceptual foundations of their fields of engineering. Recommended reading: Parts I, II and III. Intermediate. In a course on the conceptual foundations for automation and control sciences and as a supplement to traditional courses on control theory and robotics. Recommended reading: Chapters and parts recommended at the introductory level and Parts IV and V. As a companion to a course on intelligent systems and on human supervisory control including an introduction to Multilevel Flow Modelling, its tools and applications. Recommended reading: Parts and chapters as above with an additional focus on Chaps. 15 and 16. Advanced. In a course on AI application in industrial automation including methods for knowledge representation and reasoning. For PhD students or researchers and advanced developers in industry. Recommended reading: As for intermediate level but with an additional focus on formalization of actions in Chaps. 11, 12, and 13.

References 1. G. E. Cummings. “Operator/Instrumentation Interactions During the Three Mile Island Incident”. In: IEEE Transactions on Nuclear Science 27.1 (1990). 2. L. Bainbridge. “Ironies of Automation”. In: Automatica 19.6 (1983), pp. 775–779. 3. G. Baxter, J. Rooksby, Y. Wang and A. Khajeh-Hosseini. “The Ironies of Automation . . . Still Going Strong at 30?” In: Proceedings of ECCE 2012 Conference. 2012. 4. Aristotle. De Anima. Penguin Classics, 1986. 5. V. Hubka and W. E. Eder. Theory of Technical Systems: A Total Concept Theory for Engineering Design. Springer Verlag, 1988.

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6. G. Pahl and W. Beitz. Engineering Design - A Systematic Approach. Berlin: Springer, 1996, p. 544. 7. N. P. Suh. “Complexity in Engineering”. In: CIRP Annals 54.2 (2005), pp. 46–63. 8. W. ElMaraghy, H. ElMaraghy, T. Tomiyama and L. Monostori. “Complexity in engineering and manufacturing”. In: CIRP Annals 61.2 (2012), pp. 793–814. 9. Y. Umeda and T. Tomiyama. “Functional Reasoning in Design”. In: IEEE Expert Intelligent System and Their Applications” 12.2 (1997), pp. 42–48. 10. M. S. Erden, H. Komoto, T. J. V. Beek, V. D’Amelio, E. Echavarria and T. Tomiyama. “A Review of Functional Modeling: Approaches and Applications”. In: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22 (2008), pp. 147–169. 11. B.-A. Sharfstein. The Dilemma of Context. New York University Press, 1989. 12. E. Goffman. Frame Analysis. London: Penguin Books, 1974. 13. D. A. Schön. The Reflective Practitioner. Basic Books, 1983, p. 374.

Part I

Background

The purpose of Part I is to present current uses of functional concepts in engineering of safety critical systems relevant for SCPS, and give an overview of existing artefact modelling and applications of functional concepts in engineering. Chapter 2 defines technical artefact and social-cyber-physical-systems (SCPS). It includes also an introduction to human-artefact relations in SCPS and a presentation of the overall view that technical artefacts are parts of action systems. Chapter 3 describes objectives and current practices in the design and operation of SCPS including documentation and the relevance of functional concepts for addressing complexity. Existing design methodologies and concepts of operation and their support for functional thinking are mentioned. A summary of problems of using functional concepts in the industry is given. Chapter 4 presents an overview over existing frameworks methodologies for functional modelling covering contributions from the artificial intelligence community and researchers within mechanical engineering and manufacturing, process control, human factors and safety critical systems. Challenges in satisfying the needs of functional modelling of SCPS which motivates the development of the foundations presented in Parts III, IV, and V are presented.

Chapter 2

Technical Artefacts and Humans

The purposes of this chapter are to explain the nature of a technical artefact, and to introduce social-cyber-physical-systems (SCPS) as the particular instance of artefacts considered. An introduction to human-artefact relations in SCPS is given. Finally, the overall view that technical artefacts are parts of action systems is presented. This view is foundational for the approach to functional modeling taken in the book.

2.1 What Is a Technical Artefact? Technical artefacts are the objects of design in engineering domains and include mechanical, chemical, electrical, and automated control systems. Examples of technical artefacts can be found everywhere: tools, machines of all sorts, cars, bicycles, fridges, pumps, heat exchangers, distillation towers, robots, manufacturing systems, power plants, wind turbines and so on. Technical artefacts are accordingly abundant but they should be distinguished from natural objects like stones, mountains and rivers which are not designed and therefore have no functions unless used by human beings for a purpose. Technical artefacts can be found on any level of physical size and complexity. Since roads and buildings are both physical and purposeful they, strictly speaking, also should be considered to be technical artefacts. Functional concepts are therefore also of relevance for civil engineering and architects (see e.g. Mitchell [1]). In this book the focus is on industrial processes. Actions are often involving the use of technical artefacts. Actually there is a whole range of situations where the concepts of action and function are relevant one way or the other for explaining and understanding technical artefacts. This will be illustrated by some cases with increasing complexity. First Case The first case to consider is a natural object i.e. an object which is not made to serve a purpose. Natural objects can cause changes when e.g. a piece of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_2

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rock falls from a cliff and crushes everything on its way. But the behaviour of the falling rock does not invite us to say that it is acting because there is no intention involved. Likewise the rock cannot fail in causing a change because there is no norm or intention from which its behavior can be judged. However, what if the rock is positioned by a human so it falls by itself, or is pushed over the cliff with the intention of making harm to the environment or to somebody passing by? In such a case the rock is used as a means to achieve the objective of a human. Even though the rock is participating in an action, it does not act by itself, it is an instrument or means for the human action. In addition to explaining the fall of the rock by natural laws, conditions and reasons for its falling can be given by reference to the intentions of a human. The stone is accordingly ascribed a instrument function in the context of a human intention and the rock can fail depending on its ability to serve this function. Ascribing the function to the rock implies also that it is only the intended effect on the environment that matters. Another physical object having the same effect could accordingly serve the same function. This case show that concepts of action and intention are relevant for explaining and understanding even simple situations where humans interact with natural objects i.e. not technical artefacts designed for a purpose. But human intentions are even more relevant for understanding technical artefacts. They can obviously be seen as very complex objects whose behaviour can be explained by laws of nature, but since they are made to fulfil human needs and maybe even to take over human work, it is important to understand their roles in actions as substitutes for humans. The complexity and behaviour of technical artefacts cannot be understood without reference to the context of intentions which underlines their design and use. Second Case The first case can be expanded to illustrate how the relevance of human intentions becomes obvious and inevitable for even simple technical artefacts. For this purpose assume that the rock is being shaped by humans to make the most harm possible when it falls down the cliff i.e. it has been turned into an artefact. In addition a device is constructed to detect by-passers at the foot of the cliff and a mechanism to push the rock so it falls down to hit the by-passers. The simple case has now been developed into a “killing” machine whose behaviour still can be explained in physical terms, but the purpose of the control mechanism, the modes of failing and the reasons for the particular shape of the rock cannot be understood without reference to the aims of the human designer. The example illustrates that the roles of human intentions in explaining and understanding technical artefacts is increasing the more automated and adapted to a particular purpose they become. Third Case At the other end of the scale comprising highly automated industrial process plants involving human operators, knowledge about physical phenomena and natural laws also play a significant role in decision making. But the operator also needs to understand the designers intentions to be able to diagnose and intervene in abnormal situations when the automated systems fail. Experience from numerous accidents in process-industry show that the interaction between the operator and an automated plant is of critical importance for handling abnormal high risk situations, in particular when they are not anticipated by the plant designer.

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In such circumstances, the operator needs knowledge about design intentions of plant components and subsystems and their functions in order to make informed decisions. Knowledge of system functions is necessary for making sense of and respond to abnormal situations. Fourth Case Autonomous robots being able to generate their own goals and plans comprise another more extreme case of technical artefacts. It could be argued that they are acting, as their behaviour cannot be understood without reference to goals and plans, but these artefacts are still designed to be means for satisfaction of human needs. This is also the case for robots which by design, are able to generate their own goals. The ability to give reasons rather than causes for the behaviour of technical artefacts is accordingly becoming of increasing importance for design and operation of highly automated or autonomous systems. It is not only necessary for human operators to understand the functions of the artefacts they are operating. It is also important that designers of fully automated systems document their designs and make them transparent to public regulators and convince them that they are safe and sustainable.

2.2 Socio-Cyber-Physical Systems Above the general relevance of functional modelling for representation of technical artefacts has been presented. However, a particular challenge is to apply concepts of functional modelling on complex industrial systems which typically comprise several diverse technologies such as mechanical-, electrical-, chemical-, and information technology, which are the objects of design within associated scientific and professional fields of engineering and human factors. Such systems, often called socio-cyber-physical systems (SCPS), are dependent on natural physical environments as well as contexts of social needs, constraints and practices. The challenges they represent for functional modelling will be outlined in the following. Analysis and synthesis of the interactions between the social, cyber and the physical subsystems are challenging tasks because they rely on knowledge of features of both the whole and the parts. A study of each part requires modelling approaches and concepts which are specialized to the technologies and sciences of each field with a focus on structure and behaviour of the part. A study of the whole requires modelling approaches which can represent the functions that the parts serve in achieving the overall purpose of the whole SPSC system which is to deliver products to the customer in a reliable, safe, efficient, and sustainable way. The distinctions between the three technical artefacts P, C and HMI comprising a SPCS, shown in Fig. 2.1, are reflections of the functions they serve in achieving the purpose of the whole system, but not on a level of details sufficient for reasoning about their interactions and the ways the SPCS can fail in achieving its purpose. In the following the functions and their interdependencies will be explained on

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Fig. 2.1 Elements of socio-cyber-physical systems and their interactions with the designer and the human operator

an overall level, including how each of the elements is dependent on external environments providing resources for constraining the operation of the SCPS. Figure 2.1 also illustrates the overall human-technology and technology-technology relations between the designer, operator, and the three technical artefacts P, C and HMI. Understanding the nature of these relations is important for functional modelling and will be discussed in more detail in Chap. 5. Socio-cyber-physical systems as shown in Fig. 2.1 can be found on many system levels. It can be found on the level of an entire production plant, on the level of a subsystem having its own local control and human machine interface, and on the

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Fig. 2.2 Interactions between the process, the natural environment and the consumer can be divided into accomplishments (production) and avoidances (safety)

level of individual equipment like coffee machines. In addition, SCPS systems may be distributed into a network of subsystems interacting by exchange of raw materials and products (see Fig. 2.2) or information.

2.2.1 The Physical Process The purpose of the physical process (P) is to produce products from raw materials and energy delivered by other productions or the natural environment (see Fig. 2.2). It interacts both with the cybernetic and the social (human) subsystems as illustrated in Fig. 2.1. P is dependent on services of the natural environment, which provides energy and material resources required for the production and receive waste products. These services of the environment are taken into account in process design to meet sustainability requirements. P is also dependent on the consumer i.e. the user whose needs define requirements to the products. The functions of the physical process must accordingly include such dependencies on the natural environment and the consumer. The physical process P interacts with the cybernetic subsystem C which comprises the instrumentation and the automated controls. The automation-process

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interaction provided by the instrumentation includes from a functional point of view two relations: (1) a relation of observation where P is the object observed and C is the observer, and (2) a relation of intervention or control, where P is the controlled object and C is the controller. In both cases, the interaction is between the two technical artefacts P and C. The human operator (H) interact directly with the process (P) of the control and instrumentation system through the operation support system (HMI) as indicated in Fig. 2.1. This system includes the human machine interface and associated computer based systems for decision support.

2.2.2 Control and Instrumentation The purpose of the control and instrumentation subsystem is to monitor the performance of the process, to evaluate the production in relation to design and operational intentions, and to intervene in the process in order to compensate for performance deviations in safety or efficiency caused by uncertainty or unforseen disturbances. The instrumentation artefacts transform between physical events and information. Two types of instrumentation are used: sensors and actuators. The purpose of the sensor is to transform physical events into information and the purpose of actuator is to transform information into physical events in the process. The control and instrumentation systems are traditionally modelled using concepts of systems and information theory representing causal relations between signals. The functions of subsystem C in Fig. 2.1 are therefore fundamentally different from the physical process P where the interactions between the parts are caused by interchange of materials and energy. C is an information system using signals or representations, meaning that the information processing involve causeeffect relations between events or representations of states of the physical system P, and information about objectives and commands and their conversion into to physical action. The design of the control subsystem therefore rely on knowledge of cause-effect relations between states in the physical process, but includes also knowledge of control objectives and principles for extraction of meaning from process data.

2.2.3 The Operation System The operation system (O) includes the human operator (H) and a support system (or the human machine interface, HMI) used to translate operator commands into physical events or to acquire and present information about the state of the process. These interactions between the operator and the process through the operation support system are often mediated by the control and instrumentation artefacts.

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Sometimes however, the human operator interacts directly with the process through instruments (sensors or actuators) provided for manual control. The function of the human operator in current highly automated industrial processes is to supervise the process and the automated controls, and to intervene in abnormal operational situations where the behaviour of the automated system is moving beyond design specifications. Operators are trained to fulfil the requirements of their supervisory task and are supported with information by the operation support system in the control room. Human operators are also responsible for maintenance of process equipment in order to ensure that it is in working order and properly calibrated. These maintenance functions will not be discussed here. Functional analysis of the interaction between the operator and the other elements of the SCPS requires two separate perspectives; • an identification of the operators control and supervision functions as e.g. specified in the task requirements (i.e. the operator as a system component) • a consideration of the natural dispositions of humans to engage in goal directed interaction with the physical world (i.e. the artefact as an object of human action). The capacity for goal directed behaviour and training makes the operator fit for his control and supervision task but she/he may be substituted with automated control systems having similar capacities and therefore the ability to serve the same functions. The implications of the cognitive abilities of human operators for the functional representation of the interaction between operator and system will be discussed in Chaps. 5 and 13. The role of the human operator is depending on the process in question. In many industries, the automation has taken over the task of operating and controlling the process and the operators role is reduced to be a supervisor. But in other cases the human operator is directly involved in the operation in abnormal situations. In both cases the operator is dependent on the operation support system (HMI) for information about system states and means of intervention which are relevant for the task.

2.3 Human-Artefact Relations It is accordingly not meaningful to discuss technical artefacts without considering the role of human designers and users. The relation between humans and artefacts can be seen from three perspectives: • the artefact is an embodiment of design intentions • the artefact is an object of human action • the human operator is a systems component These perspectives define three different contexts for assignment of functions to the artefacts and the human.

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The Artefact as an Embodiment of Design Intentions Here the artefact is seen as a means for satisfaction of humans needs for safe, efficient and sustainable manufacturing of products. Satisfaction of these needs require that the process can be controlled. It is therefore provided with means for observing the states of the production process and for means of intervention. The functions of the artefact are seen in the context of human goals and objectives. The Artefact as an Object of Human Action In this view the artefact is seen from the perspective of the human i.e. as an “umwelt” in the meaning defined by Uexküll [2] i.e., the environment as experienced by the human and reflected in his/her skills, competences and objectives. The functions of the artefact (how it works) seen in the context of the human operator’s experience become accordingly in focus. A model of these functions would accordingly represent the Umwelt of the operator i.e. how the artefact is experienced as an object for goal oriented action. This view of a technical artefact as an object of human action is important for the design of operator decision support systems. Functional models representing the Umwelt can here be used to design the content of information presentation systems. The Human Operator as a Systems Component It is important also to consider the function of the human operator seen as a system component (Rasmussen [3]) in order to understand the role of automation in technical artefacts. The purpose of automation is to substitute human physical and cognitive functions with the operations of machines. In addition to having physical and cognitive functions when operating technical artefacts, humans also have organizational functions by being members of operational teams. Such teams and other organizational structures are social artefacts. Cooperative aspects of social artefacts can be simulated by software agents (see e.g. Jennings et al. [4]) but will only be discussed to the extent that they are relevant for functional modelling of the interaction between control agents in the SCPS. In principle, the designer could also be seen as a systems component serving functions of the whole. This perspective would be relevant when using information technology for automation of the task performed by a human designer. This possibility of creating technical artefacts with self-designing features is interesting in theory, but is probably remote for technical reasons in spite of the recent progress in machine learning. Conceptually it seems far fetched, at least in risky industries, because of the challenges it would impose on the representation of contextual information including human values and opportunities for action in unfamiliar safety critical situations. It would also be problematic for ethical reasons i.e. not desirable. The Control System as a Systems Component The functional perspective on the operator explained above can also be applied to the automated control systems. This is expressed in the theorem “every good regulator of a system must be a model of that system” which means that it must include representations of the object under control. The theorem is central to cybernetics and was initially presented by Conant

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et al. [5]. It was later reformulated in the internal model principle of control theory by Francis et al. [6].

2.4 Technical Artefacts Are Parts of Action Systems In conclusion, the proposition presented in the book is therefore, that foundations for functional modelling should be addressed within the framework of social interpretation where a technical artefact is as part of a system of actions including the natural environment, the human operator and the designer. The functional relations between the subsystems of the technical artefact are embedded in an overall means-end structure relating the means of action with a set of common goals or ends. Analysis of the means-end structure of the artefact is therefore included as the initial step to set an overall context for building a functional model. This means-end analysis requires in technical artefacts like a SCPS an overall distinction between actions and functions required for accomplishing goals of production and those required for avoiding safety critical situations. This view on the interaction between individual parts of a technical artefact, the environment and the human operator differs from traditional approaches to modelling in engineering, which is dominated by the natural sciences’ conception of causal physical relationships, as determined by quantitative laws of nature. This view precludes a systematic description of the purposes and intentions underlying the design and use of a technical artefact, and thus the perspective of action. Action is, of course, on an overall level central to engineering practice, but the concept does nevertheless not play a corresponding role in the development of the models used traditionally in the analysis and synthesis of technical artefacts. Adopting an action perspective in functional modelling implies an emphasis on reciprocal relations and interactions between the SCPS and its subsystems, the designer and the operator: • The artefact is a materialization (embodiment) of the designers intentions using physical means for their realization. • For the human operator, the artefacts and its subsystems are means for achieving his/her goals. From a perspective of the whole, the operator is a means for achieving the overall goals of production and safety. • The natural environment is a means of meeting production and safety objectives i.e., it has functions in the contexts of accomplishing goals and avoiding hazards to the equipment. Reversely, the artefact and the human operator are seen as means of maintaining sustainability of the environment i.e., avoiding harm to the natural environment. • Interactions between the technical artefact, the operator, and the natural environment are perceived as purposeful and are described by relations of meaning. Thus, the action perspective provided by the social framework of interpretation differs from the view of the natural framework of interpretation (i.e., the natural

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sciences) where interactions are described by events of no significance beyond the meaning of physics, chemistry and evolutionary biology. • Knowledge about means-end relations and functions of technical artefacts is shared between designers and operators, and is therefore inter-subjective.

References 1. W. J. Mitchell. The Logic of Architecture. Massachusetts: The MIT Press, 1992. 2. J. von Uexküll. A Foray into the Worlds of Animals and Humans. University of Minnesota Press, 2010. 3. J. Rasmussen. “The human as a systems component”. In: Human Interaction with Computers. Ed. by H. T. Smith and T. R. G. Green. Academic Press, 1980. 4. R. Jennings and S. Bussmann. “Agent-Based Control Systems”. In: IEEE Control Systems Magazine June (2003), pp. 61–73. 5. R. C. Conant and W. R. Ashby. “Every Good Regulator of a System Must be a Model of that System”. In: International Journal Systems Science 1.2 (1970), pp. 89–97. 6. B. A. Francis and W. M. Wonham. “The internal model principle”. In: Automatica 12 (1976), pp. 457–465.

Chapter 3

Functions in Design and Operation of SCPS

The purposes of this chapter are to introduce existing engineering frameworks for documentation and modelling of SCPS, and to explain how functional concepts currently play an important but not fully developed role in design and operation. In addition, the needs for further development and formalization of functional concepts to make them applicable for design and operation of SCPS are identified.

3.1 Functions in Engineering The concept of function is a natural part of the engineering vocabulary for the very simple reason that technical artefacts, apart from being physical, are the objects of design and operation, and their functions should be seen relative to these contexts. In engineering there are accordingly strong reasons for the relevance of the concept of function. The value of functional concepts for engineering in the formulation and solution of design problems was realized early (see e.g. Leaver et al. [1]). Simon [2] went a step further by defining engineering science as “the sciences of the artificial” and argued for the central importance of the concept of function. Jørgensen et al. [3] presents a more recent overview demonstrating the broad relevance of functional concepts for design and operation of chemical products and processes. Concepts of function are used to cope with the complexity of decision making during design (see Suh [4] and ElMaraghy et al. [5]) and by human operators when dealing with plant upsets in operation (see Rasmussen et al. [6, 7]). In brief, the concept of function provides an understanding of a technical artefact on a level of abstraction which is necessary for making decisions in design and operation. The elimination of concepts of purpose and function from the natural sciences has influenced the view of engineering as applied science. However, in spite of this, concepts of function and purpose are still used within all engineering disciplines because the problems of engineering cannot be addressed meaningfully © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_3

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without these concepts. Central engineering concepts like requirement, efficiency, optimality, success, and failure are logically intertwined with concepts of purpose and function. The aim of engineering science to change the world according to human needs distinguishes it from the natural sciences, whose main interests are in observing and understanding the world as nature independent of human purposes. Of course, natural scientists also intervene in the world by actions through experiments, but the purpose of an experiment is not to change the world, on the contrary one could say, but only to disturb it slightly in order to reveal its essential properties, which are considered independent on the observer.

3.2 Design of Socio-Cyber-Physical Systems Socio-Cyber-Physical Systems (SCPS) were introduced in Chap. 2 and are the types of technical artefacts in focus in this book. They include three types of artefacts; a process system P, a control and instrumentation system C, and an operation support system (HMI) as shown in Fig. 3.1. Fundamentally, the control system, the operation system (and the operators) have no purpose in themselves. From the perspective of the SCPS as a whole, their functions are to serve needs of the process system P by ensuring efficient, safe, and sustainable operation. The physical equipment, the raw materials and energy provided by the environment, the control and instrumentation systems and software, the operation system and the skills and competences of human operators are all means to the ends or goals of production. The ends of production are to achieve Fig. 3.1 Technical artefacts in socio-cyber-physical systems (see also Fig. 2.1)

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Fig. 3.2 SPCS systems include many technologies connected in a value chain

the needs of customers (the product to be produced), the owners (the economy of operations), and the social and natural environments (safety and sustainability). The relations between the means and the ends of production define the contextual constraints for the analysis and synthesis of the functions of the SCPS. The value chains with different technologies connecting the means and ends of production of the SCPS are illustrated in Fig. 3.2 Electronics and software are included in Fig. 3.2 as means (A and B) for implementation of the control and instrumentation system since the functions of control and instrumentation today in large measure are implemented by these technologies. However, they will only be discussed in this book from a control and instrumentation perspective and in the context of the needs they serve in the SCPS as a whole. The functions of software and electronics hardware, which has a particular interest for electrical engineering, will accordingly not be discussed. Functions of the control and instrumentation system are also in some cases implemented by means of process technologies from mechanical and chemical engineering. One reason for not using electronic devices can be to avoid explosions caused by sparks. This is common practice in e.g. the oil and gas industry. Another reason to avoid electronics is to ensure that functions critical for safety are not lost when power supplies fail (so-called passive safety systems using e.g. natural circulation for cooling instead of safety pumps which require electrical power for their operation). Such inherently safe systems are considered by the nuclear power industry (see e.g. IAEA responses to the Fukushima accident [8]). The arrow marked

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Fig. 3.3 The design of process, control, operation support system and operator training in SCPS are interdependent through mutual requirements and constraints

C shown in Fig. 3.2 indicates that control and instrumentation functions can be implemented by process components or subsystems.

3.2.1 Design Requirements and Constraints Specifications of production objectives and functions for the SCPS subsystems originate in design of the process system and comprise the basis for design of the control and instrumentation system as shown in Fig. 3.3. Furthermore, since control

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systems modify the response of the process to disturbances, human operators need information through the operation support system about objectives and functions when dealing with diagnosis and counteraction of operational disturbances. Without having this information it is difficult for the operator to isolate the causes of plant malfunction, predicting their consequences, and to understand how the control systems affect the result of manual interventions. This means that the operation support system must be designed to convey information to the operator about the objectives and functions of the process and control systems, and to support the operators skills and cognitive abilities which are partly provided through training.

3.2.2 Automation of SCPS The purpose of automation (C and HMI in Fig. 3.1) is to substitute human work by machines. In functional terms this means that monitoring and control functions previously performed manually by human operators are allocated to technical artefacts, the control and the instrumentation systems and to the operation support system. The purpose of early automation was to relieve humans from hard physical work. Today human operators are also relieved from stressing cognitive work by using information technology to analyse and evaluate complex control situations to acquire information, give advice, make decisions or suggest countermeasures and the execution of control plans. These functions supporting the operators cognitive work are provided by the operation support system. This approach to automation is used in risk industries like energy systems or chemical plants where humans have a central role as operators responsible for managing risk of operations. In other nonrisk industries humans play a less significant role in daily operations. Higher levels of automation are used in these industries and the human operators tasks are focused on supervision and maintenance of plant equipment. Sheridan [9] proposed a distinction between different levels of human interaction with automation shown in Fig. 3.4. The levels of automation have been expanded to ten levels in more recent work by Parasuraman et al. [10]. The figure illustrates how the role of the human operator changes when the level of automation is increased. In manual control the operator is interacting directly with the process through display and control devices, and is responsible for control and overall operation of the process. At the other extreme, fully automatic control, the operators role is reduced to be a monitor of the automated process. Supervisory control has two modes; (1) the computer monitors the human operator who is in control (left) and (2) the computer is in control and is monitored by the human operator (right). The functional significance of the different modes of interaction between the operator and the process and control systems defined by Sheridan will be analysed further in Chap. 9 using a classification of human-machine relations developed by Ihde [11].

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Fig. 3.4 Levels of automation (Sheridan [9])

Developments of Automation Historically, automation has its roots in control engineering i.e. the discipline dealing with analysis, design and implementation of closed and open loop dynamic systems, but it has developed to satisfy increasing requirements to more efficient and safe industrial processes by also including operator support functions for advanced decision making, monitoring and control. Growing demands for green, efficient and dependable production makes highly automated solutions even more important because these needs can only can be satisfied by more “intelligence.” An important source of intelligence is obviously engineering knowledge of the processes and the production which can be used by the automation to reduce pollution, resource consumption, operational cost and risk. The widespread use of digital technology has accordingly had a significant influence on the implementation of automation solutions. It has had an impact on the range of tasks which can be automated and has required extensions of the theoretical foundations of automation and control. Especially, arteficial intelligence (AI) using computers for modelling and simulating human problem solving has had a major influence by extending our understanding of how computers can be used to represent and process human knowledge and how they can enter in a dialogue with human operators engaged in supervisory control and diagnostic problem solving. AI research about the representation of qualitative knowledge including purpose and functions here plays a significant role. More recent developments of artificial agents can be seen as a further strengthening of the role of concepts for modelling and simulating intelligent behaviour. Artificial agents are accordingly exploited for automating negotiation processes in systems with distributed decision making and control such as e.g. power systems.

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Fig. 3.5 Documents currently supporting design of process, control and operations support systems

Originally the engineering documents were paper based, but are now converted into digital form to facilitate processing and distribution in the design organization and operations, but their contents have not been changed as this would require changes in the organization of the design process. In industry design decisions are often made without formal models and instead based on the reuse and adaptation of previous solutions. This may be cost effective in some situations, but can also lead to problems if design requirements are incomplete or not well understood. There is therefore an interest in changing practices by applying methods for knowledge acquisition, representation and reasoning developed by AI research to formalize and make the design knowledge explicit (e.g. object oriented databases and rule based systems).

3.2.3 Engineering Documents Used by the Industry On an overall level, the SCPS design procedure can be divided into two overall phases; conceptual and detailed design. In conceptual design qualitative models of technical artefacts are used for evaluation and documentation of alternative solutions early in the design process. In detail design quantitative models are used for adaptation of previous or optimization of new solutions.

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Design and operation of SCPS is traditionally supported in industry by several engineering documents as shown in Fig. 3.5 (excluding documents of electrical hardware and software). The documents are used as media for exchange of information between the designers of the process-, control-, and operation support systems, but they also serve the needs of plant commissioning, operation, maintenance, and decommissioning. The documentation is adapted to the needs of the work flow in the SCPS life cycle and contain essential information about the functions of the SCPS subsystems. However, they are descriptive and do not provide a sufficient basis for calculation and logical reasoning as formalized models do. The documents are either based on diagrams or text, and are qualitative representations of the artefact (the process and the controls), and use the conventions developed by the different industries for naming of functions, plant subsystems and equipment, their interconnections and topology. In SCPS of recent design these documents are digitized to support the design process and the operation. Process Flow Sheets Process flow sheets (PFS) are diagrammatic representations of the overall production functions of SCPS subsystems and the associated flows of materials and energy. These documents are developed and used in the conceptual phases of process design before decisions have been taken regarding the detailed implementation. The process flow sheets contain information about functions, however as mentioned above, information about goals and objectives is missing. Consider the example of a PFS for a flash compression plant (a subsystem of gas treatment plants) shown in Fig. 3.6. The functions shown are related to the overall purpose of production and represent the steps of transformation of the raw materials used to produce the final product. The terms used to describe functions are action verbs (supply, separate, deliver etc.) put into a sequence which relates directly to the steps required to transform raw materials and the energy supplied into products. A PFS excludes information about the physical equipment or the detailled phenomena to be used to realize the functions, furthermore the instrumentation and control systems are not shown. They depend on information about the locations of disturbances in physical topology of the plant (where to measure and actuate with control actions). This information is shown in piping and instrumentation diagrams (P&ID) to be described below. Piping and Instrumentation Diagrams Information about how functions are implemented is shown in piping and instrumentation diagrams (P&ID). These diagrams represent the physical equipment and their interconnections including instrumentation and control systems. Figure 3.7 shows a P&ID for the flash compression plant in Fig. 3.6. It shows the equipment and components i.e. the physical means used to implement the gas compression functions, including the instrumentation and controls provided to ensure that the required stability and quality of the compression process is maintained. System Control Diagrams System control diagrams (SCD) represent the signals transmitted from sensors, and signals generated by the control algorithms and

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Fig. 3.6 Overall functions of a flash gas compression plant

Fig. 3.7 Piping and Instrumentation diagram (P&ID) of a flash-gas-compression plant extended with information about material and energy streams (Lind et al. [12])

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transmitted to the actuators. SCD’s can also include discrete events dealing with control sequences and their timing (in particular for start-up and shut down operation). Several diagrammatic formalisms are used including petri nets and sequential function charts , and sometimes programing code is used to document control sequences (see e.g. Peterson [13] and the IEC-61131 standard [14]). Standard Operating Procedures Standard operating procedures (SOP) contain textual description of objectives for process operation and how the process should be operated during start-up, normal operation, and shut-down. This includes also operation of the control systems when changing from automatic to manual operation. SOP’s are usually described in text or diagrams, but are sometimes formalised using control narratives. In many industries SOP’s are digitized and made accessible for the operator though the HMI. Cause-Effect Diagrams and Fault Trees Cause-effect diagrams (CE) represent alarms and related protective actions to be used in the design of protection systems. Fault trees (FT) represent dependencies between faults and their causes. CE and FT diagrams are developed in the SCPS design as part of risk assessment which is mandatory in safety related industries (see Sect. 3.4 for more details). Displays and Decision Support The purpose of the displays is to support the operator with information about the state of the production and the equipment and to present alarms to alert the operator in abnormal situations. The displays represent the state of the process, the automated control systems, sensor readings, alarms, and means of manual intervention. The information is organized into a display hierarchy helping the operator to search through the often massive information. The displays are, as a rule, designed on the basis of P&ID’s on different levels of decomposition of the system. The displays may also include access to computerized procedures helping the operator to deal with start-up, shut-down, and decision support systems for handling abnormal situations predicted by the designer (so-called design based accidents).

3.3 Approaches to SCPS Design Two different approaches to design of a SCPS, the subsystems and their interactions can be distinguished: • the traditional design approach which has its origin in the early development of process automation in the last century • the systems centered design approach which has been proposed to cope with problems in overall design of SCPS and for improving the interaction between the human operator and the technical artefacts

3.3 Approaches to SCPS Design

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Fig. 3.8 A traditional linear design approach with interactions

3.3.1 The Traditional Linear Design Approach In the traditional approach the design procedure is carried out sequentially in a linear fashion (with iterations) as shown in Fig. 3.8. The approach is based on the presumption that the operator is a source of failure, and the control systems are therefore designed to automate as many tasks as possible. The tasks which cannot be automated, and therefore should be executed by the operator, are to supervise the process and the automated controls i.e. to monitor, diagnose and intervene when required in abnormal situations which are not predicted by the process and control engineers. The traditional approach reflects the separation of process and control engineering and human factors. These professions have different scientific backgrounds which hinder easy exchange of information between the design phases. The focus is accordingly on design of the parts and not the system as a whole. The transfer of information is based on the engineering documents as illustrated in Fig. 3.9. The design procedure is starting with the PFS (1) followed by the P& ID (2) and the SCD (3) leading to revised PFS (4) and P& ID’s (5). Subsequentially detailled design is undertaken which again leads to a new version of P& ID’s (6) followed by SCD’s (7) followed by development of CE and FT’s (8) and SOP’s (9). Finally, the displays and decision support system are developed (10).

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Fig. 3.9 Information exchange by design documents. Arrows indicate dependencies and numbers indicate the design sequence

A problem with the traditional approach shown in Fig. 3.8 is that the unidirectional flow of requirements from process design to control design, and from this to design of the operation system and operator training, fails to acknowledge that the process design is constrained by control requirements. For example, controllability and observability requirements to the process may constrain the choice between alternatives in design of the process. Furthermore, the design of both the process and the automated controls are also dependent on humans. An operator may not be able to perform his/her supervision task if the process and control design pose unrealistic demands on his/her cognitive abilities. Ignoring these dependencies may lead to costly design iterations or suboptimal design of the whole SCPS. There is therefore a need for a systems centered design approach which ensures that design constraints of both the whole and the parts are taken into account. It is a purpose of the foundations presented in this book to provide a framework for such a systems centered design approach.

3.3.2 System Centered Design Changes to the traditional design approach to be more systems centered has been proposed both by designers of the process and the automation systems. The main idea is to decompose the overall design task into subtasks. Many tasks are involved

3.3 Approaches to SCPS Design

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Fig. 3.10 Systems centered design approach

such as product design, process design, control design, formulation of operational procedures and design of operator support system etc. Each of these tasks comprise a set of more specific subtasks, where different aspects of product/process modeling may be effectively utilized to provide a basis for resolving the task. Obtaining a satisfactory resolution for each subtask is strongly dependent on other tasks as the product/plant design develops and on the overall efficiency and environmental constraints. Thus, the product/process and control design requires an overall structured approach. Furthermore, the plantwide considerations related to energy and material efficiency, process safety, and environmental aspects also require identification and representation operational objectives. In a systems centered approach the subgoals at each stage needs accordingly to be documented to guide the design process (Fig. 3.10). The development of systematic tools for process synthesis and design, and control design in process engineering has been both directly and indirectly influenced by a functional view from the relatively early developments through means-end analysis at the task level to an approach using function oriented but phenomena-based building blocks (PBB). By this approach the limitations of the traditional focus on one-to-one relations between functions (called unit operations) and equipment were eliminated and even greater material, energy, and economic benefits were achieved. PBB’s can be seen as abstract phenomena based elements representing functions

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of physical balance volumes such as mixing, phase contact, phase transition, phase change, phase separation, reaction, energy and momentum transfer or stream division. PBB functions are associated with phenomena rather than components or devices. These approaches have all facilitated the development of more-sustainable processes (Jørgensen et al. [3]).1 However, even though a functional view in different forms already has had a significant influence in process design it has not yet been formalized. There is therefore a need for methods like functional modelling to express design goals and their relations to functions, behaviour and structure of the whole SCPS. The concept of function presented in Chap. 7 as a set of dispositions selected for a purpose provides a formalization of the FBB’s. Human Centered Design Human factors researchers and practitioners have also proposed to bring in human factors early into the design procedure starting already in the conceptual phase to ensure that operational requirements and cognitive limitations of the human operator are taken into account in the design of automated controls and decision support systems. Such an approach to automation is often called human centered, but is actually systems centered because its realisation has implications for design of the SCPS as a whole. The problems with the traditional design approach was pointed out by Bainbridge [15] who argued that, contrary to expectations, the reliability of highly automated systems is reduced when the level of automation is increased. The reason is that the operator is taken out of the loop and cannot maintain the skills required to intervene in highly critical situations and may make errors when the automated control fails. This means that the traditional design approach, which presumes that the operator is fallible, at the same time assumes that the control system designer is infallible. An assumption which is not supported by evidence from industrial accidents caused by design errors. A notable example is the Three Mile Accident where the operations support system to a nuclear power reactor by design did not provide key information required by the operator for an assessment of the situation and appropriate response. This design error led eventually to a partial meltdown of the reactor core (see Cummings [16]). Bainbridge’s argument against full automation in safety critical industries is still valid even in times where industry may be tempted to increase the level of automation by using machine learning algorithms to substitute human operators. These new technologies face two challenges: firstly, from a both a regulators and an operators perspective, acceptance of the technology in safety related industry require transparency of its black box operations, and secondly safety critical events are by nature not very frequent and it may therefore be difficult to acquire sufficient data to ensure useful learning results.

1 This

paragraph is partly based on input from Prof. S. Bay Jørgensen.

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Cognitive System Engineering A human centered approach to automation is represented by a influential research direction called Cognitive System Engineering (CSE) (see e.g. Rasmussen et al. [17], Vicente et al. [18], Hollnagel et al. [19]). The idea of CSE is that the operator support system should be designed to reflect operators’ problem solving strategies. Based on field studies of fault diagnosis tasks and the general finding from cognitive psychology that human behaviour is goal oriented, it is proposed that the process and the control system should be represented by a so-called abstraction hierarchy of means-ends and part-whole relations. The CSE concepts suggest to match the operational concepts with human cognitive skills in order to make their decisions in management of abnormal operating situations less vulnerable to error. The proposal is in stark contrast to the traditional design approach to human machine interfaces where process and the control systems are represented mainly by their physical subsystems, components and their interconnections i.e. with a lack of explicit representation of system goals and functions. It is also suggested that the mismatch between the information presented and what the operators require in dealing with abnormal operational situations is a potential source of error which has been created by the traditional design principles. CSE is human centered but its implementation has implications for design of the process and the automation as well. Coping with the constraints between means and ends in operation mentioned above require consideration of the system as a whole in the design procedure. Goals and functions of operation cannot be made explicit without knowing goals and functions of the process and the controls. Solving the problem in the human machine interface requires accordingly changes in the approach to process and automation design to be systems centered and to be more concerned with modelling the relation and transfer of knowledge between system design and operation. The modelling frameworks proposed by CSE are described in more detail in Chap. 4.

3.4 Design for Reliability and Safety SCPS are often subject to severe requirements to reliable and safe operations in order to avoid adverse consequences of failures for humans, environment and equipment. Concepts and methods have therefore also been developed to address these needs in the design procedure.

3.4.1 Reliability The design of SCPS for reliability is based on the concepts of redundancy and diversity. Redundancy means that a function is implemented by several separate

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Fig. 3.11 The SCPS as a barrier between hazards and the environment

means in order to provide alternatives of operation and ensures, that a system can continue operating in spite of failure of a subsystem or components. Diversity is stronger than redundancy by requiring that the alternatives are implemented using different technologies. In this way common cause failures, caused by the use of common technologies, are avoided. The analysis of means-end relations (see Chap. 17) which is part of the foundations for functional modelling, is therefore important for the design of reliable systems.

3.4.2 Safety The design for safety of SCPS includes development of means of avoiding critical situations. Within such a thematic focus on avoidance the functions of the SCPS as a whole can be represented as shown in Fig. 3.11 (see also Fig. 2.2). Means of avoidance can be divided into preventions which are a priori measures, and protections which are a measures used a posteriori when the prevention has failed. The following avoidances can be distinguished: • prevention of harmful releases of energy and materials and subsequent protection of humans and the environment • prevention of abnormal operational conditions and subsequent protection of the equipment • prevention of disturbances leading to economic loss and subsequent protection of the production • prevention of unauthorized access and subsequent protection of equipment and assets The design of safe SCPS systems is accordingly influenced by many factors and is in practice obtained by following international standards and guidelines. In addition a number of qualitative and quantitative methods have been developed for risk assessment including barrier analysis and analysis of failure causes and consequences using e.g. fault trees, and design of protective actions. These methods are used as part of hazards and operability studies. Outcomes of these studies are documented in fault trees (FT) and cause-effect diagrams (CE) (see Fig. 3.5) which are used as part of the overall design procedure.

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The industry has also developed overall design principles for guiding the development and operation of safe SPCS. The methods have been primarily been developed by the nuclear industry, aviation, and the chemical industry. This includes also industry specific principles to break down the barriers. An example is the definition of safety functions for nuclear power plants which are principles used to prevent releases of radioactive material to the environment common to reactors of the same type (PWR or BWR) (see Corcoran [20]).

3.4.3 Defense in Depth The industry applies two basic principles in the design of safe systems which are used in design of hardware and software and in organization of the operators work. The first principle, already mentioned, is to establish active or passive barriers against safety critical events i.e. events which can be harmful to environment, humans and the technical systems. In the second principle, called defense in depth in the nuclear industry, several barriers or levels of defense are connected into chains so that the development of a critical event into an accident require the breakage of several barriers (see IAEA [21]). Automated control systems and the human operator contribute to safety as active barriers as shown in Fig. 3.12. The levels of defense shown in Fig. 3.12 represents a functional decomposition of the barriers in Fig. 3.11. Barrier concepts play a central role in the nuclear industry and has also been adopted by other industries. The chemical industry used it in the LOPA methodology (layer of protection analysis) for analysis of plant vulnerability in safety critical events (see e.g. Willey [22]). A barrier is fundamentally a functional concept but unfortunately it has many meanings and may therefore hinder a systematic and consistent analysis of safety of a technical artefact. Figure 3.12 illustrates the problem by the circumstance that the barriers (the vertical lines) are implemented by using a diverse set of mechanisms. Even though the barrier concept provides structure to the formulation of the safety problem, it is too abstract to be useful for formal analyses which require distinctions between the different causal mechanisms (see e.g. Petersen [23]). It is also necessary to distinguish functions like barriers, which are means of avoiding something, from functions which are means of accomplishment. These relations between causality and functions are discussed in Chaps. 6, 7 and 15. Passive Safety Systems Recent development within nuclear power industry includes the application of so-called passive safety systems. In order to reduce plant vulnerability to loss of electrical power, the physical process is designed so that its response to safety critical situations is independent of active systems (e.g. pumps and control devices) which require electrical power for their operation. A case in point is the Fukushima disaster [8] where a tsunami disrupted the power

Fig. 3.12 Levels of defense, countermeasures and failure mechanisms in evolution of an accident

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supply to the primary reactor cooling pumps causing a meltdown of the reactor core. Modelling functions of safety systems (the barriers) is therefore a problem of particular interest for industry managing risky productions. The challenge is here to model systems whose purpose is to avoid situations which are undesirable either in themselves or because they have possible adverse consequences. It is therefore clear that means-end concepts and the complementary related concepts of countermeasures and hazards would be applicable for analysis and design of these kinds of system (see Chap. 17).

3.4.4 Existing Methods for Modelling Safety Functions Several approaches have been developed to model the functions of safety systems (see e.g. Haddon [24], Trost et al. [25], Sklet [26] and Hollnagel [27]). The methods include also quantitative methods for predicting system reliability and qualitative methods such as fault tree and hazards analysis, which are used for identification and analysis of causes and consequences of failures. However, in spite of this progress there are still discussions about basic issues such as the precise meaning of the concepts used in describing safety aspects of a system. Good examples demonstrating these problems are the concepts of safety functions, barriers, control, and defence in depth. On a qualitative common sense level there is general agreement about the meaning of these terms. However, when attempting to build models of process plant safety in order to analyze consistency or completeness of safety requirements, it becomes clear that the ambiguity of the common sense terms is a problem and that there is a need for formalized concepts with a precise meaning. Methods have also been developed for “Functional Safety” which comprises system design of safety-related electronics hardware and software (see e.g. IEC61508 [28], a generic standard, providing the framework and core requirements in specific standards for e.g. the process and nuclear sectors). These methods are specific to the application of electronics in control and instrumentation and do not deal with safety issues at the level of the whole SCPS considered here.

3.4.5 Summary Within the nuclear industry there was an early focus on the safety of the technical artefacts but it is also of importance for other industries. Operational experience has shown that accidents often involve a complex interaction between technical, human, and organizational factors. The industry and the regulatory bodies have therefore in more recent years been interested in methods for risk assessment which addresses

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the safety problem from an overall systems perspective. One of the challenges is to define concepts that are generally applicable across different technologies and domains, but at the same time sufficiently precise to separate the distinguishing features of safety functions of the technical, human and organizational subsystems. Functional modelling has accordingly an important role to play in management of safety of SCPS. It will be applicable both by the industry in design and operation of facilities, but also by the public regulators responsible for approval and monitoring of the safety of SCPS. The conceptual formalisation provided by functional modelling will make it possible to analyse safety requirements and also enable systematic methods for design of safe systems, which at present is based on good practices. Finally, formalised models of safety functions can be used by an operator support system to deliberate about safety critical operational situations and to suggest remedial protective actions. A particular challenge in SCPS design in safety critical industries is to balance production requirements with safety requirements. Safety systems are designed to intervene and take control of the system in critical situations. However, in near critical situations, systems provided for normal operation are still in operation and can thereby prevent further consequences. Control systems for normal operation are therefore also involved in prevention functions. Understanding the interactions between systems provided for serving both normal production goals and safety goals is therefore necessary in order to be able to detect malfunctions, predict possible consequences critical to safety, and proposing countermeasures for mitigation the situation.

3.5 Quantitative Models Used by Industry The engineering documents are, as mentioned, informal, qualitative and mainly used for documentation of design decisions. They do not support reasoning or logic inferences. Quantitative models are also used by the designers but mainly for detailed design and optimisation. The earliest were heuristic methods using rules to select a flowsheet by using insight and know how (i.e. qualitative) at the level of unit operations. Hybrid methods combine information from phenomena-based, heuristic and mathematical programming methods. Predictive quantitative models are used as much as possible to provide sufficient physical insights to reduce the network of possible flowsheet alternatives. (see e.g. Jørgensen et al. [3]). In detailed design of the process, the equipments, materials, energy and their interactions are described by concepts of natural science and mathematical quantitative models developed are used by mechanical, chemical, and electrical engineers for analysis and synthesis. The detailed design of the process is therefore relying on natural laws of physics and chemistry and the relevant tools of mathematics for formulation of models representing quantified cause-effect relations between physical and chemical phenomena.

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Current practice does accordingly not use formal models of SCPS functions in the conceptual design phase. The quantitative mechanistic models used in detailed design do not apply here because information about implementation details is lacking in the conceptual design phase and thereby limits application of quantitative calculations. Models of Human Machine Interaction Researchers in human machine systems have also applied control theoretical methods for modelling human behaviour. Using these models, it is possible to analyse the interaction between the technical artefact and the human operator. However, the models are not applicable for modelling cognitive (knowledge based) processes involved in reasoning about abnormal operational situations. For this purpose modelling methods including decision theoretical concepts and methods for knowledge representation and reasoning having their roots in cognitive science and AI has been used for simulation of cognitive behaviour of operators (see e.g. Cacciabue [29]). But methods of knowledge representation and reasoning are traditionally not adopted by process and control designers for modelling cognitive aspects such as design intentions and their relations to system functions. Such models are required for representation of the process and the controls as an object of cognition and deliberation for the operator. There is accordingly a mismatch regarding the use of models by process and control engineers and human factors, which makes it difficult to analyse and evaluate the performance of the system as a whole, especially in unfamiliar abnormal operating situations, which are of particular interest in the design of safety critical systems.

3.6 Summary of Problems The brief overview of documents and models supporting the design of process, control and operations presented above identify a lack of explicit information about system goals and functions which is essential for design of the SCPS as a whole. The following paragraphs summarizes the main issues relevant for the foundations of functional modelling with particular focus on the relations between the process and the control systems and their implication for operations. Functions and Naming Conventions Whereas process flow sheets (PFS) have a clear functional meaning (by using action terms) this is not the case for piping and instrumentation diagrams. They actually pose a problem because the equipment depicted in the diagrams have names which often have functional connotations, describing how they work in the context of the system they are part of. Take as examples the cooler and the level control in Fig. 3.7. The P&ID shown represent physical devices but their names reflect the functions they have in the flash gas process i.e. what they do i.e. cooling and regulation. Other examples are the pump and the motor which have connotations to the internal workings of the device. The pump is a device which can push a fluid by rotation of the impeller, and a motor is a device which can transform electrical energy into shaft rotation. However, the

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functions of the pump and the motor can only be understood by also considering their functional relations to the streams of materials and energy which are the objects of the pumping action. Stream designations are added in Fig. 3.7 to convey this point. In addition a physical entity can have several functions depending on the context, and naming it by a noun with verb like connotations is usually indicating its main or proper function and therefore tied to a specific context of use. If the pump is seen in another context of use it can be ascribed a function which is its “main” in that particular context. Naming by its main function in a particular context can therefore be confusing or even hinder the ascription of functions in other contexts which can be required in creative problem solving. The psychological phenomenon called functional fixation is about the difficulties humans can have in solving problems where a thing should be used in a way which is not its usual function (see e.g. Duncker [30] who demonstrated the value of functional concepts for human problem solving). The solution of operational problems like fault diagnosis and planning of remedial actions require the ability to separate structure from function. The naming of devices is accordingly inconsistent seen in view of a distinction between what the device is supposed to do (the function) and how it is implemented by hardware components. In conclusion, whereas the process flow sheets represent the what aspect of the whole system, the semantics of piping and instrumentation diagrams is confusing the distinction between the what and the how. The semantic issues mentioned here are aspects of the general function-structure problem which will be discussed in Chap. 7. Problems in Information Sharing The documents include results of design decisions but do only rarely contain explicit information about design intentions and operational objectives i.e. they document the what and the how of the design but not the why. By leaving this information about the whole physical process and the control implicit, it becomes difficult to transfer knowledge about intentions between process and control system designers and to the designer of the human machine interface. The design rationale is also of importance for achieving an increased level of intelligence of the control systems and for improving the decision support provided by the operation support system to the operator. This includes formalization of system goals and objectives and their relations to the functions and the equipment. One of the purposes of functional modelling is to make this information explicit and accessible through the operator support system. Implicit Design Motives Another aspect of lacking documentation of design intentions is the absence of explanations for choices between alternatives which require the distinction between the because-of and the in-order-to motives of an action. Information about design motives can be important in reconfiguration and retrofitting and will be explored in more detail in Chap. 10. When there is a need of reconfiguring the control and instrumentation system it is important to know why some alternative solutions have been discarded due to

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Fig. 3.13 A safe and an unsafe burner control system

safety concerns and should be avoided. The design solution of a control system is motivated by a primary control objective (the in-order to motive) but can also be motivated by operating experience (the because-of motive) e.g. when the selection of a means of control serving the primary objective, also is a means of avoiding undesirable events experienced in the past. A design solution can accordingly be preferred because its behaviour in a possible failure situation is safer compared with the alternative. Example: The air-gas burner case shown in Fig. 3.13 illustrates the problems in identifying implicit design intentions from the implementation documented in a P&ID. The control system (comprising FC, FFC, the control valve and the sensors) regulates the flows of air and gas to the burner. The purpose of the control system is to ensure proper combustion of the gas and is obtained by maintaining a constant ratio R between the gas flow Fg and the flow of air Fa . This control requirement can be implemented in two ways A and B by using a combination of a ratio (FFC) and a flow controller as depicted below. One way (B) is to use the measured gas flow rate to determine the setpoint for the air flow. The other way (A) is to use the measured air flow rate to determine the set-point for gas flow. An important factor in choosing between the two solutions is the behaviour of the control valve which is designed to open in case of lack of instrument air powering the valve movements (not indicated in Fig. 3.13). The designers prefer solution A where the measured air flow rate determines the setpoint for the gas flow. This is considered more safe than the other design option B where the measured gas flow rate determine the set-point for air flow. B is unsafe because previous operational experience has shown that the control valve may fail by closing when loosing instrument air (instead of opening as intended). The consequence of this failure will be a potentially risky situation with no air supply

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to the burner. The consequence of the same failure in solution A will be no gas supply which obviously is critical to heat production but not to safety. A functional model of the burner system used in conceptual design of the burner system would abstract from the design of the control systems and include two objectives related to production (to ensure proper combustion) and safety (to prevent unintended releases of gas. The subsequent choice between alternative solutions to the control design problem will then be influenced by safety considerations. The function of the control system in solution A is to ensure a constant ratio between air and gas flows, which can be inferred from the P&ID. However, the other design intention, to prevent unintended release of gas in case of a control valve failure, cannot be inferred without knowing the designers motive to reject solution B. A functional model of the burner system used by the designer should include both functions of the control system, as regulator of the gas supply and as a barrier against unintended release of gas. This information would be important if the configuration is changed from A to B by another control system designer. However, the information would not be relevant for an operator supervising configuration A. The example illustrates accordingly the dependence of functional modelling on the context of use. The lack of explicit representation of the intentions of the control designer is also a problem when retrofitting old control and instrumentation systems with new technological solutions. Due to the fast development of information technology the lifetimes of control and instrumentation systems are considerably shorter than for the process systems and is therefore often changed several times during the lifetime of the whole SCPS. New information technology will often facilitate the implementation of more intelligent solutions to a control and instrumentation problem, but if the only documentation available is an electronic circuit diagram, programming code or informal functional specifications, the intentions of the control systems designer behind the old solution are hidden. It can therefore be very difficult to infer what the designer had in mind. Safety critical situations can occur if a design intention is neglected or implicit. For example if a component has more than one function and one of them is neglected during retrofitting. Sensors also may be used in operations for a purpose not originally intended by the designers. Control and Protection Systems Information about the purpose, the why, of the control systems is not represented in P&ID or SCD’s. Only the topology of control loops is shown i.e. how it is implemented by instrumentation and control devices. The behaviour of the control loops as devices for signal transmission and transformation are specified in the SCD’s as discussed above, but the functions served by the control systems in the context of the process, i.e. their purpose, is implicit. Even though control engineers may claim that they can read information about purposes from such diagrams, it is in their minds but not in the document. It is dependent on knowledge about the control designers intentions and their relations to the overall goals of the process. As a consequence, piping and instrumentation diagrams cannot be used to communicate knowledge about the purpose of control

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systems, information which can be crucial for an operator in dealing with an abnormal situation. In summary, the system control diagrams (SCD) represent what happens in the control system when it responds to events in the process i.e. the logic, but not the reasons why the response is produced. This would require references to the control requirements of the process i.e. seeing the control system in the context of the operational conditions of the process. The SCD also lacks information about how the signals are transmitted and generated by the underlying technology which today mainly is based on digital electronics and computers and communication networks. However, old analogue technologies (electronics, pneumatics and hydraulics) are still used where appropriate. The CE and FT diagrams depict the protection logic but lack information about the reasons for the response i.e. the underlying semantics of the hazards (indicated by the alarms) and the countermeasures used for protection. Operator Support The lack of explicit representation of control system purposes in display mimics is a particular problem for operators trying to understand the behavior of an automatic control system. Explicit information about control system goals and functions is necessary for the operator both in situations that require manual intervention and in supervising the operation of the automated control functions. If only the means of control are known (i.e. the control algorithm and the physical structure of the automated controls) it may be difficult for the operator to find alternative solutions to a control problem. The plant condition or the causal structure may have changed during a disturbance so that the control algorithm or strategy do no apply any longer in the actual situation and the operator must find other courses of action. Information about the means and ends of control and their status should therefore ideally be available to the operator at the interface. The operation support system should inform and advise the operator in situations not predicted by the designer. This need requires knowledge about design rationale and assumptions i.e. not only how the system works but also what it is supposed to do and why it is provided by the designer. Diagnostic Problems Causal knowledge is important for making decisions in diagnosis of operational disturbances. However, piping and instrumentation diagrams (P&ID) do not represent causal interactions between subsystems, equipment and their parts which are responsible for their functions. The only source of causal knowledge is related to the input-output behaviours of the process components and their connections. However, the causality is also dependent on the interactions between material and energy streams and between components of the streams. Furthermore, the streams interact via equipment through their internal parts, which are not represented in a P&ID. The diagram is accordingly not rich enough to represent how equipment work in the context of other equipment and streams of materials and energy i.e. their functions. A P&ID is not sufficient to capture knowledge which is important on the overall plant level.

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Another problem is that the control system change the causal structure of the process system in ways which only can be inferred from P&ID and the SCD’s by experts knowing the functions and objectives of control systems.

References 1. E. W. Leaver and J. J. Brown. “A Functional Morphology of Mechanisms”. In: Automation (1955), pp. 37–41. 2. H. A. Simon. The Sciences of the Artificial. Cambridge: The MIT Press, 1981. 3. S. B. Jørgensen, M. Lind and N. Jensen. “Functional Modelling View on Product and Process Engineering in Design and Operations”. In: Industrial and Engineering Chemistry Research 50 (2019), pp. 11129–11146. 4. N. P. Suh. “Complexity in Engineering”. In: CIRP Annals 54.2 (2005), pp. 46–63. 5. W. ElMaraghy, H. ElMaraghy, T. Tomiyama and L. Monostori. “Complexity in engineering and manufacturing”. In: CIRP Annals 61.2 (2012), pp. 793–814. 6. J. Rasmussen and M. Lind. “Coping with complexity”. In: Proc. 1st European Annual Conference on Human Decision Making and Manual Control (EAM). Ed. by H. G. Stassen and W. L. T. Thijs. Delft, Holland, 1981, pp. 69–91. 7. J. Rasmussen and M. Lind. “A Model of Human Decision Making in Complex Systems and Its use for Design of System Control Strategies”. In: Proc. American Control Conference (ACC82). Chicago, USA, 1982. 8. IAEA. The Fukushima Daiichi Accident: Technical Volume 1, Description and Context of The Accident. Tech. rep. International Atomic Energy Agency, 2015. 9. T. Sheridan. Telerobotics, Automation and Human Supervisory Control. Cambridge, Massachusetts: The MIT Press, 1992. 10. R. Parasuraman, T. Sheridan and C. D. Wickens. “A Model for Types and Levels of Human Interaction with Automation”. In: IEEE Transactions on Systems, Man, and Cybernetics -Part A: Systems and Humans (2000). 11. D. Ihde. Technology and the Lifeworld. Bloomington, USA: Indiana University Press, 1990. 12. M. Lind, J. Wu and X. Zhang. Multilevel Flow Modelling Guideline, For Oil and Gas Production Systems. Tech. rep. Department of Electrical Engineering, Technical University of Denmark, 2019. 13. J. L. Peterson. Petri Net Theory and the Modeling of Systems. Prentice Hall, 1981. 14. IEC. IEC 61131-3 Programmable Controllers. Tech. rep. International Electrotechnical Commission, 2013. 15. L. Bainbridge. “Ironies of Automation”. In: Automatica 19.6 (1983), pp. 775–779. 16. G. E. Cummings. “Operator/Instrumentation Interactions During the Three Mile Island Incident”. In: IEEE Transactions on Nuclear Science 27.1 (1990). 17. J. Rasmussen, A. M. Pejtersen and L. P. Goodstein. Cognitive Systems Engineering. New York: John Wiley, 1994. 18. K. J. Vicente and J. Rasmussen. “Ecological Interface Design: Theoretical Foundations”. In: IEEE Transactions in Systems Man, and Cybernetics 22(4) (1992), pp. 589–606. 19. E. Hollnagel and D. D. Woods. Joint Cognitive Systems: Foundations of Cognitive System Engineering. CRC Press, 2005. 20. W. R. Corcoran, D. J. Finnicum, F. R. Hubbard, C. R. Musick and P. F. Walzer. “Nuclear Power-Plant Safety Functions”. In: Nuclear Safety 22.2 (1981), pp. 179–191. 21. IAEA. Defense-in-depth in Nuclear Safety. Tech. rep. INSAG10. Vienna: International Atomic Energy Agency, 1996. 22. R. J. Willey. “Layer of Protection Analysis”. In: Procedia Engineering 84 (2014), pp. 12–22.

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23. J. Petersen. “Countermeasures and Barriers”. In: Proc. Annual Conference of the European Association of Cognitive Ergonomics(EACE2005). Chania, Greece, 2005. 24. W. Haddon. “Energy Damage and the Ten Countermeasure Strategies”. In: Human Factors 15.4 (1973), pp. 355–366. 25. W. A. Trost and R. J. Nertney. Barrier Analysis. SCIE-DOE-01-TRAC-29-95. Idaho Falls, Idaho USA: Technical Research and Analysis Center, Scientech Inc, 1995. 26. S. Sklet. “Safety Barriers: Definition, classification, and performance”. In: Journal of Loss Prevention 19 (2006), pp. 494–506. 27. E. Hollnagel. Barrier and Accident Prevention. Ashgate, 2004. 28. IEC. Functional safety of electrical/electronic/programmable electronic safety related systems (IEC 61508). International Electrotechnical Commission, 1998. 29. P. Cacciabue. Modelling and Simulation of Human Behaviour in System Control. London, UK: Springer-Verlag, 1998. 30. K. Duncker. On Problem Solving. Massachussets: Psychological Monographs,58,8, 1945.

Chapter 4

Existing Frameworks for Artefact Modelling

This chapter presents frameworks for artefact modelling which have been proposed by engineering disciplines for design and by human factors for operation. The frameworks are motivated by the need for improving the quality of design and operation and to support designers and operators in making informed decisions. An overview of methods for functional modelling developed by academia is also presented. The methods are currently not widely adopted for use in SCPS.

4.1 A Framework for Design Industrial processes and their subsystems are designed to perform specific functions and there is often, but not always, a direct relation between objectives to be achieved by the process and the physical subsystems and equipment to be used i.e. between the means and ends of production. This relation between goals or objectives, functions, and the physical components is decomposed in the design procedure into several steps in order to manage the complexity of the design problem. As mentioned in Chap. 3, an overall distinction is made between two phases of the design process: conceptual and detailed design, often called problem formulation and synthesis. The two phases bridge the gap between having a need and to find a solution, and comprise decisions between alternative solutions on several levels of abstraction between the means and the ends. • Conceptual design includes: – identification of the site of construction and available raw materials – identification of goals of production, safety, efficiency, and sustainability – specification of the related technical objectives to be achieved by the process designed

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– decomposition of the technical objectives into functions to be implemented by the physical equipment or human operators – allocation of functions to physical equipment or tasks to human operators • Detail design includes: – design of physical equipment and training of operators to acquire the skills and competencies required for the allocated task Design decisions on the two levels can be made either top down starting with the goals or bottom up by starting with the physical equipment and the skills of operators. Often decisions on the two levels are combined in an iterative procedure. The better the process can be modelled the fewer iterations are needed. With a good model iterations can almost be eliminated. The distinction between conceptual and detailed design reflects an overall meansend framework for practical reasoning separating the concerns of the designer when choosing between alternative solutions: • WHY should the artefact A do it (the end)? The answer to this question describes the reasons why the functions are required in terms of an objective of production, safety, efficiency, and sustainability • WHAT should A do (the functionality)? The answer to this question is about the functions, in abstraction from their reasons (why) and the implementation (how) • HOW should A do it (the means)? The answer to this question describes how the functions are implemented by the behaviour of physical components or human operators The distinction between the three concerns and the associated levels of abstraction was proposed by Rosenman et al. [1] as a model of the design object, the artefact which could be any of the three SCPS subsystems; the process system, the control and instrumentation systems, and the operation support system. By ascribing purposes and functions to the artefact, the model represents it as an embodyment of the designers intentions. The artefact model is therefore fundamentally different from models used in detail design which are based on laws of physics and chemistry. The latter models represent a physical object without reference to human intentions including the reasons for its existence or use. The general definition of the concept of function as a description of how something works in a particular context (see Chap. 2) is consistent with the artefact model shown in Fig. 4.1. The latter is however more concrete with respect to the meaning of context being the objectives of the process, the control or the operation system, and the meaning of the terms “works” being the behaviour and function of the process and control equipment, and the operator. Rosenman et al. (op. cit.) define purpose, function, behavior, and structure as follows: purpose means the intention, aim or goal related to human activity function is the result of behavior and is interpreted according to human values within a particular sociocultural environment as enabling certain purposes to be fulfilled

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Fig. 4.1 The design object, concepts, environments, and reasoning processes (from Rosenman et al. [1])

behavior is the processes carried out by a particular structure in given circumstances structure is the state of the object, in a given physical environment

These definitions assign a double role to functions as being enablers of purposes and the result of behaviour. However, this is in contradiction with Fig. 4.1 where functions are seen as belonging to the physical environment (by the effect) whereas the definitions above seem to indicate that functions also belong to the human socio cultural environment. The resolution of this contradiction requires two investigations; one dealing with the distinction between behaviour and function to be considered in Part III, and the other a clarification of the concepts of goals and objectives and their relations to functions which is considered in Part V. The investigations lead to the revised framework for functional modelling proposed in Chap. 19. The use of the revised framework in design will not be discussed in this book.

4.1.1 Two Types of Decomposition Rosenman et al. (op. cit.) also explain how the three levels of means-end abstraction, the purpose, the function, and the behavior of the artefact each can be decomposed into parts and thereby forming whole-part hierarchies. The decompositions which are shown in Fig. 4.2 play an important role in the design procedure. The objects of

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Fig. 4.2 Decompositions of aspects of the artefact model (from Rosenman et al. [1])

the decomposition are the three aspects of the artefact model, the purpose, functions, and behavior. The decomposition of model aspects should be distinguished from a decomposition of the object of design into its parts such as subsystems or components. This type of decomposition is shown in Fig. 4.3 where an artefact is decomposed into two parts each represented by their function, behavior, and structure.

4.1.2 Two Types of Complexity The two types of decomposition (into means-end and part-whole aspects) can be used to distinguish between two types of complexity of an artefact.

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Fig. 4.3 Decomposition of an artefact into its parts (from [1])

The first type of complexity can be expressed by the number of elements and relations between purposes, functions, and behaviour shown in Fig. 4.2. This aspect of complexity can accordingly be measured by the number of many-to-many relations connecting the decomposed levels of abstraction. In this view complexity is a property of the system model and can be reduced or increased by aggregation or decomposition. System complexity is therefore relative to the artefact features represented and dependent on what the model is used for in design and operation. The second type of complexity can be measured by the number of parts necessary in order to provide a representation of the system. Whereas the complexity measure mentioned above is depending on many-to-many relations between means and ends, it is in this case related to the number of subsystems of the artefact and their organization into levels in a whole-part hierarchy.

4.1.3 Reasoning with the Artefact Model An artefact model can be used reasoning in conceptual design. Two types of reasoning are proposed by Rosenman et al. [1] as indicated in Fig. 4.4. One type, the teleological, is used to reason about the relations between purposes and functions. The other type, the causal, is used to reason about the relations between behaviour and structure. The revised framework for functional modelling presented in Chap. 19, which is based on the relations between functions and means-end concepts developed in the

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Fig. 4.4 The artefact model and reasoning processes in design (from Rosenman et al. [1])

book, implies a different view on teleological and causal reasoning processes and their relations to the artefact model.

4.1.4 Problems with Rosenman’s Artefact Model Rosenman et al. suggest that purposes are only relevant for the whole artefact and not to its parts. This raises some conceptual problems as there is a need for more distinctions to be able to represent the relations between the features of the whole artefact and its parts (the arrows). For a SCPS it is necessary to be able to represent the functions of the process, the automated control systems, and the operator each have in achieving the purpose of the whole. This includes a distinction between process objectives and control objectives i.e. the quality obtained concerning the suppression of disturbances and reduction of the effect of uncertainties. Another problem is a confusion created by the way the term purpose is used by Rosenman et al., which ignores the distinction between two types of motive for a purposeful action: the in-order-to meaning i.e. the end to be achieved, and the because-of motive comprising the beliefs and experiences of the actor. Rosenman et al. refer to aims and goals when describing purposes i.e. to the in-order-to motive but the because-of-motive is also required for an artefact to have a purpose for a designer or an operator (more about this in Chap. 10). Finally, the application of the artefact model is faced with some difficulties when applied to modelling complex artefacts. The difficulties emerge as a problem of

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separating the levels of abstraction. One problem is that sometimes objectives and functions seem to be the same. Another problem is to distinguish between functions and behaviour when considering artefacts which involve design decisions on several whole part levels. What is seen as the behaviour of an artefact from one perspective can be seen as functions of its parts from another perspective. These apparent confusions can be resolved by a closer analysis of the concept of function, and means-end concepts as presented in Parts III, IV, and V.

4.2 A Framework for Operation The artefact model shown in Fig. 4.1 represents the system as an embodiment of the designers intentions, but the artefact is also an object for the human operators observations and interventions. Models representing the artefact from an operational perspective as an object of use are accordingly needed. Furthermore, a model is needed to represent the functions of the human operator as explained in Chap. 1. Frameworks for operation have been developed for the purpose of analysis and design of human interaction with technical artefacts. The decision ladder, the abstraction hierarchy and the SRK (skill-rule-knowledge) models proposed by Rasmussen [2] are such models which have attained considerable attention and support in the human factors community. They were proposed as part of the CSE framework for human centered design mentioned in Fig. 3.5. The models represent the human operator as a systems component. They are functional models according to the general definition presented in Chap. 1 because they represents the working of the operators mind in the context of reasoning and making decisions in operation of a technical artefact i.e. his/her cognitive functions. This means, using terms of Rosenman et al.’s artefact model, that these functional models do not represent his/her psychological or physiological behaviour and anatomy (structure).

4.2.1 A Model of Decision Making in Supervisory Control A simplified and slightly revised version of Rasmussen’s decision ladder is shown in Fig. 4.5. It is revised here by including the cyclic nature of the decision procedure in operation. The model depicts the sequence of tasks to be performed by an operator making rational informed decisions when supervising and coping with operational upsets. It shows the cognitive functions of the human supervisor but not how decisions are made on the basis of his/her mental model (the abstraction hierarchy discussed below) and observations. The decision ladder/cycle belongs to a family of similar models developed for design of human machine interaction (Schrenk [3]) and for management decision making (Kepner et al. [4]). Chapter 13 presents details of Rasmussen’s decision ladder and another closely related model developed by AI researchers for

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Fig. 4.5 A decision cycle depicting the functions and overall objectives of operation (a simplified and revised version of Rasmussen’s decision ladder [2])

implementation of artificial agents, the so-called BDI architecture (belief-desireintention). These models only represent the functions and not the objectives i.e. of supervisory control (see Chap. 13 for a discussion of this point). The decision cycle shown in Fig. 4.5 separates an operator’s concerns in supervisory control into the why, what and how of operations as follows: • WHY intervene? (situation analysis) – Is there a need for intervention in the process? ∗ Is there a deviation from normal operation? ∗ What are the causes? .∗ What are the consequences? . .

– Is there a deviation from objectives of production? ∗ are objectives of production under threat?

.

• WHAT to do? (action planning and execution) – decide to intervene – what should the objectives and means of intervention be?

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• HOW to intervene in the process and control systems? – what is the plan of intervention? – how to execute the plan? • EXECUTE the plan – intervene in the process – monitor the effect of the intervention Lessons from industrial experience have shown that human operators often shortcut the rational sequence shown in Fig. 4.5 by for example, jumping to preparation of plans of action immediately after having found the cause of the disturbance (shortcuts is a significant feature of Rasmussen’s model shown in Chap. 13). They make such jumps based on stereotyped rules connecting causes of disturbances with known countermeasures in order to reduce the mental load involved in following a more rational procedure. When making a shortcut the human operator is ignoring that the situation may call for a change in the objective of operation and consideration of alternative countermeasures. In order to ensure that the human operator is aware of the situation and can respond appropriately, the human machine interface therefore should facilitate rational decision making i.e. use the causes identified to evaluate the consequences, decide for the action to be taken and so on. This type of knowledge based behaviour is not guided by stereotyped rules but requires design knowledge about the process, the equipment used, its functions, and the objectives, including knowledge of the means and ends of operation. The human machine interface should accordingly be designed so that the operator can effectively perform situation analysis, action planning, and execution of tasks, which otherwise would be cognitively demanding without the information and decisions support provided by the operation support system.

4.2.2 The Abstraction Hierarchy Rasmussen [2] proposed the use of a so-called abstraction hierarchy (AH) as a basis for design of the information content of the human machine interface in control rooms (Fig. 4.6). The abstraction hierarchy can be seen as a representation of the operators mental model of the artefact used when making knowledge based decisions and interventions in control of an abnormal situation. As mentioned in Chap. 1 this also means that the abstraction hierarchy is a representation of the work environment as perceived by the operator as an object of his/her observations, evaluations, and interventions. The abstraction hierarchy also includes the idea of whole-part decomposition (and is therefore sometimes called an abstraction decomposition hierarchy). It is,

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Fig. 4.6 Rasmussen’s [2] abstraction hierarchy with teleological and causal reasoning paths

for the reasons given above, related to Rosenman et al.’s artefact model shown in Fig. 4.1. However, there are some important differences which need to be pointed out: • Rosenman’s model relates teleological reasoning to purpose and functions and causal reasoning to behaviour and structure. This separation is not found in the abstraction hierarchy which suggests causal reasoning and teleological reasoning as overlapping the levels of abstraction. A clarification of this point requires a closer analysis of the relations between causes, intentions, and function, and the relation between function and behavior (see Chaps. 6 and 7). • The abstraction hierarchy is a representation of the operators mental model of the artefact under operation i.e. as an object for supervision, and not as an object of design. This means that the perspectives and organizing principles of the levels of means-end abstraction in the two models may be different. Furthermore, the reasoning procedures are different in design and operation.

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Reasoning in operation is about relations between past, current, and possible future states of the artefact. The operator has two related tasks; (1) to supervise the artefact to ensure that it performs according to designers intentions and (2) to intervene if there is a deviation. This reasoning is based on a concept of causality which is in the meaning of effective causes (using Aristotle’s distinction between material, effective, formal, and final causes (see Sachs [5])). In Rosenman’s case the causes and reasons are related to material causes and the purpose of the artefact, not to the purpose of the operators intervention. • The boundary between design and operation is fluid, in particular in abnormal operational situations where an operator needs to create new opportunities for action i.e. reason as a designer. An example is the Fukushima accident (see IAEA [6]) where cooling of a nuclear reactor was lost due to loss of electrical power. Alternative means of cooling should here be deviced by the operators during the accident. • The design artefact model is relevant for situation analysis in operation when it represents a norm for comparison of the actual state of operation with the intentions i.e. a detection of a need for intervention as a deviation from operational objectives. However, the levels of abstraction and their details must be compatible with the operators task and responsibilities. All functions which are provided by the designer may not be relevant for operation of the artefact. The common use of means-end and function concepts and reasoning in both design and operation of technical artefacts motivate the search for unified modelling concepts. However, as mentioned above, the concepts should be used with caution. Even though they are related there are important differences because the objectives and the means of the artefact and its subsystems and the means and ends of operation are not the same. One of the aims of functional modelling is to distinguish and combine the types of ends, means and functions required for modelling the relations between a technical artefacts and the operation.

4.2.3 Problems with the Abstraction Hierarchy The abstraction hierarchy also suffers from some problems to be resolved: • The meaning of the levels of abstraction is unclear. For example the level of generalized function seems to refer to functions of aggregates of components or of subsystems, which are obviously relevant for the designers use of design patterns (standard functions), but not necessarily for the operator unless there is a means of intervention in the subsystem level. Usually, means of intervention are related to the plant components and their locations, unless the operation and the control system allows the operator to intervene in this level through control functions, but then the location of the intervention is through the human machine interface and not on the components themselves.

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Comparing with the engineering documents described in Chap. 3 it is realized that the level of generalized function is similar to the representation provided by a PFS diagram, and that the level of physical function is similar to the representation provided by P&ID’s. However, the levels of functional purpose or abstract function are not included in the engineering documents. • Even though the conceptualization of the relation between means and ends will match engineering principles of design, it may obscure causal interactions which does not directly relate to the overall production purpose, but relate to the realisation of subordinate design intentions. Principles for chemical process design are often based on a hierarchical approach to explore design solutions (Smith [7], page 8). This approach can cause causal interactions in the system that are not directly related to achieving the overall design objectives but which are relevant for the diagnosis and response to abnormal operating conditions. The means-end decomposition needs to take this into consideration if a functional model should be used for decision support in operations, and there may be a potential conflict in the choice of levels of abstraction between the traditional engineering decomposition (like the one shown in Fig. 3.6) and the means-end decomposition required for decision support by functional reasoning. • The abstraction hierarchy assumes that the object represented is a technical artefact. It does not therefore apply as a representation if the work domain is a natural object. In such a case there are no intentions and consequently the levels of means-end abstraction does not make sense. It may be applicable if the work domain is a biological system, but here the levels of abstraction should represent levels of organisation developed by evolutionary processes and not by design. • In addition, the abstraction hierarchy is not sufficient for planning and execution of control actions unless it contains information about the functions of the means of observation (the sensors), and means of intervention or control (actuators) available for the operator, including their relations to the process functions which are the object of intervention. This means that the artefact model for operation should also include the intentions of the instrumentation and the control system designer. However, it is not obvious how this should be done systematically without revising the framework.

4.3 Overview of Methods for Functional Modelling Functional modelling has been a subject of research by academics in several decades and the scientific literature on the subject is huge. Several reviews have been published covering significant parts of the research until 2008. An early historical review of research done before 1994 was presented by Chandrasekharan [8]. A series of reviews has later been published covering the development until 2008 (see e.g. Hirtz et al. [9], Chandrasekharan [10], Far et al. [11], VanEck et al. [12], Erden et al. [13]).

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A brief overview is given in the following which cover selected research issues which are of particular relevance for functional modelling of SCPS and for the work presented in the book. Other contributions to the field of functional modelling which are not directly relevant to the foundations are excluded. The research efforts considered are for convenience divided into contributions from three different communities. They are guided by different research aims but have to some extent exchanged ideas about functional modelling: • artificial intelligence on knowledge representation and reasoning • mechanical engineering design and manufacturing • process control, safety and autonomous systems (robotics) Artificial intelligence research focuses on the use of computers for knowledge representation and reasoning (including machine learning) and deliver in this way general theories, methods, and tools which can be applied in mechanical engineering and manufacturing, process control and safety, and autonomous systems for formalizing and reasoning about knowledge in those domains. Different relations exist between mechanical engineering and manufacturing, and process control and autonomous systems. The first field of engineering is focused on methods and tools for product design whereas the other two fields are focussed on methods and tools for intelligent control and operations of dynamic processes. Methods for product design have obvious relevance for design of components used in construction of process control systems like SCPS and of autonomous robots but the main focus is on design of the dynamic interactions between components and subsystems rather than on the components themselves seen as products (the design of the products to be produced by a chemical process is ignored here). These differences in aim of the engineering communities may explain why the exchange of knowledge of functional modelling between them has been somewhat limited. The context dependence of functions and the difference in aims and purpose of the systems considered may be the reasons why it has been difficult to find a common basis for functional modelling even within the individual engineering disciplines (see e.g. Kroes [14], Vermaas [15–17] and Vermaas et al. [18]). The difference in aims between the communities may also explain the selective nature of the reviews mentioned above which do not provide sufficient coverage of topics which are relevant for SCPS such as modelling the functions of control and protection systems and their interactions with the functions of the physical process.

4.3.1 Knowledge Representation and Reasoning Functional concepts have been a topic of research in artificial intelligence within a larger effort on using computers for simulation of human problem solving and qualitative reasoning. Simon [19], one of the fathers of AI, mentions the central importance of functional concepts for intelligent systems. Freeman and Newell [20] observe that reasoning by function is ubiquitous and mention that the means-

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end reasoning of the general problem solver (GPS) offers a model of functional reasoning. Means-end relations play a central role in the foundations presented in this book. Representation and reasoning about functional knowledge was investigated by deKleer [21] and included the use of causal and teleological reasoning in analysis of electronic circuits. Franke [22] investigated how to derive and use descriptions of purpose, a subject which is investigated in this book in the context of SCPS. Keuneke [23] proposes a typology of functions by actions terms including: to make, to maintain, to prevent, to control and to allow. These action terms are rather abstract and require interpretation when used. The question about function types is a key research issue which has been addressed by the engineering communities as described later. It is addressed in this book by using theories of action and semiotics. Chandrasekharan et al. [24] presented a definition of function as effect (in contrast to purpose) and indicated hereby the relation between functions and causality. They also distinguished two ways of ascribing functions, to the device or to its environment [25]. Both theoretical contributions are relevant for the foundation of functional modelling and are discussed in Chaps. 6 and 7. Chandrasekharan and his associates contributed also with several important research results relevant for applications of functional modelling. Sembugamoorthy et al. [26] considered the use of functional knowledge in diagnostic problem solving, Chandrasekharan et al. [27] and Iwasaki et al. [28] considered the use of functional representations in design. Tanner et al. [29] investigated the more general relation between task structure and domain functional models. Functional Ontologies The development of ontologies is a central part of research in knowledge representation. It takes advantage of the ability of natural language to capture functional knowledge and create taxonomic structures sometimes using a consensus approach to deal with ambiguity, vagueness, inconsistency and different use of language among various engineers. A consensus approach to construction of ontologies aims at creating agreements among experts about their domain concepts but suffers from the lack of a logical basis for construction of functional primitives as attempted in this book. It can therefore be difficult to decide whether a given function should be considered a primitive or a composite concept. Furthermore, the concepts do not necessarily support reasoning about functions and cannot directly be shared with non experts. This formalization problem connected with acquisition of functional knowledge from natural language and by agreements can be circumvented by using formalized concepts of action to construct domain ontologies as done here, and by using these concepts to create an artificial language mimicking the ability of natural language to deal with shifts in context and perspective. Such artificial languages do not match exactly with expert concepts and should be learned as other artificial languages. On the other hand, the logical basis gives a much stronger support for reasoning. Furthermore, non experts learning the artificial language can get access to the functional knowledge of experts. Functional ontologies for particular domains should be developed by using generic (i.e. domain independent) elementary functions derived

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from theories of action and semiotics as presented in this book. Such a formalization is required for support of functional reasoning which has its origins in the meansend logic of practical action. Contribution to the development of functional ontologies have been presented by Kitamura and coworkers [30]. The ontologies presented show how functions can be distinguished according to context of use. Specific studies are conducted on categorization of process faults (see Kitamura et al. [31]) and design knowledge (Kitamura et al. [32] and Koji et al. [33]). Contribution also included deployment and sharing of functional knowledge (Kitamura et al. [34, 35]). Computer Models of Language Another area of AI research relevant for functional modelling and its foundations is computer modelling of natural language. The work done by Schank and his co-workers is of particular interest (see e.g. Schank et al. [36–38]). The overall aim of this research is to develop and use language models for simulation of cognitive processes involved in text understanding, and the role of memory for cognitive processes like reminding and learning.1 The language models used by Schank and his coworkers are based on representations of knowledge structures including the deep semantics of verb phrases. Verb semantics is of particular relevance for functional modelling since functions in general are expressed by verb phrases. This is the main reason why the semantic of verbs and semiotics in general play a key roles in the foundations. Software Agents AI research on modelling and simulation of cooperative behaviour of goal oriented agents is also relevant for modelling functions of intelligent control system which are reasoning about goals and plans. The beliefdesire-intention (BDI) software architecture presented by Wooldridge [39] is of particular interest here and will be discussed in Chap. 13. Agent communication in multiagent systems represented by speech acts [40], can be seen as functional representations of elementary acts of communication because they describe the intended effect of the act rather than the underlying causal mechanisms of information exchange. Functions of communication will not be discussed in the book.

4.3.2 Mechanical Engineering and Manufacturing Concepts of function have an important role in mechanical engineering in design methods which systematically separate and combine knowledge of requirements (functions) with implementation aspects (structure and behaviour). The relevance of functional concepts for mechanical engineering was discussed early before the advent of computers. An article by Leaver et al. [41] mentions for example the 1 A practical outcome of this research is software for case based reasoning which has been considered for engineering applications in design and diagnosis.

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use of functional morphologies in the design of mechanisms. Pahl et al. [42] also mentions functional knowledge as essential for engineering design. Umeda et al. [43] and Tomiyama [44] considers the functional reasoning in the context of design and the practical problems to be addressed in order to make it applicable in the industry. Szykman et al. [45] presents efforts at NIST, USA to develop computational framework for the creation of design repositories for the manufacturing industry including representation of functional knowledge. Hirtz et al. [9] presented an overview of the field reconciling and evolving effort up to 2002 and presented an extensive list of function and flow types which will be discussed shortly below with respect to its relevance for representation of functional relations between control and process functions and safety functions in SCPS. Vermaas and coworkers have contributed to the theoretical foundations and development of functional modelling in engineering design in the context of the research traditions of mechanical engineering and manufacturing described above [46–51]. Early examples of methods for representation of functional knowledge include SADT and IDEF0 (“Icam DEFinition for Function Modeling”, where ICAM is an acronym for “Integrated Computer Aided Manufacturing”). SADT was developed by Ross [52] and has later been developed into IDEF0. SADT was originally developed for design of large scale systems (see also Marca et al. [53]) and was used in the development of CAD systems. IDEF0 is a methodology for modelling functions of manufacturing systems. Other methods have been developed for representation and reasoning about functions within mechanical engineering (see e.g. FunctionBehavior-State Modeller (FBS) developed by Umeda and Tomiyama [43]) but will not be discussed here where the focus is on the conceptual level. Functions and Flows There is a general agreement in the engineering communities that the concept of flow play an important role in the description of functions of technical artefacts (see also the discussion by Borgo et al. [54]). The idea is that a function (being described by a verb phrase) denotes a change or transformation of a property of some object, which can be an individual identifiable thing or it can be a stream of things (another word for flow). The overall relation between functions and flows can be explained tentatively using the function block diagram (FBD) formalisms proposed by originally IDEF0 (and SADT) shown in Fig. 4.7. A function in FBD represents a transformation of some inputs into some outputs influenced by control information and some resources. The inputs, outputs, and the control would in a technical artefact be flows of different types. Hirtz et al. [9] presented an overview of function and flow types shown in Fig. 4.8. The typologies have been obtained by agreement among experts by compiling and integrating inputs from several sources. A question relevant for modelling is, what kind of functions and flows can be combined? (in FDB diagrams the answer would be rules for connection of function blocks). The question has been addressed by Hirtz et al. (op. cit) but will not be considered here since the answer depends on the typologies of functions and flows used. The granularities of

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Fig. 4.7 A function block

Fig. 4.8 Function and flow types according to Hirtz et al. [9]

the typologies is questionable in particular because their relevance depend on the context of use. Thus some but not all types of functions and flows in Fig. 4.8 are relevant, and some are missing for modeling functions of control and operation. Several types in Fig. 4.8 seems to be most relevant for making decisions between different technologies in product design, which is not really relevant for functions of process control and safety where causality related to interaction of mass and energy balances plays a dominant role. The purpose of the foundations presented in this book is to present a more fundamental basis for definition of functions which can capture the needs of process control and safety of SCPS, and which is not consensus based but based on theories of action and semantics. Problems Identified Several problems have been identified in using the typologies and the representational scheme provided by function blocks for functional modelling in process control safety. They are discussed briefly here:

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• Inputs outputs and causality. The interactions between functions are in FBD’s specified by input output relations and the functions themselves represent directed causal relations from inputs to outputs. By being directed, the diagrams do not support the distinction between upstream and downstream influences. The diagrams show only downstream influences, but upstream relations are also required for representing the interaction between flows and accumulations of energy and mass. They are both required for design of control systems and for diagnostic reasoning. • Structure and function. The typology does not facilitate a clear distinction between functions and the objects they are ascribed to. The tertiary level in the flow typology thus represents features of materials and energy forms which are related to the dispositions physical entities rather than to their functions (how they are used for a given purpose). The relation between structure and function is clarified in the foundation through the introduction of concepts of disposition and roles (Chaps. 6 and 12). • Objectives and functions. Objectives are not distinguished from functions. FBD represents the functions required for fulfillment of the process objectives. Each output of a function block therefore represents a corresponding subobjective. However a more clear distinction is required because an objective can be that a function is executed or maintained. There is accordingly a confusion of the objectives and the transformations i.e. the functions used to achieve them. This is a specific problem for FBD but reflects also an underlying lack of distinctions, which is a particular problem in representation of control functions. • Control functions. The relations between physical functions and functions of the control systems are described by signals in the typology in Fig. 4.8. It is sufficient for describing control on the implementation level but is not sufficient for representing the purposes of control system which are reflected in the meaning carried by the signals (in contradiction to having status and control as subordinate to signal in the typology and addressed by concepts of semiotics in the foundations). Furthermore control functions should be defined in relation to process objectives but objectives are not included in the typology. • Support functions. There is no distinction between functions of a process and its control systems and the supporting or enabling subsystems. The distinction is necessary in order to represent modes of operation and control functions related to start-up of processes and for switching between redundant units. • Safety functions. The typology does not include concepts required for modelling functions of safety systems. Fundamental safety concepts like barrier, prevention and protection are missing. The fundamental problem for the typology (and FBD) is that a safety function may have an input (e.g. a fault condition) but does not have an output. The result of a successful execution of a safety function is that a potential hazardous consequence of a failure is NOT realized i.e., the output is a negated state. A solution to this problem requires the theories of action presented in Chaps. 10 and 11.

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4.3.3 Process Control, Safety and Autonomous Systems The domain of process control includes energy systems of different types including conventional and nuclear power and gas and oil production systems. It also includes chemical and bio-chemical engineering. Functional concepts and approaches are in these domains used for design of process plants and their control systems (see e.g. Jørgensen et al. [55]). However, it is also clear that there is a lack of firm conceptual and methodological foundations for functional modelling to improve the quality of decisions made in design and operation. Academic research in functional modelling has not yet had a large impact in the industry partly because the theories about knowledge representations available needs to be matured through assimilation and further development by engineering professionals. Modelling and reasoning tools which can be integrated in existing workflows are also required. Functional thinking also plays a central role in the development of new advanced human machine interfaces for process operation. Some industries like nuclear power has adopted the ideas of means-ends analysis of work domains proposed by Rasmussen [2]. Functional concepts formed accordingly the basis for an integrated approach to control room design in nuclear power plants proposed by Pirus [56]. The development of protection systems for safety critical processes such a nuclear power and chemical production has also adopted functional concepts through barrier analysis and concepts of defense in depth. Functional concepts also play a significant role in research on development of systems with different degrees of autonomy such as intelligent control systems and robots. The concepts are in the design of software architectures combining high level cognitive functions with low level control tasks. Three research contributions within the domain of process control and safety, and autonomous systems will be mentioned below. They all present challenges which are addressed by the foundations presented in this book.

4.3.3.1

Multilevel Flow Modelling (MFM)

MFM is a methodology for representing the goals and functions of complex process plants on multiple levels of abstraction. MFM builds on basic conceptual distinction between goals and objectives of the process plant, and its functions and its structural elements. Overviews and an introduction to MFM are presented in Lind [57–61]. The basic concepts of MFM are shown in Fig. 4.9 and will be illustrated by an example below. It is seen that MFM concepts includes only some of the concepts of function in the typology shown in Fig. 4.8 but they include others which does not appear in the typology, in particular causal relations and means-end relations and some of the control functions. Another difference is the inclusion of objectives (targets and hazards) and that information about flows are included as attributes of the functions.

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Fig. 4.9 Concepts of multilevel flow modelling

MFM was developed in response to the needs for improving human machine interaction in highly automated plants. The needs for improvements became obvious when accidents in nuclear power plants (e.g. Three Mile Island, TMI) showed that deficiencies in human machine interaction can be the cause of serious accidents. Research before and in the aftermath of TMI developed systems engineering principles (e.g. Cognitive Systems Engineering, CSE developed by Rasmussen et al. [2]) which included a functional approach to modelling the power plant and the cognitive processes involved in operators decision making. MFM was initially developed as a language for modelling goals and functions of complex technical physical processes which should be used for information presentation and decision support for plant operators in control rooms. However, during the development it was found that the basic modelling problems in supporting operator decisions, are related to more general foundational issues in modelling complex engineering artefacts. During the development it was also found that there was a need to extend the traditional modelling frameworks of engineering inherited from natural science and mathematics with concepts from the human and social sciences in order to be able to represent information about mind dependent aspects such as goals and functions, and thereby to be oriented towards action rather than constrained by the theoretical view of natural science. The basic idea of MFM can be illustrated by Fig. 4.10 which represents the functions of a conventional power plant by the interactions between equipment or subsystems connected by flows of mass and energy. This type of representation has similarities with process flow sheets used in process engineering (see Chap. 3) which facilitates the acquisition of knowledge required for building MFM models

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Fig. 4.10 A power plant as a set of interacting mass and energy flows

(Lind et al. [62]). Three types of flow are indicated in Fig. 4.10: flows of mass, flows of energy mediated by mass flows, and energy flows created by work or transfer of heat or electricity. The distinctions are important for the analysis of means-end relations and are therefore emphasized in MFM by separate levels of abstraction (see the example below). Information flow is also considered in relation to control systems but are not shown in Fig. 4.10 (see Lind [63] for details). MFM research did not start with the fundamentals presented in this book but by addressing modelling challenges facing engineers and operators when coping with the complexity of industrial processes. A plausible engineering hypothesis based on means-end abstractions presented by Rasmussen [2] motivated the author’s development of the MFM modelling language and the supporting software tools for model building and reasoning. During the early development of MFM it became clear that the idea of the abstraction hierarchy proposed for the human interface should be revised in order to be able to represent the true means-end complexity of the process and the automated control systems (Lind [64]). In this re-orientation the concept of function and its fundamental relations to the means-end relation and action proved to be of key importance. The development also included MFM capabilities for causal reasoning (Lind [65]) and the extension with modelling concepts for representation of barriers and defense in depth structures relevant for modelling of safety functions (Lind [66]). MFM has been used to represent various industrial processes including nuclear power and chemical engineering plants (Lind et al. [67]). Applications of MFM

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includes model based situation assessment and decision support for control room operators (Petersen [68, 69], Zhang [70] and Lind et al. [71]), diagnostic reasoning (Kirchhübel [72] and Kirchhuebel et al. [73]), hazop analysis (Rossing et al. [74] and Wu et al. [75]), safety design (Wu et al. [76, 77]), alarm design (Us et al. [78]), reasoning and planning of control actions (Larsen [79], Heussen et al. [80] and Song et al. [81]). MFM is supported by knowledge based tools for model building and reasoning. Example: Energy transfer system. An application of MFM concepts is shown below by a simple energy transfer system comprising a water circulation loop with two heat exchangers and an associated support system for lubrication of the circulation pump (Fig. 4.11). The example is used later in Chaps. 7 and 15 for explanation of basic concepts. The overall purpose of the system shown in Fig. 4.11 is to transfer heat between two heat exchangers by means of water circulation. The water circulation loop is driven by a pump which is lubricated with oil. The water circulation and the lube oil systems are equipped with flow measurements FM1 and FM2 and associated controllers CON1 and CON2 dealing with lube oil and water flow regulation. The MFM model shown in Fig. 4.11 represents the goals and functions of the heat transfer system without control systems. The MFM model can on an overall level be seen as being composed of three sub-models representing different views on the water circulation system. The first view (starting from the top) represents systems aspects related to water circulation and comprises of the flow structure labeled MFS1, a maintain relation, and the objective O1. This part of the model represents the overall objective of the water circulation, which is to maintain the required flow of water. The flow

Fig. 4.11 The heat transfer system and its functions represented by MFM

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structure contains the functions provided to circulate the water. In this simplified model the transport function T1 is the means used for water circulation. The second view is partially overlapping with the first because what is seen here as a means (the transport T1), is in the second view seen as an end. Transport T1 is related to the means of transport which is the pumping represented by the energy flow structure EFS1. T1 and EFS1 are related by a means-end relation called a producer-product relation in MFM. The flow structure EFS1 is decomposed into the flow functions representing the services provided by components of the pump system (including the energy supply) in order to achieve the end, the transportation of water represented by T1. The third view is related to the second view through the energy transport T2, an enable relation and an associated objective O2 which is the end to be maintained by the functions contained in the flow structure MFS2. The flow structure MFS2 represents the functions involved in the lubrication of the pump and the objective O2 represents the condition that should be fulfilled in order to ensure that the pump is properly lubricated. This condition should be satisfied in order to enable the pump to provide its functions. The flow functions inside MFS2 accordingly represent the functions of the pump lubrication system. Even though this simple example does not utilize all the concepts of MFM, it demonstrates that MFM can represent, in a clear and logical way relations between the goals and functions of a system. MFM can also represent the functions of control and protection (not included in Fig. 4.11) systems as shown in Fig. 4.12. The application of the means-end schema is unique to MFM and provides the ability to deal systematically with multiple levels of functional abstraction and a systematic treatment of the relation between plant and control functions. Contributions from Other Research Groups Other research groups have contributed to development of Multilevel Flow Modelling and its applications. Larsson et al. [82–84], Öhman [85, 86] and Dahlstrand [87] have contributed with algorithms for diagnosis, reliability analysis, failure mode analysis, and consequence analysis. Gofuku et al. [88–93] have contributed with methods and algorithms for diagnosis, counteraction planning, causal explanations, failure mode and effect analysis, fault tree analysis, and operating procedures. Yoshikawa and coworkers [94, 95] have contributed with applications of MFM for modelling the nuclear fuel cycle and used MFM for diagnosis of a pressurized nuclear water reactor (PWR). Yang et al. [96, 97] used MFM for reliability analysis of nuclear power plants and its digital instrumentation and control systems. Kang et al. [98] used MFM for analysis of accidents in PWR’s with passive safety systems. Paassen et al. [99–102] developed MFM based principles for alarms, the use of MFM for modelling mode changes, and reasoning methods for MFM. MFM Research and the Foundations Applications of MFM and its tools demonstrated that it had a considerable potential for diagnosis of abnormal operational

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Fig. 4.12 MFM models of heat transfer system example with control and protection functions

situations in complex industrial systems. Reflection on the source of this potential led to the identification of a set of underlying general conceptual and logical schemas from theories of action and semiotics, which can explain the representational and reasoning power of MFM. The schemas can be used for both enriching MFM and to propose extensions of the language for engineering domains beyond the original application domain of material and energy processes. The foundations presented in this book addresses the following questions which emerged from the experience of developing and using MFM: • how are functions of control systems different from functions dealing with mass and energy flow? The answer to this question requires a distinction between different types of causality (see Chap. 6) and a separation of the means and ends of control (see Chap. 16) • what is the rationale for the choice of the rather limited set of functions presented in Fig. 4.9. This question is addressed in Chaps. 10 and 11: the answer is given by a set of fundamental transformation types which are developed from an action theoretic and semiotic basis and which should be used to construct function types in a given context. • what is the relation between function and physical structure (i.e. components and equipment)? An answer to this question (initially proposed in Lind [103])

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is to introduce roles as links between physical structure and functions. A more complete answer is given in Chap. 12 • why should interchange of mass, energy and information flows used to represent interactions between functions? The answer to this question requires a distinction between dyadic and triadic causality and an explanation of the advantages of ascribing functions to control volumes for mass and energy balances defined by physical boundaries • how are distinct practices with different goals, functions and structure combined systematically into an action system through means-end relations and the associated functions. The answer is presented in Chap. 15 • can multilevel flow modelling be supported by an underlying conceptual framework which provides a logical integration of concepts of goals, objectives, functions, roles and structure? An answer to this question is presented in Chap. 19

4.3.3.2

System Safety and Reliability

The principles of levels of means-end abstraction suggested by Rasmussen [2] and applied in MFM, have also been developed for application within system safety and reliability by Modarres and coworkers (Modarres [104–106], Chen and Modarres [107], and Modarres and Cheon [108]). The method called GTST-MPLD (Goal Tree Success Tree—Master Plan Logic Diagram) represents a decomposition of the overall safety goals into subgoals and functions and connect them with possible countermeasures by success paths. A few studies make comparisons between GTST and MFM (e.g. Jalashgar [109–111]). Nordvik et al. [112] presents an application of GTST for real-time supervision of hazardous plants.

4.3.4 Autonomous Systems Sanz, Bernejo-Alonso and coworkers have developed principles for design of autonomous systems building on functional concepts, in particular for the design of architectures of intelligent system with self-reflective features using a model of the goals and functions of the control system for reasoning about its interaction with the system under control. This research combines ideas from agent systems [113] and functional modelling. Sanz et al. [114] presents design patterns for intelligent systems and Sanz et al. [115, 116] discuss design for self-awareness. BernejoAlonso et al. [117] proposed an ontology for autonomous systems. Rodriguez et al. [118] proposed also a functional modelling language which integrate information about function and structure. The foundations presented in Chap. 15 principles for integration of physical and cognitive functions in so-called action systems which may be of relevance for modelling of complex architectures for autonomous systems.

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References 1. M. A. Rosenman and J. S. Gero. “The Why, The How and The Why in Design”. In: Applied Artificial Intelligence 8 (1994), pp. 199–218. 2. J. Rasmussen. Information Processing and Human Machine Interaction. New York: North Holland, 1986. 3. L. P. Schrenk. “Aiding the Decision Maker - A Decision Process Model”. In: Ergonomics 12.4 (1969), pp. 543–57. 4. C. H. Kepner and B. B. Tregoe. The Rational Manager. New York, USA: Mc-Graw Hill, 1965. 5. J. Sachs. Aristotle’s Physics. New Brunswick, USA: Rutgers University Press, 1995, p. 260. 6. IAEA. The Fukushima Daiichi Accident: Technical Volume 1, Description and Context of The Accident. Tech. rep. International Atomic Energy Agency, 2015. 7. R. Smith. Chemical Process Design. New York: McGraw-Hill, Inc, 1995. 8. B. Chandrasekharan. “Functional Representation: A Brief Historical Perspective”. In: Applied Artificial Intelligence 8 (1994), pp. 173–197. 9. J. Hirtz, R. B. Stone, D. A. McAdams, S. Szykman and K. L. Wood. “A Functional Basis for Engineering Design. Reconciling and Evolving Previous Efforts”. In: Research in Engineering Design 13 (2002), pp. 65–82. 10. B. Chandrasekharan. “Representing function: Relating functional representation and functional modeling research streams”. In: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19 (2005), pp. 65–74. 11. B. H. Far and H. Elamy. “Functional reasoning theories: Problems and perspectives”. In: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19 (2005), pp. 75–88. 12. D. VanEck, D. A. McAdams and P. E. Vermaas. “Functional Decomposition in Engineering: A Survey”. In: Proceedings ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Las Vegas, Nevada, USA, 2007. 13. M. S. Erden, H. Komoto, T. J. V. Beek, V. D’Amelio, E. Echavarria and T. Tomiyama. “A Review of Functional Modeling: Approaches and Applications”. In: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22 (2008), pp. 147–169. 14. P. Kroes. “Formalization of Technical Functions: Why is That so Difficult”. In: Proceedings of TMCE 2010 Symposium. Ancona, Italy, 2010, pp. 155–165. 15. P. E. Vermaas. “Technical Functions: Towards Accepting Different Engineering Meanings with an Overall Account”. In: Proceedings of the TMCE 2010 Symposium. Ed. by I. Horvath, F. Mandorli and Z. Rusak. Ancona, Italy, 2010. 16. P. E. Vermaas. “Accepting Ambiguity of Engineering Functional Descriptions”. In: Proceedings International Conference on Engineering Design, ICED11. 2011. 17. P. E. Vermaas. “The Coexistence of Engineering Meanings of Functions: Four Responses and their Methodological implications”. In: Artificial Intelligence for Engineering Design and Manufacturing 27 (2013), pp. 191–202. 18. P. E. Vermaas, D. van Eck and P. Kroes. “The conceptual Elusiveness of Engineering Functions: A Philosophical Analysis”. In: Philosophy of Technology 26 (2013), pp. 159–185. 19. H. A. Simon. The Sciences of the Artificial. Cambridge: The MIT Press, 1981. 20. P. Freeman and A. Newell. “A Model of Functional Reasoning in Design”. In: Proc. 2nd International Joint Conference in Artificial Intelligence. 1971. 21. J. deKleer. Causal and Teleological Reasoning in Circuit Recognition. Tech. rep. AI-TR-529. 545 technology Square, Cambridge, Massachusetts: MIT Artificial Intelligence Laboratory, 1979. 22. D. W. Franke. “Deriving and Using Descriptions of Purpose”. In: IEEE Expert 41–47 (1991). 23. A. M. Keuneke. “Device Representation - The Significance of Functional Knowledge”. In: IEEE Expert (1991), pp. 22–25.

References

79

24. B. Chandrasekharan and J. R. Josephson. “Representing Function as Effect”. In: Proceedings of the Fifth International Workshop on Advances in Functional Modeling of Complex Technical Systems. Ed. by M. Modarres. Paris, France, 1997, pp. 3–16. 25. B. Chandrasekharan and J. R. Josephson. “Function in Device Representation”. In: Engineering with Computers 16(3/4) (2000), pp. 162–177. 26. V. Sembugamoorthy and B. Chandrasekaran. “Functional Representation of Devices and Compilation of Diagnostic problem Solving Systems”. In: Experience, Memory, and Reasoning. Ed. by J. Kolodner and C. Reisbeck. Lawrence Erlbaum Associates, 1986, pp. 47–73. 27. B. Chandrasekharan, A. K. Goel and Y. Iwasaki. “Functional Representation as Design Rationale”. In: IEEE Computer Special Issue on Concurrent Engineering (1993), pp. 48–56. 28. Y. Iwasaki, R. Fikes, M. Vescovi and B. Chandrasekaran. “How Things Are Intended to Work: Capturing Functional Knowledge in Device Design”. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93). Ed. by R. Bajcsy. Vol. 2. Chambery, France: Morgan Kaufmann Publishers, San Mateo, California, 1993, pp. 1516– 1522. 29. M. C. Tanner and A. M. Keuneke. “The Roles of the Task Structure and Domain Functional Models”. In: IEEE Expert (1991), pp. 50–57. 30. Y. Kitamura and R. Mizoguchi. An Ontology of Functional Concepts of Artifacts. Tech. rep. AI-TR-99-1. Institute of Scientific and Industrial Research, Osaka University, 1999. 31. Y. Kitamura and R. Mizoguchi. “An Ontological Analysis of Fault Process and Category of Faults”. In: Proceedings of Tenth International Workshop on Principles of Diagnosis (DX-99). 1999, pp. 118–128. 32. Y. Kitamura and R. Mizoguchi. “Ontology-based Description of Functional Design Knowledge and its Use in a Functional Way Server”. In: Expert Systems with Applications (2002). 33. Y. Koji, Y. Kitamura and R. Mizoguchi. “Towards Modeling Design Rationale of Supplementary Function in Conceptual Design”. In: Proceedings of the TMCE. Lausanne, Switzerland, 2004. 34. Y. Kitamura, M. Kashiwase, M. Fuse and R. Mizoguchi. “Deployment of an Ontological Framework of Functional Design Knowledge”. In: Advanced Engineering Informatics (2004). 35. Y. Kitamura, Y. Koji and R. Mizogucho. “An Ontological Model of Device Function and Its Deployment for Engineering Knowledge Sharing”. In: Proceedings of the First Workshop FOMI 2005 - Formal Ontologies Meet Industry. Castel-nuovo del Garda (VR), Italy, 2005. 36. R. C. Schank and K. M. Colby. Computer Models of Thought and Language. San Francisco: W. H. Freeman and Company, 1973. 37. R. C. Schank and R. Abelson. Scripts, Plans Goals and Understanding - An inquiry into Human Knowledge Structures. New Jersey, USA: Lawrence Erlbaum, 1977. 38. R. C. Schank. Dynamic Memory Revisited. Cambridge, USA: Cambridge University Press, 1999, p. 302. 39. M. Wooldridge. Reasoning About Rational Agents. Cambridge, Massachusetts: The MIT Press, 2000. 40. J. R. Searle. Speech Acts: An Essay in the Philosophy of Language. Cambridge: Cambridge University Press, 1969. 41. E. W. Leaver and J. J. Brown. “A Functional Morphology of Mechanisms”. In: Automation (1955), pp. 37–41. 42. G. Pahl and W. Beitz. Engineering Design - A Systematic Approach. Berlin: Springer, 1996, p. 544. 43. Y. Umeda and T. Tomiyama. “Functional Reasoning in Design”. In: IEEE Expert Intelligent System and Their Applications 12.2 (1997), pp. 42–48. 44. T. Tomiyama, T. J. van Beek, A. A. A. Cabrera, H. Komoto and V. D’Amelio. “Making function modeling practically usable”. In: Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2013). 45. S. Szykman, R. D. Sriram, C. Bochenek and J. Raz. “The NIST Design Repository Project”. In: ed. by R. Roy, T. Furuhashi and P. K. Chawdhry. Advances in Soft Computing Engineering Design and Manufacturing. London: Springer-Verlag, 1999.

80

4 Existing Frameworks for Artefact Modelling

46. P. E. Vermaas. “Modelling Functions as Operation on Material, Energy and Signal Flows: Conceptual Problems and Possible Solutions”. In: Proceedings TCME 2008. Ed. by I. Horvath and Z. Rusak. Izmir, Turkey, 2008. 47. P. E. Vermaas. “The Flexible Meaning of Function in Engineering”. In: Proceedings of International Conference on Engineering Design, ICED09. Stanford University, Stanford, CA, 2009, pp. 113–124. 48. P. E. Vermaas and P. Garbacz. “Function Decomposition and Mereology in Engineering”. In: Handbook of Philosophy of Science. Ed. by A. Meijers. Vol. 9. Philosophy of Technology and Engineering Sciences. Elsevier BV, 2009. 49. P. E. Vermaas. “Focussing Philosophy of Engineering: Analyses of Technical Functions and Beyond”. In: Philosophy and Engineering. Ed. by I. de Poel and D. E. Goldberg. Vol. 2. Philosophy of Engineering and Technology. Springer Science, 2010. 50. M. Carrara, P. Garbacz and P. E. Vermaas. “If engineering function is a family resemblance concept: Assessing three formalization strategies”. In: Applied Ontology 6 (2011), pp. 141– 163. 51. P. E. Vermaas. “On the Formal Impossibility of Analysing Subfunctions as Parts of Functions in Design Methodology”. In: Research Engineering Design 24 (2013), pp. 19–32. 52. D. T. Ross. “Structured Analysis (SA): A Language for Communicating Ideas”. In: IEEE Transactions on Software Engineering 3.1 (1977), pp. 16–34. 53. D. A. Marca and C. L. McGovern. SADT-Structured Analysis and Design Technique. McGraw-Hill, 1988. 54. S. Borgo, M. Carrara, P. Garbacz and P. E. Vermaas. “A Formalization of Functions as Operation on Flows”. In: Journal of Computing and Information Science in Engineering 11 (2011). 55. S. B. Jørgensen, M. Lind and N. Jensen. “Functional Modelling View on Product and Process Engineering in Design and Operations”. In: Industrial and Engineering Chemistry Research 50 (2019), pp. 11129–11146. 56. D. Pirus. “Why and How a Functional Information System Improves Computerized Operations”. In: Proc. Inter. Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC&HMIT2006). Albuquerque, New Mexico, 2006. 57. M. Lind. “Modelling Goals and Functions of Complex Industrial Plant”. In: Applied Artificial Intelligence 8.2 (1994), pp. 259–283. 58. M. Lind. “The What, Why and How of Functional Modelling”. In: Proceedings of International Symposium on Symbiotic Nuclear Power Systems for the 21’st Century (ISSNP). Tsuruga, Japan, 2007, pp. 174–179. 59. M. Lind. “An overview of Multilevel Flow Modeling”. In: Nuclear Safety and Simulation 4.3 (2013), pp. 186–191. 60. M. Lind. “Functional Modelling of Complex Systems”. In: Risk Management in Life-Critical Systems. Ed. by P. Millot. London: ISTE-Wiley, 2014. Chap. 6, pp. 95–114. 61. M. Lind. “An Introduction to Multilevel Flow Modeling”. In: Nuclear Safety and Simulation 2.1 (2011). 62. M. Lind, J. Wu and X. Zhang. Multilevel Flow Modelling Guideline, For Oil and Gas Production Systems. Tech. rep. Department of Electrical Engineering, Technical University of Denmark, 2019. 63. M. Lind. “Control functions in MFM: Basic Principles”. In: Nuclear Safety and Simulation 2.2 (2011), pp. 132–139. 64. M. Lind. “Making Sense of the Abstraction Hierarchy in the Power Plant Domain”. In: Cognition Technology and Work 5(2) (2003), pp. 67–81. 65. M. Lind. “Reasoning about Causes and Consequences in Multilevel Flow Models”. In: Proceedings of ESREL2011. Troyes, France, 2011. 66. M. Lind. “Modeling Safety Barriers and Defense in Depth with Multilevel Flow Modeling”. In: Proceedings of First International Symposium on Socially and Technically Symbiotic Systems (ISST2012). Okayama, Japan, 2012.

References

81

67. M. Lind, H. Yoshikawa, S. B. Jørgensen and M. Yang. “Modeling Operating Modes for the MONJU Nuclear Power Plant”. In: Nuclear Safety and Simulation 3 (4) (2012), pp. 314–324. 68. J. Petersen. “Knowledge Based Support for Situation Assessment in Human Supervisory Control”. PhD thesis. Lyngby, Denmark: Department of Automation, Technical University of Denmark, 2000. 69. J. Petersen. “Situation Assessment of Complex Dynamic Systems using MFM”. In: Proceedings of 8th. IFAC/IFIP/IFPRS/IEA Symposium on Analysis, Design and Evaluation of Human-Machine Systems. Kassel, Germany, 2001, pp. 645–650. 70. X. Zhang. “Assessing Operational Situations”. PhD thesis. Technical University of Denmark, Kongens Lyngby Denmark: Department of Electrical Engineering, 2015. 71. M. Lind and X. Zhang. “Functional Modelling for Fault Diagnosis and its Application for NPP”. In: Nuclear Engineering and Technology 44.6 (2014), pp. 753–772. 72. D. Kirchhübel. “Combining Functional Modelling and Reasoning with Online Event Analytics.” PhD thesis. DK2800Kongens Lyngby, Denmark: Department of Electrical Engineering, Technical University of Denmark, 2019. 73. D. Kirchhübel, M. Lind and O. Ravn. “Dynamic Reasoning in Functional Models for Multiple Fault Diagnosis”. In: Computers and Chemical Engineering (2019). 74. N. L. Rossing, M. Lind, N. Jensen and S. B. Jørgensen. “A Functional Hazop Methodology”. In: Computers in Chemical Engineering 34.2 (2010), pp. 244–253. 75. J. Wu, L. Zhang, J. Hu, M. Lind, X. Zhang, S. B. Jørgensen, G. Sin and N. Jensen. “An Integrated Qualitative and Quantitative Modeling Framework for Computer-Assisted HAZOP Studies”. In: AIChe Journal 60.12 (2014), pp. 4150–4173. 76. J. Wu, M. Lind, L. Zhang, X. Zhang, S.B. Jørgensen and G. Sin. “Validation of a Functional Model for Integration of Safety into Process System Design”. In: 12’th International Symposium on Process Systems Engineering and the 25th European Symposium on Computer Aided Process Engineering. Copenhagen, 2015. 77. J. Wu, M. Song, X. Zhang and M. Lind. “Identifying Safety Objectives and Functions for Emergency Shutdown in the Design Phase by Using Functional Modelling”. In: Chemical Engineering Transactions 86 (2021). 78. T. Us, N. Jensen, M. Lind and S. B. Jørgensen. “Fundamental Principles of Alarm Design”. In: International Journal of Nuclear Safety and Simulation 2.1 (2011), pp. 44–51. 79. M. N. Larsen. “Deriving Action Sequences for Start-Up Using Multilevel Flow Models”. PhD thesis. Department of Automation, Technical University of Denmark, 1993. 80. K. Heussen and M. Lind. “Representing Causality and Reasoning about Controllability of Multi-level Flow-Systems”. In: Proceedings IEEE International Conference on Systems, Man and Cybernetics SMC 2010. Istanbul Turkey, 2010. 81. M. Song, X. Zhang, M. Lind and A. Gofuku. “A Rule-based Approach for Planning CounterActions with Multilevel Flow Modelling”. In: Proceedings of 27’th International Conference on Nuclear Engineering ICONE27. Ibaraki Japan, 2019. 82. J. E. Larsson. “Diagnostic Reasoning Strategies for Means-End Models”. In: Automatica 30.5 (1994), pp. 775–787. 83. J. E. Larsson. “Diagnosis based on explicit means-end models.” In: Artificial Intelligence 80(1) (1996), pp. 29–93. 84. J. E. Larsson, F. Dahlstrand, B. Öhman and J. Tuszynski. “Reliability Analysis Based on Multilevel Flow Models”. In: Proceedings of the 6th Workshop on Functional Modeling. University of Maryland, College Park, Maryland, USA, 2002. 85. B. Öhman. “Discrete Sensor Validation with Multilevel Flow Models”. In: IEEE Intelligent Systems 17.3 (2002), pp. 55–61. 86. B. Öhman. “Failure Mode Analysis using Multilevel Flow Models”. In: Proceedings of the 5th European Control Conference. Karlsruhe, Germany, 1999. 87. F. Dahlstrand. “Consequence Analysis Theory for Alarm Analysis”. In: Knowledge-Based Systems 15(1) (2002), pp. 27–36.

82

4 Existing Frameworks for Artefact Modelling

88. A. Gofuku and Y. Tanaka. “Development of an Operator Advisory System: Finding Possible Counter Actions in Anomalous Situations”. In: Proc. 5’th International Workshop on Functional Modeling of Complex Technical Systems. Paris, France, 1997, pp. 87–97. 89. A. Gofuku and Y.Tanaka. “Derivation and Evaluation of Plausible Counteractions by Combining Qualitative Reasoning, Empirical Knowledge and Numerical Simulation”. In: Proc. 5’th World Multiconference on Systemics Cybernetics and Informatics(SCI2001). Orlando, Florida (USA), 2001. 90. A. Gofuku and Y. Zheng. “Causality Explanation Generation based on Multilevel Flow Model for Operator Support Systems”. In: Proc. SICE Annual Conference 2005. Okayama, Japan, 2005. 91. A. Gofuku, S. Koide and N. Shimada. “Fault Tree Analysis and Failure Mode Effects Analysis Based on Multilevel Flow Modeling and Causality Estimation”. In: Proceedings SICE-ICASE International Joint Conference. Bexco, Busan, Korea, 2006, pp. 497–500. 92. A. Gofuku and A. Ohara. “A Systematic Fault Tree Analysis Based on Multilevel Flow Modelling”. In: Nuclear Safety and Simulation 1.2 (2010). 143–149. 93. M. Song, M. Yang and A. Gofuku. “Functional Modeling for Operating Procedure Tasks of a Chemical and Volume Control System in PWR”. In: Nuclear Safety and Simulation 6.2 (2015), pp. 155–166. 94. J. Liu, H. Yoshikawa and Y. Zhou. “Application of Multilevel Flow Modeling to Describe Complex Processes in a Nuclear Fuel Cycle”. In: Proceedings CSEPC 2004 Cognitive Systems Engineering in Process Control. Sendai, Japan, 2004, pp. 114–120. 95. J. Ouyang, M. Yang, H. Yoshikawa and Y. Zhou. “Modeling of PWR Plant by Multilevel Flow Model and its Application in Fault Diagnosis”. In: Nuclear Science and Technology 42.8 (2005), pp. 695–705. 96. M. Yang, Z. Zhang, M. Peng and S. Yan. “Modeling Nuclear Power Plant with Multilevel Flow Models and its Applications in Reliability Analysis”. In: Proceedings of International Symposium on Symbiotic Nuclear Power Systems for the 21’st Century (ISSNP). Tsuruga, Japan, 2007. 97. W. Q. Chen and M. Yang. “Reliability Analysis of Digital I&C System in NPP Using MFM”. In: Proceedings of First International Symposium on Socially and Technically Symbiotic Systems (SSTS2012). Okayama, Japan, 2012. 98. I. S. Jeon and H. G. Kang. “Accident Mitigation with Passive System in Existing PWR’s Based on MFM method”. In: Proceeding Third International Workshop Functional Modelling for Design and Operation of Engineering Systems. Kurashiki, Japan, 2018. 99. M. M. van Paassen and P. A. Wieringa. “Alarms, Alerts and Annunciators”. In: Journal A 38.4 (1997). 100. M. M. van Paassen and P. A. Wieringa. “Describing Process Mode Changes with Multilevel Flow Models”. In: Proc. 5’th International Workshop on Functional Modeling of Complex Technical Systems. Paris, France, 1997. 101. M. M. van Paassen and P. A. Wieringa. “Perspectives for Annunciator Systems”. In: Proceedings of EAM European Annual Manual 97 (EAM97). 1997. 102. M. M. van Paassen and P. A. Wieringa. “Reasoning with Multilevel Flow Models”. In: Reliability Engineering and Systems Safety 64.2 (1999), pp. 151–165. 103. M. Lind. “Knowledge Representation for Integrated Plant Operation and Maintenance”. In: Proceedings of Seventh American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies. Las Vegas USA, 2010. 104. M. Modarres. “Functional Modelling of Complex Systems Using a GTST-MPLD Framework”. In: Proceedings 1st International Workshop on Functional Modelling of Complex Technical Systems. Ispra, Italy, 1993. 105. M. Modarres. “A Pragmatic Approach to a Function-Centered Ontology of Complex Physical Systems”. In: 2nd International Workshop on Functional Modeling of Complex Technical Systems. Copenhagen, Denmark, 1994.

References

83

106. M. Modarres. “Functional Modelling of Complex Systems with Applications”. In: Proceedings Annual Reliability and Maintainability Symposium. IEEE, 1999. 107. L. W. Chen and M. Modarres. “Hierarchical Decision Process for Fault Adminstration”. In: Computers and Chemical Engineering 16.5 (1992), pp. 425–448. 108. M. Modarres and S. W. Cheon. “Function-centered Modelling of Engineering Systems using the Goal Tree-success Tree Technique and Functional Primitives”. In: Reliability Engineering and System Safety 64 (1999), pp. 181–200. 109. A. Jalashgar and M. Modarres. “Identification of hidden failures in control systems: A Functional Modelling Approach”. In: Proceedings of 2nd International FLINS Workshop. 1996. 110. A. Jalashgar. “Function-Oriented System Analysis: Putting GTST, MFM and HMG Methods in Perspective”. In: Proceedings of 5th International Workshop on Functional modelling of Complex Technical Systems. Paris-Troyes, 1997. 111. A. Jalashgar. “Goal-oriented system modelling: justification of the approach and overview of the methods”. In: Reliability engineering and Systems Safety 64 (1999), pp. 271–278. 112. J. Nordvik, N. Mitchison and M. Wilikens. “The Role of the Goal Tree-Success Tree model in the Real-time Supervision of Hazardous Plant”. In: Reliability Engineering and System Safety (1994). 113. M. Wooldridge. Multiagent Systems. John Wiley, 2002, p. 348. 114. R. Sanz, M. Segarra, A. deAntonio, F. Matia, A. Jimenez and R. Galan. “Design Patterns in Intelligent Control Systems”. In: Proc. IFAC 14th World Congress. Beijing, China, 1999. 115. R. Sanz, I. Lopez and C. Hernandez. “Self-awareness in Real-Time Cognitive Control Architectures”. In: Proc. AAAI Fall Symposium on Consciousness and Artificial Intelligence: Theoretical foundations and current approaches. 2007. 116. R. Sanz, C. Hernandez and M. Rodriguez. “The Epistemic Control Loop”. In: Proc. CogSys 2010 - 4th International Conference on Cognitive Systems. 2010. 117. J. Bernejo-Alonso, R. Sanz, M. Rodriguez and C. Hernandez. “An Ontology-based Approach for Autonomous Systems Description and Engineering”. In: ed. by R. Setchi, I. Jordanov, R. J. Howlett and L. C. Jain. Knowledge-Based and Intelligent Information and Engineering Systems (Lecture Notes in Computer Science). Berlin-Heidelberg: Springer, 2010, pp. 522– 531. 118. M. Rodriguez and R. Sanz. “Development of Integrated Functional-Structural Models”. In: ed. by R. M. deBrito Alves, C. A. O. do Nascimento and E. C. Biscaia. Computer Aided Chemical Engineering. Elsevier, 2009, pp. 573–578.

Part II

Preparatory Foundations

The purpose of Part II is to introduce topics which are considered preparatory to Parts III, IV and V. Chapter 5 includes a presentation of modelling as a cognitive process. A distinction between frameworks of interpretation and their roles in modelling is emphasized and it is explained that functional modelling is based on a social framework of interpretation. Chapter 6 presents an introduction to concepts of causality relevant for functional modelling of SCPS. This includes the dependence of causal explanations on dispositions, a distinction between general and singular causation, dyadic and triadic causality, and an analysis of the relation between cause-effect relations and of the means-end relations which has a particular relevance for functional modelling.

Chapter 5

Modelling as a Cognitive Process

The purpose of this chapter is to discuss cognitive aspects of the modelling procedure with a particular emphasis on the model relation and the frameworks of interpretation involved in functional modelling of technical artefacts. Chapter 3 explained the central importance of functional concepts for solution of a range of engineering problems and provided thereby the motivations for development of concepts and methods for functional modelling. For an engineer or systems analyst trained in traditional modelling methods based on the application of concepts of natural science (and associated branches of mathematics), the main challenge will be that the systematic modelling of system functions requires inclusion of contextual knowledge of purposes and goals, which cannot be expressed by concepts of natural science. Concepts of function and purpose have effectively been eliminated from almost all branches of natural science except from biology. Therefore the true nature of man-made systems like technical artefacts cannot be properly described exclusively by concepts of natural science.

5.1 The Model Relation Models play in general a large role in all engineering disciplines as tools for generation, evaluation and optimization of design alternatives, including the identification and assessment of risk. They also play a central role in operation of technical artefacts for prediction of behavior and in failure diagnosis. In both cases models serve as tools for human decision making. However, models also play an increasing role as components of intelligent systems giving them the ability to respond autonomously to changing conditions and circumstances in an automated production process. Models play accordingly an increasingly dominant role in design and operation of technical artefacts. Building and validation of models are

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key concerns in systems engineering and require insight in the model building procedure. A convenient starting point for a discussion of the cognitive aspects of modelling is the model relation proposed by Rosen [1] and shown in Fig. 5.1. Rosen introduced the relation in discussions about modelling in natural science (or more precisely in biology) having a particular focus on modelling anticipatory behaviour of living systems. Rosen’s model relation is adapted here so that it can be applied to modelling problems in engineering and analysis of technical artefacts. The natural system in Rosen’s model is substituted by an artefact in Fig. 5.1. The main difference between modelling in natural science and engineering science is that the object of modelling in the latter is an artefact (or an artefact interacting with a natural system) whereas it is a natural system in the former. The model relation includes two fundamental relations encoding and decoding between the artefact, the object of modelling, and the model. The model is a formalized system representing artefact features which can be used for inference in problem solving. The encoding relation is established by the model building process. Furthermore the decoding relation is the translation of inferences produced by using the model to make predictions or propose actions for design, modification or control of the artefact. Models are tools for problem solving and when used to represent experience of the past they can predict unanticipated future situations based on observed evidence, or they can be used to forecast consequences of actions. In such cases the model is used for problem solving. In other situations a model is constructed as a means of

Fig. 5.1 The model relation (adapted from Rosen [1])

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making sense of observations and other evidence so it e.g. can be used for solving diagnostic problems. In addition to using a model in solving a problem of design or operation, the model building itself (the encoding) is also a means for problem framing and formulation. The distinction between problem framing and problem solving was originally introduced by Dewey [2] and later emphasized by Schön [3] who argues that problem solvers like engineers and other professionals are engaged in a reflection with the situation in order to frame the problem. The framing includes the identification of concepts which can be used to make sense of a problematic situation in design or operation, and then using the concepts to construct a model which defines the problem and subsequently can be used to solve it. The distinction between problem framing and formulation, and problem solving is related to the encoding and decoding relations as shown in Fig. 5.2. The following section will take a closer look at the encoding relation in order to understand the different stages of the modelling activity.

5.1.1 Encoding The encoding relation can be decomposed into three steps; perception, interpretation and representation (see Fig. 5.3).

Fig. 5.2 Modelling in problem formation and solving

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Fig. 5.3 The encoding relation decomposed into perception, interpretation and representation

Perception The perception of a system and its elements is an intellectual process which is elusive and by many considered a “mind craft” which cannot be explained but requires practical experience. The perception is based on tacit knowledge. An understanding of the elements involved in the perception can be obtained through Polanyi’s [4] theory of tacit knowledge and Rolf’s [5] analysis of professional knowing. Rolf explains Polyani’s theory as follows (translated from Swedish by the author): Tacit knowledge is like an instrument or a tool similar to the blind man’s stick which is directed towards reality in order to acquire knowledge of and investigate the environment. In the background there is an “in order to” i.e. a purpose which is part of the tacit knowledge.

Polanyi considers knowing as an activity, a process. Tacit knowing is characterized by the function in a goal directed process. Polanyi considers “tacit knowing” as the same as perceiving. As all activities it has an aspect of finality i.e. it is goal directed. . .

Interpretation Modelling a product, process, system or device is a cognitive process of interpretation where meaning is assigned to primary perceived data based on a set of predefined conceptual schemata. Concepts and associated schemata constitute a framework of interpretation and is used in modelling to make sense of the events and phenomena in the problem under investigation. Conceptual schemas play a central role in human cognition and has been investigated by researchers in cognitive science (for an overview see Brewer and Nakamura [6]). This research has had a significant impact on the development of knowledge based systems, including methods for knowledge acquisition and schemes for knowledge representation (e.g. frames proposed by Minsky [7]). Cognition also relies on tacit knowledge and is dependent on concepts and theories from the tradition as shown in Fig. 5.4. The frameworks of interpretation introduced later, can be seen as concepts from the tradition. The foundations of functional modelling makes the frameworks of interpretation explicit so it becomes part of the focal dimension.

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Fig. 5.4 The perception process links together material from the situation with concepts which are passed on by tradition or language. The linking is guided by purposes and intentions (adapted from Rolf [5])

Representation Representation of concepts is the activity of expressing them into a formal system suitable for some kind of processing by hand or machine. Schemas of representation linking concepts with their representational forms are some of the key concerns of the field of Artificial Intelligence but will not be discussed further here.

5.2 Frameworks of Interpretation Concepts of physics and chemistry serve traditionally in engineering as interpretation frameworks for encoding knowledge of technical artefacts and decoding the results of model inferences. However, human beings, including engineers, also apply other frameworks of interpretation to make sense of events and phenomena in the world. Of particular interest here are frameworks of interpretation using concepts of intentions, purposes, means and ends, goals and functions which are significant aspects of technical artefacts.

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5.2.1 Goffman’s Frame Analysis Goffman [8] distinguishes in his frame analysis two frameworks as the primary frameworks of interpretation. He names them the natural and the social frameworks and defines them as follows (ibid, p. 22): Natural frameworks identify occurrences seen as undirected, unoriented, unanimated, unguided “purely physical”. Such unguided events are ones understood to be due totally, from start to finish, to “natural” determinants. It is seen that no willful agency causally and intentionally interferes, that no actor continuously guides the outcome. Success or failure in regard to these events are not imaginable Social frameworks, on the other hand, provide background understanding for events that incorporate the will, aim, and controlling effort of an intelligence, a live agency, the chief one being the human being. . . What (the agent). . . does can be described as “guided doings”. These doings subject the doer to “standards,” to social appraisal of his action based on its. . . efficiency, economy, safety. . . , and so forth.

An illustration of the two primary frameworks on a power plant example is shown in Fig. 5.5. When the power plant is interpreted within the natural framework it is seen as a purely physical object. Observed events and phenomena are accordingly undirected and unguided. But when applying the social framework of interpretation, the power plant is seen as purposeful by being directed by the controlling effort of its designers and operators and by providing functions and services for the benefit of society. From this example it is obvious that both primary frameworks of interpretation are relevant for understanding technical artefacts. This is also reflected in engineering practice where quantitative mathematical models of physical and chemical aspects of the artefact are used together with semiformal qualitative models (diagrams) representing design intentions. The natural framework of interpretation traditionally plays a dominant role in modelling approaches to engineering problems, and its application has been highly successful. As a result, other sources of knowledge belonging to other frameworks of interpretation, but relevant for formulating and solving engineering problems, are

Fig. 5.5 Goffman’s primary frameworks of interpretation applied on a power plant

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in general undervalued. An exception is Simon [9] who proposed a science of the arteficial based on functional thinking to be foundational to engineering. The two frameworks of interpretation will be discussed in more detail later in this chapter. It will be suggested that concepts of function provides a bridge between the natural and the social frameworks of interpretation. An exploration of the nature of this bridge is the main topic of Parts III, IV and V. Searle’s Brute and Social Facts Searle [10] made a distinction between brute and social facts which is related to Goffmann’s natural and social frameworks of interpretation. Searle argues that knowledge of functions of physical equipment and systems are social facts because these objects are made for a purpose and refer therefore to human intentions. The assignment of functions to technical artefacts is accordingly involved in the construction of social reality. Searle proposes a distinction between different concepts of functions as shown in Fig. 5.6. Causal agentive functions related to actions play a particular role in modeling of technical artefacts (Parts III and IV). Searle distinguishes also between two senses of truth to clarify the difference between the inter-subjective and the objective. Social facts are inter-subjective and true in an epistemic sense by representing humans shared knowledge about the world (which includes knowledge of functions). Physical (brute) facts are objective and true in an ontological sense by describing what exists (taken in the sense of physical existence). Figure 5.6 shows that descriptions of the function of a physical device must ultimately refer to physical phenomena i.e. to so-called brute facts. Interpretations given within the natural and the social frameworks of a technical artefact may

Facts

Brute Physical Facts

Mental Facts

Functions are always ultimately assigned to physical phenomena

Intentional

Nonintentional

Collective = Social Facts Singular All others

Assignment of Function

Nonagentive functions

Agentive functions

Causal agenti functions

Status functions = Institutional Facts

Linguistic

Fig. 5.6 Searle’s classification of functions (adapted from [10])

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therefore use the same terms but have different meanings depending on whether they refer to something physical (brute facts) or to inter-subjective intentions or purposes (social facts). This insight of Searle is crucial in order to understand that the two frameworks of interpretation are not independent but interplay during the process of modelling technical artefacts as explained in Sect. 5.4. Note that in spite of the inter-subjective nature of artefact functions it is only meaningful to ascribe a function to a physical entity if it is capable of realizing it. For functions relevant to modelling of technical artefacts this requirement is essential and indicates an intimate relation between functions and causality which will be explored in Chap. 6 and Part III.

5.2.2 The Natural Framework of Interpretation According to the definition above phenomena and events are in the natural framework seen as strictly physical and chemical and occurring in space and time. This means that they are explained as the effects of interactions between material objects caused by natural forces and conditioned by laws of nature. The natural framework can be applied on any object in the material world including objects like stones, mountains, waterfalls, chemical reactions, and the likes, but also to man made physical objects like robots, production processes, and other technical artefacts. The models created within this framework will be adequate for explaining and predicting physical behaviour, but since they are focussed on physical phenomena and mechanisms they are insufficient in themselves for understanding the role an entity play as part of a technical artefact seen as a whole. Observer and Value Independence A key aspect of the natural framework of interpretation is that different observers of the same phenomena are assumed to provide equivalent interpretations. The interpretations are assumed to be observer independent, or in other words objective, and can therefore be validated experimentally by comparing the model with observed behaviour of the modelling object. Interpretations within the natural framework are also claimed to be value independent, which means that it is impossible to distinguish between successes and failure. Models based on this framework can accordingly not be used for failure prediction without being complemented with other types of interpretation which include value distinctions (e.g. the social framework). A similar point was raised by Polanyi [4, chapter 11] who argued that the principles controlling the operation of a machine cannot be completely attributed to underlying physical or chemical mechanisms. In order to understand a machine, you need to distinguish between when it works versus when it does not work i.e. fail. The purpose of the machine must be a perquisite for such an understanding. Although interpretations within the natural framework are assumed to be objective, the selection of phenomena to be included in a model is problem dependent and thereby relying on the problem solvers knowledge and interests. Two model

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builders may accordingly agree on the interpretation of phenomena but disagree in their selection if their models are used to solve different problems, or if they have different modelling preferences (e.g. level of detail).

5.2.3 The Social Framework of Interpretation Within the social framework, interpretations are observer dependent by relying on inter-subjective factors or points of view or interests shared by others. The concepts of intention, action, means-ends are here of central importance when describing the meaning of actions and the relations between human values and technical artefacts. This is in stark contrast to the natural framework, where interpretations are objective by being largely independent of the observer and value neutral. However, intersubjectivity does not mean dependency on the personal attitudes of an individual observer. It means that the interpretations are shared by members of a society (e.g. designers and users of artefacts) and therefore considered true in an epistemic sense according to Searle (op.cit.). The social framework of interpretation includes other sub-frameworks (also mentioned by Goffman) not relevant for technical artefacts. Lifeworlds Schutz [11] developed the phenomenological concept of the lifeworld which has strong connections to the social framework of interpretation, and considered the division or stratification of the lifeworld into provinces of meaning with different relevance structures. The province of meaning relevant for modelling technical artefacts is the everyday lifeworld which includes the inter-subjective goal-oriented interaction between humans and technical artefacts (as opposed to the fantasy world). Habermas Logic of the Social Sciences Habermas [12, pp. 76–77] identified three different social frameworks for understanding the plan or intention of a social system: • a plan can be understood teleologically, in which case it is based on the artisan model of instrumental action through which an end is reached through appropriate means • a plan can be understood dialectically, in which case it is based on the dramaturgic model of communicative action, in which an author makes an experience transparent through the role playing of actors • a plan can be understood by using a model borrowed from biology. According to this model, systems are understood as (1) organized unities that under changing circumstances, maintain themselves in a specific state through self-regulation and reproduction and/or (2) as being evolved through a selection process Habermas applies this division to a comparison of different approaches to functionalism within social science. The results of his analysis are of interest here because the three sub-frameworks apply different concepts of function. It is

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therefore necessary to specify the more specific framework of interpretation applied when using functional concepts. Each of the three sub-frameworks assume different causal mechanisms for the attainment of adaptation or fitness between the system and its environment (design, dialogue, self-regulation, reproduction and selection). The concept of function relevant for modelling technical artefacts is the artisan model i.e. the first in Habermas list. However, the second concept of function derived from communicative action is not immediately relevant. The third concept of function derived from biology is relevant for modelling technical artefacts in two ways. • It is relevant for modelling processes in biochemical engineering. Modelling the functions of e.g. an automated fermentation process require a combination of two concepts, one related to the functions of the automated control and chemical engineering processes (the artisan model) and another evolutionary to the functions of living organisms explained by self-regulation and reproduction. • It is relevant on a broader level as an evolutionary model explaining how artefact functions develop historically by a procedure of selection based on human experience from using technology. This approach is proposed by Simondon [13] and may be relevant for the study of the procedure of artefact design but will not be investigated here.

5.2.4 Interpretation of Time and Space Time and space play a central role when describing the behaviour of human beings and technical artefacts, including their functions. The question to be considered in this section is how the natural and the social frameworks of interpretation deal with time and space.

5.2.4.1

Time

According to Greek philosophy there are two concepts of time cronos and kairos which are related to the distinction between the two frameworks. The two meanings of the concept of time are explained by Smith [14] as follows: • Time understood as cronos is the concept of time as measure: This characterization (of time) combines the three essential features of chronos. There is, first, the elements of change, of motion, of process which lasts through or requires a length of time: for Aristotle, time is not identical with movement, but it nevertheless cannot be thought apart from movement. Secondly, there is the fact that, an appropriate measuring unit being given, the quantity of the movement and the elapsed time before and after Time so conceived is both a frame or container in and through events take place in an actual order of happening. . .

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• Time understood as kairos refers to qualitative aspects of time related to the significance of events to human affairs: Three distinct, but related, concepts are involved in the notion of kairos. It means, first, the right time for something to happen in contrast with any time; this sense of kairos is captured by the English word timing. . . Second, kairos means a time of tension or conflict, a time of crisis implying that the course of events poses a problem which calls for decision at that time. Third, kairos means a time when an opportunity for accomplishing some purpose has opened up as a result of the problem that led to the crisis. Thus kairos means the time when something should happen or be done, the right or best time; it means the time when an opportunity is given for creative action or for achieving something or a special result that is possible only at this time. . .

The two meanings of time are clearly related to the distinction between the two frameworks of interpretation introduced above. Cronos is relevant for the description of both actions and physical phenomena whereas kairos is only relevant for description of human actions e.g. and their timely execution. There is no right time in the world of physics. When describing behavior as temporal developments it is accordingly necessary to distinguish between behavior of purely physical objects in chronological time, and behavior of human agents and their interactions with artefacts in terms of timing, which include its functions as shown later. 5.2.4.2

Space

Space can also be associated with two different meanings depending on the framework of interpretation. In the natural framework of interpretation space is seen in its geometrical aspects. However, within the social framework spatial entities are assigned meaning or functions in relation to the means and ends of action. Schutz and Luckman [11] present an analysis of the structure of the lifeworld of agents like humans or organisms. Even though the analysis is of the structure of the common sense world of humans and other animate organisms, their analyses are highly relevant for understanding how to structure the spatial environment of an agent (robot) equipped with limited sensorial, manipulatory and mobility resources. The limitations of the capability of the inner environment of the robot leads to a stratifications of its outer spatial environment: • The world within actual reach – Zones of direct action – Zones of mediated action • The world within potential reach – Restorable reach – Attainable reach The principle of the stratification is to divide the world into spatial zones according to what can be reached by the agent given its capabilities and the opportunities for action. The stratification is therefore based on knowledge about the

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means of action available (observational and manipulation capability of the actor) and the opportunities for action given the spatial characteristics of the environment. This mutual relation between the actor and the environment is presented in Chap. 7 as the principle of reciprocity which determine the ascription of functions to artefacts. The spatial environment can also be ascribed functions in relation to the goals of the robot. Well known concepts used in robotics exemplify the purposeful use of space i.e. its functions; • landmarks which are locations in space having special meaning as guides for navigation • pick-up and put-down stations which are locations used by a robot to load or unload goods It is realized that the meaning or function of spatial locations, their affordance for action, and entities within a social framework of interpretation play a key role in action planning for both humans and robots. Space plays also a role in the design and operation of industrial processes. Process components and equipment are located in space and can be decomposed into spatial parts. Locations of equipment and knowing where to intervene are also important for the human operator.

5.3 Modelling Stances Dennett [15] proposes three different modes or stances an observer can adopt when describing or explaining complex systems; an intentional, a design or a physical stance: In the intentional stance the system is described as if it has intentions. A stance which is inviting when describing systems whose behavior appear to be directed by goals In the design stance the system is described by the functions intended by the designer In the physical stance the system is described by the materials or the hardware used for its realisation

The intentional and the design stances are clearly related to the social framework of interpretation since they both relate to the representation of knowledge involved in the interaction between humans and technical artefacts, in particular when the artefact is seen as an embodiment of the designers intentions. The physical stance applies the natural framework of interpretation. However, the stances are not identical to the distinction between the frameworks. They represent different standpoints an observer or modeller of a system can take to make sense of its behaviour in a particular context of investigation. The term intentional system coined by Dennett [16] is therefore not implying that the system

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has intentions, it only appear to the observer as if it has beliefs, expectations, values and goals. The stance chosen depends accordingly on the explanatory purpose of the description given by the observer, which could be to serve as a medium of communication between members of a team of designers or operators, or it may be implemented in a model used for automated reasoning and decision support. A system can accordingly in principle be described as an intentional system without being designed to be one. The intentional stance is often used when describing technical artefacts which exhibits goal directed behaviour such as control systems. For example, even a simple system like a room thermostat with feedback and feedforward mechanisms can be described as if it has intentions since its response to disturbances can be described as oriented towards a goal—to keep the temperature at a given setpoint (feedback), and to be guided by expectations (feedforward). It seems accordingly to have preferences and behave according to a plan. However, the behavior produced by feedforward and feedback loops can also be explained by the causal mechanisms of the elements used for implementation. These mechanisms comprises the means of control and is the basis for implementing the functions of feedforward and feedback loops. Rosenblueth et al. [17, 18] used this as an argument against the use of functional concepts for explanation of goal directed systems using feedback. The argument ignores that the concept of control cannot be defined without reference to objectives or intentions (see Rescher [19]) and that its interaction with the system it is controlling only can be understood with that in mind. Control systems invite therefore to descriptions in the intentional stance when seen in the context of the whole e.g. the system under control. But still, control systems do not have intentions in themselves. The description by intentional concepts such as goals, beliefs and plans specify functions of the control system and is therefore given in the design stance. Functional modelling of control systems is discussed in Chap. 16. The intentional stance is also highly relevant for modelling interaction in communication (see e.g., speech acts by Searle [20]). This modelling approach has been promoted by AI researchers in so-called agent systems. According to this theory an agent is a social entity and can negotiate goals and communicate with other agents. Agent technology is currently being exploited by industry for the implementation of intelligent distributed control systems, or more broadly autonomous systems (see e.g. Jennings and Bussman [21]).

5.4 The Hermeneutics of Modeling The two frameworks of interpretation and the associated systemic viewpoints are mutually interdependent in two ways when building models of physical behaviour and functional models. The interplay between them can be illustrated as shown in Fig. 5.7 which is an instance of the hermeneutic circle known e.g. from theories of text interpretation. The process of text interpretation is not linear but circular

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Fig. 5.7 The hermeneutics of artefact modelling

because the meaning of the single words (the parts of the text) depends on the meaning of larger sentence structures (the text as a whole) and vice versa. Building a functional model can be seen in a similar way, as a constructive hermeneutical procedure revealing the meaning of the physical interactions in the context of the intentions of a human designer or operator. There are two relations between the physical structure and phenomena of the technical artefact and its functions (see Fig. 5.7):

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Fig. 5.8 The spiral of interpretation in modelling

• The relevance or significance of a physical phenomenon (the parts) for inclusion in a physical model depends on system functions (the whole). The functions provide criteria for selection of physical phenomena to be included in the model. This is the case both when modelling the functions of an existing system, and when modelling in conceptual design i.e. before the system exists. • The assignment of functions to the system is conditional on physical phenomena and structures. The depiction of the procedure for interpretation in building a functional model as a circular movement is a slightly misleading simplification. It can better be described as a spiralling movement as illustrated in Fig. 5.8, starting with an initial hypothesis about the significance of the whole or the parts which is subsequently elaborated at each turn of the spiral. Figure 5.8 shows two modelling procedures A and B which have different purposes both relying on the two frameworks of interpretation, but in different ways as depicted in Fig. 5.7. Firstly, the process of building a functional model is relying on knowledge of physical phenomena which are guiding the behaviour of the artefact. This knowledge is necessary in modelling an existing artefact. Secondly, the procedure of physical modelling of a technical artefact is relying on knowledge of its functions in order to select phenomena of relevance for inclusion in a physical model. Two types of purpose are guiding the development of physical and functional models of a technical artefact; (1) the purpose of the artefact (its function) and (2) the purpose of the model i.e its use in solving an engineering problem.

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References 1. R. Rosen. Anticipatory Systems. Oxford, UK: Pergamon Press, 1985, p. 436. 2. J. Dewey. Logic: The Theory of Inquiry. Ed. by J. A. Boydston. Vol. 12. The Later Works, 1925–1953. Carbondale, USA: Southern Illinois University Press, 1986, p. 793. 3. D. A. Schön. The Reflective Practitioner. Basic Books, 1983, p. 374. 4. M. Polanyi. Personal Knowledge. London: Routledge and Kegan Paul, 1958. 5. B. Rolf. Profession, tradition och tyst kundskap. Lund: Nya Doxa, 1991. 6. W. F. Brewer and G. V. Nakamura. The Nature and Function of Schemas. Research rep. 325. Center for the Study of Reading, University of Illinois at Urbana-Champaign, 1984. 7. M. Minsky. “A Framework for Representing Knowledge”. In: Mind Design; Philosophy, Psychology, Artificial Intelligence. Ed. by J. Haugeland. Bradford Books, 1981. Chap. 3, pp. 95–128. 8. E. Goffman. Frame Analysis. London: Penguin Books, 1974. 9. H. A. Simon. The Sciences of the Artificial. Cambridge: The MIT Press, 1981. 10. J. R. Searle. The Construction of Social Reality. The Free Press, 1995. 11. A. Schutz and T. Luckmann. The Structures of the Life-World, Vol 1. Evanston: Northwestern University Press, 1973. 12. J. Habermas. On the Logic of the Social Sciences. Cambridge: The MIT Press, 1989. 13. G. Simondon. On The Mode of Existence of Technical Objects. University of Minnesota Press, 2017. 14. J. E. Smith. “Time, Times and the "Right Time"”. In: The Monist 53 (1969), pp. 1–13. 15. D. C. Dennett. The Intentional Stance. Cambridge, Massachussets: The MIT Press, 1993. 16. D. C. Dennett. “Intentional Systems”. In: The Journal of Philosophy 68.4 (1971), pp. 87–106. 17. A. Rosenblueth, N. Wiener and J. Bigelow. “Behavior, Purpose and Teleology”. In: Philosophy of Science 10 (1943), pp. 18–24. 18. A. Rosenblueth and N. Wiener. “Purposeful and Non-Purposeful Behavior”. In: Philosophy of Science 17 (1950), pp. 318–326. 19. N. Rescher. “The Concept of Control”. In: Essays in Philosophical Analysis. Ed. by N. Rescher. Pittsburgh, USA: University of Pittsburgh Press, 1969. Chap. VII. 20. J. R. Searle. Speech Acts: An Essay in the Philosophy of Language. Cambridge: Cambridge University Press, 1969. 21. R. Jennings and S. Bussmann. “Agent-Based Control Systems”. In: IEEE Control Systems Magazine June (2003), pp. 61–73.

Chapter 6

Causality

Causality and the concept of function are closely related and theories of causality are therefore important for the foundations of functional modelling. The purposes of this chapter are to introduce different concepts of causality, to explain how they are related to the frameworks of interpretation introduced in Chap. 5, and provide the background for Chaps. 7 and 10. A generally accepted theory of causality is not available. Instead there are many apparently competing or complementary accounts offered. The problem is about the metaphysics of causality, and not about the logics of the cause-effect relation which is well developed and is used in formalised reasoning.

6.1 Concepts of Causality Several researchers argue that it is not possible to formulate a general theory of causality because the meaning of the concept, apart from its logical meaning, is dependent on the scientific field or domain of application (see e.g. Menzies and List [1], and Illary and Russo [2]). The logical aspects of causality (see e.g. Pearl [3]) are obviously important for reasoning about causality (and thereby functions and actions), but they are of secondary importance problem framing and formulation. In modelling the primary focus is on distinctions between different types of causality and their associated conceptual schemas (the use of conceptual schemas in modelling was discussed in Chap. 5). Technical artefacts like socio-cyberphysical-systems (SCPS) involve several interacting technologies with different types of causal mechanisms, and their modelling are therefore dependent on such distinctions to define the functional relations between the parts. An important conceptual distinction is to see the relation between cause and effect as either a dyadic or a triadic relation. Dyadic causality is involved in interactions between physical entities whereas triadic causality is involved in interactions © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_6

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involving representations such as exchange or processing of information. It will be shown in this chapter that both dyadic and triadic causal relations are necessary for modelling SCPS functions. In a broader perspective, Collingwood [4] suggested, that the concept of causality is used in three different senses which has been developed since antiquity and need to be properly distinguished. Of particular importance for functional modelling is Collingwood’s observation, that in one of the senses, the relation between cause and effect should be seen as a means-end relation involving the interaction between humans and natural objects and technical artefacts. These metaphysical aspects of causality are highly relevant for functional modelling since they relate to the overall distinction between the natural and the social frameworks of interpretation introduced in Sect. 5.2, and to finer distinctions within the social framework of interpretation to be presented in this chapter.

6.2 Defining Causality Most current theories of causality relies on a definition originally proposed by Hume [5], who presented an extensive conceptual analysis of the concept of causality in human reasoning. Hume proposed the following definition: we may define a cause to be an object, followed by another, and where all objects similar to the first are followed by objects similar to the second“..”or in other words if the first object had not been, the second never had existed

Mumford et al. [6] present in an overview of more recent accounts on causality the following reformulation of Hume’s definition: A cause-effect relation is a relation between two events (states), the cause and the effect. The cause is a sufficient condition for the effect to occur. The condition is not necessary because an effect can be caused by several causes. In addition, the cause and the effect are connected by a counterfactual condition saying that if the cause did not happen, the effect would not occur.

Hume’s definition has two parts. The first part define causation as a binary or dyadic relation between the cause and the effect. The cause and the effect are connected by a regularity condition i.e. the effect always follows if the cause occurs (sufficiency). The second part is the counterfactual aspect of causation i.e. if the cause did not occur the effect would not happen. The counterfactual condition is important because the effect is more than just a happening. It is the consequence of an intervention i.e. it is caused by something or somebody, in which case it depends on human intentions and actions. The significance of the counterfactual aspects of causation for the relation between causes and intentions (and therefore for functional modelling) will be demonstrated in Chap. 10.

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6.2.1 Changing or Becoming Mumford and Anjum’s reformulation of Hume’s definition emphasizes that causes and effects are events which are distinguished by different states, and by this it presupposes the existence of entities which persist during the change, and having changeable states. But since the causal relation is defined as a relation between changes and not the entities themselves it is an open question what these entities can be. Can they be things or persons or even a process? These questions will be addressed by Mumford’s dispositional approach to causality described below. A more fundamental question can also be raised whether becoming as an event (rather than changing which presuppose the existence of something) can enter a causal relation i.e. be a cause or an effect? To change and become are two types of transient occurrences which form the basis of process philosophy where processes are considered ontologically primary and things are seen as bundles of processes (see Rescher [7]). Chapter 7 will explain that this metaphysical position is not tenable in engineering where functions are ascribed both to things and processes. However, different types of creation or becoming can be distinguished when modelling technical artefacts. Becoming can involve the appearance of an entity, and the reverse its disappearance or destruction. Another meaning of becoming can be when a new object is created from other objects by assembling them into a whole. The new object exists only as a potentiality before it is actualized when assembling the parts. Example: Modelling technical artefacts includes changes of position, amounts and composition of things, but can also include more abstract changes like ameliorations (improvements) and degradations. Assembling is a key process in discrete manufacturing, but includes also chemical reactions, mixing, separation, and conversions where new products are created. Finally, as shown later, creation of meaning is involved in modelling of instrumentation and control systems.

6.2.2 Contiguity in Time and Space It is sometimes claimed that cause and effect should be contiguous in time i.e. the effect is always happening at the same time or later than the cause. This condition seems to be universally applicable for interpretations of the concept of causality relevant for modelling technical artefacts. Reverse causation has been suggested by some researchers i.e. that current states or events can be determined by future states or events. But this idea seems to confuse two types of determination between events; the causal and the teleological. These two types of determination is included in the means-end relation as discussed later (see Sect. 6.6).

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It is also sometimes claimed that causes and their effects should be contiguous in space. This means that the caused and the caused entity (item1 and item2 in Fig. 6.1) should be closely located in space. For example, contiguity in space is often seen as a condition for exchange of materials and energy, but this is not always the case. Energy can be exchanged by radiation which can happen between items which are separated in space i.e. the spatial contiguity condition is not satisfied. Likewise, exchange of information in communication does not require spatial contiguity, at least not when considering causal relations involved with transfer of meaning. However, the exchange of information requires a physical medium. This means that to describe the causal relations defined by the means of communication spatial contiguity may be necessary. The relevance of the spatial contiguity requirements depends accordingly on the context.

6.3 Causality as a Dyadic Relation Causality can, according to Mumford et al., be defined as a binary or dyadic relation between events or states of some entities. The two entites represented by causal roles, the agent and the object, can be seen as terminals of the causal relation as shown in Fig. 6.1. The roles are abstract representations of the capacity for change of the two entities when seen in a particular context. An entity having several capacities or dispositions for change can accordingly have different agent or object roles depending on the context. The introduction of entities having changeable states requires accordingly further distinctions between different causal relations depending on the type of state in

Fig. 6.1 Dyadic causal relation between two items. The state/event of item1 is the cause of the state/event of item 2 (the effect)

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question. The distinction between states of potentiality and actualization is of particular importance for functional modelling. It is required in order to understand the difference between the effect (and intentions) of design actions which is to establish a potentiality for change, the technical artefact, and the effect of the actions of its user or operator—to create an opportunity for change and to actualize it. The relations between states of potentiality and actualization are also important because actualization presuppose a potential and opportunity for change (the technical artefact should be designed before it can be operated). The different types of state is related to the distinction between general and singular causality discussed below. The significance for functional modelling is explored further in Chaps. 11 and 14.

6.3.1 Dispositions An influential theory about causality has been formulated by Molnar [8] and Harré et al. [9]. They suggest that cause-effect relations should be explained by the dispositions of things or persons which include causal powers and the liability to be changed. This means that some entities have the ability to make changes, to be the causer or agent, and other entities have the liability to be changed, to be the object of change (or caused). The two roles agent and object introduced above are called causal roles in order to distinguish them from other roles (circumstantial) which will be introduced later in Chap. 12. Mumford [10] defines dispositions in the following way: Disposition is a term used in metaphysics to indicate a kind of property, state of condition. Such a property is one that provides for the possibility of some further specific state or behaviour, usually in circumstances of some specific kind. Terms as causal powers, ability, propensity, and other, can be used to convey the same idea. The general criterion for something to be a disposition is that the appropriate kind of behavior, the so-called manifestation, need not be to be actual. Hence, something can be disposed to break though it is not broken now. The disposition is thought to be a persisting state or condition that makes possible the manifestation. Because dispositions make other events or properties possible, they are often understood in relation to counterfactual conditional sentences. Something being fragile is somehow related to the conditional that if it is dropped, it will break. The antecedents of the conditional identifies the stimulus for the disposition. The consequence defines the manifestation of the disposition.

The cause-effect relation and its underlying items with causal power (the agent role) and liability to be changed (the object role) is shown in Fig. 6.2. An important aspect of the dispositional theory is that causal powers and liabilities can be assigned to physical entities on arbitrary levels of structural decomposition. For example, a pump system can be assigned the causal power to move a fluid, or a part of the pump e.g. the impeller can be assigned the power, depending on the needs for including or ignoring details of the pump. The definition above makes an important distinction between a disposition and its manifestation. Since events or states are manifestations of dispositions it may

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Fig. 6.2 Dyadic causal relation between two items including their dispositions

be concluded that dispositions only can be assigned to things or persons and not to processes which are sequences of events (unless a process can be represented as a thing). This conclusion will be revised in Chap. 15 to include actions as well. The distinction between a disposition and its manifestation (the event or state) is also of central importance for the definition of failures. A failure can be a lack of ability (the disposition) or opportunity to cause a desired effect, or it can be the lack of its manifestation. Reciprocity Dispositions come in pairs of causal powers and corresponding liabilities for change. This reciprocity was emphasized by Bunge [11], who included dispositions (called causal propensities) in an ontology of the natural sciences, and related them to causation and natural laws. More recent theories of causality proposed by e.g. Mumford [12] explain natural laws as representations of dispositions. Bunge’s observation, that dispositions always come in pairs, is relevant also for functional modelling as functions of an entity are to be considered as subsets of dispositions selected according to the intentions of the designer or user (see Chap. 7 for details). Example: An event like “temperature of X is increased” is associated with something X having a property, the temperature, which is related to a disposition of X—to be heatable. Using the “language” of dispositions, the thing X must

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be heatable i.e. be liable or have the capacity to be heated in order to have a temperature, and some other thing Y should be able-to-heat i.e. have the causal power to heat X (e.g. be a source of heat). Failing to heat X could accordingly be explained by (1) X not being heatable, (2) Y not being able to heat, or (3) the lack of an opportunity to heat.

6.3.2 General and Singular Causation The distinction between general and singular causality is important for causal reasoning in both engineering (i.e. about technical artefacts) and in medicine (see e.g. Pedersen [13]). General causality is defined as the ability of something to cause something or being caused and is accordingly related to its dispositions. Singular causality, on the other hand, is defined as a relation between particular events or occurrences in time or space i.e. between manifestations of dispositions. General causality is a precondition for singular causality of the simple reason that if there is no dispositions for change there cannot be any manifestations. The relation between general and singular dyadic causality is illustrated in Fig. 6.3.

Fig. 6.3 Dyadic general and singular causal relations between two items

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The distinction between general and singular causality is important for functional modelling by introducing a separation between functions as dispositions i.e. as potentialities, and functions as manifestations of dispositions i.e. as actualizations. Causal Fields and Complexes Mackie [14] proposed a theory of singular causality which contributes with an additional distinction between conditions and causes and the concept of causal fields. The theory was applied by Pedersen [13] to explain how causal reasoning in medical diagnosis is based on a causal field, representing the background context for reasoning about causes and effects. The same analysis applies to engineering contexts as explained by Pedersen et al. [15]. The notion of a causal complex is illustrated in Fig. 6.4. A causal field comprising a network of causal complexes and a causal path is illustrated in Fig. 6.5. Petersen [16] showed that changes in the causal field is involved when changing focus in the causal reasoning in the context of functional modelling.

Fig. 6.4 Graphical illustration of Mackie’s theory of singular causality (Pedersen [13]). Each box in the tree is called an effective causal complex. Each line in the box is a non-redundant factor or component, A cause is defined as a non-redundant component of an effective causal complex

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Fig. 6.5 Tree of causal complexes and a causal path (adopted from Pedersen [13])

Fig. 6.6 A causal chain. The dyadic causal relation is transitive and can create chains of causes and effects

6.3.3 Causal Chains The dyadic relation between a cause and its effect is transitive since an effect can be the cause of another effect as illustrated in Fig. 6.6. Analysis of causes and effects is faced with a problem of termination. Thus it can be difficult to decide what is to be considered as the prime cause or the final effect of a causal chain. The termination of a chain is, both at the beginning and at the end, ultimately decided by pragmatic factors. However, the termination problem is solved when seeing causality as an aspect of the means-end relation (see Sect. 6.6).

6.3.4 Causation by Exchange of Mass and Energy A particular view on dyadic causality is to see it as explained by exchange of material or energy (see Dowe [17]). This view, called physicalism, is compatible with the dispositional approach to causality if dispositions of an item are defined as

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the ability to transmit (as an agent) and receive or transform mass or energy (as an object). Dowe’s theory excludes obviously causal relations which are not connected with exchange of these kinds of quantities (such as information) but still relevant for modelling communication and control in technical artefacts. It may be argued that transfer and processing of information cannot be done without a physical medium or carrier and thereby involves exchange of mass or energy (electric signal). However, the assignment of causality depends on the level of abstraction or context required to express the purpose of communication or control. In one context concerned with the means of communication or control, the intention is to use signalling (e.g. energy or mass transfer) as a carrier of information, in another context the intention is related to the effect or meaning of the information transferred. Two contexts of causal assignments are accordingly involved which are related as means and ends.

6.4 Causality as a Triadic Relation The dyadic concept of causality is sufficient for modelling physical interactions and is relevant for modelling functions of technical artefacts involving the transfer or transformation of materials and energy. However, it is insufficient for modelling causal relations involved in generation, transfer, and transformation of information that require a triadic concept of causation as suggested by Deely [18]. Triadic causal relations (and associated functions described in Chap. 13) are associated with mind dependent phenomena and are often called signs.

6.4.1 Signs Triadic causality was originally proposed by Pierce (see e.g. Freeman [19]) by his distinction between firstness, secondness (the dyadic relation) and thirdness (the triadic relation). Signs are according to Pierce triadic relations. A sign stands for something to the idea it produces, or modifies ...That for which it stands is called its object; that which it conveys, its meaning or representamen; and the idea to which it gives rise, its interpretant.

Signs are the subjects of the field semiotics, where the generation and transformation of meaning by sign processes is called semiosis [18]. Several versions of the sign concept have been proposed. Here the terminology proposed by Morris [20] will be used (see Table 6.1). The sign relation, also called the semiotic triangle, includes as shown in Fig. 6.7 three roles; an interpretant, a signifier (the object of interpretation, the “cause”) and the signification (the meaning produced, the “effect”). The interpretant can be seen as a mediator of a dyadic causal relation between the signifier and its signification.

6.4 Causality as a Triadic Relation Table 6.1 Comparing sign concepts of Peirce and Morris

113 Peirce Object Representamen Interpretant

Morris Signifier Signification Interpretant

Fig. 6.7 The semiotic triangle is a triadic causal relation between three items; item1 (an interpretant) establishing a causal relation between item2 (the signifier) and its meaning item3 (the signification). Note that item3 can be a signifier in another sign relation

Example: Chandler [21] gives the following example to illustrate the meaning of the sign concept: “The traffic light sign for stop would consist of a red light facing traffic at an intersection (the signifier), a vehicle halting (the signification) and the idea that a red light indicates that vehicles must stop (the interpretant)” The three roles involved in a sign relation (interpretant, signifier and signification) are also reciprocal because each of the roles conceptually presupposes the other two. Conceptually they form a triad. The triadic causality defined by a sign is not related to the dispositions for change of something as in the case of dyadic causality. In triadic causality, as defined here, there is nothing changed. The causality of the sign is related to dispositions of the interpretant exclusively, and the effect of the sign is creation or becoming (not change) of some signification of the signifier. The distinction between dyadic

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and triadic causality accordingly relies on the two types of transient occasions, changing and becoming. The regularity condition, the counterfactual aspects, and the contiguity in time of dyadic causality apply also for triadic causality. The distinction between general and singular causality also seems to apply as well. Signals Signs and signals should be clearly distinguished. The concept of a signal is information theoretic and corresponds in semiotics to the sign vehicle or signifier. A signal is opposed to a sign by being only its physical embodiment. Signals are potential sign vehicles because their function in transmitting a message require an interpreter (see Nöth [22, p. 80]). As explained in Chap. 7, this distinction is important for functional modelling of control systems which traditionally are seen as signal processing systems. Signals involve transfer of energy or material, and they are therefore connected by dyadic causal relations.

6.4.2 Modes of Signifying The triadic causality of a sign is dependent on assumptions regarding the nature of the interpretant. It can, as explained below, be based on social conventions i.e. on the interaction with other minds, or on the experience of an individual cognitive agent interacting with the physical world. According to Pierce signs can be subdivided into symbols, icons, and indexes corresponding to three modes of signifying or ways a signifier can have signification for an interpreter. Chandler[21, pp. 36–37] describes the three modes of signifying as follows: 1. Symbol/symbolic: a mode in which the signifier does not resemble the signified but which is fundamentally arbitrary or purely conventional—so that the relationship must be learned e.g. languages in general(...), numbers, morse code, traffic lights... 2. Icon/iconic: a mode where the signifier is perceived as resembling or imitating the signified ...- being similar in possessing some of its qualities... 3. Index/indexical: a mode in which the signifier is not arbitrary but is directly connected in some way (physically or causally) to the signified—this link can be observed or inferred: e.g. natural signs (smoke, thunder...), medical symptoms (pain,..., pulse rate), measuring instruments (... thermometer, clock, spirit level), signals (a knock on a door, a phone ringing), pointers (...)

The three modes of signifying are listed in decreasing order of conventionality. When considering control and supercision of technical artefacts all modes of signifying may be involved. Signifying by Convention A standard example of signifying by convention is the use of pieces of metal (coins) for exchange of monetary value (i.e. they have the function of being money). This means that their functions is, when exchanged between people, to signify monetary value and to cause corresponding behavior of the humans using them. This kind of causal relation is not based on a disposition of coins to signify value, but on an agreement between participants of a community

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involved in a particular praxis (exchange of monetary value). The mind dependent causality involved here is a triadic sign relation between a signifier—the coin, its signification—the monetary value, and an interpretant—the social conventions associating monetary value with the coin. Signifying by convention is also relevant for modelling technical artefacts. The stop light in Chandlers example is signifying by conventions expressed by the traffic rules. However, conventions are also involved when associating value i.e. cost or benefits to materials and energy. It plays an important role when choosing between design alternatives and in operational decisions. For example control actions making tradeoffs between the cost of an intervention in the process, and the expected benefit of the response obtained (so-called optimal control), are based on criteria including the values assigned to materials and energy. The signification of materials and energy (their value) is based on agreements established by the market. Signifying Through Experience in Action Morris [20] applied triadic causality in a semiotic analysis of the stages of action in goal oriented behaviour of an organism. The analysis describes the relations between values or goals of an organism, the stages of an action, and the associated representations (significations) of the environment. The relations are defined through preferences or dispositions to act, learned through experience from interacting with the environment (the interpretant in the triadic relation). These preferences, guiding a goal oriented agent in its interactions, can also be seen as habits or internal mental representations achieved through its adaptation to the environment. According to Morris, the causal roles (the signifier, the interpretant, and the signification) of the entites involved depends on the stage of an action. Morris applies the analysis of the behavior of living organisms, but it is also applicable for a causal analysis of a control system seen as a cognitive artefact. The difference is that control systems are designed, and that the representations it is using of the environment (its signification) are acquired through instruction or programming (which also may include learning algorithms) according to the control designers values, plans and preferences. The relevance of Morris’ analysis for modelling the cognitive functions of human agents and artefacts like control systems or robots will be discussed in more detail in Chaps. 13 and 16. Affordances Triadic dispositional theories of causality (see e.g. Toyoshima [23]) have also been applied to represent affordances, a concept proposed by Gibson [24] to represent how goal oriented organisms perceive the environment. The basic idea is that items in the environment of an organism afford actions, for example a chair affords sitting (is perceived as seat-able—the significance of the chair in the situation) and may be recognized for this purpose by an organism having the appropriate perceptual dispositions. The affordance is accordingly ascribed by the organism to the chair in the context of this disposition and an intention to be seated. The concept of affordance can accordingly be seen as a psychological interpretation of the concept of function.

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The concept of affordance is closely related to Uexküll’s [25] biosemiotic concept of the “umwelt” representing the environment as perceived by animals and humans. The two concepts of affordance and umwelt are relevant for coping with different viewpoints in functional modelling including the embodiment relation between a human and an artefact (see Chap. 9). The theory of affordances has been applied by researchers in human machine interaction (see e.g. Albrechtsen et al. [26]), but has also been used for modelling the relations between technical artefacts like robots and their environments. The types of causality and their relations defined above are illustrated in Fig. 6.8.

Regularity and Counterfactuals (Hume)

Contiguity in Time

Causal Powers and Dispositions (Harré and Madden)

Agent (causer)

Object (caused)

General and singular causality

Causality as a dyadic relation

Contiguity in Space

Exchange and conservation of mass, energy and momentum (Dowe)

Fig. 6.8 Causality types for technical artefacts

lnterpretant

Meaning or Signification (caused)

Signifier (causer)

Causality as a triadic relation

Exchange of information

Behavioral preferences (Morris)

Speech Acts (Searle)

Affordances (Toyoshima)

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6.5 Three Notions of Causality The two types of causality introduced above for modellÃng technical artefacts are summarized in Fig. 6.8. However, the distinction between dyadic and triadic causality cannot explain the difference between cause-effect and means-end relations which is often confused. Collingwood [4] distinguishes between different senses of the word cause which contribute to such a clarification. Collingwood’s study, published in 1938, has recently gained recognition in social theory (see e.g. Wide [27]). However, it does not include the distinction between dyadic and triadic causal relations, as well as other relevant and more recent contributions from cognitive sciences such as the dispositional approach to causality and theories of communicative action (see e.g. Searle’s [28] speech acts) . These theories which are of relevance for functional modelling will be set into the context of Collingwood’s distinctions below. Collingwood argues that the concept of causality should be seen in a historical perspective. His point is, that current conceptions has been inherited from the Greeks and was developed through times, and that some of the current misconceptions about the concept is due to an ignorance of this historical background. Collingwood proposes the following distinctions between three senses of the term causality; • causality in history (sense I) • causality in practical natural sciences (sense II) • causality in theoretical natural science (sense III), These distinctions are all relevant for functional modelling of technical artefacts and will therefore be explained below. Sense I This is the original concept of causality used when talking about social interaction between humans. According to this a person A can cause somebody B to do something either through giving some advice which influences the behaviour of B, or by giving an order or command. Two types of causes influencing the behaviour of B are involved here:1 • an efficient cause—a situation or state of affairs existing (the advice given by A to B) • a final cause—a state of affairs to be brought about by B (the command given by A to B) The two causes correspond to the two types of motive for an action defined by Schutz [29]; the because-of motive, and the in-order-to motive (to be discussed in Chap. 10).

1 The efficient cause and final cause refers to Aristotle’s distinction between four causes; the material, efficient, formal and final causes. These distinctions are used by Collingwood but are today considered of historical interest only.

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These two notions of causality implies free will since person B, in spite of the motivations put forward by A, can decide to ignore the advice or not to act according to the command. Free will is relevant for the study of human-human interaction but not for modelling the interaction between humans and technical artefacts unless it is seen as an intentional system (see Sect. 5.3). Causality in sense I also includes acts of communication i.e. transfer of information. The causal relation between A and B involves two triadic causal relations with two interpretants, the sender and receiver of a message. The theory of speech acts proposed by Searle [28] and used in the branch of artificial intelligence called agent systems is of particular interest here. Sense II Within the practical natural sciences, including engineering and medicine, which are concerned with intervention in the world through action, cause-effect relations are equivalent to means-end relations. This is the key interpretation of cause-effect relations used in functional modelling of technical artefacts and their interactions with humans. Here the causal relation is defined as follows by Collingwood: A cause is an event or state of things which it is in our power to produce or prevent, and by producing or preventing which we can produce or prevent that whose cause it is said to be

The distinction between conditions and causes, which is emphasized by Mackie [14], is also mentioned by Collingwood as an aspect of causality used in the practical sciences. A cause is here one of the conditions in a causal field, selected from the practical interest by an agent in producing or preventing an event or state of affairs. The selection of one of the conditions to be considered as the cause depends accordingly on both an aim or interest and on the available means for action, and is called the principle of relativity of causes by Collingwood. A consequence of this principle is that the same effect can be explained by different causes depending on the point of view (i.e. the condition selected). In most situations there are several agents involved and different causes may be proposed. This many-to-one aspect of the causal relation in the practical sciences is a challenge when investigating accidents, diagnosing system failures or diseases. A classical example is the misclassification of the cause of an accident in industrial plants as error of the human operator even though a closer study reveals a design error in the human machine interface. In addition to the principle of relativity, the same cause can have many effects (depending on the other conditions). The means-ends relation is therefore a many-to-many relation (see also Chap. 17).2 Sense III The theoretical sciences, including physics, chemistry etc., are only concerned with understanding the world and is only intervening with the world when making experiments. Russel [31] argues that in theoretical sciences causal

2 Gasking [30] claims that recipes can cause something. But recipes or procedures are more like the

manner in which some means are used in action. The distinction between the means and manner of acting are discussed in Chap. 10.

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relations should be abandoned and substituted by natural laws represented by mathematical functions. This tradition is inherited by engineering when they apply physics and chemistry when modelling artefacts.

6.5.1 The Three Senses and the Frameworks of Interpretation Collingwood’s distinctions between different senses of the concept of causality are reflected in the two frameworks of interpretation which were introduced in Chap. 5. Following Collingwood and Russel (op. cit.), causality is substituted by natural laws in the natural framework of interpretation, but the two other notions of causality (sense I and II) are relevant for the social framework of interpretation and thereby for the functional modelling of technical artefacts.

6.5.2 Non-causal Connections Kim [32, p. 22] discusses causality as a special case of determination relations which also includes non-causal connections. The idea is that there are other types of determination, having the same fundamental properties as the causal relation (e.g. regularity and counterfactuality), which cannot be explained physically e.g. by exchange of mass or energy. Kim uses the example of the death of Greek philosopher Socrates by poisoning “causing” his wife Xantippe to become a widow. The dependency is in this case through the change of martial status i.e. a triadic causal relation based on conventions. Technical artefacts include non-causal connections which cannot be seen as cases of triadic causality. One example is a containment relation between two physical objects A and B. Assume for example that A contains B and that the location of A is changed by some force. Here the consequence will be that the location of B changes also. Accordingly, the change of A’s location can be seen as causing the change of B’s location even though there seems to be no exchange of energy or force involved. It may therefore be appropriate to say that the position of A and the position of B are related by a non-causal connection. However a closer analysis of the situation will reveal that the determination relation can be explained on another level of detail by exchange of energy between A and B. The distinction between a causal (dyadic or triadic) and a non-causal relation may accordingly depend on the level of abstraction required. It is accordingly necessary to allow combinations of causal and non-causal connections into more general chains of determination as shown by the example in Fig. 6.9.

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Fig. 6.9 A chain of determination containing causal and non-causal connections

6.6 The Means-End Relation Collingwood’s observation, that the cause-effect relation in sense II is a meansend relation, is of fundamental importance for understanding functions of technical artefacts. The ascription of functions to technical artefacts implies causality as well as intentions, and the basic idea of the means-end relation is to provide an integrated concept capturing both causality and intentions. Functions and the meansend relation are therefore somehow related. Chapter 17 will show this in detail. A means-end relation can be depicted as shown in Fig. 6.10 as a vertex connecting two nodes P (the means) and Q (the end). The means and the end are the terminals of the relation. The nodes connected at the terminals become in this way a means (P) and an end (Q) by being connected through the relation. The abstract representation of the means-end relation shown in Fig. 6.10 does not reveal what kinds of entities P and Q can be in order to enter a means-end relation. This abstraction is deliberate since different types and means and ends are possible, and because it is of interest to investigate what can be said in general about the means-end relation independent of the concrete nature of both P and Q. A means-end relation and its associated nodes expresses information about intentions. Two types of intentions are actually involved here; (1) the end Q itself as an expression of what should be achieved, and (2) an intention to use the means P to do it. Means-end relations can therefore, through the associated purpose, be used to express the intention of the system designer or another agents intention. The intention to use the means for the end is motivated by the agent by his/her previous experience, including knowing the means which have been used before successfully before to accomplish ends or objectives of design or operation.

6.6.1 Teleological and Causal Aspects This can also be expressed by two aspects of the means-end relation, a teleological and a causal. When P is a means for an end Q it is implied that P is used by an

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Fig. 6.10 A means-end relation between two items P and Q

Fig. 6.11 The two aspects of the means-end relation; the teleological and the causal

end

effect

causality

agent

teleology

Q object

P means

cause

agent with the intent of achieving Q. This is the teleological aspect of the relation. Furthermore P should also be able to produce or maintain the end Q. This causal aspect of the means-end relation is motivated by the agents experience, that the means can cause the end. Conceptually, P cannot be a means if it is not both defined with the end Q in mind and being able to produce it i.e. P should have the required dispositions.3 These are the preconditions for P and Q being related by a means-end relation. Accordingly there are two directions of determination involved in the meansend relation as indicated in Fig. 6.11 by the arrows on the vertices connecting P and Q. In the teleological aspect, it is the end which determines the means. In the causal aspect it is the cause which determines the effect. The teleological and the causal aspects of the means-end relation are accordingly mutually connected into a circle of determination (end->means->cause->effect->end) (see also Højrup [33]). The relation between the causal and the teleological aspects of the means-end relation solves the termination problem of causal chains mentioned in Sect. 6.6 since the choice of initial cause and final effect in a chain is dependent on the means and the end which are related to dispositions i.e. to general causality. The circle of determination shown in figure is accordingly conceptual and not a logical dependency between singular events. The means-end relation accordingly implies that it is impossible to think about something as being a cause without thinking about it teleologically. The reason why

3 The causal aspect of the means-end relations is accordingly of the general and not of the singular type.

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humans perceive the world as causal is that it is seen in a teleological perspective (causality in sense II according to Collingwood (op. cit.)). Conversely, reasoning about means and ends would be daydreaming or even useless if there was no causal relations to ensure that the ends can be achieved. The relation between cause and effect is usually seen as an objective aspect of a situation. But as mentioned above, the causal relation is only meaningful in a meansend context which includes the teleological relation representing the intentions of an agent. The two concepts of teleology and causality have been the subjects of debate in the philosophy of science through centuries and has often been seen as mutually incompatible with the result that one is eliminated in favor of the other. The teleological relation refers to notions of purpose and intentions and has therefore a subjective or inter subjective content, and is conflicting with objectivity and the concepts of the natural sciences like physics. It is compatible with the aims of engineering sciences which consider the use of physical mechanisms as means for design of technical artefacts.

6.6.2 The Means-End Relation as a Conceptual Schema The two aspects of causality and teleology depicted in Fig. 6.11 comprise a conceptual schema for making sense or interpretation of an event or a situation. By applying the schema meaning is created through the distinctions it makes when ascribing the roles of being agent, object, means and end to entities or occurrences in the situation. Means-end analysis and functional modelling are based on the schema. The schema can be applied in a variety of ways to a situation by different role associations and provides thereby the possibility of multiple representations of the same situation. Not all interpretations will however necessarily be valid. Principles for construction of functional models or other methods are required to ensure validity of the models based on the means-end scheme. Such principles and rules will be domain dependent. A distinction between design and operation, as two separate types of praxis, having different means and ends will also be required.

References 1. P. Menzies and C. List. “The Causal Autonomy of the Special Sciences”. In: Emergence in Mind. Ed. by C. McDonald and G. McDonald. Oxford: Oxford University Press, 2010, pp. 108–129. 2. P. Illari and F. Russo. Causality: Philosophical Theory Meets Scientific Practice. Oxford University Press, 2014. 3. J. Pearl. Causality. 2nd ed. New York: Cambridge University Press, 2013. 4. R. G. Collingwood. An Essay on Metaphysics. Martino Publishing, 2014.

References

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5. D. Hume. An Enquiry Concerning Human Understanding. Ed. by L. A. Selby- Bigge and P. H. Niddich. 3rd ed. Clarendon, 1975. 6. S. Mumford and R. L. Anjum. Causation: A very Short Introduction. Oxford University Press, 2013. 128 pp. 7. N. Rescher. Process Metaphysics - An Introduction to Process Philosophy. New York: State University of New York Press, 1996. 8. G. Molnar. Powers: A Study in Metaphysics. Oxford University Press, 2006. 9. R. Harré and E. H. Madden. Causal Powers. Oxford: Basil Blackwell, 1975, p. 191. 10. S. Mumford. “Dispositions”. In: Routledge Encyclopedia of Philosophy. Routledge, 2001. 11. M. Bunge. Treatise on Basic Philosophy: Ontology I- The furniture of the world. Vol. 3. Treatise on Basic Philosophy. Dordrecht, Holland: D. Reidel Publishing company, 1977. 12. S. Mumford. Laws in Nature. Abingdon, OX14 4RN: Routledge, 2004. 13. S. A. Pedersen. “Causal and Diagnostic Reasoning”. In: Nordic Journal of Nursing Research 10.2-3 (1990), pp. 10–28. 14. J. L. Mackie. “Causes and Conditions”. In: Causation. Ed. by E. Sosa and M. Tooley. Oxford University Press, 1993. Chap. 1, pp. 33–55. 15. S. A. Pedersen and J. Rasmussen. “Causal and Diagnostic Reasoning in Medicine and Engineering”. In: Cognitive Processes and Resources. Proceedings of 2nd MOHAWK Workshop. Vol.3. Ed. by H. B. Andersen, S. A. Pedersen, C. Cacciabue and J. Reason. 1991. 16. J. Petersen. “Focus and Causal Reasoning in Disturbance Management of Complex Dynamic Systems”. In: Proceedings 19th European Annual Conference in Human Decision Making and Manual Control (EAM2000). Ed. by P. C. Cacciabue. Ispra, Italy, 2000. 17. P. Dowe. Physical Causation. Cambridge University Press, 2000. 18. J. Deely. Basics of Semiotics. American University Press, 1990. 19. E. Freeman. The Categories of Charles Peirce. Chicago: The Open Court Publishing Co., 1934. 20. C. Morris. Signification and Significance. Cambridge: The MIT Press, 1964. 21. D. Chandler. Semiotics: The Basics. London: Routledge, 2002. 22. W. Nöth. Handbook of Semiotics. Indiana University Press, 1990. 23. F. Toyoshima. “Modelling Affordances with Dispositions”. In: Proceedings of The Joint Ontology Workshop JOWO 2018. 2018. 24. J. J. Gibson. The Ecological Approach to Visual Perception. 270 Madison Avenue, New York, NY 10016: Psychology Press, 1986. 25. J. von Uexküll. A Foray into the Worlds of Animals and Humans. University of Minnesota Press, 2010. 26. H. Albrechtsen, H. H. K. Andersen, S. Bødker and A. M. Pejtersen. Affordances in Activity Theory and Cognitive Systems Engineering. Tech. rep. Risø- R-1287. Roskilde, Denmark: RisøNational Laboratory, 2001. 27. S. Wide. “Causation and Reason: R. G. Collingwood and Causal Analysis as the Essence of Social Thinking”. In: Distinktion Journal of Social Theory 18.2 (2017), pp. 173–195. 28. J. R. Searle. Speech Acts: An Essay in the Philosophy of Language. Cambridge: Cambridge University Press, 1969. 29. A. Schutz. Reflections on the Problem of Relevance. New Haven: Yale University Press, 1970. 30. D. Gasking. “Causation and Recipes”. In: Mind 64.256 (1955), pp. 479–487. 31. B. Russel. Mysticism and Logic. Unwin Books, 1963. 32. J. Kim. Supervenience and Mind. Cambridge: Cambridge University Press, 1993. 33. T. Højrup. State, Culture and Life-Modes: The Foundations of Life-Mode Analysis. Routledge, 2018.

Part III

The Concept of Function

The purpose of Part III is to present a detailed conceptual analysis of various aspects of the concept of function and their implications for principles of SCPS decomposition. Chapter 7 provides an overview of various aspects of the concept of functions and its relation to dispositions (causality) and structure. Two aspects of functions are distinguished—as a transformation and as a role which links the concept of function to the concept of action discussed in Part IV. Chapter 8 presents a summary of definitions found in the literature and compare them with the concepts developed in Chap. 7. Chapter 9 presents perspectives and decompositions of the whole SCPS into its parts relevant for functional modelling. The distinction between structure and functions is used to systematically derive modelling perspectives which can be used in a system centered and function oriented approach to design of a SCPS and its subsystems.

Chapter 7

Aspects of Functions

This chapter explores aspects of functions in order to clarify the meaning of this concept in the overall context of technical artefacts. At the same time it will provide the background for the discussion of concepts of action and means-end relations presented in Parts IV and V.

7.1 What Are Functions? The concept of function is understood by most people and is used in ordinary discourse about interactions between human beings and physical objects like tools, machines, industrial processes, and about social interaction. Functions have therefore also a natural place in the vocabulary of the engineering professions. Definitions of the concept of function found in dictionaries are often confused with other concepts like purpose, goal, objective and action. Another complication is that two alternative views on functions exist. In one view, the teleological, they are seen as related to purposes, goals and objectives i.e. to intentions whereas the other view, the causal, define them in terms of effects. It will be shown in the following that in domains like engineering dealing with design and operation of technical artefacts, the two different views can be joined by seeing functions in a wider context of action and means-end relations. It will also be shown that the conceptual foundation for functional modelling of technical artefacts can be developed by using theories from human and social sciences, which are traditionally not seen as relevant for engineering.

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7.1.1 A General Definition A function can on a very general level be defined as follows: the function of something (S) describes how it works (W) in a particular context (C)

This definition is obviously open to interpretation and therefore insufficient for any practical use, unless it is given within a particular context. Apart from the general and various domain specific definitions, there is no universal agreement on the concept of function. A unifying definition of the concept of function should be valid for all types of things and contexts and it is therefore doubtful that such a unification is possible or even useful. The definition captures the basic intuition about the concept of function, but has several interpretations depending on the answers to three questions. Some interpretations which are relevant for modelling the functions of technical artefacts are explored in the following: • what is something (S)? – – – –

it could be a material physical object it could be somebody i.e. a person it could be a process it could be a region or location in space

• how is the context (C) defined? – it could be another something, a natural object or an object designed or used for a particular purpose – it could be situations involving goal directed action of humans engaged in design and use of a technical artefact – it could be a norm or tradition • what is the meaning of works (W)? – it could mean that S has a causal effect on something in C – it could mean that S participates in or undergoes a change of something which is expected or intended by a designer or a user in C The emphasis of the contextual nature of functions is very important. It means that the function of something (S) is not one of its properties, but is ascribed to it in a context (C). A main challenge of functional modelling is to cope with context. Biological Functions It can be asked whether this definition of function extends to biological systems also? A question which is relevant for modelling biotechnical processes. It could be claimed that functions of biological systems are not ascribed but immanent properties of natural living systems (i.e. have ontological status),1 but

1 Michael May is acknowledged for pointing out that the ascription of functions is only valid for technical artefacts or natural objects (including living systems) when used for a human

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here the context is not human intentions but evolution of the species, and the concept of function serve to understand patterns in their development. Homeostatic behavior of biological systems (i.e. the ability to maintain their state) are often associated with goals and may therefore imply intentions in some form, but homeostatis can equally well be explained by regularities in response to disturbances created by feed back loops or survival mechanisms. They can operate without goals but create behaviour which appear to be oriented towards a goal because of the regularity in response. The decisive point is whether goals (i.e. information about future or desirable states) can interact causally with actual physical states in the biological system, which is another way of saying that the organism should have representations i.e. a mind. In such cases the behavior of the organisms is causally dependent on the actual state of affairs but also on representations of the future state. The behavior is accordingly both oriented and directed towards goals and is explained not by biology but by cognitive psychology.2 Functions and Semiosis Chapter 6 demonstrated that concepts of semiotics play an important role in functional modelling of technical artefacts by the distinction between triadic and dyadic causality. However, semiotics can also be applied on the more abstract cognitive levels of model building and the use of a functional model for making inferences. The relation between S, the context C and the function F is triadic and can accordingly be given the semiotic interpretation shown in Fig. 7.1. The triadic relation between them, which is conceptual, is a complex sign because the modes of Fig. 7.1 A semiotic interpretation of the triadic relation between something S, the context C, and its function (see also Fig. 6.7). The relation between S and F which is mediated by C is not physical but conceptual

purpose. The functions of genetically engineered biological systems have functions which are either immanent (by nature) or ascribed to them (by design). When immanent, the concept of function is based on evolutionary criteria. In design of e.g. a biochemical reactor, the functions ascribed to the biochemical processes is related to human intentions. 2 Sommerhoff introduced the distinction between goal oriented and goal directed behavior in [1].

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signifying involved cannot be described by the three basic types; symbolic, iconic or indexical (see Chap. 6). Further analysis of the complex sign relation in Fig. 7.1 requires insights in the model building process including the use of conceptual schemas as contexts for interpretation in the encoding process shown in Fig. 5.2. What is the function of a functional model? Following the generic definition, the answer is “how it (the functional model) works in a particular context.” Here the context is some reasoning task (e.g. diagnosis of artefact failure based on some evidence (e.g. observations)) using the model (the signifier) to make inferences about artefact functions. The function of the model (the signification) would be a set of rules describing how the model is used together with evidence to make inferences. These semiotic aspects related to model building and use for reasoning open interesting avenues for further investigations of the semiotic foundations of functional modelling, including the use of conceptual schemas in model building and using functional models of artefacts as part of reasoning architectures. The first topic is of interest for formalisation and automation of the model building process. The latter is of potential interest for modelling control and supervision systems in SCPS applying model based reasoning. However, these topics are outside the scope of this book and will not be pursued further.

7.1.2 Natural Language Natural language (NL) is often used to express functional knowledge. In fact many sentence forms with associated syntax and semantics can be used to express functions, but since this book is not about natural language as such, only the sentence forms presented in the following sentences will be used as a basis for the discussion. Case 1: Case 2: Case 3:

The function of is to do The function of is to do by using The function of is to do in order to achieve

The sentence forms in case 1, case 2 and case 3 represent three interpretations of the word “works” namely as “what is done”, “how it is done” and “why it is done”. The how and why can be expressed by the sentences above but are often implied or given in engineering documents where the sentences appear in text or is expressed by diagrams. Case 2 and 3 are sometimes combined to include why, what and how something is done in one single sentence. As shown later in this chapter, the relations between why, what and how form the basis for hierarchies of functions. In natural language the item having the function is described by a noun phrase, and “what is done” is described by a verb phrase which simply can denote a change or transformation of some sort, or it can also involve a noun phrase denoting the item being transformed or changed in some aspect. Many verb phrases in natural language refer to actions (apart from verbs representing state of affairs), but conceptually functions are not identical to actions. Functions and actions may be confused because the verb phrase in a statement about

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functions refers to something being transformed or created as the outcome of an action. As discussed later in Chap. 10, descriptions of an action include more than a specification of what is done i.e. the change or transformation it is causing. Another important difference is that a function describes a potential for change whereas an action describes its actualization. In addition, specification of an action includes other aspects such as the motives of the actor for acting i.e. reasons and intentions. Such aspects are not explicitly included in a function statement. However, they belong to the context wherein the function is ascribed to the item and illustrates the relevance of the contextual layering depicted in Fig. 1.1. The means and ends of acting are included in the extended forms shown above (case 2 and case 3), which can be considered as mixed forms expressing functions in the context of actions organized in a means-end hierarchy as explained later in this chapter. Example: The heat transfer system including a pump shown in Fig. 7.2 will be used as a main example for the discussion of the aspects of function in this chapter. Pumps are widely used in process plants and are interesting objects for discussion because they have a variety of functions. A pump may not seem to be a very complex system seen as a physical object. However, the variety of its functions makes it suitable to introduce basic aspects of the concept of function which also are relevant for modelling more complex SPCS systems like power plants or chemical processes. Such large scale systems appear complex in terms of the sheer number of physical parts and interconnections, but are also complex due to functional interactions between the parts. Both types of complexity are important in design and operation. The heat transfer loop in Fig. 7.2 comprises two heat exchangers (HE1 and HE2) connected by a circulation loop including a pump (PMP1). The type of fluid used for heat transfer has no significance for the present discussion but it will be assumed for convenience that it is water. Other details which are not relevant for the present purpose will be ignored such as the power supply for the pump motor and the systems serving as energy sources and sinks for the heat transfer system. The water flow rate F in the circulation loop is maintained by the controller (CON1) on the basis of readings obtained from a flow measuring device (FM1). The purpose of the temperature controller (CON2) is to regulate the temperature in the outflow from the heat exchanger (HE1). This is done by compensating deviations in the temperature readings obtained from the measuring device (TM1), by increasing or decreasing the set point for the flow rate of circulated water when the temperature increases or decreases.

7.2 The Aspects Using the sentence form defined in case 1, a function of the pump can be expressed as “the function of the pump is to transport water.” This sentence clearly expresses knowledge of the pump which is relevant both to plant design as a statement of

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Fig. 7.2 A heat transfer system

what the designer intended the pump to do, and as a statement informing what it is actually used for in operation but this context is implicit. The design intention and the actual use of an item are supposed to be the same, but in operational situations not anticipated by the designer, an operator may use an item for another purpose than intended. In such situations the intentions of the designer and the human operator are different. It is therefore necessary to distinguish between the design function and the use function, and in addition for complex systems like SCPS, to see functions in different contexts which are reflected in the three fundamental perspectives described in Chap. 9. In order to address these and other questions about the meaning of function statements, the heat transfer system will be used to discuss general aspects of the concept of function.

7.2.1 Explanations Ascription of a function to a thing have an explanatory role, and Wright [2] claims that the function of something should explain its existence i.e. why it was made or how it evolved. This claim brings the thing into a context of design intentions or evolution, but functions can also be ascribed to natural objects whose existence cannot be explained by reference to the intentions of a designer or evolutionary criteria. However, the function of a natural object can explain why it is selected by a human user with a particular intention. To be ascribed a function is accordingly not limited to technical artefacts, and the classification of a thing as a technical

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artefact or as a natural object depends on the context. For example when a technical artefact is used with an intention which is not compatible with the design function it must be considered a natural object since it is ascribed functions not intended by the designer. Functional explanations explain why something exists or is used, the reasons for being there, and are often distinguished from causal explanations which explain how things behave. However, this distinction is unfortunate because functions also have causal aspects as discussed below. Furthermore, causal explanations are always made within a (sometimes implicit) context of intentions (what the system is made for or how it is used). The distinction between the two types of explanations is accordingly not categorical but a question of difference in emphasis on intentions or causality.

7.2.2 Intentions A function is accordingly ascribed to something, which means that the function of the thing is not one of its existential properties. The function is ascribed to it in a context of both causality and human intentions. The function will therefore be expressed by reference to existential properties of the thing such as its dispositions, but the meaning or content of the function will refer to intentions of the designer or user. Knowledge of functions are social facts and belong accordingly to the epistemology of things and not to their ontology. For example, the function of a pump “to transport the fluid” is not one of its physical properties like its weight, the shape or speed of the impeller, but is inherently related to a particular interest or intention of a designer or a user and to other things in its environment. The pump could accordingly be ascribed other functions in other contexts depending on the designers or users intentions. If the pump was part of a cooling loop (i.e. a system having the function to cool something) it could equally well have been ascribed a function related to the cooling such as “to transport energy”. Alternatively, a user of the pump could have a particular interest in the inner operations of the pump parts and their interaction with the fluid, and ascribe it the function “to produce pressure.” It is here seen that a function of a technical artefact is relative both to intentions i.e. what it is made to do or what it is used for, and to the level of decomposition into spatial parts of itself and its environment which is relevant for expressing the intentional focus on a particular subsystem. Intentions seen in the context of technical artefacts have their origins in the needs or wishes of somebody (the designer or operator) or an organisation, the socalled direct needs.3 However, it could also be a subsystem of a technical artefact which has a there-of derived need which is neccessary in order to satisfy or serve a 3 Langkjær

[3] has proposed the distinction used here between direct and derived needs.

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superordinate direct need. Finally it would also require knowledge of dispositions relevant for particular needs. Functions of things are relative to intentions and actions of a human i.e. they do not have intentions themselves. In contrast actions have intentions and they therefore relate to things in a different way than functions do. Intentions are intrinsic to the definition of actions whereas they are extrinsic for functions. Aspects of action including the intentions are discussed in detail in Chap. 10. Functions and Objectives Intentions are expressed by objectives which are often confused with functions. However, their distinction is important for functional modelling. Achinstein [4] presents the following distinction between ends (i.e. objectives) and functions: Although functions are intimately related to ends the two should not be identified. Functions as well as ends can be given by infinitive and verb+ing nominals; yet ends but not functions can be given propositionally. My end might be that I make money, but my function (as chief fundraiser, say) is not that I make money or that money be made by me but simply to make money or making money). Nevertheless, for any function there is an associated end which can be formulated in a propositional way by a that-clause nominal. And the item with the function is a mean to this end. For this to be the case the associated end will need to be a “generalized” one in this sense: its propositional formulation will include no reference to the item to which the function is attributed. If my function in this organization is to make money for the organization then the associated end is that money be made for the organization, not that I make money for the organization or that money be made for the organization by me. And if this is my function then I am (or a) means by which this generalized end is to be achieved. The association between functions and ends, then, comes to this. If x’s function is to do y then that y is done is a generalized end (given in propositional form) for which x is a means.

This can be summarized in the following sentence: the objective is that is done

Objectives therefore define states to be accomplished (targets) or avoided (hazards) whereas a function specify the transformation or process required to reach the target or to avoid the hazard. Achinstein’s distinction does not completely resolve the confusion between functions and objectives. Sometimes an objective can be to execute the transformation rather than to accomplish or avoid its result. However, this problem is easily resolved by realizing that in such cases the objective refer to the state of execution of the transformation rather than the transformation itself. The relation between objectives and functions will also be discussed in Part V as an aspect of the means-end relation.

7.2.3 Decomposition of Functions and Objectives Achinstein’s distinction between function and objectives is used in functional modelling to exchange between two temporal perspectives, from the process (the

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function, the doing or the means) to the result to be obtained (the objective - the end or result intended). The distinction leads to two different ways of decomposition: • the objective is decomposed into sub-ordinate objectives to be accomplished at the same time • the associated function is decomposed along a temporal scale into subfunctions. Control Systems The relation between functions and objectives are of particular importance for functional modelling of control systems and the associated objects of control. The function of a control system is ascribed in a context of extrinsic “design” intentions similar to functions of other technical artefacts as explained above, but due to their goal directed behaviour control systems have also ascribed intrinsic intentions (the control objectives) derived from the extrinsic ones. These intrinsic intentions are mindlike properties of the control functions representing desirable states of the object of control. The function of the object of control is accordingly ascribed in the context of the derived intrinsic intention. Control systems and their functions will be analysed in detail in Chap. 16.

7.2.4 Dispositions Since functions describe changes (see the verb phrase above) it is only meaningful to ascribe a function to an item if it can make something happen, be transformed, create something or be created. This means that the item referred to in the noun phrase having the function, under proper conditions, can cause the change or transformation involved or is capable of being transformed. For functions of technical artefacts this requirement of causality is obvious since the purpose of the artefact is designed to make the transformation happen or create something. It would therefore have no meaning to ascribe a function to an item if it does not have the required ability to transform other items or being itself transformed. As mentioned in Chap. 6 such abilities are also called dispositions and are closely related to causality. The distinction between the concepts of function and disposition is of crucial importance for separating the mind dependence of functions (the intentions) from their dependence on the physical (the dispositions). The dispositions of an item includes all possible ways it could interact with its environment and are usually expressed by natural laws when considering physical items, by habits when considering human operators, and programs when considering intelligent machines. The functions of an item are a subset of its dispositions (see Mumford [5]) which is useful in the context of human intentions. This means that dispositions and functions are expressed by the reference to the same terms used to describe changes or transformations but have different meanings. The selection of functions from the set of dispositions of a physical item or an operator is dependent on human choice between alternative means of action. This selection process is different from evolutionary selection of biology. The concept of function in engineering and biology are therefore different. In engineering functions

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Fig. 7.3 Pump knowledge divided into aspects depending on context

are ascribed to artefacts in the context of actions and intentions. In biology functions are immanent to the nature of life and a consequence of the selection mechanisms of evolution. Example: The realization of the transport function of the pump shown in Fig. 7.3 is dependent on the availability of physical entities (shaft, impeller, casing) and an environment (water inlet and outlet) which can realize the function. The realization has both structural and dispositional features. The physical parts of the pump should be available and properly configured and the parts (the structure) should provide the causal powers (the disposition) for the pumping. The material transported (the water) should be liable to movements and pushing (its dispositions) in order to be transportable.

7.2.5 Behaviour Functional explanations of systems are often contrasted with explanations of their behaviour referring to mechanisms (as on the level of dispositions and physical structure in Fig. 7.3). However, explanations of behaviour describes manifestations of dispositions specific to particular conditions i.e. singular causation and not general causation based on dispositions as mentioned in Chap. 6 (see Fig. 6.3). The relevance of using the two types of explanations depends on the application and on system complexity. Functional and dispositional concepts are efficient for explanations of complex systems with many interacting physical parts since they by

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nature facilitate the formulation of levels of abstraction taking into account contextual factors as intentions and spatial and temporal boundaries of the environment. The power of functional explanations is less obvious when considering systems with fewer spatial or temporal parts and simple physical behaviour. In such cases behavioural explanations are often sufficient.

7.2.6 Function and Structure The problem of assigning functions to physical things is called the functionstructure problem. Here it is usually implied that structure refers to a division of the world into spatially interconnected items persisting in time i.e. into what usually is called material objects. In the context of SCPS it would be process components like pumps, tanks, heat exchangers, and streams of materials or mixtures. According to this interpretation of the function-structure problem, it is about deciding the level of spatial decomposition which is appropriate for the ascription of a given function to the thing in some context considering its intended use for somebody. Technical artefacts like industrial processes are composed of spatial elements on various levels of aggregation, such as components, equipment, and subsystems. However, the functions of industrial processes also involve chemical reactions and exchanges of different types of energy between streams of materials and their components. This means that a spatial whole-part decomposition of technical artefacts includes items which could be either material streams and their components, and equipment and their parts and subsystems. The function-structure problem is also relevant for biology. The Russian neuropsychologist Luria [6], who studied the effect of brain damages caused by war injuries, was interested in the relation between physical spatial structures of the brain and its cognitive functions. These functions are obviously not related to design intentions but to biological evolution. But the function-structure problem is similar to the problem encountered when modeling higher level cognitive functions of control systems, except that in the latter case the relation between structure and function can be explained by the intentions of the control system designer.

7.2.7 Device- and Environment Centric Functions The distinction between design problems at the component and the subsystems level has motivated Chandrasekharan and Josephson [7] to propose two viewpoints of function; an environment-centric and a device-centric viewpoint. The distinction is made in order to explain why functions sometimes are associated with the physical components or devices themselves and their parts, and in other cases to the effect or consequences of the devices behaviour on its environment i.e. the whole of which it is part. In both cases the function is ascribed to the same spatial entity (the device).

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Fig. 7.4 Dispositions of the coil and the environment

The device-centric viewpoint, which is often used in engineering, considers the function of the component or device in abstraction from its particular context and is therefore relevant for design of components having a standardized function. In the environment-centric viewpoint the ascription of function to the device refers to the effect it has on its environment. The two viewpoints correspond to two different contexts of ascription of functions and imply a distinction between what the component or device designer intended the pump to do, and what it was intended to bring about by the process designer through the effects of its behaviour on the environment. The example below illustrate the distinction between the device-centric and the environment-centricviews. Example: Consider a simple case of a electrical heater positioned in air and connected with a voltage source. The heating element is a metal coil which has the following dispositions (see Fig. 7.4):4 • the coil is a conductor which has the disposition to lead a current of electrons I if it is connected to a voltage source with a voltage differential of V. The current I is determined by Ohms law .(I = V /R) • the flow of electrons in the voltage field will create an equivalent amount of heat power .P = R ∗ I 2 (Joule’s 1’st law). • the coil is heat-able i.e. it has a disposition to absorb the heat created by the current and thereby increase its temperature. • the surrounding mass of air has dispositions to be both heat-able and the ability to transport heat. This means that its temperature will increase when exposed to a source of heat from the coil which then will be distributed to the surrounding mass of air.

4 Quantitative expressions are not really necessary for understanding the distinction between device- and environment-centric functions. They are only included to satisfy readers familiar with equations. What matters here are the qualitative causal relations between the current and the voltage and the heat power which are implied by the equations.

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What is the function of the heating coil; is it to heat the environment or is it to determine the current flow in the circuit? The answer to this question depends on the point of view. If the function of the coil is to determine the current (the devicefunction), the heating effect will be an undesirable sideeffect which may need cooling. If the function is to heat the environment (the environment-function), the ability to determine the current becomes a means to achieve the heating. The distinction between the device and the environment level is not absolute, but depends on the context of design and operation. For example, in the context of pump design and operation, the device-function of the impeller (a part of the pump) is to create pressure, and the environment-function of the impeller is to move the fluid contained in the pump. The device and environment centric functions are not different types of function, but represent different uses of the concept of function in different contexts. They both satisfy the general definition proposed in Chap. 1: the function of something (S) describes how it works (W) in a particular context (C)

Accordingly the choice between the two viewpoints cannot be decided without considering the context of design or operation and the conceptual relations between causality, functions and intentions. Ontological Assumptions The decomposition of a physical process into spatial elements is based on the ontological assumption that material things are the fundamental items of the world. This assumption has been questioned by Rescher [8] who suggests that processes (taken in a general sense) rather than things or objects may be more basic for understanding the world. Within such a view the decomposition of the whole into parts is more related to temporal structure rather than spatial structure, and the function-structure problem becomes a matter of ascribing functions to segments of temporal sequences i.e. to processes. The primacy of processes proposed by Rescher is a metaphysical position called process philosophy, which is supported by several philosophers (see e.g. Browning and Myers [9]). However, even though material things (equipment or materials) from this point of view should be seen as bundles of processes, it does not necessarily make sense to adopt this position in an engineering context. The choice of point of view and level of abstraction depends on the problem to be solved and not only on metaphysical preferences. Process engineering terms include equipment and materials which can be aggregated spatially into systems and compounds having equipment and materials as their component parts. Furthermore, equipment and materials can be decomposed into parts or components. These spatial entities on various levels of whole-part abstractions can be involved in processes changing their properties, amounts or locations in space. The design and operation of technical artefacts involve causal interactions between things (equipment and materials) and processes (energy and momentum). Intentions of design and operation are related to both.

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Causation and the Function-Structure Problem An important aspect of seeing causation as being connected to exchange of energy, material and momentum (Dowe [10]), is to take advantage of the principles of conservation which can be applied on arbitrary levels of spatial decomposition (called control volumes in thermodynamics). Decomposition of a system into interacting control volumes is compatible with Harré’s theory of dispositions [11] since the roles of agents and objects can be associated with control volumes and can thereby be used to solve function/structure problem. Without the principles of conservation and balances, it would not be obvious how to relate function and structure and at the same time reflect the causal structure of the system. Note that the decomposition of a SCPS process into control volumes may cut across spatial boundaries of component or subsystem. The possibility of providing systematic mappings from structure to function through concepts of mass and energy balances was investigated by Everett [12, 13], who concluded that the mapping was not unique and knowledge of goals was necessary in order to disambiguate the mapping. This finding is in agreement with the presumption that functional ascriptions need to consider both physical structure, the dispositions and the design intentions. Control Functions and Structure The association of structure and function through thermodynamic control volumes applies only for dyadic causality i.e. modeling of the functions of physical process in a SCPS system. There are no laws or principles which in the same way govern the exchange of information and meaning involved in control which can be mapped into spatial structure. Control engineering terms refer both to things with spatial locations (e.g. sensors and actuators) but also to processes describing temporal dynamic aspects of the interaction with the object of control. The purpose of control systems is to ensure that these interactions take place, but their functions cannot always be ascribed to spatial entities but must also be ascribed to processes. In a similar way, the operator may be seen as a spatial entity (as a “systems component”) but his/hers function in operations is related to the cognitive temporal processes involved in monitoring and control of the physical process. Control functions rely on triadic causality involved in the exchange of information and meaning which is not constrained by laws of conservation or by spatial boundaries. The principles used to relate control functions to structure is related to sign processes, commonly called semiosis, and their temporal structure. Chapter 13 introduces a semiotic model of action defining such principles, and Chap. 16 applies the model to the analysis of control actions.

7.2.8 Roles and Transformations The function of the pump “to transport water” describes what the pump does to another object i.e. a transformation of something, here the water. The sentence indi-

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cates accordingly the roles played by the pump and the water in the transportation, the pump being the causer or agent, and the water the caused entity or the object of the transportation. The agent and object roles are mutually related by being two elements of a causal relation, and relative to the transformation ascribed (see Chap. 6). Furthermore, a physical entity may have several roles depending on the transformation involved. If the pump is considered as part of a cooling loop, the water will serve two roles by being both the object transported (assuming that the design purpose is cooling) and by being the agent transporting the heat removed by the cooling system. However, the heat removal could be realized by another different type of entity. The entities and the roles ascribed to them are therefore connected by many-to-many relations. A role can accordingly be seen as a binary relation between function and the entity realising the role, the structure. The introduction of the role concept and the distinction from the transformative aspects of a function/action contributes to a clarification of the relation between function and structure. The agent and object roles introduced in Chap. 6 and exemplified here are related to causality. Chapter 12 will introduce other role types related to intentions. Roles and Transformations as Function Attributes By the introduction of roles a function can be decomposed into the transformation (change of a state of affairs) and the roles involved (abstract representations of the entities involved). The transformation and the roles can accordingly be seen as attributes of a more abstract concept - the function. The decomposition of a function into its transformation and roles is not always required. It is a modelling decision depending on the need for making explicit the distinction between different roles in a reasoning task. For example, when using a functional model for fault diagnosis, the ability to decide between two root causes for a failure of the water transportation would depend on a distinction between; (1) the lack of power of the pump (the agent) to move the water, or (2) the failure of the water to be moveable i.e. to serve as the object of transport. A distinction between the two roles is also necessary to express temporal constraints of pump operation. For some pumps the object of transportation (the water) should be available before the agent (the pump) is enabled to prevent pump failure. This temporal constraint cannot expressed or reasoned about without the distinction between roles. Note that functional concepts are necessary for the distinction between the transformation and the roles. A purely physical view on the interaction between the water and the pump would not reveal the constraints which are essential for successful operation of the pump system. The distinction between a function as a transformation or as a role implies a separation of dynamic and static aspects of the function. Transformations refer to changes whereas roles refer to aspects of the function which are persistent or static. Roles are accordingly representations of entities involved and are abstract features of the situation which persist during the time frame of the transformation. While transporting the water, the pump and the water accordingly maintain their roles as

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agent and object. Their existence and ability to fulfill the roles are preconditions for actualizing the transportation i.e. having the proper dispositions. Conditions The fulfillment of causal roles is often also conditional on circumstances not directly related to the function to which the role is associated. Thus the object role of the water in the pump example is conditional on the water being in a fluid state (i.e. that it is able to be moved), and the agent role of the pump is conditional on proper lubrication of its bearings (i.e. that it has the ability to rotate). In addition, even when the agent and the object roles are enabled i.e. there is a potential for transportation, it is also necessary that there in an opportunity for interaction between the pump impeller and the water i.e. that the amount of water is sufficient to ensure that they can interact. The important distinction between causes and conditions was introduced by Mackie [14] and explained by Collingwood [15] to be relevant for means-end causality, and therefore to functions (see Chap. 6). The purpose of control actions in SCPS can be to establish and maintain availability of the roles associated with system functions. These enabling actions are important ingredients in the transitions between different phases of e.g. a startup plan. The relation between roles and action phases will be discussed in Chap. 14.

7.2.9 Latent and Manifest Functions Merton [16] introduced distinctions between manifest and latent functions and between functions and dysfunctions in sociology which are equally applicable when modeling technical artefacts. The four concepts listed below are distinguished according to two different criteria; intentionality and recognition (adapted here to the context of technical artefacts).The distinctions shown in Fig. 7.5 can also be explained by the relation between dispositions and functions. Thus, the dispositions not relevant for a given purpose could be harmful or of no consequence. If they are harmful they would be dysfunctions. Dispositions not recognized but providing an opportunity for achieving a desirable outcome or being a risk would be latent functions. • functions are intended and beneficial by having a positive (i.e. desirable) effect on the environment. • manifest functions are recognized, intended and beneficial and are the consequences that people observe or expect. • latent functions are not recognized, unintended and beneficial. • dysfunctions are unintended and harmful by having a negative (i.e. undesirable) effect on the environment. Dysfunctions of artefacts are failures and latent functions could be unexploited opportunities for an operator engaged in goal oriented interaction with the environment. Discovery of latent functions may provide new opportunities for an operator when dealing with familiar operational situations or in coping with unfamiliar risks.

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Fig. 7.5 Manifest and latent functions (Merton [16])

7.3 Abstraction Ascribing a function to an entity does not in general specify why and how it is achieved. This possibility of abstracting from the means used to implement the function or the ends to achieve is an important aspect of functional ascriptions. It allows the consideration of alternative means for the same function, and the possible use of the same means for achieving alternative ends. Functional ascriptions are therefore effective in conceptual design where decisions about the physical realization have not yet been taken, or where alternative means are provided in the design solution to ensure redundancy in system operation. Similarly, the functional ascription to a system can help an operator to focus her/his attention on an operational problem (such as loss of cooling) rather than directing the attention on the means required to solve it (e.g. pumping). Functional ascriptions on different levels of abstraction support accordingly explicit control of focus in problem solving situations.

7.3.1 Two Types of Abstraction Ascription of function to an entity involves two types of abstraction with different focus of attention either on the entity itself or on the effect is has on its environment (see Fig. 7.6).

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Fig. 7.6 Interaction between an entity and its environment

The first type of abstraction is about selecting details relevant for a particular interest. Following from the distinction between dispositions of an entity and its functions, the ascription implies a selection among a the set of dispositions of the entity and its environment which it influences and are used to accomplish an objective of design or use. Here the selection is made on the basis of an intention of design or use. The second type of abstraction has a focus on the interaction between the entity and the environment, expressing the effect the disposition of the entity in focus has on other entities in its environment. This type of abstraction is guided by the principle of reciprocity of dispositions mentioned in Chap. 6. Reciprocity of Functions Functions are accordingly ascribed to an entity S in the context of other systems in its spatial or temporal environment E with which it is designed to interact with the intention of achieving some end. This relativity of functional ascriptions to context applies of course also for entities in the environment E. Commitments are therefore also made concerning ascription of the functions of the entities in the environment E of S when a function is ascribed to S. These commitments are expressions of the fitness of the system S to its environment E as being part of an intentional context. Two commitments are made which together define these reciprocity relations between function ascriptions: • The first commitment is the assumption that the system S has the required dispositions to realize the function, and that the function fits with the functions of other entities in the intentional context both with regard to transformations and the roles involved. • The second commitment is the assumption that proper external conditions are satisfied so the system S is not only capable of realizing the function but also can be enabled and actualized. As the conditions can be enabled or inhibited by states of other entities or events in the environment, it is realized that the fitness relation between the entity and its environment can be conditioned. What have been said here about S can also be said about any other entites considered parts of its environment within the given goal context. The fitness relation linking the function of S with the function of systems in its environment can therefore be conditioned (enabled or inhibited) from both sides.

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Fig. 7.7 The functions of an item (here a pump) can be given on different levels of abstraction and ordered in a hierarchy of specifications

The conditions discussed above have their roots in the intentional nature of the system. Without knowing the intentions or purposes of a system it would not be possible to select between the unbounded set of possible conditions which in principle could be defined. The conditions ensure that there is a potential and an opportunity for transformation or change. Opportunities play, as will be shown in Chap. 14, a central role in the temporal development of actions and they serve accordingly as context for the reciprocity of functional ascriptions. Autonomy Gouldner [17] emphasized that the reciprocity of functional ascriptions has implications for the definition of autonomy. Reciprocity is an expression of dependence, and autonomy of an entity in a functional sense therefore means absence of reciprocity in its functional ascription with functions of other entities of the same system. Autonomy is accordingly related to a context of intentions.

7.3.2 Levels of Abstraction Functions of the Parts Functional ascriptions can be given to an item on different levels of categorical specification. Consider as an example the pump described in Fig. 7.3. Its function can be ascribed by abstracting from the particular material substance transported by a pump (e.g. water) and its functions will be “to transport fluid”. Furthermore, the function “to transport” could be ascribed to the pump without specifying the item transported. Here the first abstraction is done by generalising the object of action and the second is to apply the principle of reciprocity and make the object of transport implicit. The three levels of abstraction can presented in an inheritance hierarchy as shown in Fig. 7.7. The principle of subordination of the levels is that a level is subordinate to the level above if it implies a categorical specialization. Functions of the Whole Another type of abstraction is to provide functional ascriptions of the system as a whole from different subordinated view points. An important principle of subordination is to see the whole system as organized in a system of actions based on the means-end relation introduced in Chap. 6 and to be explored further in Chap. 17. The functions of the whole can in this way be organized into a means-end hierarchy which can support deliberation and reasoning about means and ends in design or operations of a technical artefact.

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Fig. 7.8 Nesting of why, what and how explanations of actions and a corresponding hierarchy of functions connected by means-end relations

As mentioned above, the means-end relation is reflected in natural language by the sentence form: The function of is to do by using in order to achieve

Such sentences can be nested into hierarchical structures since the means for a function can be another function. The basic principle of subordination in such a hierarchy can be explained by the distinction between why, what and how explanations of actions (see also action aspects described in Chap. 10). The why explanation addresses the end or purpose to be achieved i.e. the motives or reasons for the existence and use of the technical artefact, the what explanation describe what is done with the artefact (the function) and the how explanation describes the means used to realize the functions of the artefact. The nesting of why, what, and how explanations create a hierarchy of actions and associated functions as illustrated in Fig. 7.8. The actions and the associated means-end relations between them comprise the context for ascription of function to the artefact and reflects the contextual layering introduced in Chap. 1. Such function hierarchies can be used as shown in Fig. 7.8 in a search for means and causes by a series of how questions, or a search for reasons or motives by a series of why questions. Deng [18] propose a distinction between “action functions” and “purpose functions” which is related to the distinction between means (the action functions) and ends (the purpose functions). However, the terms “action function” and “purpose functions” are confused according to the analysis of the concepts of action, purpose and function presented above. The terms refer to the distinction between two levels

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Fig. 7.9 A hierarchy of functions of a power generation and supply system

in a function hierarchy shown in Fig. 7.8 and is accordingly related to the distinction between means and ends to be discussed in Chap. 17. Deng’s (op.cit.) proposal create accordingly confusion by not making distinctions between the concept of function and its use in specific contexts. Example: An example of a function hierarchy for a power generation and supply system is shown in Fig. 7.9. The example shows how the pump function (energy conversion) is the means of circulation of the water, which again is the means of power production, which again is a means of distribution of power. Functional hierarchies are efficient for modelling large complex systems by being able to represent its functions on several hierarchical levels connected by means-end relations.

7.3.3 Functions, Wholes and Parts The ascription of functions to entities also involves considerations of whole and part relations. This ascription is non trivial because of the many-to-many relations mentioned above which are established between functions and physical structure through the roles.

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Actually the relation between functions, wholes, and parts is somewhat deeper as suggested by Emmet [19] who claims that the definition of function is conceptually dependent on the distinction between wholes and parts. Emmet expresses the dependence by the following criteria: 1. The object of study can be considered as forming a system taken as a unitary whole. 2. The unitary whole must be ordered as a differentiated complex, in which it is possible to talk about whole-part relationship. 3. The parts will be the elements which can be shown to contribute to fulfilling the purpose for which the ordered whole has been set-up, or, if it has not been purposefully set up, to maintaining it in a persistent or enduring state.

7.3.4 Function and Failure Concepts of failure and function are closely connected, and by using the definition of function given above a failure can be defined as: A failure is a deviation from the way something works according to expectations or intentions of its designer or user

The close connection between the concepts of function and failure may be the reason why people sometimes find it easier to define functions of something in the negative by explaining how it fails. Failures and their relations to functions (and objectives) will be explained in detail in Chaps. 7 and 18. The dependence of failure on design intentions is confirmed by the definition offered by del Frate [20]: Failure is the inability of an engineering process, product or system to meet the design team’s goals for which it has been developed.

Failure Types From the view of a user (operator) interacting with a natural environment or a technical artefact, failures can be the following: • A failure can be related to the intentions when the user interacting with the artefact expects a result which is not achieved. Here it is assumed that the user is rational so that his/her intentions and expectations are compatible. • A failure in use is sometimes defined as a deviation from the normal, in which case it is about experience from using the natural environment or the technical artefact. The experiences from past use based on empirical evidence are given the status of a norm for evaluation of future deviations. The user’s intention is accordingly not to intervene unless there is a deviation from the norm. The failure is accordingly not a deviation from the user’s intentions when intervening but a deviation from his/her beliefs, which are based on previous experience. Functions can also be ascribed to objects of nature whose existence or behaviour cannot be explained by human interests. Such objects fail when their behaviour

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deviate from expectations based on previous experience of use or operation, and their function are defined by the causal effect they have on their environment within this context of experience and expectations. Since technical artefacts can also be considered to be natural objects due to their realization through physical phenomena and mechanisms, the two concepts of failure can be applied to them distinguished by different contexts of functional ascription, intentions or experience. Biological systems are natural systems but are also objects of operation in the biochemical industry. Therefore, they have functions that can fail according to how they normally behave according to experience, but being living systems they can also be ascribed functions and failures by reference to an evolutionary context. A distinction between two types of failure should accordingly be considered: • Failure by not achieving the intention of design or use. • Failing by not behaving according to previous experience or deviating from what is considered normal. The distinction between two types of failure disappear when the intention is defined by reference to previous experience. The following causes of failure can be identified by considering the dependence of functions on dispositions and structure introduced above: • Failure in dispositions. Not providing the dispositions required for the function (transformation or roles) to be realized e.g. due to missing conditions. This applies both to dyadic and triadic causality. • Failure in structure. Not providing the structural basis for the disposition required.

7.4 Validation of Functions The association of functions with intentions is often mistakenly seen to imply an element of subjectivity, which makes functional models suspect. Hempel [21] argued that functional explanations should be avoided in science due to problems of verification and testability and that nomological explanations based on natural laws should be used instead. The problem is that if functions are subjective they cannot be validated, at least not by following the same principles as used for models of the physics of things which are objective and consequently can be validated by experiments. However, functions are only subjective in an ontological sense while they are objective in an epistemic sense because having a function is a fact which cannot be disputed i.e. is true according to the knowledge shared by a community of designers and users (see Searle [22]). Validation of functions is therefore dependent on agreement. However, agreement is not sufficient for technical artefacts. Validation by reference to the physical design basis or experiments is also required in order to demonstrate that the artefact is able to realize the function.

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The problem with validation of functions has its origin in a lack of distinctions between subjectivity and inter-subjectivity and between what Searle (op.cit.) calls social facts and brute physical facts. According to Searle, a functional ascription of the pump to transport water, is subjective in an ontological sense because the truth of the statement depends on knowledge about the designers or users intention i.e. it does not describe an existential property of the pump. On the other hand, the ascription is objective in an epistemic sense because having this function is a social fact which cannot be disputed in the context of the designers and users of a technical artefact. It is true according to the knowledge shared by a community of designers and users knowing what things are made and used for. In accordance with this distinction Searle distinguishes between physical and social facts, and for him the use of functional concepts is part of the construction of social reality. Searle also explains that natural language plays a crucial role in this construction. Two Stages of Validation Since functions are ascribed in a context of intentions and also rely on causal mechanisms, the validation problem can be divided into two: • Validation of causality. Here the problem is to demonstrate that the system represented can cause the transformation and fulfill the roles implied by the function. The demonstration can be done by objective means through experiments. • Validation of intentions. Here the problem is to demonstrate that the intention behind the functional ascription is correctly understood and formulated. This demonstration cannot be done by objective means since it relies on the expectations of designers or users i.e. is mind dependent. However, the intentions can be agreed inter-subjectively between designers and users. If a function cannot be validated by testing the associated causal relation or disposition, it does not necessarily mean that the functional ascription is false, it may still express what the designer intended. However, it could be that the designer has failed to realize the function according to his/her intention i.e. there is a failure in embodyment of the intention. Finally, the validation of a function may fail if the designers intention cannot be realized in the physical world.

References 1. G. Sommerhoff. Analytical Biology. London: Oxford University Press, 1950. 2. L. Wright. “Functions”. In: The Philosophical Review LXXXII (1973), pp. 139–168. 3. A. Langkjaer. Contributions to a General Normology or Theory of Purpose Setting. Copenhagen: Dansk Videnskabs Forlag, 1961. 4. P. Achinstein. The Nature of Explanation. Oxford: Oxford University Press, 1983. 5. S. Mumford. Dispositions. Oxford: Oxford University Press, 1998. 6. A. R. Luria. The Working Brain: An Introduction to Neuropsychology. Basic Books, 1974. 7. B. Chandrasekharan and J. R. Josephson. “Function in Device Representation”. In: Engineering with Computers 16(3/4) (2000), pp. 162–177. 8. N. Rescher. Process Metaphysics - An Introduction to Process Philosophy. New York: State University of New York Press, 1996.

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9. D. Browning and W. T. Myers. Philosophers of Process. New York: Fordham University Press, 1998. 10. P. Dowe. Physical Causation. Cambridge University Press, 2000. 11. R. Harré and E. H. Madden. Causal Powers. Oxford: Basil Blackwell, 1975, p. 191. 12. J. O. Everett. “A Theory of Mapping from Structure To Function”. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Mateo, CA, 1996, pp. 1837– 1843. 13. J. O. Everett. “Topological Inference of Teleology: Deriving Function from Structure via Evidential Reasoning”. In: Artificial Intelligence 113 (1999), pp. 149–202. 14. J. L. Mackie. “Causes and Conditions”. In: Causation. Ed. by E. Sosa and M. Tooley. Oxford University Press, 1993. Chap. 1, pp. 33–55. 15. R. G. Collingwood. An Essay on Metaphysics. Martino Publishing, 2014. 16. R. K. Merton. Social Theory and Social Structure. New York: The Free Press, 1957. 17. A. W. Gouldner. “Reciprocity and Autonomy in Functional Theory”. In: Symposium on Sociological Theory. Ed. by L. Gross. Row, Peterson and Company, 1959, pp. 241–270. 18. Y. M. Deng. “Function and Behavior Representation in Conceptual Mechanical Design”. In: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16 (2002), pp. 343– 362. 19. D. Emmet. Function, Purpose and Powers. New York: MacMillan and Co, 1958. 20. L. del Frate. “Failure: Analysis of an Engineering Concept”. PhD thesis. Technical University of Delft, Holland, 2014. 21. C. G. Hempel. Aspects of Scientific Explanation. London: The Free Press, 1965. 22. J. R. Searle. The Construction of Social Reality. The Free Press, 1995.

Chapter 8

Definitions

The purpose of this chapter is to use the analysis of various aspects of function presented above to unify definitions of functions proposed in the literature which have different emphasis on the different aspects: 1. Function as purpose i.e. as related to intentions 2. Function as effect i.e. as related to on causality (see Chandrasekharan et al. [1]) 3. Function as an interface between two systems i.e. as an expression of reciprocity (see e.g. Simon [2]) 4. Functions as doings i.e. as related to action (see Achinstein [3]) 5. Functions as variable mappings The first three definitions on the list can be seen as different perspectives of the concept of function as shown in Fig. 8.1. The first definition relates functions to ends or purposes and emphasize that a function can only be defined within a context of intentions. The second relates functions to their causal factors such as the dispositions of the means used for their realization. The third definition expresses the reciprocity between functions of two systems serving the same objective. The fourth definition unifies the first and second definitions as shown below. The fifth definition is based on the matehematical concept of function as a variable mapping.

8.1 Functions as Doings Achinstein [3] distinguishes between three meanings of the concept of function namely design-functions, use-functions, and service functions which are related to different intentional contexts. Achinstein explains the distinctions by the following example: Suppose that a magnificent chair was designed as a throne for a king, i.e. it was designed to seat the king. However, it is actually also used by the king’s guard to block a doorway

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Fig. 8.1 Function as purpose, effect and interface between two systems P and Q

in the palace. Finally, suppose that although the guards attempt to block the doorway by means of that chair they are unsuccessful. The chair is so beautiful that it draws crowds to the palace to view it, and people walk through the doorway all around the chair to gaze it. But its drawing such crowds does have the beneficial effect of inducing more financial contributions for the upkeep of the palace, although this was not something intended. What is the function of the chair?

The design-function of the chair is “to seat the king”, its use-function is “to block the doorway” and its service-function is “to attract the crowd”. In the first two cases the intentions of the designer and the guard explains why the chair is in the kings chambers or at the doorway. Design-functions and use-functions of the chair explain accordingly why it exists or has a role in a situation. In the last case where the chair has the service-function to be an attraction it is not part of an intention or a plan. The ascription of a service function to the chair accordingly does not explain why the chair is there and the meaning of function should here not be taken in its teleological sense but in its meaning as effect. The (service) function of the chair is to attract people because the chair and its interaction with the visitors unintentionally serve some financial needs of the king and his household. The chair was not brought to the castle with the intention of attracting people. The following definitions of design-, use- and service functions are proposed by Achinstein (op.cit) to clarify the distinctions between the three meanings: The design-function of x is y if the function x was designed to serve is to do y. The designfunction of an item x is what x was designed to do. If x was produced (created, established,

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appointed, placed where it is, etc.) by design to be or to serve as a means of doing y then x’s function is to do y. The use-function of x is y if the function x is used to serve is to do y. The use-function refers accordingly not to something not about x’s origin or placement as the design function does, but about its use. The function x is used to serve is to do y (or to enable S to do y), which will be true if and only if x is used as a means of doing y (or as a means of enabling S to do y). The service-function of x is y if what x in fact does, the performance of which is a function that it serves, is y. This statement is claiming something stronger than simply what function x was designed or is used to serve.

The definition of design-, use- and service-functions as different types of doings indicate that Achinstein sees the concept of function as closely connected with the concept of action. Design and use functions are directly related to the intentions of the designer or the user of the technical artefacts. However, as discussed below by an example, service-functions are only indirectly related to intentions. As will be demonstrated below by an example different types of use need to be distinguished.

8.1.1 An Example from Process Control The example used by Achinstein to explain the distinctions between different meanings of the concept of function is not taken from the domain of industrial systems which are in focus in the present work. However, their relevance for engineering can be demonstrated by adopting the three meanings to the context of design and operation of process control systems. Take the heat transfer system shown in Fig. 7.2 as an example. Example: The heat transfer system. Design-function. Consider here the pump in the water circulation circuit. The intention of the process designer is that the pump should be a means for circulating the water. The ability of the pump to move water through a pipe (under proper conditions which will be ignored here) is the designers reason for placing the pump in the circulation circuit. The designer selected the pump based on hers/his previous experience that it will work. The purpose of the pump to move the water is therefore its design-function. Furthermore, the pump is meant to be used by the operator (user) of the system according to its purpose. In normal operational situations the use-function of the pump is therefore derived from the design-function. Use-function. Now, suppose that the heating transfer system one night in a very cold winter fails to transfer heat because a lump of ice in the pipes prevents the water from circulating, and thereby the heat from being transported from the heat source to the sink. The pump cannot perform its design-function because a condition for proper function is not satisfied. However, the operator of the heat transfer system happens to know from experience and thermal hydrodynamics, that when the pump is running it will heat up the water due to friction losses,

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and that this heat may eventually (with some luck) melt the ice so the water can flow again. In order to get out of the predicament, the operator therefore decides to melt the ice by using the pump as a heat source, and in this way recover normal operations of the heat transfer system (assuming that the ice does not enter the pump and prevents its proper function). In this fault situation, the usefunction of the pump is not to transport water but to be a heat source. Note also that the use-function and the design-function are different because the designer and the user have different intentions. The designers intention is to circulate the water and the operator’s urgent intention is to melt the ice. These intentions refer to different situations since the process designer is concerned with normal operating conditions whereas the operator is dealing with an abnormal situation. The process design intention could obviously be modified to avoid the fault. It is often the case that experience from using a technical artefact is used to improve the design. Service-function. To complete the example; in the very moment on that night, when the piping was blocked by the ice and the water stopped its circulation, the operator wakes up because he/she could not sleep because the gentle sound from pumping the water vanished. The operator did not like to sleep in complete silence, and a service-function of the pump was therefore to be a source of noise. By the blocking of the pipe the pump was no longer able to provide this function. It is realized that this function was not intended by the designer or the operator even though it actually served a need. The sound production is not intended by the designer but is a side effect of the design of the pump and its operation. The designer may actually consider it to be a dysfunction and may try to reduce or eliminate the sound altogether. But as seen from the example, such side effects can be beneficial anyway and have service-functions. It is accordingly necessary not only to consider the intented and the manifest functions but also the tacit needs of the user (to be alarmed if the pumping function fails), and the latent functions of the pump as a sound emitting device. Comments The distinction between design-function and use-function is context dependent by being related to two stakeholders, who in the current case share interests. But in other situations the designer and the user could have conflicting interest (e.g. sabotage). The distinction between design-function and use-function also depends on the level of automation. The designer could for example equip the pump with some programmable device which can detect the fault situation and automatically change the function of the pump from water circulation to heat production. By increasing the level of automation in this way the design-function of the pump has been changed to include both water circulation and heat production. If the user is not supposed to control the pump it has no use-function. However, if the operator must intervene in situations where the pump and its automation fails it has use-functions related to monitoring and diagnosis.

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8.2 Functions as Variable Mappings A common approch in engineering is to use a mathematical concept of function to represent the relation between variables representing properties of plant equipment or the material processed. A function is then defined quantitatively as a mapping between input-, internal- and output-variables. The mathematical functions defined in this way can be associated with subsystems (structure) on any level of physical detail, and solves in this way the function-structure problem, essentially by redefining it using a mathematical concept of function. The definition of function mathematically as a variable mapping is different from the teleological definition of function which is proposed in this book as the way a thing work in a context. The mathematical definition is convenient for many types of engineering calculations (e.g. dynamic simulations), but does not address the relations between causal and intentional structure which is captured by means-end relations and the associated teleological concept of function. The explicit means of representing these relations provided by functional modelling facilitates formulation of design and operational requirements which are qualitative and difficult to capture by quantative mathematical functions, but necessary e.g. for efficient solutions of problems in design and operation of technical artefacts. The two approaches to the definition of function, the mathematical and the teleological, are associated with two different conceptions of failure. Defining functions as variable mappings lead to a definition of failure as a deviation in plant variables from their normal or expected values. In contrast, when functions are defined teleologically or in means-end terms, failures are related to lack of an ability or disposition of components or materials or to achieve operational conditions specified by the plant designer or user. The action theoretical analysis of the heat transfer system presented in Chap. 16 show that the action system structures derived for the control systems can be mapped into a traditional signal block diagram showing relations between variables, however with a loss of the information which is important for identification of failure causes as lacks of dispositions of equipment and materials. The analysis by action systems concepts reveal accordingly knowledge which is implict or tacitly assumed in a block diagram but necessary for diagnosis and remediation of failures.

8.2.1 Discussion It can accordingly be concluded that the distinction between design-function, usefunction and service-function is entirely related to different intentional contexts. The different functions of the pump in the example obviously refer to different physical dispositions of the pump i.e. its properties and potential behaviours. But the dispositions cannot be labeled according to the distinctions between design, use or service function since the labelling depends on the context of stakeholders and

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levels of automation etc. The same disposition can serve different functions in the same situation for different stakeholders depending on their needs and intentions. Another important observation related to context is that functions are defined by Achinstein [3] as doings, i.e. by using terms referring to actions. But an action is conceptually more comprehensive than a function because it also incorporate the motives of the actors. The concept of action and its distinctions from functions will be discussed in detail in Part IV. A distinction will also be made between doing something, bringing it about, and letting it happen as three types of action which all are guided by intentions. The intention when doing something is to intervene in the state of affairs. When bringing something about the result of the action has an intended consequence. When letting something happen the intention is to refrain from intervening. A user of a technical artefact can decide not to intervene in its operation and let it produce the desired result on its own. The decision not to intervene is intentional an motivated by the users experience. The three ways of acting provide different contexts of ascribing functions including the motives of the actor and other aspects of action not associated with functions. Functions of Natural Objects The distinctions between design-, use- and servicefunctions applies with some restrictions also for natural (i.e. not made by humans) objects. Natural objects have no designer and accordingly they cannot have designfunctions. But they can be used intentionally by an actor as a means to achieve a desired outcome or objective and can therefore have use-functions. As an example, a stone can be used to hammer in nails. Another example is to use nature as a source of raw materials or energy or as a storage of waste products. In such cases the natural environment is used intentionally and must be considered a part of the technical artefact and being functionally integrated with it. A technical artefact can also be considered to be a natural object when its behaviour is interpreted in a context without intentions i.e. when physics and chemistry is used to analyse properties of human-made objects. As explained in Chap. 5, the natural framework of interpretation is applied to an object which also belongs to a social context of needs and purposes.

8.3 Summary The resulting definitions of function are summarized in Fig. 8.2 Functions of Doings The relations between doings and functions to be explored in more in detail in Part IV can be summarized as follows: • The function of a thing or a person (how they work in a particular context) – can be the transformation or change of something resulting from doing – can describe the role they have in the performance of the doing

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These two meanings of the concept of function implies a distinction between what is changing and what is persistent in a situation. They are often confused and their distinction is therefore a key topic in the book

Functions of artefacts

Function as role and transformation

Functions of natural objects

Function as Effect (causality)

let something exist

make something happen let something happen

Non-Intentional something happen

Fig. 8.2 Summary of definitions

Fig. 8.3 Merton’s and Achinstein’s distinctions combined

Service function

Function as purpose (intentions)

Use Functions

make something exist

Design functions

Intentional

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• A doing can have a function when it produces a result in one context which is bringing about a consequence desirable in another context of intentions Latent and Manifest Functions Merton’s [4] distinctions between functions, dysfunctions, latent, and manifest functions presented in Chap. 7 (Fig. 7.5) can be combined with Achinstein’s (op.cit.) distinctions between design-, use- and servicefunctions as shown in Fig. 8.3. The distinction between functions and dysfunctions is relative to the intention. A failure is accordingly dysfunctional.

References 1. B. Chandrasekharan and J. R. Josephson. “Representing Function as Effect”. In: Proceedings of the Fifth InternationalWorkshop on Advances in Functional Modeling of Complex Technical Systems. Ed. by M. Modarres. Paris, France, 1997, pp. 3–16. 2. H. A. Simon. The Sciences of the Artificial. Cambridge: The MIT Press, 1981. 3. P. Achinstein. The Nature of Explanation. Oxford: Oxford University Press, 1983. 4. R. K. Merton. Social Theory and Social Structure. New York: The Free Press, 1957.

Chapter 9

Modelling Perspectives and Human-Artefact Relations

Modelling the functions of technical artefacts like SCPS involve a selection and combination of different perspectives depending on the use of the model in design or operation. The chapter explains how the selection is based on distinctions between different contexts of intentions and causal mechanisms and is an important step in framing the modelling problem and choosing its level of abstraction. A distinction is made between three modelling perspectives and it is shown how it relates to the system centered design approach discussed in Chap. 3.

9.1 Two Contexts of Action Considering the functions of a SCPS there are two overall contexts of action involved as depicted in Fig. 9.1 with different actors and associated means, ends and functions. • the context of design • the context of control and operation Both contexts of action involve relations between humans and artefacts which are important for functional modelling. The changes or transformations of state of affairs involved in the two contexts belong to different phases in realization of the overall purpose of the SCPS. The first phase is to establish a potential for change or creation of something, which is the aim of designing the artefact. The second phase is to actualize the potential, which is the aim of controlling and operating the artefact to accomplish the desired product quality and level of safety, and to maintain the capability to do so. This important distinction between phases is action theoretic and will be discussed in more detail in Chap. 14.

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Fig. 9.1 Two contexts of action and ascription of functions to technical artefacts (see also Fig. 2.1)

9.2 Human-Artefact Relations The human-artefact relations are involved in the modelling perspectives and will therefore be introduced first.

9.2.1 The Designer-Artefact Relation The purpose of SCPS design is to define goals, objectives, and means for realization of the three technical artefacts, the process system, the control and instrumentation system, and the operation support system. These artefacts are three different embodiments of the intentions of the process, control, and human factors designers. The embodiment relation implies here that intentions in the mind of the designer are given material form i.e. externalized in the technical artefact through the design procedure (see Kroes [1]). The embodiment is expressed by the design functions of the artefacts, the objectives they are intended to achieve (the mind dependence), and the means used for realization (the material dependence). Figure 9.2 depicts the embodiment relations between the designer and the three SCPS subsystems.

9.2.2 The Operator-Artefact Relation The human-technology relations proposed by Ihde [2] will be used in the following to describe different types of operator-artefact relation. This includes the concept

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Fig. 9.2 The embodiment relations between designer and subsystems

of embodiment which unfortunately is used in a different meaning than in relation to the designer-artefact relation. The distinction betweeen the two meanings are discussed on page 166 after introducing the operator-artefact relations. Ihde used a phenomenological1 approach to determine modes of human interactions with the world. The theory can be seen as a typology of relations between a cognitive agent (human) and his/her environment which suggests principles for functional decomposition of systems including technical artefacts and cognitive agents. The main idea is to represent an operator’s interaction with the world (the process and control artefacts) on a level of abstraction reflecting his/her intentional focus. Four human-technology relations are distinguished representing increasing human involvement with technology in the world, which in the current interpretation is a world of artefacts including the process and the control and instrumentation systems: • • • •

the background relation the alterity relation the hermeneutic relation the embodiment relation

1 Phenomenology was developed by Husserl [3], and Merleau-Ponty [4] for the study of conscious-

ness (and thereby intentions) and its relations to the external world. It plays an important role in the analysis of human perception and cognition.

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The Background Relation Here technologies are the background of human existence rather than being experienced in themselves and being objects of interaction. The background relation is schematised by Ihde as .human(technology/world) which means that the human interacts without making distinctions between technology and world. This relation is not relevant for functional modelling. The Alterity Relation Here the human interacts with technology with the world as a background. The alterity relation can be schematised as .human− > technology − (world). In the present context of technical artefacts this relation describes situations where the operator is engaged in the operations support system as an object for interaction. The alterity relation is obviously relevant for designing the interaction between the operator and the operation support system (HMI) (Fig. 9.3). The Hermeneutic Relation Here technology (the HMI) and the world form a unit, and human perception and behaviour is directed at how the world is represented

Fig. 9.3 The alterity relation: the operation support system (HMI) is the object of the operator’s intentions

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Fig. 9.4 The hermeneutic relation: observing and intervening in the process using the operations support system either directly (dotted) or by bringing about the desired effect by using the control and instrumentation system

through technology (Fig. 9.4). The hermeneutic relation can be schematised as human− > (technology − world). The technology serve here as an extension of the means of observation and interpretation, or the means of intervention. In the CSE approach to human machine interface design (see Chap. 4), the functions of the HMI are to establish these two hermeneutic relations by using a functional model (e.g. the abstraction hierarchy) of the process and the control and instrumentation systems (the world of artefacts) for information presentation, reasoning and intervention.

.

The Embodiment Relation Here technology and the human operator form a unit which is directed at the world (see Fig. 9.5). The embodiment relation can be schematised as .(human − technology)− > world. In situations relevant for functional modelling of SCPS the aim of the operator is to supervise the world either by monitoring and intervening. When the operator’s attention is on the effect of his/her actions on the world and not on the means used (the HMI), the technology gets embodied and becomes phenomenologically an extension of the users body and mind. This abstraction from technology as a means for observation or intervention breaks down if the technology fails in serving its function, and the human enters into an alterity relation with technology.

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Fig. 9.5 The embodiment relation: observing and intervening in the process and control artefacts through the operation support system

9.2.3 Comments Level of Automation The levels of automation defined by Sheridan [5] (shown in Fig. 3.4) can be seen as realizations of the embodiment relation (manual control) and the hermeneutical relation (supervisory and fully automatic control). Operator Training Operators of complex technical artefacts like SCPS always receive training in order to be able to operate them. The operator’s behaviour is accordingly shaped by the designer to comply with his/her intentions of use. In this narrow sense the operator’s behaviour may be considered to be an artefact whose functions may be allocated to a monitoring and control artefact. The allocation of functions between human and machine and training are central issue in the early and late phases of automation design. Training includes skills on the task level (embodiment), skills of using the HMI to supervise (hermeneutic), and the skills required to use the technologies implementing the HMI (alterity). Two meanings of embodiment. It is realized that it is necessary to distinguish between two meanings of embodiment: as externalisation or as inclusion (see Fig. 9.6). Ihde’s embodiment is a relation of inclusion since the artefact is

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Fig. 9.6 Two different embodiment relations between humans and technical artefacts

phenomenologically included in the user’s body. The technical artefact as an embodiment of design intentions is a relation of externalization because the intentions in the mind of the designer is given material form in the external world. The relation of externalization may accordingly be schematized as (designer− > artef act)− > world. Human-Artefact Relations and Control Systems Ihde’s analysis may also be applied to classification of functional relations between a technical artefact having cognitive functions and its environment (e.g. intelligent control systems) and can therefore be used for a more general analysis of functional relations, in particular for defining levels of abstraction representing the intentional focus of a cognitive actor (human or artefact) interacting with their environments through triadic causality (see Chap. 6).

9.3 Modelling Perspectives in SCPS Design The design of each of the SPCS elements P, C, and HMI in Fig. 2.1 is divided into subproblems, based on a decomposition of the overall production objectives to be accomplished, and objectives for avoiding situations critical to humans, the environment and economy (see Fig. 9.7). It is common practice to divide SCPS design problems into component, subsystem, and system level problems. Motives for this decomposition are to reduce complexity of the design problem, and to be able to use standard components or subsystems. Each of the component, subsystem and overall system designers have their individual objectives and associated views on the system. As shown later, the

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Fig. 9.7 The two overall objectives for SCPS design

operators have also their particular objectives and associated views on the system depending on their work context. The following three perpectives will be considered in functional modelling of SCPS systems. • the process perspective • the control and operation perspective • the work domain perspective Each of the perspectives represents a particular focus on the relations between causal and intentional aspects of SCPS subsystems, and reflects a distinction between three different functional views of the overall design problem. The focus is accordingly on functions rather on systems. The perspectives are combined in the modelling of functional relations between the SCPS subsystems. They are related to the system centered design approach as depicted in Fig. 9.8, but through the perspectives, the design process becomes focused on causal interactions between subsystems and their relations to objectives i.e. on their functions. The divisions into subsystems and into the three perspectives for functional modelling are overlapping as shown in Fig. 9.8. This means that both the process and the control systems are involved in modelling functions in the process perspective.

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Fig. 9.8 Functional modelling perspectives and the systems centered design approach

The functions of the control systems are here seen in the context of the process. In the same way, both the process and the operation system (i.e. support system and the operator) are involved in modelling functions in the control and operation perspective. The functions of the process is here seen as an object of control, and the operation system is seen as a supervisory control agent (i.e. the hermeneutic relation). The overlappings between perspectives and subsystems are reflections of the distinction between function and structure and the general principle of reprocity for functional ascriptions introduced in Chap. 7. Other Perspectives Two other perspectives for functional modelling can be considered: the automation and the product perspectives. In the automation perspective the focus in on the functional relations between the control systems and the operation support system, and the software and hardware used for implementing their functions. However, as mentioned in Chap. 1, electronics and computer technologies are not considered in this book and the automation perspective is therefore excluded. In the product perspective, which is also excluded here, the focus is on the functional relations between the product and the consumer.

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9.3.1 The Process Perspective The process perspective has its focus on the functions involved in the transformation of raw materials and energy into products and on the countermeasures (barriers) required to avoid harmful releases of materials or energy. This perspective involves dyadic causality (see Chap. 6) and applies in design of the process system and its exchange of raw materials and energy with the natural environment and of products with the consumer.

9.3.1.1

Process Design

Process design can be subdivided into component design, subsystem design and overall system design. The three design problems are illustrated below using the heat transfer system shown in Fig. 7.2. Component Design A component like a pump (see Fig. 7.3) is obviously much less complex than a complete process system in terms of the number of parts and its operation But a pump may appear complex, because the functions of its parts are highly interdependent and can be combined to achieve a variety of functions for the pump as a whole in its interaction with the fluid pumped and the upstream and downstream components and subsystems. The challenge for the pump designer is to choose the pump materials, shape the shaft, impeller, casing, and the dimensions of the inlet and outlet ports so that the pump is able to satisfy a range of uses with different objectives of performance, safety and economy. The function of the pump in this context is to convert rotational energy into a source of potential energy (pressure) which can be described by concepts of energy conservation and conversion. The pump design also involves specification of the objectives of the pump control system including set-points2 for flow rate or speed, outlet pressure and other conditions necessary for proper operation of the pump. Means to realize these requirements are considered in the control and operation perspective. Component design also includes identification of risks and specification of alarm settings for safety critical variables (e.g. shaft speed or temperature of bearings) and countermeasures to be considered in the control and operation perspective. Subsystem Design The challenge for the designer of the heat transfer subsystem is to choose a pumping device which is capable of circulating the fluid in the loop, to choose the fluid to be used for carrying the heat, and to choose a heat exchanger having the required heat transfer capacity. The functions of the heat transfer loop can accordingly be described by concepts of heat transfer and mass and energy

2 The set-point defines the target value for a physical variable X to be controlled i.e. the value to be achieved or maintained by the control system or the operator.

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balances. The function of the pump in this context is to transport the fluid, a function which in the context of designing the heat transfer can be described by concepts of mass conservation. The designer is accordingly using the pump together with other devices to comprise a subsystem implementing the heat transfer function. Subsystem design also includes subsystems as countermeasures to be activated in case of accidents (e.g. so-called engineered safety systems for core cooling in nuclear plants). Subsystem design also includes specifications of control objectives related to the interactions between subsystem components and the elimination of disturbances. Identification of risks of e.g. overheating and leakages and specification of alarm settings and countermeasures to be taken are also considered. These requirements are included as a basis for design in the control and operation perspective. Overall Process Design The challenge is here to design the interaction between the heat transfer system and the connected systems serving as heat sources and sinks. The solution of this design problem is based on functional modelling of conservation laws for mass and energy. It also includes specification of control objectives regarding suppression of disturbances and balancing of mass and energy flows and storages. Overall process design may also include identification of risks and specification of alarm settings and objectives for protective control actions (e.g. shut-down systems).

9.3.2 The Control and Operation Perspective This perspective applies to the design of the control and operation systems. The focus is here on the interaction between the control and intrumentation systems and the process seen in the context of objectives for operation and control (derived from the process perspective). Figure 9.9 represent this perspective as a decomposition of the SCPS into two subsystems connected by a control relation.3 Control and Instrumentation Design The challenge in design of the heat transfer control system in Fig. 7.2 is to provide the required conditions for operation. That is, to decide and control the shaft speed of the pump so that it delivers the required flow of fluid independent of upstream or downstream disturbances and within safe and economical range of operation. Furthermore, the set-point for the flow rate (measured by sensor FM1) which is decided in the process design is used to achieve the desired temperature (measured by sensor TM1). The function of the pump in the context of operation is to manipulate the fluid flow i.e. to be a means of intervention or control.

3 The relation includes both control and operational aspects and may therefore be called an control and operation relation. However, the term control relation is used here for convenience.

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Fig. 9.9 The control and operation perspective

The control tasks are here realized by the two control systems CON1 and CON2 connected in a so-called cascade and their associated instrumentation TM1 and FM1. The functions of the control and instrumentation are based on triadic causal mechanisms (see Chap. 6) including conversion of physical states in information, and conversion of commands into physical states. In existing engineering practice control systems are described by using dyadic causal relations between signals. This means that the meaning i.e. the information carried by the signal, is implicit. The functions of a control system is accordingly described by the means used rather than by the purposes it has in the process or in the coordination with other control systems (e.g. the communication between CON1 and CON2). These purposes can only be expressed by triadic causality as interpretation of the signals i.e. by reference to their meaning. Human Supervisory Functions Control and operation of a SCPS subsystem like the heat transfer loop considered above, was in the past the responsibility of humans, but today tasks like these are taken over by automation, and the operator’s role is to supervise the automated system and the process aided by the operations support system in the evaluation of system performance and the execution of intervention. The operator is accordingly engaged in control of the process through the HMI as shown in Fig. 9.10. Comment Human intentions are obviously relevant for understanding their mutual interactions and their interaction with technical artefacts, but are intentions also relevant for interactions between artefacts? This question has a particular relevance for modelling control systems whose functions include perception and interpretation of observations, execution of actions, and other functions which imply intentions. The use of representations (i.e. models) of the object under control, as an element

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Fig. 9.10 The operator is involved is supervision of the process and the control system (the work domain) through the HMI

of the control functions, furthermore emphasizes that description of the interaction between the control system and the process under control must refer to intentions. Rescher [6] argues that the concept of control has strong connotation to intentions and purpose. This is in some contrast to well established control theory where intentions have no particular explicit role.

9.3.3 The Work Domain Perspective In this perspective the focus is on the interactions between the human operator and his/her work domain, which includes the process and the control systems and the HMI (see Fig. 9.11). The work domain perspective presupposes an allocation of control tasks between the automated control systems and the operator (done in the control perspective) and applies in the design of the HMI and in operator training.

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Fig. 9.11 The work domain perspective

Within the work domain perspective there are two overall functions to consider: • the functions of supervision which includes diagnosis and mitigation of disturbances (the hermeneutic relation in Fig. 9.12) – functions for monitoring and intervention in the process and the control systems – functions for decision support • the functions provided by the HMI hardware and software to support the operations and supervisory functions (the alterity relation in Fig. 9.12) Only functions for supervision will be discussed further here. Supervision Functions There is here a need for representing the functions of the process (the AH) and control artefacts. Objectives for design of the human supervisory functions is to ensure that his/her perception of the situation is adequate, i.e. the process and the control systems are presented at the HMI so that it conforms with his/her mental model and problem solving strategies (the idea of a joint cognitive system proposed by CSE).

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Fig. 9.12 Human-technology relations and steps in the design of the operator’s work

9.3.3.1

The Abstraction Hierarchy

Rasmussen [7] used concepts of function in his abstraction hierarchy (AH) describing the human supervior’s mental model of the process and control systems (see Chap. 4). The AH is proposed as a model to be used in designing of the information content of the HMI. The AH present the process and control systems by the five levels of functional abstraction organized into groups related to a subdivision of the supervision task into three subtasks: • overall system operation – functional purpose: production flow and system objectives – abstract function: causal structure, mass and energy and information flow topology • subsystem operation – generalized functions: standard functions and processes, feedback loops, heat transfer

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• component operation – physical functions: electrical, mechanical, chemical process components and equipment – physical form: physical appearance and anatomy, material and form, locations The naming of the levels of abstraction in the AH reflects a distinction between concerns of different control and operation tasks in a SCPS. The tasks may be executed by the same operator or divided amongst a team of operators responsible for each subtask. The AH is a functional representation of the process and control artefacts. The levels in the AH reflect the separation between the perspectives of the component designer (physical form and physical functions), the subsystem designer (generalized functions), the overall system designer (functional purpose and abstract functions). The AH comprises in this way a convenient manner of organizing plant knowledge according to the division of work. It represents knowledge shared between the designers and the operators. However, the functions in the AH are not functions of the operator but the functions of the process and control artefacts. The operators functions are to control and supervise the SCPS and require therefore a representation of the SCPS as an object of observation, evaluation, and intervention. These means and ends of control are not included in the AH. There is accordingly a need for a more generic modelling framework where these aspects can be represented (see Lind [8]). This involves a detailled analysis of the functional relations involved in action systems which is presented in part IV. Such an investigation is also required if the direct coupling between the decomposition of the design and supervisory problems is relaxed. A case in point is in the development of Multilevel Flow Modelling (MFM) for diagnostic reasoning (see e.g. Lind et al. [9]) where the levels of abstraction required to support formalized reasoning about causes and consequences, do not necessarily correspond to the levels of abstraction proposed by the AH. The AH levels are defined by the division of the operators work whereas the levels of abstraction in MFM are consistent with the logic of means-end reasoning and depending on the depth of the causal reasoning required. A potential mismatch between the levels of abstraction derived from the operators work, and the levels derived from an analysis of the causal relations in MFM is a challenge when using the MFM reasoning for diagnostic support of the operator. The challenge is to communicate functional knowledge between two actors having different levels of abstraction in their functional representations of the world. Comments The functions represented on the different levels of abstraction in the AH can be defined using the general definition proposed in Chap. 1 as describing how the components, the subsystems and the overall system works in different contexts of supervision. The different namings of functions on the different levels is a reflection of the interest of the operators having different roles in superving the plant. There is accordingly no need for the distinction between different types of function as suggested in the AH. What distinguishes functions on the levels of

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abstraction in the AH are the contexts of ascription (C) and not the concept of function used. Functions of the HMI Interaction The layout of the proces and control system information in individual display pages and their organisation into display systems should be designed to facilitate operator task dependent needs for acquistion of status information and his/her dialogue with the decision support system (e.g. using a functional model as a basis for its reasoning).

9.4 Summary Figure 9.13 present in conclusion the relations between modelling perspectives and modes of interaction between the operator and the SCPS.

Fig. 9.13 The three perspectives for functional modelling are interconnected in design of the SPCS subsystems and involve three different modes of interaction with the operator

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References 1. P. Kroes. Technical Artefacts: Creations of Mind and Matter - A Philosophy of Engineering Design. Springer, 2012. 2. D. Ihde. Technology and the Lifeworld. Bloomington, USA: Indiana University Press, 1990. 3. E. Husserl. Logic Investigations. Oxon, UK: Routledge, 2001. 4. M. Merleau-Ponty. The World of Perception. Routledge, 2010. 5. T. Sheridan. Telerobotics, Automation and Human Supervisory Control. Cambridge, Massachussets: The MIT Press, 1992. 6. N. Rescher. “The Concept of Control”. In: Essays in Philosophical Analysis. Ed. by N. Rescher. Pittsburgh, USA: University of Pittsburgh Press, 1969. Chap. VII. 7. J. Rasmussen. Information Processing and Human Machine Interaction. New York: North Holland, 1986. 8. M. Lind. “Means and Ends of Control”. In: Proc. IEEE Conf. Systems Man and Cybernetics. The Hague, Holland, 2004. 9. M. Lind and X. Zhang. “Functional Modelling for Fault Diagnosis and its Application for NPP”. In: Nuclear Engineering and Technology 44.6 (2014), pp. 753–772.

Part IV

Concepts of Action

The purposes of Part IV are to present conceptual analyses of the concept of action, and to explore formalizations which can be used in the development of languages for functional modelling. Chapter 10 provides an overview of different types of action distinguished by their causal and intentional aspects including a basic distinction accomplishments and avoidances, and between doing something and bringing it about. Chapter 11 presents a detailled analysis of dyadic transformative aspects of action showing that a formalised causal account of action proposed by von Wright can be used to define a basic set of elementary dyadic transformations comprising the basis for elementary functions. Chapter 12 introduces a formalisation of action roles based on Greimas’ theory of actants. It is shown that elementary roles involved in physical actions can be defined and applied for modelling SCPC functions. Problems of role interpretation are discussed. Chapter 13 introduces triadic transformations involved in cognitive actions i.e. actions based on representations and their meaning. A theory developed by Morris relating stages of action with types of interpretation is used to define basic cognitive actions. It is argued that the semiotic analysis of stages of action is foundational to existing models of operators decision making and models used in artificial intelligence for design of agent systems. Chapter 14 supplements Chaps. 11, 12, and 13 with an analysis relations between phases of action involved in their temporal unfolding from having a potential to act to the actualisation of the intended result. Chapter 15 combines the results of the previous chapters in this part into a comprehensive theory of action systems. The theory combines physical and cognitive actions through relations (called embeddings) established thorugh the roles. A morphology of embedding forms is presented which can be used in the decomposition of SCPS subsystems into actions which each can be associated with functions (as transformations or roles).

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Chapter 16 presents an analysis of control actions and explains how the theory of action systems can be used to represent the relations between process and control systems in a SCPS. The theory is demonstrated by an example.

Chapter 10

Action Aspects and Types

This chapter explains the relations between concepts of action, causaliy and intentions, and distinguish between different action types. Actions are closely connected with causality and making changes to the world. To act is to actualize the potential and an ability for making changes. But actions are more than just making things happen they are also guided by intentions such as motives, values, goals, and objectives. Thus, a change in the world caused by an action is not only happening, it is also intended by the actor, who has a reason to act. Actions should therefore be distinguished from changes which just happen without a reason. Two types of action and thereby intentions are involved when talking about technical artefacts; • the designers intention in making the artefact • the operators intention in using the artefact The aspects and types of action described in this chapter are general and applies in principle for actions done by both the designer and the operator. However, the actions done by the designer require investigations of the design procedure which is not considered in detail in this book, the discussion is therefore mainly focussed on the process, the control system or the human operator seen as action systems (see Chap. 2).

10.1 The Aspects This section presents an overall description of different aspects of action. The aspects constitute a comprehensive set, but are not necessarily considered to be relevant for all types of action. The description of the aspects is mainly based on Rescher [1] with minor adaptations to adjust it to the SCPS context and terminology developed in this book. Extensions are also made including research by others e.g.

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Anscombe [2] and Schutz [3]. The analysis of the aspects reveal that action, purpose and function are interrelated but distinct concepts. Rescher presented the following list of action aspects and corresponding explanatory questions: • Actors: who did it? • Type: what was done? • Modality: how was it done? – the manner: in what way? – the means: by using what? • Setting: in what context? – temporal aspect: when? – spatial aspect: where? – circumstantial aspect: under what circumstances? • Rationale: why was it done? – causality: what caused it to be done? – finality: with what aim was it done? – intentionality: in what state of mind?

10.1.1 The Actors The actors may be individuals or a group but may also refer to physical non animate entities that have causal powers i.e. are capable of changing its state or being changed. The concepts of causal powers are explained by Harré et. al. [4] and is the basis for a theory of causation as explained in Chap. 6.

10.1.2 The Type The act type describes the result of the action i.e. the change or transformation of the world state produced which can be defined on different levels of abstraction. It can be generic (“the opening of a valve”, “the turning of a rudder”) by only mentioning the type of object involved in the action. It can also be specific like “this valve” or “this rudder”. A detailed specification of the act-type may accordingly include the types of objects or the particular concrete objects involved in the action. It could also be a further qualification of the result of the action e.g. “turning the rudder by 5.◦ ”.

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10.1.3 The Modality The modality describes how an action is accomplished and includes the means used (“the operator closed the valve by means of the motor”) and the manner of acting (“the operator closed the valve by starting the motor and letting it run for five minutes”). The means of action would in technical artefacts be physical entities like valves or motors or interventions by an operator. The aspect of manner describes the procedure or recipe to be applied when using the means and refers to a plan (e.g. step 1: ‘start the motor’ and step 2: ‘let it run for five minutes’). In the following a plans will also be considered as a means of action.

10.1.4 The Setting The setting of the action locates the action in time and space. Thus “open the valve” may e.g. be specified to take place every hour at a specific time or dependent on when the event occurs. The location of the action is not defined in this case, but in other cases the location should be specified in order to ensure that the condition for action is satisfied (e.g. the closing of a robot hand when picking up a block assumes that the hand is located at the block, or the turning of a rudder on a ship when at a certain location). The setting also includes the circumstances required for the action to be possible and actualized. These circumstances may involve the conditions of the actors for being able to interact. The nature of these circumstances are explained in more detail in Chaps. 12 and 14.

10.1.5 The Rationale An action can be explained in three ways as a response to the question “Why did the actor do it?” • It can be given a causal explanation as when the behaviour of an actor is explained as a response to an outside disturbance. • An action can also be explained by reference to its finality i.e. to what the actor aims to accomplish i.e. the objective of the action. Thus the action of a pump can be explained by referring to the aims of the designer. However, since intentions are not attributed to material objects like pumps, the meaning is here that the pump is an embodiment of the designers intentions as explained in Chap. 9. The aim or objective of an action can be: – the result of the action – a consequence of the result – a condition for another action.

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These distinctions will be elaborated below and in part V. • An action can also be explained by giving the reasons why the action was done i.e. the beliefs and expectations that motivated the decision to act. In cases where actions are ascribed to physical items, this aspect would give the reasons why the object was selected by the user for a particular aim or according to the designers goals and values. Explanations by reasons refer accordingly to the state of mind of a user or a designer. Anscombe [2] makes a clear distinction between the intention and the motives for action. The intention is what is aimed at, the result or objective to be accomplished, and the motive is what determines that aim. Explaining an action by its finality is accordingly to make a reference to an intention, and explaining by the motive is referring to the agents beliefs and expectations. A similar distinction is made by Schutz [3] between two types of motives for actions: the in-order-to motive (the intention) and the because-of motive (the beliefs and expectations). The distinction between intention and motive is of importance for understanding the design and uses of technical artefacts. As an example consider an operator who plans to use a pump for water transport (the intention). The motive for this aim is that the operator knows by experience that a pump is able to do it (the expectation that the pump is a solution to his/her problem). Without this motive it would not be rational to propose it in the first place. The pumping of water therefore can fail in two ways. When operated the pump can fail by not accomplishing the operators intention due to some unpredictable events, or the human operator may fail because his/her motivation for using the pump for water transport can turn out to be unwarranted.

10.1.5.1

Purposes

Purposes are often confused with both objectives and functions. The confusion can be resolved by the distinction between two uses of the concept of purpose proposed by Anscombe [2]; • the actors purpose of acting • the purpose of the object of action Functions and objectives are also often confused but in a way which has nothing to do with the confusion of objectives with purposes. Chapter 17 will explain the difference between functions and objectives. The Actors Purpose of Acting The purpose of an action may refer to the final aim or objective as defined above, and in such cases purposes and aims or objectives seem to be synonymous. However, purposes also refer to the motives for acting which should be distinguished from the objective. Bunge’s [5] analysis of the concept of purpose concludes that purposeful action is motivated by experience and therefore presupposes learning and memory. To act with a purpose therefore means:

10.1 The Aspects

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Fig. 10.1 Purposeful action and time

• there is an aim to be achieved (the in-order-to motive) • the action is motivated by the agents previous experience (the because-of motive). Both conditions should be satisfied. A purposeful action assumes accordingly a dependency of past experiences and the future objective as depicted in Fig. 10.1. Only rational actions have this link between the past and the future. An action can have an aim without being motivated by past experiences in which case it would be considered irrational. The Purpose of the Object in Action The purpose of an object in action refers to the role it has as a participant in the action i.e. to its function. The object can be used as a means or a tool by the agent to achieve the aim, or it can be the object transformed by the action. Functions of objects are therefore described in the context of action as mentioned before. The purpose of an object can be defined by reference to a single actor or it can be described in the context of shared understanding between several actors i.e. inter-subjectively. The purposes of tools are shared by a community of actors. Example: A stone age artisan. The actions of a stone age artisan hitting a flintstone with his pressure stick has a purpose when they are motivated by his previous experience, that repeatedly hitting the stone with the stick in a particular manner, an object (an axe) can be created of the flintstone which is fit for hunting. However, he may also be able to create an object with the same properties by accident so to speak, by hitting the stone without taking advantage of his previous experience and not thinking about hunting. In the latter case the artisan’s actions have the purpose to hit the stone but do not have the purpose to create an object which can be used for hunting. The example illustrates that proper use of the concept of purpose requires careful consideration of context. Usually contexts of purpose are deeply nested. The stone age artisan knowing his task would motivate the actions by reference to both

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previous experiences in hitting stones with a stick club and to the fact that an object produced in this way can be used for hunting. The nesting of contexts of purpose is a reflection of the interdependence of different types of praxis (stone shaping and hunting).

10.2 The Types Functions are ascribed to technical artefacts in the context of action, and the possible separation of functions in different types is therefore dependent on how actions can be distinguished. The distinctions will be used as a basis for formalisation of dyadic transformation types in Chap. 11, and by this the transformative aspect of functions. Formalisations of the role aspect of functions will be considered in Chap. 12.

10.2.1 Some Distinctions Different types of action can be distinguished, depending on the criteria used, and in fact each of the aspects of action listed can be used to define such criteria. Here the focus is on criteria which have to do with causal and intentional aspects of actions. Overall Distinctions Two overall preliminary distinctions between action types can be made. The first distinction is between accomplishments and avoidances where the idea is to separate actions which are aimed at something desirable (accomplishments) from actions which are aimed at avoiding something undesirable or harmful (avoidances). This distinction is based on criteria expressing preferences or values of the actor, regarding the state of affairs produced by the action. Consequently, this distinction is of obvious relevance for distinguishing safety actions (avoidances) from other types of actions in SCPS used to make products (accomplishments). The second distinction proposed by Kotarbinsky [6] is between constructive, preservative, destructive, and preventive actions. They can be related to accomplishments and avoidances in the following way: • accomplishments – constructive actions – preservative actions • avoidances – destructive actions – preventive actions

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It will be shown in Chap. 11 that these overall distinctions can be supported by a more formal analysis of causal and intentional aspects of the transformational aspects of actions (and thereby functions). More Fine Grained Distinctions As a precursor to the formal analysis in Chap. 11 five more fine grained distinctions between action types will be introduced. They relate to the distinctions between accomplishments and avoidances introduced above and are based on causal criteria and on the distinction between the because-of and in-order-to motives defined by Schutz [3] as rationales of action. All five action types mentioned below are dependent on intentions but in different degrees. The analysis excludes accordingly non-intentional actions. The five more fine grained types of action are: • • • • •

making something happen letting something happen doing something forebearing something bringing about something

The actions will be explained in order to clarify the meaning of the verbs making, letting, doing, forbearing and bringing-about. The definitions found in dictionaries are not sufficiently distinct for the present purpose. For example, the meaning of the verbs letting and forbearing needs to be distinguished using the causal and intentional criteria. Making Something Happen An actor A who is making something happen (is an agent) is the cause of the change in state of affairs. It is accordingly assumed that the actor both has an ability and opportunity to make changes. In addition it is also necessary to assume the existence of another actor B (an opponent) trying to counteract the changes made of A. The presence of an opponent is a prerequisite for ascribing a causal influence to A. To make something happen also means to intervene intentionally in state of affairs and there is accordingly an in-order-to motive “to make a change”, but here the motive does not imply any expectation concerning the specific result or outcome of the action. To make something happen is accordingly an intentional action. It will be called an intervention in the following. Letting Something Happen When an actor A lets something happen, another actor B is causing the change of affairs intended by A. The actor B can accordingly be seen as a kind of substitute for A. When letting something happen it is a condition that the agent A both has an ability and opportunity for intervening in state of affairs, but is refraining from doing so. The reason to refrain from intervening is that the actor A expects that the substituting agent B will make something happen. The actor has this expectation as a because-of motive for the action but has no in-orderto motive i.e. there is no “intention in action (following Anscombe’s [2] distinction between “intentional action” and “intention in action”).

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Forbearing Something Forbearing something is similar to letting it happen because the actor refrains from acting, but when forbearing, the actor A also has an in-order-to motive indicating the expected result of the intervention of a substitute B in state of affairs. This kind of action is relevant in some cases when modelling interactions between a control system (A) and a dynamic environment (B, an artefact or natural environment). Doing Something When doing something the actor A intervenes in state of affairs with an in-order-to motive regarding the specific change of state of affairs to be obtained in the world (the objective). This means that doing something is distinguished from making something happen by also having an intention in action (the result). To do something implies accordingly also the existence of an opponent B. Note that the doing is not a means to achieve the result produced since the result aimed at is an intrinsic aspect of the doing. Bringing About Something A distinction between doing something and bringing it about was introduced by Danto [7]. It can be explained by a discrimination between the result of a doing and its final aim. Sometimes the result intended by acting is identical to the final aim i.e. the result achieved is the outcome intended by the actor, but when bringing about something, the final aim is a causal consequence of the result and not the result itself. It should be noted that an opponent actor B is still assumed, otherwise the opportunity and causal power of actor A (the agent) to produce a result cannot be accounted for (having the intended consequence). Whereas doing something is not a means of achieving the intended result, bringing about something can be considered as means for its achievement. The main distinction between doing something and bringing it about is accordingly a difference in the intentional focus.

10.2.2 Discussion The conceptual distinctions between intervention, letting, forbearing, doing, and bringing-about are summarized in Fig. 10.2. Note that the types shown in Fig. 10.2 are complementary to the distinction between accomplishments and avoidances. Using the distinctions between different action types in the description of concrete actions is faced with interpretation problems because the same action can be described in several ways depending on what is assumed to be known about the intentions or motives for action. For example, bringing about something can be described as a doing if the difference between the result and its causal consequence is ignored. If it is not known whether the agents intention in action (the in-order-to motive) is in the result Z or in its consequence Y, it cannot be decided whether the action is “bringing-about Y by doing Z”, or it is “doing Y”.

10.2 The Types

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Fig. 10.2 Types of intentional actions and their motives

In a similar way, doing something cannot be distinguished from making it happen if the intention in action is unknown. Again, making something happen cannot be distinguished from letting it happens if the causal influence of an agent is unknown or ignored. Finally an action cannot be distinguished from a mere change if the presence of an agent is unknown or ignored, The change of state of affairs could simply be caused by the inner dynamics of the environment or by another unknown agent. The relevance of the distinctions for functional modelling of technical artefacts will be illustrated by examples in the following. The examples show that the distinctions are of fundamental significance for understanding human interaction with technical artefacts, in particular when describing actions and associated functions on different levels of abstraction. Examples: Consider as the first example an operator opening a valve. This action can have different aims depending on the intentional focus. The aim (the in-ordermotive) of the valve operator can be the result “the valve is open”, or it can be that “the flow rate of water through the valve is increased”. In the first case the “opening the valve” is a doing whereas in the latter it is a case of bringing about, because the increase of water flow rate is a consequence of the valve opening— the result of the action. The causal connection between the valve position and the flow rate is given through the valve design and the properties of the fluid. The same physical event “opening the valve” can accordingly be described as either a doing or a bringing about. The two descriptions have different meanings because they refer to different intentions. Another situation can be considered where the operator forbear to “let the valve open.” This is a common situation when operators have their attention directed towards automated equipment. The

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operator does not intervene because he/she expects that the control system will do it (its design function). Consider as the second example operation of the water circulation subsystem in the heat transfer system shown in Fig. 7.2. Depending on the intentional focus of the operator different descriptions of his/her actions on the circulation system can here be provided. If the operator is increasing the speed of the circulation pump his/her aim can be to increase the transfer of heat from the source to the sink (a consequence of speed increase), but it can also be to increase the flow rate of the circulated water. As above, the two descriptions differ by their intentional focus. In the first case it as a bringing-about because the increase of the heat flow is a consequence of increasing the pump speed. The causal connections responsible for this is the influence of the pump speed on the water flow, and the ability of water to carry heat. The latter description is also a case of bringing about where the focus is on the flow of water. Two different descriptions of the same physical situation can accordingly be proposed depending on the focus of the operator. The two descriptions provide different selections or abstractions of physical aspects and associated assumptions, depending on what particular problem the description is used to solve. The distinction between action types is accordingly important for the selection of the level of abstraction and by this to be able to provide different explanations depending on the intentions.

References 1. N. Rescher. “Aspects of Action”. In: The Logic of Decision and Action. Ed. by N. Rescher. University of Pittsburgh Press, 1967. 2. G. E. M. Anscombe. Intention. Oxford: Basil Blackwell, 1957. 3. A. Schutz. Reflections on the Problem of Relevance. New Haven: Yale University Press, 1970. 4. R. Harré and E. H. Madden. Causal Powers. Oxford: Basil Blackwell, 1975, p. 191. 5. M. Bunge. Treatise on Basic Philosophy Vol.4: A World of Systems. Dordrecht: D. Reidel Publishing Company, 1979. 6. T. Kotarbinski. Praxiology: An Introduction to the Science of Efficient action. Pergamon Press, 1965. 7. A. C. Danto. Analytical Philosophy of Action. Cambridge: Cambridge University Press, 1973.

Chapter 11

Dyadic Transformations

The purpose of this chapter is to use the action types described in Chap. 10 to define basic generic dyadic transformations which are formalizations of the causal aspects of physical actions, and of interpretations which can be used to express possible intentions of the agent. The transformations represent the transient aspects of actions and is therefore complementary to the role types (described in Chap. 12) which are persistent aspects of actions i.e. what remains the same during a transformation. The generic transformations and role types are key foundations of functional modelling by defining the two aspects of functions involved in physical actions.

11.1 Von Wright’s Theory of Action A formalization of the transformations involved in different physical actions according to their dyadic causal aspects can be obtained by applying and extending a theory of action types developed by von Wright [1, 2]. The advantages of the theory is its simplicity and that it is generic so it can be applied for a range of transformations relevant for technical artefacts and their operations. The extensions and interpretations of von Wright’s theory proposed here provides formal definitions which capture both (dyadic) causality and intentionality. It is clear that the formalizations obtained are only valid for actions which satisfy the basic assumptions of his theory. The original purpose of von Wright’s theory of action types was to provide a logical definition of the concept of action that can serve as a basis for legal judgements. A key point of the theory is how to distinguish an action logically from a change of state of affairs that can happen in a world without the intervention of an agent. Such a formalization is relevant for legal judgements of whether an agent can be considered responsible for his/her actions. However, as shown below, the

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_11

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Fig. 11.1 An action is defined by two situations of change

theory is equally important for the analysis of the transformations of state of affairs involved in the interaction between technical artefacts and humans. Von Wright defines an action by comparing changes in the state of affairs in two different situations as shown in Fig. 11.1. The first situation is hypothetical and describes what would happen if there was no intervening agent. The second situation is the actual one where the agent A has intervened. Given these two situations, von Wright defines an action by the difference between the change of state of affairs in the hypothetical situation, and the change happening in the situation where the action is actualized. Since the first situation is hypothetical, an action is defined by a counterfactual conditional. Counterfactual conditions are involved in defining causality as mentioned in Chap. 6. Later it will be shown that counterfactuals are also essential for the analysis of control actions and therefore are necessary for understanding many technical artefacts. The definition of an action/transformation by the distinction between the actualized and the hypothetical situation implies the presence of an opponent which can be seen as representing the inner dynamics of the world or the influence of some other agent. The presence of the opponent is not discussed by von Wright because the role of the actors involved in action are not part of his theory. His theory is relevant for understanding a function as a transformation. However, roles are important for understanding other aspects of functions and will be discussed in Chap. 12. Von Wright’s theory introduces three propositions p, q and r representing state of affairs, and two operators T (then) and I (instead). The T operator represents the change of state of affairs from p to q caused by the action by the formula pT q, which will be called a change schema in the following. The operator I describes the relation between the actualized q and the hypothetical state of affairs r by the formula qI r.

11.1 Von Wright’s Theory of Action

r H yp si oth tu e at ti c io a n l

Fig. 11.2 T and I operators in the action schema as relations between state of affairs p, q and r

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p

Instead-of (I)

Then (T)

q

Actualized situation

The logical structure of the transformative aspects of an action is expressed by combining the two operators T and I and the state of affairs .p, q and r into the formula pT qI r, which will be called an action schema. The schema is read as follows: Initially the state of affairs was p then (T ) q occurred instead of (I ) r. The notion of change implies sequential time because the schema pT q means that p precedes q. The action schema also implies the notion of branching time since the I operator distinguishes two possible futures, the actualized and the hypothetical. The two meanings of the operators are illustrated in Fig. 11.2. The theory proposed by von Wright is valid for any choice of state of affairs represented by p, q and r and has therefore potentially a wide range of interpretations. However, the judgments involved in representing state of affairs should not be underestimated. Most situations invite to a multitude of interpretations depending on the significance of a change as it is seen in a particular context. As shown later, the theory and the extensions presented here makes it possible to formalize transformations and related concepts like functions and objectives, but it does not help in deciding how to represent a particular situation. A consideration of the judgements involved in representation of concrete situations is therefore necessary and requires a distinction between the situations and their representation by propositions. A transformation is defined by state of affairs, which are observable in principle through suitable experimental setups and by proper definition of the propositions involved. It refers accordingly to overt aspects of an action but not to covert aspects since there are no references to the intentions or motives of the agent. The definitions can accordingly not, as they are, be used to distinguish between intentional and non-intentional actions as discussed in Chap. 10. As this distinction is important in functional modelling, the theory will be extended in the following to include the intentions of the agent. Such an extension is also necessary in order to define success and failure of actions. An initial step in this direction is von Wright’s definition of elementary changes and actions.

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11.2 Situations The definitions of changes and actions presented above refer to propositions (i.e. statements about situations) and their combination in schematic structures by operators, but they contain no explicit references to the situations. However, the distinction between propositions and situations is important. A characteristic feature of propositions is that they do not directly represent situations but refer to them. This relationship is explored in the following. Let it first be assumed that the domain of action is defined by a set of possible situations W where W = {w1 , w2 , . . . wM }

(11.1)

P = {p1 , p2 , . . . pN }

(11.2)

.

and a set of propositions P .

where each proposition will be true or false depending on the situation. The correspondence between the set of situations W and the set of propositions P can then be formally defined by the mapping (w, pi ) |→ ϕ(w, pi ) ∈ {T, ⊥}

.

(11.3)

where .ϕ is a truth function and .w ∈ W . There is accordingly a truth function .ϕ(w, pi ) for each proposition .pi which may map more than one situation into true (or false). Conversely, several propositions may be true for each situation. This is not surprising since propositions refer to object attributes or to relations between objects. An object attribute or relation may be an aspect of several situations, and a situation may be defined by several attributes and relations. Situations, and the propositions they make true, will therefore generally be related by many-to-many mappings. The judgements mentioned above involved in representing state of affairs and by defining propositions, are accordingly expressed by the truth functions. Usually focus is on selected aspects of the situations which are of particular interest and not to consider all the propositions which may be true or false in each situation. In the case of elementary changes and actions described below only the defining proposition p is of interest and the situation is accordingly seen under the aspects defined by p. The elementary change or action will apply for all the initial or target situations where p is true. Aspects of the situations, which are not included by the object attributes or relations referred to in .pi , are accordingly neglected.

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11.3 Elementary Changes and Actions Von Wright proposes a set of so-called elementary change types and a corresponding set of elementary actions considered in the following. The elementary change types are defined by considering two situations represented by the propositions p and .¬p. With this restriction the four types of elementary change shown in Table 11.1 can be distinguished . Here .¬p means that p is not true and the change schema .¬pT p should read ‘.¬p then p’. The four types of elementary changes are simply derived as the set of possible combinations of propositions p and .¬p in the change schema pT q. Each type of elementary change is defined in the table by a schema and a description. The schema defines the logical structure of a change whereas the description expresses the meaning of the schema. Von Wright does not discuss the significance of the distinction between a schema and the corresponding description. It is shown later that the distinction is important for using the elementary actions to represent intentions and to give a formal analysis of the distinctions between interventions, lettings, forbearances and doings. The elementary actions are not sufficient for formal analysis of actions which bring about something. This require a consideration of composite actions, which will be introduced later in Sect. 11.8.1. Since the elementary change types in Table 11.1 are defined in abstraction from the distinction between states of potentiality and actualization, the change types are applicable to both general and singular causality. This includes also the elementary transformation types which can be derived from them.

11.3.1 Situations, Propositions and Elementary Changes Each proposition p in the set P divides the set of situations W into two subsets . Wp and .W¬p = W \ Wp . They contain the situations for which the truth function .ϕ(w, p) is true (.Wp ) and false (.W¬p ) respectively. With this division of W into subsets the elementary changes can be represented as transitions between .Wp and .W¬p as shown in Fig. 11.3. These graphic representations of the changes are extended in appendix A so they can also be used to represent the transformative aspect of actions. The transitions depicted are abstract representations of the elementary changes, but in reality a change will always take place between concrete situations and not .

Table 11.1 The elementary changes

Schema ¬pTp . pTp . pT ¬p . ¬pT ¬p .

Description p happens p remains p disappears p remains absent

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Fig. 11.3 Change graphs of the elementary changes

Fig. 11.4 Elementary changes as transitions between concrete situations in W .

between sets of situations. This is illustrated in Fig. 11.4 for a simple case with only five situations .w1 , w2 , w3 , w4 and .w5 . The “abstract” transitions between subsets of W can be implemented by several alternative “concrete” transitions as illustrated in Fig. 11.4.

11.3.1.1

Negation and Intentions

The negated proposition .¬p in the schema for elementary changes raises some interesting issues concerning their meaning, which has to do with intentions. Above .¬p was defined by a truth function on a subset of world situations complementary to those defined by p. However, Bergson [3] argues that in the real world an expression like .¬p does not make sense, since the physical world will always be in another state

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say q (different from p). The expression .¬p represents a judgement of a judgement, and judgements are dependent on mental states like intentions. The first judgement is in the selection of the proposition p and its associated truth function .ϕ. The second judgement is in the negation of p which according to Bergson only has meaning in the context of an intention or plan. He argues that in .¬p the judgement is an evaluation of the situation relative to p which has a particular relevance for the agent e.g. in relation to a plan of achieving or maintaining p. It is unknown to the present author whether von Wright was aquainted with Bergson’s argument, but it is nevertheless important for using the theory of elementary changes and actions in the analysis of intentions presented below.

11.4 Intervening The elementary interventions are obtained from the elementary changes in Table 11.1 simply by extending the change schema with the hypothetical state of affairs that would be obtained if the agent did not intervene. Since the intervention would change the state of affairs, it is necessary that the state of affairs realized by the action is different from the hypothetical state that would be obtained if the intervention was not done. The resulting four interventions are shown in Table 11.2, each corresponding to an elementary intervention. As with the changes, each intervention is characterized by a schema and a description conveying the meaning of the action schema.1 The four elementary interventions can, as pointed out by von Wright, only be actualized under some conditions. Thus, it is not possible to .make p remain if .p is not already true and if the state of affairs will vanish unless caused to remain. This can be exemplified by the action ‘keep the valve open’, which is only meaningful if the valve is already open, and the valve will close if the act is not

Table 11.2 The elementary interventions and their conditions Schema ¬pTpI ¬p . pTpI ¬p . pT ¬pIp . ¬pT ¬pIp .

Description make p happen make p remain make p disappear make p remain absent

Condition p is not and will remain absent unless caused to happen p is but will disappear unless caused to remain p is and will remain unless caused to disappear p is not but will happen unless caused to remain absent

1 The naming of the elementary intervention types are slightly different from those used by von Wright. An example: the type .¬pTpI ¬p i.e. “make p happen” is named “produce p” by von Wright. Producing has connotation to intentions and causality whereas making only has connotations to causality. The names shown in Table 11.2 have therefore been chosen to express the causal interpretation of actions, and von Wrights terms produce, destroy, maintain and suppress are used to express intentional aspects of doings.

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done. These conditions are of a logical nature as they are intrinsic to the definition of an intervention i.e. through the meaning of the terms p, q and r in the formula pT qI r and through the constraints that are imposed on these terms. The concept of an intervention therefore implies the notion of an opponent i.e. some disposition of the environment that may hinder the agent in e.g. .make p happen. The nature of the counteraction depends on the type of intervention. Correspondences between interventions and conditions are shown in Table 11.2. The elementary types of intervention do not define whether the action is successful or failing i.e. whether the opponent is defeated or not. However, according to the present definition of an intervention, the opponent is always defeated i.e. fail because the agent is considered to be the cause of the change. Formalized definitions of success and failure of elementary actions will be discussed Sect. 11.10. Examples: The four types of elementary interventions can be illustrated by assuming that p represents the proposition “the valve is open.” First consider . ¬pT pI ¬p representing an action changing a world where .¬p is true, into a world where p is true. Thus . ¬pT pI ¬p whose description is .make p happen is in the example represented by the sentence “the valve is being opened.” The schema . pT ¬pIp , whose description is .make p disappear, represents the action “closing the valve.” The schema . pT pI ¬p represents an intervention that preserves the state of affairs of the world in the feature described by p on two successive occasions. In the example the schema would therefore represent the action “keeping the valve open.” Finally the schema . ¬pT ¬pIp represents an action that keeps the world preserved in the feature described by .¬p. This action therefore represent .make p remain absent i.e. a situation where the valve is closed but will open unless an agent does not keep it closed. The valve could be a pneumatic valve designed to open in the event of air loss. If the air is lost the operator could manually suppress the opening of the valve by keeping it closed. Other examples of manipulation of physical objects in space, translocation and containment, can also be used to illustrate the elementary intervention types as shown in Fig. 11.5. Translocation has a general interest because the dynamics of physical domains are described by so-called state spaces. These spaces are abstract but are usually explained and represented in terms of a physical space metaphor. In state spaces a change, and accordingly also an action, is defined by the transition between two locations. The examples are obviously also relevant for other domains where the space metaphor can be used.

11.4.1 Passive Domains Von Wright’s definition of elementary action types assumes that the world is inherently dynamic. Defining an elementary action by a counterfactual conditional implies that there is always an opponent trying to counteract the agent. Thus, .make p happen means to counteract an opponent who would .make p remain if

11.4 Intervening

Fig. 11.5 Interventions by an agent translocating or containing an object A

199

200 Table 11.3 Elementary interventions in a passive domain

11 Dyadic Transformations Schema . ¬pTpI ¬p . pT ¬pIp

Description make p happen make p disappear

the agent does not intervene. To .make p remain means to counteract an opponent trying to .make p disappear. This again means that each of the four elementary actions implies dynamic interactions between the agent and an opponent. However, not all situations include this persistent interaction between the agent and an opponent. It is therefore necessary to consider how the elementary intervention types would apply in such cases. To make this point clear, use as an example the translocation of objects standing on a table (i.e. the proposition p refers to the location of an object on the table surface). Horizontal movements of an object on the table are clearly instances of the elementary intervention types .make p happen and .make p disappear. However, it is not immediately clear what meaning should be given to the intervention .make p remain since the object would remain at its position on the table if it is left alone i.e. without intervention by an agent. There is apparently no opponent in this situation as this would require that the agent should intervene persistently in order to make the object remain at its location. The same argument goes for the intervention type .make p remain absent since p would remain absent independently on the agents action. Elementary interventions in such passive domains would therefore include only the two types shown in Table 11.3.

11.5 Letting As mentioned in Chap. 10, to let something happen is an action where the agent decides to abstain from intervening and let the state of affairs change by its own dynamics. As stressed by von Wright, it must be assumed that the agent both has an opportunity to act and is able to intervene in order to talk meaningfully about a letting i.e. to deliberately abstain from doing something. It must also be assumed that state of affairs would have been different had the agent intervened rather than abstained from acting. Elementary lettings can be obtained from the elementary changes shown in Table 11.1 by an extension of the change schema to include the counterfactual situation. But in this case, the actualized state must be the same as the hypothetical state of affairs that would be obtained, if the agent did not intervene (which he/she does not). The resulting four elementary lettings shown in Table 11.4 are therefore obtained, each corresponding to an elementary change type. As with the interventions, each letting is characterized both by its schema and its description. As the four elementary lettings describe actions where the natural dynamics or dispositions of the environment (or by the intervention of another agent), is the cause of the change, the propositions q and r must be the same i.e. the schema for

11.6 Doing

201

Table 11.4 The elementary lettings and their conditions Schema .¬pTpIp .pTpIp .pT ¬pI ¬p .¬pT ¬pI ¬p

Description let p happen let p remain let p disappear let p remain absent

Condition p is not and will happen p is and will remain p is and will disappear p is not and will remain absent

lettings must be pT qI q. There is then clearly a distinction between lettings and interventions because the produced q and the hypothetical state of affairs r must be different for interventions. However, the difference between something happening and letting it happen is not completely clarified by this. The difference can only be expressed if the belief or expectations behind the agent’s decision to abstain from acting is introduced in some way in the schema. The concept of a letting implies the notion of a substitute, because the dynamics of the environment produces the change intended by the agent. The change is caused by another agent which in this way substitutes the agent. The substitute is causing the change of state of affairs instead of the agent. A substitute should not be considered as a helper since this would imply concurrent intervention by the helping agent. Helpers will be discussed in Chap. 12. The conditions for letting something happen are expressed in the action schema pT qI r through the equality of q and r meaning that the state intended is the state that will occur when the agent is not intervening in the environment (see also Table 11.4). To let something happen then implies a decision not to act justified in the belief that the environment will change without intervention i.e. the dynamics of the environment will make it happen. The formula does not express these beliefs in its present form and extensions are required also in order to characterize certain types of failure. These extensions will be introduced in Sect. 11.6. Examples of lettings: The four types of lettings can be illustrated by examples of valve operations. Thus the schema . ¬pT pIp whose description is .let p happen will correspond to the sentence “let the valve open.” The schema . pT ¬pI ¬p representing the action .let p disappear will correspond to “let the valve close.” The schema . pT pIp represents an action with the description .let p remain. The schema would therefore represent the action “let the valve stay open.” Finally the schema . ¬pT ¬pI ¬p with the description .let p remain absent represents an action where the agent “let the valve remain closed.”

11.6 Doing Doing something means to intervene with a because-of and an in-order-to motive and therefore includes both causal and intentional aspects. The intention in action

202

11 Dyadic Transformations

is the “in-order-to” motive (see Schutz [4]). The action types for interventions and lettings introduced above were based on causal criteria. In order to explain how in-order-to motives can be expressed consider the general action schema pT qI r. Here the extension can be carried out by investigating ways by which in-orderto motives can be assigned to the action types for interventions and lettings. By observation a causal type can be assigned to an intervention (provided that the situation p is defined) but this is not possible with doings because observations alone do not account for the agents preferences or values. Even though motives are not observable von Wright’s theory can be used to propose in-order-to motives which the agent may have given observations of state of affairs before and after the intervention, and the possibilities of interpretation offered by different descriptions of the action types.

11.6.1 Descriptions of Elementary Actions Assuming that there is only one description for each schema leads to semantic problems that can be resolved by accepting two descriptions of the same schema. Each of the descriptions is an interpretation of the action and represents a possible in-order-to motive. The possibility of multiple descriptions of the same schema can be illustrated by discussing the consequences of reducing the eight action types for interventions and lettings to four by substituting p with .¬p in the action schemas. A reduced set obtained in this way is shown in Table 11.5. The reduction shown is possible in principle due to the logical form of the action schema.2 To see how the reduced set is derived from the full set of eight elementary actions consider an example. Example: Consider the intervention with the schema . ¬pT pI ¬p and description .make p happen. If p is substituted with .¬p the description .make ¬p happen and the corresponding schema .¬(¬p)T ¬pI ¬(¬p) are obtained. However, the latter is logically equivalent to the schema . pT ¬pIp , which has the description .make p disappear.

Table 11.5 A reduced but problematic set of interventions and lettings

Intervention ¬pTpI ¬p make p happen . pTpI ¬p make p remain .

Letting ¬pTpIp let p happen . pTpIp let p remain .

2 The meaning of .¬p is here according to the rules of propositional logic. The semantic problems is a consequences of ignoring Bergson’s argument against negation mentioned in Sect. 11.3.

11.6 Doing

203

Table 11.6 Action schemata for interventions and forbearances and corresponding descriptions Schema .¬pTpI ¬p .pTpI ¬p .pT ¬pIp .¬pT ¬pIp .¬pTpIp .pTpIp .pT ¬pI ¬p .¬pT ¬pI ¬p

Description 1 make p happen make p remain make p disappear make p remain absent let p happen let p remain let p disappear let p remain absent

Description 2 make .¬p disappear make .¬p remain absent make .¬p happen make .¬p remain let .¬p disappear let .¬p remain absent let .¬p happen let .¬p remain

In this way two descriptions can be derived for each action schema as shown in Table 11.6. It would be tempting to conclude that the two descriptions for each action have the same meaning, but this is not the case as explained in the following. Doings and forbearances will be considered separately because they require different explanations.

11.6.2 Description of Interventions The two descriptions for each elementary intervention have different meanings because they refer to two different but possible in-order-to motives i.e. to two types of doing. This can be seen by considering e.g. the action description .make p happen referring to the state of affairs (p) resulting from the intervention whereas the description .make ¬p disappear refers to the state of affairs (.¬p). The two descriptions have therefore different meanings even though they logically are the same since they have the same schema . ¬pT pI ¬p . The distinction between two different descriptions of the same intervention is important when explaining events or changes as results of an agent’s action. When the action is described as .make p happen the focus is on the result p of the action whereas when it is described as .make ¬p disappear the focus is on the actual state .¬p. The problem with the reduction proposed in Table 11.5 is that the two descriptions have different meanings. They refer to two situations where the agents have different preferences corresponding to doings that have different in-order-to motives. This difference in meaning should be expressed and the reduction should therefore be avoided. The differences will be expressed by extending the schema to include information about in-order-to motives and by descriptions of doings which reflect the intention, in contrast to descriptions of interventions which only reflect the causal meaning.

204

11.6.2.1

11 Dyadic Transformations

Promoting and Opposing

The relations between schemas for interventions and types of doings are shown in Figs. 11.6 and 11.7. The doings are grouped horizontally into two types, distinguished by two complementary action verbs promoting and opposing. According to the first type of description the doing is a promotion of state of affairs (e.g. .producing p) whereas the doing is opposive according to the other description (e.g. .destroying ¬p). It can accordingly be concluded that the same intervention can be described as two different doings depending on the intention; one which promotes a future situation (an accomplishment), another which is opposed to the present state of affairs (an avoidance). Sometimes the two types of doing, corresponding to the same intervention, can be applied at the same time when i.e. the agent’s intention is .producing p and therefore, due to the common causal structure expressed by the intervention, at the same time realizes .destroying ¬p. In such cases .producing p cannot be seen as a means of .destroying ¬p since the two descriptions are different interpretations of the same event. There is no causal relation involved between the two doings as there should be in a means-end relationship. There may be other situations where it would also be relevant to apply both descriptions e.g. when the agent has dual intentions.

Fig. 11.6 The two descriptions of the intervention . ¬pTpI ¬p correspond to two types of doing p and .destroying ¬p

.producing

11.6 Doing

205

Fig. 11.7 Relations between changes and action schemas and types of doings

The distinction between promotive and opposive doings corresponds to the distinction between accomplishments and avoidances mentioned in Chap. 10. A doing is an accomplishment if the intention is to achieve something desirable like reaching a target state. An avoidance is a doing whose aim is to avoid a state or situation which is considered not desirable to the agent i.e. a hazard. Remarks About Opposive Actions It can be seen from the analysis above that doings opposing state of affairs p (i.e. avoidances) can be subdivided into actions that suppress and destroy. This distinction is based on a difference in the initial state of affairs, but doings that suppress state of affairs can be subdivided further into preventive and protective actions. A type tree of opposing doings is shown in Fig. 11.8. This sub-typing of the suppress action presupposes that the agent is interacting via the environment with another agent. Consider an agent A that intervenes in an environment with the intention of suppressing state of affairs p (i.e. .suppressing p), which would otherwise be produced by an opponent B. If successful A prevented B from .producing p. An act of prevention is accordingly described from the agent’s perspective. The same doing described from the perspective of the environment would be that A protects the environment against B’s attempt to produce p.

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11 Dyadic Transformations

Fig. 11.8 Types of avoiding

Table 11.7 The elementary promotive doings

Table 11.8 The elementary opposive doings

11.6.2.2

Type producing p maintaining p producing.¬p maintaining.¬p Type destroying.¬p suppressing.¬p destroying p suppressing p

Schema ¬pT p.I ¬p . pT p.I ¬p . pT ¬p.Ip . ¬pT ¬p.Ip

.

Schema ¬p.TpI ¬p p.TpI ¬p p.T ¬pIp . ¬p.T ¬pIp

.

Representing in-Order-to Motives

Considering the preferences of the agent which are represented by the motives, two interpretations of the schema pT qI r can accordingly be proposed for doings. When doing something, the agent is assumed to have an in-order-motive regarding the actual or future state of affairs. The intention can be expressed by extending the schema pT qI r with an indication showing whether p, q or r is in the intentional focus of the agent. A simple notation using bold types will be applied so that .pT qI r denotes a doing where the intention is to promote future state of affairs q, and .pT qI r is a doing where the intention is to oppose the present situation p. Using this notation the promotive and opposive types of doing can be represented as shown in Tables 11.7, 11.8, and 11.9. The opposive types of doing are applicable for situations involving risk, where the agent intervenes in order to protect a system from being in a hazardous situation p (i.e. .destroying p or .destroying ¬p) or preventing that the system is entering a possible future situation p involving risk (i.e. .suppressing p or .suppressing ¬p).

11.7 Forbearing

207

Table 11.9 Beliefs and because-of motives for promotive doings Intervention producing p maintaining p producing.¬p maintaining.¬p

Belief p is not p is p is p is not

Because-of motive p remains absent unless caused to happen p disappears unless caused to remain p remains unless caused to disappear p happens unless caused to remain absent

Fig. 11.9 The distinction between opposive and promotive forbearances depends on the expectations of the agent

11.7 Forbearing Types of doings were distinguished by the in-order-to motives of the agent, but such motives are not involved in the case of forbearances. An agent forbearing something refrains from acting and lets the dynamics of the environment change state of affairs. The decision of the agent not to intervene is motivated by expectations regarding the future state of affairs of the environment. In circumstances where the agent expects the environment to change by its own dynamics into an intended state of affairs, the agent has no reason to intervene. Figure 11.9 shows how descriptions of forbearances can be distinguished by the expectations of the agent, and Fig. 11.10 shows the relations between changes, action schemata and descriptions for forbearances.

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11 Dyadic Transformations

Fig. 11.10 Relations between change schema and action schemata and descriptions for forbearances Table 11.10 The elementary promotive forbearances

Type forbear that p happen forbear that p remain forbear that p disappear forbear that p remain absent

Schema ¬pT p.Ip p.TpIp . pT ¬p.I ¬p . ¬p.T ¬pI ¬p .

11.7.1 Representing Because-of Motives Considering the motives of an agent represented by the expectations (the becauseof motive), two interpretations of the action schema pT qI r can be proposed for forbearances. When forbearing something the expectations is referring to the actual p or to the alternative (counterfactual) future r which occurs if the agent forebears to act. The distinction between expectations can therefore be expressed by extending the schema pT qI r with an indication showing, whether p, q or r is in the intentional focus of the agent. But having an expectation of the outcome by forbearing is the same having an intention that the outcome will occur. This means that the bold notation which was used to represent intentions in doings can also be applied here. Using this notation the promotive forbearances can be represented as shown in Tables 11.10 and 11.11 (a similar table for opposive forbearances can be produced).

11.8 Bringing About

209

Table 11.11 Forbearances, their conditions and because-of motives Forbearance forbear that p happen forbear that p remain forbear that p disappear forbear that p remain absent

Condition p is not p is p is p is not

Because-of motive p happens p remains p disappears p remains absent

11.8 Bringing About Danto [5] emphasizes a distinction between doing and bringing about something. In a doing, also called basic action by Danto, the intention in action is defined by the result produced i.e. by q in von Wrights action schema pT qI r. When bringing about a state of affairs s the intention in action is not directed towards the result q but at its consequence s. A formal definition of bringing about includes therefore a combination of a doing and a forbearance with the consequential production of q. When bringing about q by doing p, the doing is a means for making q happen. Both Danto and von Wright emphasize that basic actions should not be seen as means to their result r since the result is an intrinsic or logic part of their definition. Two types of in-order-to motive should accordingly be considered depending on whether the intention in action is directed towards the result of the action or its consequence. In the former case the intention will be described by one of the promotive or opposive intervention types. In the latter case the intention will be described by the intervention type as a means and the consequential state of affairs as a target or hazard. The following schema . ¬pT pI ¬p → ¬qT qI ¬q is used to represent the action which is described as . make q happen by producing p . The distinction between doing and bringing about is important for means-end analysis in general (see Chap. 17). It is also of interest for understanding control actions (see Chap. 16) where the target of control sometimes is not the result of the intervention itself but a consequence of the result.

11.8.1 Bringing About and Composite Actions Bringing about can be formalized using the concept of composite actions suggested by von Wright [1, 2]. In a composite action the state of affairs comprise two states p and q which both change as a result of the transformation. In the basic formulation of a composite action, the two states are independent, but in most cases they are related. It can be the case that p and q cannot be true at the same time (moving an object between two locations is an example of this), in other situations p implies q. This is the case for bringing about which contains two states, the result q and the consequence s. which are connected by a relation of implication. Composite actions,

210

11 Dyadic Transformations

their schemata and descriptions (reflecting distinction between different intentions) have been explored by Lind [6] but will not be considered here.

11.9 Elementary Objectives Achinstein [7] propose a distinction between functions as “doing p” (i.e. the transformation or change) and objectives as “p is done” (i.e. the desirable state of affairs has been achieved). This distinction can be used to define eight elementary objectives each corresponding to a doing (see also Chap. 18 for other implications of Achinstein’s distinction between functions and objectives). The eight doings are shown in Tables 11.12 and 11.13 together with the corresponding objectives. These types are only valid in one meaning of objective since an objective can also be to provide the potential for action, to perform the action without considering its possible result or to achieve the result, produced by the action or produced as one of its consequences. In order to consider these objectives it is necessary to discuss possible interpretations of p in the action types. This will not be considered here.

11.10 Elementary Successes and Failures The types of doings and the forbearances shown in Tables 11.7, 11.8, and 11.10 are based on distinctions between intentions representing possible desired outcomes. This means that the schemata used for the representation of doings and forbearances do not include the actual outcome. This information is required in order to distinguish between successes and failures of action. The specification of success and failure types is obtained by indicating in the schema for doings and forbearances, whether the outcome is the counterfactual state Table 11.12 Elementary promotive doings and corresponding objectives

Doing producing p maintaining p producing.¬p maintaining.¬p

Table 11.13 Elementary opposive doings and corresponding objectives

Doing destroying p suppressing p destroying.¬p suppressing.¬p

Schema ¬pT p.I ¬p . pT p.I ¬p . pT ¬p.Ip . ¬pT ¬p.Ip

Objective p is produced p is maintained .¬p is produced .¬p is maintained

Schema p.T ¬pIp . ¬p.T ¬pIp . ¬p.TpI ¬p p.TpI ¬p

Objective p is destroyed p is suppressed .¬p is destroyed .¬p is suppressed

.

11.10 Elementary Successes and Failures

211

or the result intended. The actual state is indicated with an underscore. Take as an example the promotive doing .producing p with the schema . ¬pT pI ¬p . A successful action will here mean that the intention to .make p happen is realized and will be expressed by an underscore as the schema . ¬pT pI ¬p . The failure of .producing p will be represented as . ¬pT pI ¬p since the actual outcome of the action is the counterfactual situation .¬p. Tables 11.14 and 11.15 show successes and failures for the eight elementary doings . Failure and success types can also be represented using dyadic transformation graphs as shown in Fig. 11.11 (see also Appendix A). Table 11.14 The elementary promotive success types

Schema

Description

.

¬pT p.I ¬p

producing p succeeed

.

pT p.I ¬p

maintaining p succeeed

pT.¬pIp

destroying p succeeed

¬pT.¬pIp

suppressing p succeeed

.

Table 11.15 The elementary promotive failure types

Schema

Description

.

¬pT p.I ¬p

producing p failed

.

pT pI.¬p

maintaining p failed

pT.¬pI p

destroying p failed

¬pT.¬pI p

suppressing p failed

.

Fig. 11.11 Transformation graphs representing failures

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11 Dyadic Transformations

The definition of control actions relies on the definition of action success and failure. The objective of control action is to ensure that process objectives are achieved i.e. to ensure success. This point will be elaborated later in Chap. 16.

References 1. G. H. von Wright. Norm and Action - A Logical Enquiry. London: Routledge and Kegan Paul, 1963. 2. G. H. von Wright. An Essay in Deontic Logic and The General Theory of Action. Amsterdam: North-Holland, 1968. 3. H. Bergson. Creative Evolution. New York, USA: Dover Publications Inc., 1998, p. 407. 4. A. Schutz. Reflections on the Problem of Relevance. New Haven: Yale University Press, 1970. 5. A. C. Danto. Analytical Philosophy of Action. Cambridge: Cambridge University Press, 1973. 6. M. Lind. Description of Composite Actions - Towards a Formalization of Safety Functions. Tech. rep. NKS-R(04)07/11 Barriers, Control and Management. Nordic Nuclear Safety Research, 2004. 7. P. Achinstein. The Nature of Explanation. Oxford: Oxford University Press, 1983.

Chapter 12

Role Types

This chapter presents a theory of role types which can be used to define functions in the meaning of role. It complements the classification of actions by their transformational aspect presented in Chap. 10. As mentioned in Chap. 7 functions also have a role aspect representing the participants in an action including the temporal and spatial locations, together comprising the circumstantial aspects of an action in Rescher’s [1] list presented in Chap. 10. Often the participants are seen as those directly involved in the transformative aspects of the action. Other items involved have circumstantial roles.

12.1 The Concept of Role The participants in a physical action, the actors, would typically be entities such as persons, things, or systems which are acting or acted upon and therefore changed in some capacity as a result or consequence of the action. The causer is indicated by ascribing the agent role to an actor. In a similar way the object role is ascribed to the actor being changed. Roles are accordingly used in the description of an action to denote the causing and the caused entity in a given context in abstraction from other features these entities may have in other contexts. Two other participant roles were introduced in Chap. 10, the substitute and the opponent. These roles were introduced to distinguish between the causal structures of letting something happen and making it happen. The concept of role has been important in the development of language semantics (here called semantic roles) and more broadly in theories of narratives. The subject of narrative theory within language studies is to develop principles for construction of texts beyond the level of the individual sentences. Each sentence can be seen as a representation of a situation governed by rules of syntax and semantics and can describe a state of affairs, a process or a state transition. At the text level beyond © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_12

213

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the sentence, the discourse, narratives representing sequences of situations bound together into meaningful higher level chunks represent scenes, plots, or thematic units. The theory of narrative is applicable for other than texts, it is also seen as a basis for explanatory forms in history (Danto [2]) and for the types of explanations given of human interaction with artefacts (Polkinghorne [3]). There has been an interest for development of generic role types which can be applied for analysis of role structures in actions and narratives across different domains. Linguists have developed role types fitted for analysis of texts. An influential example is Fillmore’s case theory [4]. In this book roles types will be investigated which are relevant for modelling functions of technical artefacts.

12.1.1 Fillmore’s Cases Concepts of action play a central role in linguistics especially for analysis of the meaning of texts describing actions or activity such as e.g. stories. The verb in a sentence is the linguistic element denoting action. In text analysis the challenge is to identify the references between a verb and other elements within the text which contribute to the meaning of a sentence. The analysis is both on the syntactical and on a deeper semantic level. These meaning structures are often called cases or semantic roles. An important research question is whether there exists a fundamental set of cases or semantic roles, but unfortunately there is no strong consensus about the definition or the number of semantic roles in linguistics. Fillmore (op.cit.) proposed a theory for case structures in natural language. The theory distinguishes surface aspects of language, such as the distinction between subject and object in sentence syntax, from deep structures related to sentence meaning. Fillmore defines a case as a binary relation between a verb and one of its arguments. The case structure for a verb is the set of cases allowed for that verb. Furthermore a case system constitutes a complete set of cases for a language. Fillmore’s cases shown in Table 12.1 are intended to be a comprehensive list. This means that all cases may not be relevant for a given action verb. Fillmore’s system of cases has been extended and elaborated by several researchers. An overview of case systems or semantic roles types can be found in e.g Bruce [5]. The agent and object roles have already been identified in Chap. 6 but several of the other cases are relevant as well. This will be shown by using the more general theory of actants developed by Greimas.

12.2 Greimas’ Actant Schema Greimas [6] proposed a very influential theory of narratives called the theory of actants, which is of particular interest for functional modelling of technical artefacts.

12.2 Greimas’ Actant Schema

215

Table 12.1 Fillmore’s cases Case (Role) Agent Counter-agent Object Result Instrument Source Goal Experience

Description The instigator of the event The force or resistance against which the action is carried out The entity that moves or changes or whose position or existence is in consideration The entity that comes into existence as a result of the action The stimulus or immediate physical cause of an event The place from which something moves The place to which something moves The entity which recieves or accepts or experiences or undergoes the effect of an action

Greimas’ theory defines six fundamental roles (called actants) with associated sets of generic causal and intentional relations.1 The schema, shown in Fig. 12.1, was originally developed by for analysis of semantic structures of narrative texts, but Greimas and Hebert [8] claimed it to be of general applicability also outside the domain of literary texts. The schema defines a set of roles which is generic to any action and is associated with interpretations dealing with causality and intentions in action. This generality is obtained through a high level of abstraction including only a very limited number of roles. However, the simplicity is not obtained without a cost. One of the problems of Greimas’ theory is to apply the generic concepts on concrete examples. This challenge is shared by functional modelling where it is addressed through the formulation of principles and guidelines for abstraction (discussed in Chap. 15). Greimas developed the schema from theories of narrative structure originally proposed by Propp [9]. The roles can theoretically be used to analyse any action, but are mostly used for analysis of texts or images. Actually, the roles make immediate sense for the description of interactions between physical artefacts and humans and one could speculate whether the schema takes its inspiration from this source, even though it is claimed that it was derived from Propp’s (op.cit) analysis of fairy tales. An action can, according to Greimas, be decomposed into six components, the roles and their relations. When using the schema for analysis each element of the action is being described as belonging to or being an instance of one of the roles.

1 Actant is a technical term introduced by Greimas, but to be compliant with more common terminology, the term role, which has a similar meaning, will be used instead. It is also more consonant with the term ‘semantic role’ used in language semantics. Brandt and Johansen [7] make a distinction between actants and roles. They see roles as a concept for representing social relations between persons rather than as a concept for representing the contributions or participation of an actor in a specific action. This distinction does not seem to be highly relevant for functional modelling of technical artefacts which does not enter into social relations. However, when required to include social relations in functional modelling (e.g. communication involved in control), the roles involved will simply be called social roles.

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Fig. 12.1 Greimas’ schema with the three oppositions and associated roles (the agent is called the subject by Greimas)

The three oppositions and associated roles in Greimas’ schema are defined as follows (Hebert [8]): The axis of desire (agent/object): The agent is what is directed toward an object. The relation established between the agent and the object through the axis of desire is in Greimas’ theory called a junction. A junction is essentially a cause-effect relation between the agent (the causer) and the object (the caused). The axis of transmission (sender/receiver): The sender is the element requesting the establishment of the junction between agent and object. The junction can be either a conjunction or a disjunction. The receiver is the element that benefits from achieving the junction between the agent and the object. The axis of power (helper/opponent): The helper assists in achieving the desired junction between the agent and the object. The opponent hinders the same.

The axis of transmission relates to two separate spheres of action A and B serving as senders and receivers of the object transformed through the axes of desire and power. This distinction which is illustrated in Fig. 12.2, is important for the causal and intentional interpretations of the roles and the transformations presented below and in Chap. 10. The role combinations connected with the three axes is an expression of the reciprocity principle for dispositions mentioned in Chap. 6 (i.e. an agent presupposes an object, a helper presupposes an opponent and an agent, and a sender presupposes an object and a receiver). Hebert mentions (op.cit.) that the definition of the sender and the helper roles was developed further by Greimas in his canonical narrative schema CNS [10] in order

12.2 Greimas’ Actant Schema

217

Fig. 12.2 Two contexts of analysis

to emphasize the manipulatory nature of the sender e.g. as the role which prompts the action by wanting-to-do or having-to-do it. Below a related elaboration of the schema is presented which is suitable for representing intentional structure in the design and use of artefacts. Greimas’ schema represents relations between role types and not concrete instances. The application of the schema on concrete actions implies accordingly an interpretation in terms of the three oppositional relations and their associated roles. An action would typically involve several roles where each role is an abstract representation of an actors contribution to the action. An actor can be associated with several roles and role can be realized by several actors. A related mapping between function and structure was discussed in Chap. 7. The roles in Greimas’ schema are very abstract and need interpretation when using the schema in the analysis of a concrete action. In particular it is not clear in what way the receiver benefits from the action. Similarly, the nature of the request made by the sender is also unclear. These problems of interpretation will be addressed in Sect. 12.4. An associated problem is the lack of specification of the transformation of the object resulting from the interaction with the agent. This also includes a specification of the nature of the object. Can it be a thing or is it a situation e.g. an event or a process? For each of these interpretations the transformation will have a separate meaning. This means that Greimas’ schema does not support a specification

218

12 Role Types

Fig. 12.3 The relations between actors and roles (see Greimas [10]

role 1

role 2

sharing

role 1

role 2

arbitration

actor

actor

Fig. 12.4 Actors can be exclusive (arbitration) or shared between roles

of what is done i.e. the action result in Rescher’s list of action aspects shown in Chap. 10. Greimas’ schema also includes the relation between roles and actors in [10] which corresponds to the distinction between function and structure in Chap. 10. Figure 12.3 shows that a role R1 can be served by a set of actors A1, A2 and A3, and conversely an actor A1 can serve several roles R1, R2 and R3. These mappings between roles and actors are of importance when applying the schema. It is difficult to assign roles to actors because the meaning of the roles and their relations along the three axes are ambiguous. In the following section two different interpretations of the schema will be presented in order to resolve this problem.

12.2.1 Sharing and Arbitration of Actors The many to many mappings between actors and roles shown in Fig. 12.3 are important when considering the sharing of actors between several actions, and how roles can be assigned to actors in different capacities in various contexts of action. Actions can in this way become interdependent through sharing or exclusive access (arbitration) to actors as shown in Fig. 12.4. Sharing and exclusive access to actors are basic mechanisms for making actions interdependent.

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12.3 Using Greimas’ Schema The aim of Greimas’ theory is to provide a semantic analysis of human interaction and can inspire, but has limited direct application in the study of interactions between humans and technical artefacts, which is the focus in the present book. Greimas’ concepts are too generic to provide the analytical power and “precision” needed for engineering applications. Such applications require models which can be validated i.e. to be “true” representations in some sense and to capture the aspects of reality relevant for a particular problem. This is demanded e.g. in safety critical domains. Greimas’ theory is attractive because of the simplicity provided by its abstract concepts, but the interpretations involved in applying it to concrete phenomena hinders the precision required for engineering domains and is an obstacle to validation. Abstraction is important for functional modelling but a middleway should be found which includes concrete particulars also. Hebert [8] proposes the following steps for using Greimas’ schema:2 1. Select the general action 2. Convert the action into an role model by first selecting the agent and the object (since the other roles are defined relative to this axis), specifying the type of junction between the agent and the object (conjunction or disjunction) and saying how and whether the junction is achieved. . . 3. Select the other actants. Each selection must be justified. . . It is a common error to loose sight of the particular agent object axis identified by the analyst, and to designate senders, receivers, helpers, and opponents that actually pertain to a different agent-object axis. The helper is not allied to the agent, but to the objectagent junction. . . Apart from this helpful advice, the use of Greimas’ schema is confronted with considerable problems of interpretation in the analysis of concrete cases. The schema makes perfect sense intuitively as a generic conceptual schema of an action which is obviously a strength, but it is at the same time also a major weakness, because the level of abstraction makes it a challenge to apply the schema consistently. Different factors contribute to this challenge: • The schema is developed for analysis of human-human interactions as found in literary texts and stories, and includes therefore presupositions about the types of intentional and causal relations involved in such domains of action. When applied in functional modelling for analysis of human-artefact relations it is necessary to distinguish the causality in human-human relations from the means-end causality of the human-artefact relation. The distinction between different notions of causality proposed by Collingwood [11] clarifies this point and was adressed in

2 Note that Hebert uses the terminology proposed by Greimas. The agent is accordingly called the subject and a role is called an actant. But apart from these terminological differences the meaning is the same.

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•

•

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Sect. 6.5. Using Greimas’ schema for interpretation of the embodiment relation between the designer and the artefact is discussed in Chap. 15. The schema has two readings. The description of the roles and their relations through the axes has double meaning because it refers to both causal and intentional aspects of an action. There is therefore a need for two different readings separating the two aspects, especially when considering the allocation of roles to actors (adressed in Sect. 12.4). Several actions: The schema represents the semantics of a single action. There is a need for rules for using the model to make sense of concrete situations involving several actions and for the sharing of actors between actions dependent on their dispositions or capacities. This challenge is approcahed by the introduction of action systems in Chap. 15. Contexts: There is a need for an identification of different types of context with rules for using the schema in a specific domain of application. Validation of models requires rules or principles for this. Furthermore, there are no rules describing how the schema is used to distinguish different interpretations of concrete situations. The same physical occurrance can be described differently depending on the perspective.3 The schema invites to a fragmentation of situations where causal relations and intentions span across several interdependent actions. This is a particular problem with sender and receiver roles.

The relations between the roles and their conditions and associated phases of an action discussed in Chap. 14 are not included in Greimas’ schema. It assumes that the roles are established, but the analysis in Chap. 14 shows that the agent and object roles and their interrelation by the axis of desire are conditional on an opportunity for interaction. This analysis can be extended to other roles and their interactions as well.

12.4 Interpretation of Roles Above it was mentioned that Greimas has provided several interpretations of the sender roles in order to elaborate the manipulative nature of the sender i.e. as either wanting-to-do or having-to do. These interpretations of the sender role relate to intentions of an actor. In the following another distinction will be introduced between causal and intentional interpretations of all the roles, not only the sender. This distinction is important for using the Greimas’ schema to represent the relations between a user of a technical artefact and its designer. It is furthermore important for understanding how actions can be related and combined into action systems.

3 Each interpretation defines a situation. This implies that there are no situations as such in the world represented. Situations are relative to an interest or perspective.

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The causal and the intentional interpretation of the Greimas’ schema represents the meaning of the roles in two different contexts, (1) in a context of causal determinants involved in the action, and (2) in a context of intentions. Thus the assignment of intentions to the helper and opponent is given within the context of the actors aim. The assisting or preventing influence of the helper and the opponent may not be intentional but only a causal consequence of the helping and opposing actors behaviour on the agent. The context of intentions defines the oppositions and the associated causal influences by types of actors having an interest in the outcome of the action. The two interpretations cannot be completely separated as each of them provide the background for the other and are therefore hermeneutically related.

12.4.1 Causal Interpretation The causal interpretation is reflected in the three oppositions in the following way: The axis of power is dealing with the combined causal influences of the agent, helper, and opponent on the object. The axis of transmission represent the causal influences of the sender on the receiver mediated by the causal influence of the agent on the object. The axis of desire represents the causal influence of the agent on the object which is a prerequisite for fulfilling the aim or desire. The causal interpretation of Greimas’ schema is shown in Fig. 12.5 and Table 12.2. The Junction The junction is essentially a cause-effect relation between the agent actor (the causer) and the object actor (the caused), but it is also an intentional relation indicating a desire or aim of transforming the object. These two aspects of the junction are intimately related through the means-end relation. The existence of a junction depends on both implict assumptions and explicit conditions associated with an underlying causal relation. Causal relations are always dependent on implict assumptions like ‘all things being equal’ (see the distinction between conditions and causes in Chap. 6), but in addition a junction could be dependent on explicit conditions which should be satisfied in order to establish the junction between the agent and the object. These conditions can be related to the agent, the object, or the transformation involved. The Helper and Opponent In the causal interpretation the helper/opponent is something, somebody, or an action which provides or hinders a condition necessary for enabling or disabling the junction (conjunction or disjunction) between the agent or the object. In this causal interpretation the helper and the opponent are means (conjunction) and countermeasures (disjunction) for the agents actions. The helper role is in the causal reading similar to an enabler or supporter.

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Fig. 12.5 A causal interpretation of Greimas’ schema Table 12.2 Causal reading of roles

Role Sender Receiver Object Agent Helper Opponent

Meaning Provider of material to be transformed Consumer of material transformed (the product) Material transformed Means of transformation Enabler of the transformation Obstacle of the transformation

12.4.2 Interpretation by Intentions The intentional interpretation of Greimas’s schema is shown in Fig. 12.6 and Table 12.3. In the axis of desire the intentional interpretation is reflected by the agent, having the aim or desire to transform the object which is provided by the sender in order to serve the needs of the receiver. The sender provides the intentional conditions for the action and the receiver’s needs become satisfied by the result of the action. The Helper and Opponent On the axis of power, the helper is representing the aim of an actor who is sharing the aim of the agent and assist in its achievement. Similarly, the opponent is representing the assignment of an aim to an actor which

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Fig. 12.6 An intentional interpretation of Greimas’ schema

Table 12.3 Intentional reading of roles

Role Sender Receiver Object Agent Helper Opponent

Meaning Requestor of the transformation Benefactor of the transformation Material in need to be transformed Actor having the aim of transforming Actor sharing the aim of the agent Actor opposing the aim of the agent

prevent the agents achievement.In the intentional interpretation the helper/opponent ensures/prevents that the desired or intended junction/disjunction is achieved. As shown later in Chap. 16 a control system has a helper role in relation to the process by ensuring that the process objective (the desire) is achieved.

12.5 Discussion Greimas’ theory is of relevance for the foundations of functional modelling because the need for modelling complex systems on several levels of abstraction can be achieved by interpretation of the basic role types. Furthermore, it is an advantage

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that there is only a limited set of generic roles. The oppositions included in Greimas’ schema has also direct interpretations in the analysis of interactions between humans and technical artefacts. As shown later in Chap. 15, the axis of desire and the axis of transmission have a direct interpretation in terms of the basic principles of production. Furthermore, the distinctions between the agent, helper and the opponent on the axis of power is about causality and control and is discussed in Chap. 16.

References 1. N. Rescher. “Aspects of Action”. In: The Logic of Decision and Action. Ed. by N. Rescher. University of Pittsburgh Press, 1967. 2. A. C. Danto. Narration and Knowledge. New York: Columbia University Press, 2007. 3. D. E. Polkinghorne. Narrative Knowing and the Human Sciences. New York, USA: State University of New York Press, 1988. 4. C. J. Fillmore. “The Case for Case”. In: Universals in Linguistic Theory. Ed. by E. Bach and R. T. Harms. New York: Holt, Rinehart and Winston Inc., 1968, pp. 1–88. 5. B. Bruce. “Case Systems for Natural Language”. In: Artificial Intelligence 6 (1975), pp. 327– 360. 6. A. J. Greimas. Semantique Structurale. Paris: Presses Universitaire de France, 1986. 7. P. A. Brandt and J. D. Johansen. “Om Tekstanalyse”. In: Analyser af Dansk Kortprosa I. Ed. by J. D. Johansen. Borgen, 1971. 8. L. Hebert. Tools for Text and Image Analysis. Tech. rep. Department de Lettres, Universite du Quebec a Rimourski, 2011. 9. V. Propp. Morphologie du conte. Paris, France: Seuil, 1970, p. 254. 10. A. J. Greimas. On Meaning. Selected Writings on Semiotic Theory. University of Minnesota Press, 1970. 11. R. G. Collingwood. An Essay on Metaphysics. Martino Publishing, 2014.

Chapter 13

Triadic Transformations and Roles

This chapter investigates the relations between triadic causality and intentions, and is in its aim similar to Chap. 11 dealing with transformation types derived from dyadic causality. A semiotic analysis is made of the three basic stages of an action; experiencing, evaluating, and intervening in the world, which provides a link between the actor’s knowledge and functions of cognition. The semiotic analysis is shown to be foundational to models of decision making used in systems engineering and is of particular interest for functional modelling of monitoring and control systems and human operators.

13.1 Action and Triadic Causality The definition of an action presented in Chap. 10 was based on the assumption that the causal aspects of an action could be represented as a dyadic relation between a causer (the agent) and the caused (the object). Dyadic relationships are valid for understanding the relation between causes and effects in physical actions, and are obviously important for representing functions of technical artefacts. However, actions in technical artefacts cannot be limited to the physical, there are other actions which are of a triadic nature, namely those connected with control i.e. observation, evaluation, and intervention in a technical artefact or natural system. As explained in Chap. 6, the causal mechanism involved is here interpretative and the effect of such actions is to create meaning (see the semiotic triangle in Fig. 13.1). The process of meaning creation involved is called semiosis (Deely [1]). Research on semiosis has been done in the context of biological systems, but as will be shown, the theories developed are equally relevant for modelling of technical artefacts having cognitive functions such as control systems.

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Fig. 13.1 The semiotic triangle

Interpretant In

Si

Sn

Signifier

Signification

13.2 Experiencing, Evaluating and Intervening Dewey [2] and Schutz [3] explain that observations and interpretations involved in the perception of a situation are sign processes determined by habits i.e. structures of knowledge and motives (distinguished by Schutz in because-of and in-order-to motives). It is accordingly clear that there is a distinct difference between experiencing something and causing something by acting. Causing something is described by a dyadic relation with terminals defining functions or roles of the caused (the object) and the causing entity (the agent). In contrast the perception of a situation is described through triadic sign relations which define functions of experience corresponding to the preferences or values of the actor associated with the stages of an action described below. Experiencing a situation therefore cannot be understood in action or means-end terms, unless the experiencing includes the workings of a mind i.e. deliberations involving choices between alternative means of observation or schemes of interpretation to make sense of the situation. The triadic causal relations between the agents experience (the interpretant), the environment (the signifier) and its meaning (the signification) in the act is therefore just as essential as the dyadic causal relations which relate to the means of intervention (i.e. the dyadic transformations discussed in Chap. 10).

13.2.1 Stages of the Act Morris’ [4] presents a semiotic analysis of action which describes how an agent associates meaning to occurrences in the environment. It is assumed in the analysis that the agent is engaged in a problematic situation i.e. there is a lack of adjustment between the environment and the agent, who therefore wants to interact with the environment in order to achieve states that in some way are valuable or desirable. The courses of action chosen by the agent are motivated by values/goals (the in-

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Table 13.1 Morris [6] three dimensions of signification Signification Stimulus properties of object Selection of objects for prefer- Appraisive Object preferences of Reinforcing properties the agent of object ential behaviour Action on object by specific Prescriptive Behavior preferences Act as instrumental of the agent behaviour Action requirements Obtaining information

Dimension Interpretant Designative Sense organs

order-to motive) and on observations of the state of the environment (the because-of motive). Morris base his analysis on a distinction between the three stages1 of an action developed by Mead [5]; perception, consummation, and manipulation. Morris makes distinctions between three dimensions of significations of the environment (the signifier) corresponding to each of the stages namely a designative, an appraisive and a prescriptive dimension. These three dimensions of signification involved in the act are related to different interpretants representing dispositions to act related to distinct preferences or values of the agent. Morris analysis is summarized in Table 13.1.

13.2.2 Cognitive Functions Morris and Mead discuss the behaviour of biological species i.e. acts of an organism, but their analyses can be generalized to include both humans and intelligent technical artefacts. In the latter case, the relations between signs and their meaning, should be understood as modes of interpretation of occurrences in the environment that are prescribed by a designer and embodied in a technical artefact. The three modes of interpretation have both a causal aspect—the preferences or dispositions, and an intentional aspect—the values. They therefore matches the general definition of a function introduced in Chap. 1 (“the function of something (S) describes how it works (W) in a particular context (C)”), where S is an organisms or artefact, W has different modes corresponding to the three values (the contexts, C). The three modes of interpretation can therefore be seen as basic cognitive functions (Fig. 13.2). The basic cognitive functions can be combined into sequence to form a basic model of a control function as shown in Fig. 13.3 (control actions are discussed in a broader perspective in Chap. 16).

1 The

stages of an action should not be confused with the phases explained in Chap. 14. Stages and phases are two complementary aspects of actions involving cognitive functions.

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Fig. 13.2 Three basic cognitive functions derived from Morris’ analysis

Fig. 13.3 The three cognitive functions are included in a control action

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13.3 Models from Cognitive Psychology and Engineering Morris’ semiotic analysis of the act can be seen as the conceptual foundation for a whole class of models of human decision making, robotics and reasoning architectures for intelligent agents in AI.

13.3.1 Norman’s Action Cycle Norman [7] developed a model of human action called the action cycle. It distinguishes between seven stages as shown in Fig. 13.4 representing cognitive functions. Norman’s action cycle does not distinguish clearly between states of knowledge as e.g.the goal or the action sequence and the cognitive functions involving interpretation which connects them in the cycle. This is more clearly distinguished in the decision model proposed by Rasmussen presented next.

Fig. 13.4 Norman’s action cycle

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13.3.2 Rasmussen’s Decision Ladder Rasmussen [8] proposed a model of human decision making called the decision ladder shown in Fig. 13.5 which share several features with Norman’s model in Fig. 13.4. In addition to making a distinctions between states of knowledge and cognitive functions, the decision ladder also represents different behavioural modes as shortcuts which also relate directly to the distinction between the three dimensions of signification in Morris’ model. The decision cycle shown in Fig. 4.5 is a simplified version of Fig. 13.5 by having a reduced number of shortcuts. However, it shows the cyclic nature of the decision process which is not included in Fig. 13.5.

Fig. 13.5 Rasmussen’s decision ladder (adapted from Rasmussen [8])

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Hollnagel’s Contextual Control Model Hollnagel [9] proposes a separation of two aspects of Rasmussen’s decision ladder into a competence model and a control model. The point is that the sequential nature of the decision ladder (the control aspect) does not fit very well with human decision making. Thus, the individual tasks in the decision ladder (corresponding to different competences) can in principle be executed in arbitrary order. This is only partly reflected by the shortcuts shown in Fig. 13.5. Hollnagel argues that the sequence of task executions (the control aspect of the decision process) is dependent on contextual factors such as the state of affairs in the system under control or the workload of the operator. The sequential nature of the decision ladder makes it therefore problematic as a general basis for analysis of real life human decision making. The question about the existence of fundamental cognitive functions, which are suggested by Morris’ semiotic analysis of action, is also addressed by Hollnagel [10]

Fig. 13.6 Belief-desire-intention architecture

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by a comparison of inductive and deductive models of decision making but no firm conclusions are made.

13.3.3 The BDI Architecture A generic functional architecture for building intelligent software agents, (called BDI), has been proposed by AI researchers (see e.g. Wooldridge [11]). The BDI (belief, desire, intention) architecture shown in Fig. 13.6 is a model of rational decision making based on reasoning about beliefs, deliberation of goals, and action planning by means-end reasoning. The BDI architecture contains the basic features of Morris’ semiotic analysis and has strong similarites with Norman’s and Rasmussen’s models of human decision making presented above.

References 1. J. Deely. Basics of Semiotics. American University Press, 1990. 2. J. Dewey. “Qualitative Thought”. In: Philosophers of Process. Ed. by D. Browning and W. T. Myers. New York: Fordham University Press, 1998, pp. 192–210. 3. A. Schutz. Reflections on the Problem of Relevance. New Haven: Yale University Press, 1970. 4. C. Morris. Signification and Significance. Cambridge: The MIT Press, 1964. 5. G. H. Mead. The Philosophy of the Act. Chicago: The University of Chicago Press, 1938. 6. C. Morris. “Signs and the Act”. In: Semiotics: An Introductory Anthology. Ed. by R. E. Innis. Indiana University Press, 1985. 7. D. A. Norman. Design of Everyday Things. Doubleday, 1988. 8. J. Rasmussen. Information Processing and Human Machine Interaction. New York: North Holland, 1986. 9. E. Hollnagel. “Models of Cognition: Procedural Prototypes and Contextual Control”. In: Le Travail Human 56 (1993), pp. 27–51. 10. E. Hollnagel. “Inductive and Deductive Approaches to Modelling Human Decision Making”. In: Psyke and Logos 5 (1984), pp. 288–301. 11. M. Wooldridge. Reasoning About Rational Agents. Cambridge, Massachussets: The MIT Press, 2000.

Chapter 14

Action Phases

The purpose this chapter is to introduce temporal aspects of actions in more details including a discussion of their relations to causal roles of technical artefacts engaged in action. A theory of narratives is first introduced as a foundational conceptual framework for analysing the temporal unfolding of an action, and is then shown to be closely related to a model of action phases developed within engineering for safety analysis of technical artefacts. The model of action phases expresses a basic distinction between possibility and actuality of an action, which is fundamental to the relation between design and operation, and to the distinction between different types of action failure.

14.1 A Logic of Narratives Bremond [1] proposed a general theory for the analysis of narratives to be applied for analysis and synthesis of action sequences in literary texts. The theory has striking similarities to models used within engineering, but is considered to be more foundational by representing generic temporal aspects of actions. Bremond’s theory comprises three elements, the narrative atom, the enclave, and the perspectives. The narrative atom and the enclave will be explained below. The third element, the perspectives, is the idea that the same event can be seen in relation to two agents with opposing interests, is only of relevance for modelling technical artefacts where design or operation is influenced by conflicting interests of stakeholders. Perspectives as an element of narrative will not be considered here.

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14.1.1 The Narrative Atom The narrative atom is the elementary sequence distinguishing the opening, actualization and the fulfilment of an action as shown in Fig. 14.1 which accordingly includes three overall phases in the temporal unfolding of an action based on the overall distinction between possibility and actualization of an action. The narrative atom is general, but will be used in an extended form with more phases in Sect. 14.2 which is more applicable to design and operation of technical artefacts.

14.1.2 The Enclave The enclave is a process of improvement or the reverse, which can be interrupted. Bremond distinguishes between different narratives depending on the types of action as shown in Table 14.1. According to the table, an action can have a favorable or an unfavorable narrative for a goal oriented agent. If the aim is to produce a new situation, a favorable narrative will be seen as an amelioration or improvement of state of affairs. Reversely, an unfavorable narrative will be seen as a degradation. The two types of action involved in Bremond’s enclave is a subset of the action types proposed by von Wright [2, 3] (see Chap. 11), but it is unclear what kind of situation the propositions p should refer to. The situations referred to in Chap. 11 are physical states. In the case of the enclave, the propositions involve mind dependent evaluations of physical states in relation to some criterion of goodness or optimization. The enclave is therefore relevant for representing the functions of optimal control (see Chap. 16) (Fig. 14.2). A more detailed version of the enclave is shown in Fig. 14.3 which includes the steps involved in the selection and use of means to attain the goal.

Fig. 14.1 Bremond’s narrative atom Table 14.1 Types of action and the enclave

Action type

Favorable

Unfavorable

Produce Maintain

Amelioration Protection

Degradation Frustration

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Fig. 14.2 Bremond’s enclave

Fig. 14.3 The process of actualizing ameliorations in Bremond’s enclave

14.2 Extending the Phases The action phases shown in Fig. 14.4 extends Bremond’s narrative atom shown in figure by representing the unfolding of an action into eight basic temporal phases: power, liability, readiness, reachability, initiation, execution, termination, and disengagement. The distinctions between phases are important for identification of conditions for successful action and for how actions can fail. Conditions can be grouped according to the phase they belong to, and an action can fail in any of the phases if one or several of these conditions are not satisfied. The phase model represents accordingly the generic conditions for successful action. The action phases is a modification and extension of a model proposed originally by Haddon [4] and extended by Petersen [5] for analysis of safety barriers in

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Design

Operation

Termination

Disengagement

Goal Is achieved

Agent and object are disengaged

Performance

Initiation The action A is initiated

The object undergoes A

Reachability The agent can “reach” the object

The agent does A

Readyness

Liability The object has the liability to undergo A

Possibility

Completion

Execution

Opportunity

The agent and the object are enabled

Power The agent has the power to do A

Potentiality

Actualization

Fig. 14.4 A model of action phases (square boxes indicate the success conditions for each phase)

industrial systems. It is realized that it can be seen as a more extended version of Bremond’s enclave. The phases shown here is further extended by the introduction of enabling (opportunity) and termination (completion). Enabling, which is necessary to ensure that there is an opportunity for action, was implicit in Petersen’s model in the phase of actualization. The extension is necessary to be able to distinguish between enabling and initiation. Initiation is, by definition, sufficient for the action to be executed and is therefore crucial for control. In contrast the enabling is necessary but not sufficient for the action to unfold. Petersen (op.cit.) shows how the phase model can be used in safety analysis of technical artefacts. The phases of potentiality and opportunity are of particular interest in the study of safety critical situations, because an efficient countermeasure against a hazard is to prevent a potential or an opportunity for an action which may produce it. They are also of interest when dealing with abnormal operating situations which can only be handled by redesign of strategies for operation and control of the process. Example: A recent example of redesign of strategies for operation is the Fukushima accident where the operational challenge after the accident was to find new ways to cool and control the reactors (see IAEA [6]). The response involved identification of available alternative resources (potentiality and opportunity), deliberation about operational goals, and execution of control actions in order to mitigate the accident (completion). The human operator’s problem solving procedure involved accordingly all the phases of an action depicted in the phase model.

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Grouping of the Phases The eight phases depicted in Fig. 14.4 can be grouped in several ways. They can be grouped into two overall phases of possibility and actualization. This grouping highlights the fundamental distinction made by Bremond (op. cit.) between the phases where the action is a virtual not yet actualized possibility and the phases where the action is actualized. Another grouping of similar importance for technical artefacts, like a SCPS, is the distinction between phases belonging to design, and to operation and control. According to the phase model, the purpose of designing the process artefact (the object of operation and control), is to provide a potentiality of action. The purposes of operation and control which are implemented by the operational system, the human operator and the automated control system, are to ensure that there is an opportunity for action and that it is properly executed and completed.

14.2.1 Possibility The first overall phase of an action is only a possibility, but for an action to be possible it is necessary that there is both a potential and an opportunity for action. The concepts of potentiality and opportunity are modal notions referring to the possibility for action and not to the actualization of the action. Potentiality The phase of potentiality is about the ability of physical entities to be the causer of changes and to be caused. Thus, for a change to be possible it is necessary that the agent has the required power i.e. that the agent can cause the change to happen in the entity being the object of action. Furthermore the physical item should have the liability to undergo the change caused by the agent i.e. serve as an object of the action. Only when these general conditions are satisfied there will be a potential for change and thereby action. The potential for action relates accordingly to the ability of the participants to engage in the action. The aim of the phase of potentiality may also be to ensure that services from support systems which are required by the agent (lubrication, electrical power etc.) or the object (e.g. heating to ensure fluidity and thereby transportability) are available. This also includes avilability of control and instrumentation systems which are required to monitor and maintain process objectives. The functions of support and control systems (so-called helpers) will be discussed in Chaps. 12, 15, and 16. Opportunity Having a potential is a necessary but not sufficient condition for action. In order to make the action possible there should also be an opportunity. This involves in general two conditions as indicated in Fig. 14.4: one of readiness and one of reachability. The readiness ensures that the power of the agent and liabilities of the object of action are not only available but also enabled, but enabling the agent and the object to be ready to engage in action is still not sufficient. The agent and the object should also be located in space and time under the right circumstances

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so that they are able to interact i.e. they should be reachable.1 The conditions of readiness are accordingly related to the intrinsic properties of the agent and the object, whereas the reachability conditions are extrinsic to them and related to their locations in time and space and other circumstances. Two meanings of the concept of opportunity related to time should here be distinguished • an appropriate or favourable time or occasion. This meaning is related to the ancient Greek term kairos meaning to be in “the right time” for action, in contrast to kronos denoting “sequential time” (Smith [7]. • a situation or condition favourable for attainment of a goal. In this meaning an opportunity has two interpretations: – a possibility offered by an actor with causal powers (an agent) – a possibility offered by an actor liable to change (an object) The opportunity is only a condition for attainment of a goal and achievement of the goal is only a possibility. Its actualization depends on whether it is the intention to achieve the goal. These two meanings are logically connected because an action is only possible if the agent has the power to exploit the opportunities offered by the liability of the object to be changed, or conversely if the object offers opportunities that match the power of the agent. This logical connection is reflected in the reciprocity of dispositions and functional ascriptions mentioned in Chaps. 6 and 7. The distinction between potentiality and opportunity is important for identification of dyadic causal relations in design and operation of technical artefacts and will therefore be elaborated in Sect. 14.3.

14.2.2 Actualization The actualization phase includes two stages: execution and completion. Execution The fifth phase is the initiation of the action, which is restricted to the satisfaction of what is usually called triggering conditions. Example: Consider the starting of a motor which has the possibility of rotating but need to be triggered by closing a power switch (the closing action of the

1 Reachability as defined here is a condition for operability and is different from the definition of reachability used in control theory. Here it means that the agent has sufficient power to ensure fulfillment of the control objective, but this definition presupposes that the operability conditions are satisfied i.e. that there is an opportunity for control.

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switch in itself can be analysed according to the same five phase schema). The triggering is a condition for the motor to rotate. In the sixth phase of performance, the action is performed and hopefully it will successfully lead to its intended result or objective. In order to specify this phase the conditions under which the action is performed (the performance conditions) and the means to be used should be identified. This phase will include aspects of control in cases where the conditions of the action are uncertain. Completion The seventh phase is the termination of the action representing the final achievement of its objective. This phase of the action is accordingly specified by the conditions under which the action should be terminated. In some cases the termination phase marks the end of an activity and would therefore have a short event like temporal duration. However, in cases where the objective is to maintain a state of the object these will have longer duration in time. In many cases the conditions achieved by the action are maintained, but in other cases the action is completed by the eighth phase of disengagement, which is typical to e.g. shut down actions in technical artefacts.

14.3 Phases and Dyadic Causal Roles The distinction between potentiality and opportunity is important for explaining how the dyadic causal relation introduced in Chap. 6 is identified either through design or in operation. The distinction reveals two types of condition which should be satisfied so that agents and objects are able to interact. The first condition is that the agent has the power to act (the potential) and the second that the context provides an opportunity for change. This formulation of the condition assumes that abilities relate to the agents and opportunities relate to actors in the context which may serve as objects. This can be reversed in an alternative formulation where the ability relates to the liability of an actor to change i.e. to be an object of action, and the opportunities to actors in the context which may serve as agents. The two conditions are derived from the reciprocity of dispositions and functions mentioned in Chaps. 6 and 7. The concepts of potentiality and opportunity have therefore two interpretations depending on how the conditions are formulated. Each interpretation represents a particular perspective of the situation. In the first interpretation the analyst has a selected agent in the foreground and potential objects in the setting in the background. In the second interpretation a selected object is in the foreground and the potential agents in the setting is in the background. The two interpretations are illustrated in Fig. 14.5.

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Fig. 14.5 Two interpretations with focus on the possible agents or objects

Fig. 14.6 An agent in the foreground

14.3.1 An Agent in the Foreground In this perspective an agent is in the foreground i.e. an actor A in this capacity is attended to by the analyst (designer or operator), and the other actors (B, . . . ) are part of the background. The objects are here a representation of the liability for change that the corresponding actors (B,. . . ) can offer the actor A under the conditions provided by a range of possible settings .Sa , S1 , S2 , .., Sn . The set of objects offered in the actual setting comprise accordingly in this perspective opportunities for action (Fig. 14.6). Example: Consider an actor B (the object) being moved by another actor (the agent). Here B (having the disposition to be move-able) offers A the opportunity to realize the agent role of being the mover. In order to realize this role A and other actors must have the power to be a mover (i.e. mover-able). The opportunities (a set of agent roles) offered to the actor B are therefore the subsets of the dispositions of A and the other actors, that are serviceable in the given

14.4 Action States

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Fig. 14.7 An object in the foreground

setting (here .Sa ) in agreement with the principle that dispositions come in pairs (see Chap. 6). The agent roles are here defined relative to the particular actor in view. In comparison, the disposition of the actor B to be an object is independent of a particular actor A.

14.3.2 An Object in the Foreground In this perspective an object is in the foreground i.e. an actor B in this capacity is attended to by the analyst (designer or operator), and the other actors (A, . . . ) are part of the background. The agents are here a representation of the causal power that the corresponding actors (A,. . . ) can offer the actor B under the conditions provided by a range of possible settings .Sa , S1 , S2 , .., Sn . The set of agents offered in the actual setting comprise accordingly in this perspective opportunities for action (Fig. 14.7).

14.4 Action States The action phases defined in Fig. 14.4 define a sequence of states for the actualization of an action which is relevant for planning. As mentioned above, the sequence is not necessarily in a strictly logical sense but is an expression of a pragmatic necessity if a goal oriented action should succeed. The sequence represents a “logic of achievement” as defined by Polani [8]. According to this logic any deviation from the sequence is a failure. In the following the action states and the transitions which are possible between them will be defined.

242 Table 14.2 States of an action

14 Action Phases State

Values

empowered liable potent ready reachable opportune initiated perf orming executing terminated disengaged completion

False or true False or true empowered AND liable False or true False or true ready AND reachable False or true False or true initiated AND perf orming False or true False or true terminated AND disengaged

Generally, achievement of each phase is dependent on the achievement of previous phases. For example, in order to actualize an action it should be possible i.e. there should be both a potential and an opportunity. Since an action can be potentially possible without being opportune, two interdependent state variables are needed to define the status of an action (an action is possible if there is a potential and there is an opportunity). Distinctions between different state variables are of particular importance for defining types of action failure. Referring to the phases of an action shown in Fig. 14.4 states of an action can be defined as shown in Table 14.2. Transitions between states are shown in Fig. 14.8.

14.5 Action Phases and Failure The distinction between potentiality and opportunity is valuable in both analysis of action failure and in action planning. In analysis of failures it helps in focussing the identification of possible causes either related to the agent or to the object, and thereby revealing the variety of ways interactions between agents and objects can break down (lack of power to change or liability to be changed). In action planning the distinction helps in identification of conditions for interaction between agents and objects. The phase model can be used to define different types of failure for an action. Each of the phases in the action will be characterized by a specific type of failure.

14.5.1 Design Failure A failure of potentiality occurs if design assumptions are not met. Failures in providing the power or liabilities for an action originate in the design phase where the physical basis for realization of system functions are determined.

14.6 Discussion

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Fig. 14.8 Action states and transitions

14.5.2 Operation and Control Failure Failures of opportunity are failures which relate to the operation of a system and occur if a condition to be established during operation is not met. It could be conditions of reachability or readyness to be established during system start-up or shut down. Control failures can occur in the phase on initiation, performance and completion. Three types of performance failures can be distinguished: • The performance is influenced by external factors causing it to fail. • The means used in performing the control action fail. • The performance can fail in uncertain environments due to unforeseen disturbances. Finally a control can fail in completion by not achieving the intended objective.

14.6 Discussion The conditions formulated above ensure that the actors involved can fulfil their roles i.e. ensure that the action is possible. Conditions in the phases of actualization are

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related to the subsequent causal interactions between the actors (assuming that they fulfil their roles). When human operators act these conditions are often taken for granted. Likewise a control engineer often assumes that many of these conditions are satisfied when designing control algorithms. When acting under normal circumstances this is unproblematic, but when the circumstances change or deviate from the expected an action can fail sometimes with catastrophic consequences. The identification of conditions for action and the role they play in the proper phasing of the unfolding are therefore important problems. The phase model is used in Sect. 14.5 to define failure modes of action. Unfortunately, it is impossible in principle to define all conditions for an action. There will always be situations where an action fails due to latent conditions i.e. conditions which have not been realized, overlooked or emerging because circumstances develop beyond the expected. The analysis of action phases and the associated conditions is therefore confronted with a fundamental problem of incompleteness which only can be adressed by learning from experience. It should be mentioned that the distinction made between conditions for action provided in the phases of opportunity and initialization, are essential for specifying how the unfolding of an action is controlled. Satisfaction of conditions of opportunity are necessary but not sufficient for the actualization of an action whereas initialization (triggering) conditions are sufficient. The distinction cannot be made on a theoretical basis but depends on requirements to safe operation of a physical system. Example: Consider the conditions for the pumping action of a pump. The enabling conditions would here involve satisfaction of lubrication requirements and the availability of a source of power for the pump. The initiation condition would be that the power switch is closed (assuming of course that the enabling conditions are satisfied). The distinction between enabling and triggering conditions ensures that the pump is operated in a proper and safe sequence. Closing the switch without ensuring that the pump is enabled (i.e. considering the closing an enabling instead of a initiation action) would potentially do harm to the pump. The outcomes of the two ways of operating the pump would make no logical difference but are significantly different from a pragmatic operational point of view. Making the distinctions between action phases is accordingly a means of representing knowledge of system operation. The significance of the distinctions can be illustrated by considering why the pumping fails if the power switch is closed before the lubrication is available. The pump fails because the bearings are damaged and the pump loses its potential for pumping i.e. a precondition for execution is no longer satisfied. In cases where it does no harm to the agent (pump) or the object, the enabling and the initiation conditions can be reversed, but this is strictly a pragmatic decision.

References

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References 1. C. Bremond. “The Logic of Narrative Possibilities”. In: New Literary History 11 (1980), pp. 387–411. 2. G. H. von Wright. Norm and Action - A Logical Enquiry. London: Routledge and Kegan Paul, 1963. 3. G. H. von Wright. An Essay in Deontic Logic and The General Theory of Action. Amsterdam: North-Holland, 1968. 4. W. Haddon. “Energy Damage and the Ten Countermeasure Strategies”. In: Human Factors 15.4 (1973), pp. 355–366. 5. J. Petersen. “Countermeasures and Barriers”. In: Proc. Annual Conference of the European Association of Cognitive Ergonomics(EACE2005). Chania, Greece, 2005. 6. IAEA. The Fukushima Daiichi Accident: Technical Volume 1, Description and Context of The Accident. Tech. rep. International Atomic Energy Agency, 2015. 7. J. E. Smith. “Time, Times and the “Right Time””. In: The Monist 53 (1969), pp. 1–13. 8. M. Polanyi. Personal Knowledge. London: Routledge and Kegan Paul, 1958.

Chapter 15

Action Systems

The aspects of actions described in Chaps. 10, 11, and 12 are valid for a single action but do not apply directly for situations involving relations between several actions. This chapter will consider how actions can be combined to form actions systems. The principles of combination integrate the results of Chaps. 11, 12, 13, and 14. They are of foundational importance for functional modelling as they provide systematic principles for decomposition of a SCPS into a system of actions. Action systems in SCPS can be created in two different ways: • An actor (a thing, person or action) in SCPS can be shared among several actions serving causal roles in accordance with different dispositions. This type of sharing of actors is called embedding. • The outcome of an action can enable another action (make it potential, opportune or trigger it). These two ways of combining actions into systems are relevant for modelling functions of SCPS subsystems. In addition, and for the sake of completeness, it is necessary also to consider the relations between the designer and the artefact as a combination of actions. Since the SCPS is seen as an embodiment of the design intentions, the SCPS designer is the actor who is a source of intentions and the SCPS subsystems and components are as actors providing the causal basis for realising the designers intentions. This separation of the intentional and the causal basis for the functions of the SCPS requires insight in the design procedure and will be investigated below.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_15

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15.1 The Concept of Practice The sharing of actors between actions through roles can be derived from a so-called practice schema. The concept of practice used here was defined by Althusser [1] as follows: By practice in general I shall mean any process of transformation of a determinate given raw material into a determinate product, a transformation effected by a determinate human labor, using determinate means (of ‘production’). In any practice thus conceived, the determinant moment (or element) is neither the raw material nor the product, but the practice in the narrow sense: the moment of the labor of transformation itself, which sets to work, in a specific structure, men, means and a technical method of utilizing the means. This general definition of practice covers the possibility of particularity: there are different practices which are really distinct, even though they organically belong to the same complex totality. . . .

Althussers’ definition of practice is very general and is going beyond the scope of functional modelling of technical artefacts by being applicable on many levels of society including also the historical development. The aim of functional modelling is to model the interaction between humans and technical artefacts but not at all levels of interaction including the social. The purpose of technical artefacts is to assist or substitute human work which means that the human-machine relation needs to be more clearly incorporated in the concept of practice. This interaction between humans and machine in practices is only hinted at in the definition given above. Althusser also mentions in his definition the possiblity of different distinct practices belonging to the same complex totality. This has been explored by Højrup [2] in his development of life-mode analysis, which is targeted at representation of different practices and their interactions in social structures, but the life-mode analysis does not include practices in interaction between humans and technical artefacts which is the focus in functional modelling. Brandt [3] and Larsen [4] use the concept of practice in the context of text analysis and production. The various applications of the practice schema illustrate its generality, but also the need for understanding how to reflect the nature of different practices to a level where the schema can be used to make models with semantics sufficiently strong for reasoning purposes. The distinction between different types of practice is used in functional modelling to decompose a SCPS into meaningful chuncks of interdependent goal oriented activities.

15.2 The Practice Schema The practice schema shown in Fig. 15.1 describes the principle of production where a material (M) is transformed (T) by an action into a product P. The practice schema is proposed by Larsen [4] in the context of semiotics studies of language production. Here the schema will be used to formulate general principles for combining actions based on dyadic and triadic causal relations found in SCPS.

15.3 Extending Greimas’ Schema and the Semiotic Triangle

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Fig. 15.1 The practice schema

The practice schema is clearly related both to the axis of desire in Greimas’ actant schema discussed in Chap. 12 and to the semiotic triangle discussed in Chaps. 6 and 13. But they need to be expanded by representing the object of action in two states: before the transformation as material for action, and after the transformation as the product. The extended Greimas’ schema focuses on the transformation of the object resulting from the interaction with the agent, helper, and opponent i.e. axes of desire and power. The axis of transmission with sender and receiver roles will be considered at the end of the chapter when discussing the designer and artefact as an action system. Production and Prevention Schemas The practice schema can be specialized into a production schema when the junction (the relation between the agent and the object) in Greimas’ schema is a conjunction. As explained in Chap. 12, this means that the agent and the helper is connected with the object with a cause-effect relation. When the junction in Greimas’ schema is a disjunction, the opponent dominates and is connected with the object, and the practice schema is specialized into a prevention schema. The production schema applies therefore for accomplishments and the prevention schema applies for avoidances (see Chap. 10).

15.3 Extending Greimas’ Schema and the Semiotic Triangle The interconnection of actions in systems is based on embeddings. Two actions H1 and H2 are embedded when H1 is an actor for a role in H2 . An actor can be a thing, a person, or an action. Specification of embeddings require extensions of the role structures of physical and cognitive actions, which are described in Greimas’ schema and in the semiotic triangle. The embedding forms are based on an extension of the two schemas and a consequent clarification of the difference between transient events in physical actions (changes) and semiotic actions (creations). The extended schemas are shown in Fig. 15.2. Greimas’ schema for physical actions implies a temporal unfolding, which distinguishes between the object state before (the material cause, to time Tb ) and after the transformation (the product, to time Ta >Tb ). The semiotic triangle implies a corresponding but implicit temporal distinction between the signifier of time Tb and the consequent creation of the significance at time Ta caused by the

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A2

A3

helper

opponent

agent

A2 A4 In

A1

Object material

Tb

Ta

helper

opponent

A5

A6

Object product

Actors and roles in physical actions

Tb

Ta

Si

Sn

A1

A3

Actors and roles in cognitive actions

Fig. 15.2 Schematics of physical and congnitive actions expanded by actors and temporal annotations

interpretant. The temporal aspect of both schemas is a direct consequence of their causal content. Hebert [5] discusses an extensions of the roles in Greimas’ actant schema with an observer i.e. an actor engaged in the collection of information. Observers are needed in functional modelling, but they will here be introduced by a distinction between practices related to 1) production of material products and supporting practices like supply of power and auxiliary functions like lubrication and component cooling all based on dyadic causality, and 2) practices related to control which have to do with interpretation of signs i.e. triadic causality and information processes. Greimas’ schema in Fig. 15.2 has been extended with a distinction between helper and opponent roles for the agent and the object. This extension is necessary to be able to explain the relationship between the different phases of an action, and to describe different ways of avoiding. Both aspects relate to the meaning of the causeeffect relationship, which in Greimas’ schema is referred to as a “junction” between the agent and the object, whose assistance or obstruction is attributed to the helper or the opponent respectively (see Hebert op. cit.). Modelling of SCPS requires the distinctions to be able to represent the basic types of functions included. In physical actions, there is a further need to distinguish between support and collaboration as two types of help (this distinction may also be relevant for cognitive actions but is not considered here). It can be seen from Fig. 15.2 that an important difference between the two extended schemas is that in a physical action the object in the state before and after

15.3 Extending Greimas’ Schema and the Semiotic Triangle

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the transformation has the same actor, whereas the signifier and the significance have different actors in a cognitive action. Another difference between the two schemas is that the cause-effect relation in the physical action is anchored in dispositions of both the actor of the agent and the actor of the object, whereas in the cognitive action it is anchored exclusively in dispositions of the actor being the interpretant. The actors of the signifier or significance do not have inherent predispositions to interpretation. It is based solely on attributes that depend on the interpretant (see the discussion on sign types in Chap. 6). Finally, the cause of the transformation in a physical action is determined by the combined interaction between the agent, helper, and opponent. However, the semiotic role structure shown in Fig. 15.2 only includes the interpretant as the causal factor, but it can be extended with a helper and an opponent so that the two role schemes become virtually identical except for the difference in the anchoring of the causal relation. An actor can thus help the interpretant by ensuring that the conditions for actualizing the necessary dispositions for interpretation. An extension with a helper is relevant for function modelling of the instrumentation in SPCS and will be discussed below. An opponent that prevents the interpretant from the interpretation is conceivable but omitted here. It may be relevant to functional modelling of cybersecurity which is an important problem for SCPS operations but outside the scope of the book. Several forms of embedding are possible based on the relationships between roles and their actors and can be defined by the extended schemas in Fig. 15.2. Embedding can in principle be established through all the roles, but for Greimas’ schema it will in the following only be described in relation to the roles associated with the axis of desire (object, agent) and power (agent, helper, and opponent). The semiotic triangle also contains these two axes, though not explicitly stated as in Greimas’ schema. The sender and receiver roles, which in Greimas’ schema are linked to the axis of transmission but are omitted in the semiotic triangle, also offer possibilities for embedding and thus setting up chains of need and realization relevant for the design process (mentioned in Chap. 12). As mentioned above, an actor can be a thing, a person, or an action. If an action H1 is an actor in relation to a role in an action H2 , it will be considered a doing whose result has a consequence which is an actor in H2 . In other words, H1 is a means for H2 in the same way that things or persons can be a means for an action. The embeddings are thus based on the general action-theoretical distinction between doing something and bringing it about, and therefore imply a means-end relationship (to be discussed further in Chap. 17). Note also that the temporal annotations to object states of two embedded actions become interdependent. It follows from the means-end relation that an action H1 can only be a means for an end determined by H2 if the time interval (Ta ,Tb ) for execution of H1 contains the time interval for the end H2 it supports. An analysis of these temporal constraints, which are determined by the embeddings, is relevant to functional reasoning for real time problems like on-line diagnosis and counteraction planning.

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Physical action

A2 Cognitive action

A2

Ag Ha

A1

Ma

Oa

In

A3

Pr So Oo

Si

Sn

A1

A3

A4

Fig. 15.3 Schemas for visualizing embedding principles

The simplified schemas shown in Fig. 15.3 are used to visualize the embedding principles. Note that in the scheme of physical actions, the agent Ag and the object (Ma or Pr ) are supplemented by helpers Ha and Ho as well as opponent roles Oa and Oo . It should also be noted that the help for the agent Ha can be either support or collaboration. This is not directly expressed in Fig. 15.3 but will be clarified later and noted in the schemas by substituting Ha with Sa (support) or Ca (collaboration). A corresponding distinction has not been introduced for the object helper Ho since it is not obvious what an object collaborator would mean. According to the definition of a collaborator, Co would be the role of an actor which contributes to the aim of the object actor in another action. The practical value of such a concept is worth investigating and may be relevant for modelling biological systems (included in the process in SCPS) which have mechanisms for self-preservation and maintenance. In Fig. 15.3 Ho is substituted by So .

15.3.1 Causal Schemas and Reasoning About Failure The causal schemas shown in Fig. 15.3 can be used to formulate generic rules for causal reasoning about failures. Considering the schema for a physical action it can be concluded that a failure of the actor A1 to change a state intended (corresponding to the product role) can be caused by one of the following failures: • actor A1 failed in its material role Ma • actor A2 failed in its agent role Ag

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• actor A3 failed in its agent role Ha • actor A4 failed in its agent role So Considering the schema for cognitive action it can be concluded that a failure of the actor A3 in its role as the signification created Sn can be caused by one of the following failures: • actor A1 failed in its role as signifier Si • actor A2 failed in its role as interpreter In

15.4 Embedding Forms for Physical Actions The schemas shown in Fig. 15.3 are used in the following to establish generic principles for combining actions by embedding. In addition to their importance as a foundation for function modeling, the forms of embedding can also be used, as indicated above, in the formulation of logical rules for causal inference, which have general validity. This means that a side benefit of using the embedded action forms when building a functional model, is a set of rules that can be used to reason about means and ends. Specification of the rules of inference for each of the forms of embedding is not addressed. The combination of actions through embedding follows a few simple rules: • only one of the helper and opponent roles can be connected to an actor (which can be a thing or an action). This rule prevents uncertainty regarding whether the helper or the opponent are dominating. • a system of embedded actions represents a single mode of action. This means that alternatives are not allowed in action system structures. They are dealt with in the means-end structures described in Chap. 17 Overall, a distinctioncan is made between forms of embedding of physical actions associated with accomplishments or avoidances. The difference between them originates in the definition of causality, which has two different temporal interpretations of the cause-and-effect relationship in an action. As described in Chap. 11, the causality of an action is defined by three situations; the situation Sp before the action, the situation Sr after the action, as well as the situation Sq that would occur if the action was not realized (the situation not intended). Note that in the schemas of physical actions in Fig. 15.3 the counterfactual situation Sq is excluded. The two situations Sp and Sr are sufficient for analysis of embeddings that contain accomplishments, but not sufficient for the treatment of avoidances, which require special treatment as described below.

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15.4.1 Chains of Accomplishment Chains of accomplishment based on embeddings of actions include three subtypes: • embedding through object and agent roles that are part of chains of production. • embedding through a helper role that is part of chains of support or collaboration.

15.4.1.1

Chains of Production

The basic principle of production is the transformation connecting Sp and Sr (i.e., Sp ->Sr ). Sp and Sr represent states of the object actor, before and after the transformation. Chains of production thus interconnect intended situations and are used to represent processes where each link in the chain contributes material to the next to achieve a desired final product. In this interpretation, the actor of the object of action is perceived as the material Ma for a transformation before its execution, and as the product Pr after its execution. The temporal association thus occurs through the object’s actor which is assumed to be attributable to states at two different times Tb and Ta . It should be noted that the actor’s state, which is linked to its physical properties and dispositions, is determined by the role and thus bound to the context of action the actor is part of. An actor who is part of a chain of production can therefore undergo changes in several different physical properties and thereby serve several roles depending on the individual action in the chain. The following embeddings are parts of chains of production: • The actor of the product (i.e., the object) in action H1 is the actor of the object in another action H2 . This type of embedding is part of simple chains of production and is shown as case (a) in Fig. 15.4). The relationship of schemas between the initial state and the result is temporal and makes it possible to use von Wright’s elementary action types to define logically possible elementary transformations of the object of action and their composition in sequences. – two elementary states can be aggregated into a compound state. – compound transformations can be formed by defining causal schemas that connect compound states. The combination of states and their transformations can be logically limited, as well as be dependent on the specific practice as shown in Lind [6] (Chap. 11 includes a brief discussion of composite actions and the associated compound transformations). • The actor of the agent in action H1 is the actor of the object in another action H2 . In this case, the material is not a physical object but an action H1 whose consequence is an actor that serves as an object (at time Tb ) for transformation in the other action H2 (case (b) in Fig. 15.4) • The actor of the product (i.e., the object at time Ta ) in action H1 is the agent of another action H2 . The consequence of the first action H1 is thus an actor having an agent role in H2 (case (c) in Fig. 15.4).

15.4 Embedding Forms for Physical Actions

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Fig. 15.4 Embedding forms for chains of production

Examples: Case a. Sequences of action in a physical process can relate to the same actor being transformed by several agents. Typical examples in SCPS are the processing of materials by a sequence of unit operations. This principle is fundamental in production of chemicals or conversion and supply of energy. Most traditional engineering production systems are composed by chains of operations where the end product of one unit is the material means for the next in the chain. Another related example is the operations of a robot in the blocks world (classical example in AI). The overall purpose of the operations is to stack blocks lying on a table so they eventually stand on top of each other. Here the actions of the robot are composed of a sequence of basic actions: grasping a block from the table, picking it up, moving it, putting it on the top of another block and un-grasping it. The selected block is the object in all the individual actions which have different purposes: grasping, picking-up, moving, putting on the top of another block, and un-grasping. Different dispositions of the block may accordingly be exploited through an action sequence (e.g. grasp-able, pick-up-able, move-able, put-able and un-grasp able). The action sequences described above are centred on the same physical item. However, action sequences can also be repetitions of the same type of action but applied on different physical items. Here the action sequence is centred on the agent and the actor object is changed at each step in the sequence. Obvious examples can here be found in manufacturing systems where the same operation is done in a sequence on different physical items. The robot example above can also be extended in this direction if several blocks are included that can be grasped in the same step. For example, if several blocks can be put on top of each other, the same action ‘put-on-top’ is repeated on each block in the set. Example: Case b. An example is here a chemical reaction transforming two or more materials into products under the release (exothermic) or consumption

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(endothermic) of energy. The transformation in H1 can then be seen as a source or sink of energy for another process for e.g. a heating or cooling process H2 ). Examples: Case c. Examples are here actions which mediate other actions, i.e. as when transportation of a material (carrying energy) is used to transport energy. The material has accordingly two roles, to be an object of transportation and to be the agent transporting energy. The physical material can serve these two roles by being both transportable and by having the disposition to contain energy. Changing the location of the material mediates accordingly transportation of energy. A second example of mediation is when the transportation or storage of a material A includes another material components B (e.g. A is a solution or mixture containing B). Here the transportation or storage of A (the product) is a means of transporting or storing B.

15.4.1.2

Chains of Support

An action H1 can be the means for another action H2 when the consequence of H1 is an actor who has a helper role in H2 . The help consists of H1 establishing and maintaining conditions for the potential and actualization of the agent or object in H2 . The two possible options of agent and object support are shown in Fig. 15.5.

Fig. 15.5 Embedding forms for chains of suppport. (a) Agent support. (b) Object support

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These embedding forms are typical for the interaction between a physical process and a supporting subsystem, as well as for the relationship between phases of action (see Chap. 14). The supporting system that performs the H1 action may be a physical process or a maintenance activity. Example: An example is here is a catalytic processes where a catalyst being the end product of a transformation (A) is a means of enabling the agent actor transforming materials in a catalyzed production process (C). This means that action A provides a condition for an actor to be the agent in action C. In this case there is a coupling between different phases of action A (execution) and C (opportunity). Action phases as contexts of analysis were considered in Chap. 14. Time and Embedded Action Phases Embedded action phases include relations between actions belonging to different temporal frames. Each frame provides a means of temporal abstraction for analysis of the interactions between actions. Two overall temporal frames are defined by the phases of actualization and the phases of possibility (including potentiality and opportunity) as shown in Fig. 14.4. These two temporal frames include interactions where an action A provides conditions of potentiality or opportunity prerequisite for the phase of actualization in another action B (see Fig. 15.6). These types of interactions are diacronic developments across time frames (not taking place in the same temporal frame), and can be analysed by using the phase model. Within the temporal frame of actualization further distinctions can be made between different stages of actualization (see Chap. 13) representing stages of cognition of the agent, and the sequential states of transformation of the object of action which are the outcome of its actualization. Within the temporal frames

Fig. 15.6 Action H1 support H2 by providing a condition for enabling the H2 object actor (object support in figure 15.5)

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of actualisation interactions occur synchronously i.e. within the same frame. This means that the outcome (its result or consequence) of an action is influencing other actions by providing conditions for their execution. Even though the analysis suggests a separation of the temporal frames, the actions of both simple and complex technical artefacts include couplings so that the actualization of some actions provides conditions for potentiality, opportunity and execution for other actions. In the first case, the result of actualizing an action could be an object or situation which represents a potential for another action not available before the action. In the latter case the result of an action could produce the opportunity to be actualized by another action. Example. The operation of an air gas burner provides a good example of interaction between two temporal frames. The purpose of the burner is to generate heat by the combustion of a mixture of gas and air. In order to actualize the burning, it is necessary to provide a potential and an opportunity for burning. This means that gas and air should be available (the potentiality) and they should be mixed in the right proportion for being burnable (the opportunity). The burning action is initialized by igniting the mixture of gas and air to create a flame. When the burning is actualized the flame provides the condition of ignition required for the burning. In summary, three actions are here interacting; the mixing of fuel and air, the ignition creating the flame, and the subsequent combustion of the fuel sustained by the heat produced. Accomplishment of each of the phases in an action can be the aim of other actions. The aim could be to provide a condition for making another action possible, or deliver the resources required. Actions can in this way be nested to arbitrary depths and thereby create a hierarchy of support actions.

15.4.1.3

Chains of Collaboration

The principle of embedding in collaboration is that an action H1 makes it possible for the agent in another action H2 to achieve the desired end state (the product for H2 ) by having the achievement of H2 ’s end state as its goal. This may include both its fulfillment and the efficiency with which the end state is achieved as expressed by the object and the intended state of the agent in H1 . The intended result (the target) of H1 is expressed by the state of the object actor (that is, in the role of being the object) and by the state of the actor that is the agent. Thus, the action H1 is an actor A1 whose role Ca in H2 is to collaborate with the agent actor A4 as shown in Fig. 15.7. The collaboration consists in that the resources of H1 contributes to the achievement of the objectives of H2 . This type of embedding is part of the interaction between control and process systems in SCPS discussed in Chap. 16.

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Fig. 15.7 Embedding forms of collaboration

15.4.2 Chains of Avoidance Chains of avoidance are used to represent action systems whose overall purpose is the avoidance of unintended conditions, and if they nevertheless occur due to failure to mitigate its consequences. Such chains relate primarily to the object, agent, and opponent roles. Chains of avoidance play a key role in modelling safety functions in SCPS and are traditionally represented barrier diagrams as mentioned in Chap. 3. The avoidance chains shown below reveal the causal relations implied in the barrier diagrams. The purpose of an avoidance action is to ensure that an unwanted condition does not occur. Thus, an avoidance differs from an action of accomplishment in that the latter’s purpose is to ensure that a desired state occurs. To understand the implications of this distinction it is necessary to analyze what it means that something is not occurring. For example, it is not immediately obvious how a chain of avoidances can be formed by pairwise embeddings of avoidances where the first one has a non-occurrence of an undesirable state as a result, which should serve as a starting point for another avoidance.

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Bergson [7] has dealt with the logical problem of negations of physical states (see also Chap. 11). He argues that when one acts in the physical world to avoid an undesirable future state, the state of the world either remains the same or a state different from the undesirable one is achieved. This means that something occurs in any case. Bergson argue therefore that a “non-occurrence” is not a physical reality, but the expression of a cognitive agent’s assessment of the outcome of the action in the context of a plan or an intention. This means that the causal schema used for accomplishments cannot be used to represent the causality of avoidance failures which is of interest in the modelling of safety functions and represented in barrier diagrams. Likewise, Greimas’ schema is not immediately applicable as its concept of transformation is described as a relationship between the pre-action situation and the desired post-action situation. But a meaningful composition of avoidances in chains presupposes that an undesirable state can be the result of an avoidance failure which then can be the starting point for the next avoidance in the chain, etc. This means that the transformation should instead be described as a relationship between the situation before the action and the undesirable situation after the action which is the consequence of an avoidance failure. It is therefore necessary to modify the meaning of the transformation of in Greimas’s scheme by applying von Wright’s definition of causality (see Chap. 11) and introduce the counterfactual state as the outcome of the avoidance failure instead of the desired state (i.e., Si ->Sq instead of Si ->Sr ). The counterfactual state Sq is precisely the state which occur when an action fails. In this respect, then, the result of the failed action is the counterfactual state of the object’s actor. The basic principle of an avoidance is therefore that the transformation connects Si and Sq where Sp and Sq represent states of the actor who is the object of the action before and after the transformation. The Pr role (the intended state) of the material in the schema for a physical action is modified accordingly to the role Zq denoting a not desirable outcome i.e. a hazard. The arrow representing the causal relation is dotted to indicate the that the outcome of an avoidance is related to the counterfactual condition. As described previously in Chap. 10, a distinction must be made between two types of avoidance, prevention and protection. Both types of avoidances can be included in embedding forms and chains. Chains of Prevention To prevent something concerns the agent’s ability to influence the object. It succeeds when the agent’s actor cannot cause the intended effect on the object because of a dominating opponent preventing this from happening. Therefore, a prevention can be described as a bringing about where and action H1 is the actor of the agent’s opponent in another action H2 (see Fig. 15.8). In other words, H1 prevents the actor of H2 ’s agent from changing the state of the object actor A3 . The prevention therefore fails when the opponent fails, with the result that the undesirable counterfactual state of A3 is realized.

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Fig. 15.8 Embedding form for chains of prevention

Chains of Protection A protection relates to the object’s ability to be affected by the agent. It succeeds when the state of the object’s actor cannot be changed because conditions for being the object of the action are not present. A protection action can therefore be described as a bringing about in which H1 is the actor of the object opponent Oo in another action H2 . The object opponent counteracts the actor A3 of the object in H2 by ensuring that the conditions for realizing the object role are not met. In other words, H1 protects the object in H2 from changing the state (see Fig. 15.8 and 15.9. Protection fails when H1 fails with the result that the undesirable (counterfactual) state of H2 is realized (the hazard Zq ).

15.4.2.1

Composite Chains of Avoidance

The two types of avoidance, prevention and protection can be composed in a chain as shown in Fig. 15.10. The chain represents a principle in the design of safety barriers where the function of first barrier is to prevent the agent from causing harm to the object. If this barrier fails another barrier protects the object from being impacted by the agent.

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Fig. 15.9 Embedding forms of chains of protection

Fig. 15.10 A composite chain of avoidances (.H1 prevents and .H2 protects)

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It should be noted that there is no information in the chain shown in Fig. 15.10 regarding the temporal sequence. The precedence of the two actions H1 and H2 depends on an evaluation of the severity of the consequences of two outcomes; (1) prevention failure followed by protection failure and (2) protection failure followed by prevention failure. The evaluation requires accordingly contextual information which is not included in the action structures.

15.5 Embedding Forms and Chains of Cognitive Actions Cognitive actions imply interpretation i.e., triadic causality. Here, like physical actions, there are the following three options for embeddings shown in Fig. 15.11: • The actor A3 for the significance Sn in a cognitive action H1 is the actor for the signifier Si in another cognitive action H2 . This type of embedding is part of simple chains (case a). • The actor A2 for the interpretant In in a cognitive action H1 is the actor of the signifier Si in another cognitive action H2 (case b). • The actor A3 for the significance Sn in a cognitive action H1 is the actor for the interpretant In in another cognitive action H2 (case c). An example of using the embedding form in case a is shown in Fig. 13.3. All three forms a, b and c are relevant for modelling control functions (see Chap. 16).

15.6 Hybrid Embedding Forms and Chains The embedding forms described above include either physical or cognitive actions. However, it is also possible to combine physical and cognitive actions into hybrid embeddings using principles like those described above for physical and cognitive actions.

15.6.1 Forms with Accomplishments In the three forms of hybrid embeddings shown in Fig. 15.12 the causal chain has its origins in a physical action of accomplishment. • The actor A1 for the object Pr in a physical action H1 is the actor for signifier Si in a cognitive action H2 (case a). • The actor A2 for the agent Ag in a physical action H1 is the signifier Si in a cognitive action H2 (case b).

Fig. 15.11 Embedding forms for cognitive actions

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Fig. 15.12 Hybrid embedding forms with causal origin in physical action .H1

15.6 Hybrid Embedding Forms and Chains 265

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• The actor A2 for the object Pr in a physical action H1 is the interpretant In in a cognitive action H2 (case c). Example: Case a. This embedding principle can be exemplified by the relation between a control system and the process under control provided by the instrumentation. Information about the result of the transformation (the process under control) is used by the control system as a means to regulate the process. Example: Case b. The embedding principle can here be illustrated by instrumentation giving information about the performance of a physical transformation to be used for supervisory control. Example: Case c. In the third example is a cascade control system where the result of a physical action is used as a setpoint of a control loop. In the three forms of hybrid embeddings shown in Fig. 15.13, the chain of causation has its origins in the cognitive action. • The actor A3 for the significance Sn in a cognitive action H1 is the object Ma in a physical action H2 (case (d)) • The actor A2 for the interpretant In in a cognitive action H1 is the object Ma in a physical action H2 (case (e)) • The actor A3 for the significance Sn in a cognitive action H1 is the agent Ag in a physical action H2 (case (f)) Combinations of cognitive and physical actions are relevant for functional modeling of instrument and control systems in SCPS as well as for modeling interactions between physical and information processes in biological systems (e.g. enzymes, which are involved both in the metabolism and control of cell functions).

15.6.2 Forms with Avoidances Hybrid forms of embedding can also be defined by combining cognitive actions with physical actions of avoidance. These forms, which are relevant for functional modelling of safety systems, will not be described in detail. They follow directly from the hybrid forms for accomplishments presented above by substituting the scheme for the physical action with schemes for prevention and protection.

15.7 Application of Embedding Forms The forms of embedding of actions developed above can be used to form action systems of arbitrary complexity that capture functional relationships through the roles and associated transformations. The application to SCPS involves two challenges:

Fig. 15.13 Hybrid embedding forms with causal origin in cognitive action .H1

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• How to deal with the contextual dependence of the ascription of functions to actors? Here the principle of reciprocity described in Chap. 7 is used. • How to cope with the potential complexity of action systems? This is the question of choosing proper level of level of abstraction depending on the context of use.

15.7.1 The Principle of Reciprocity A challenge in functional modeling of SCPS is to use the embedding forms on specific subsystems, as schematics for representing their functions. This actionoriented interpretation of the SCPS (cf. “the social framework of interpretation”) cannot be implemented on a subsystem without including information about the other subsystems, their interactions and common relation to the overall system. The principle of reciprocity of functions, which is discussed in Chap. 7, is central to solving this interpretation problem, as it systematically involves knowledge of the actions and associated functions of the surrounding systems in the modelling of a subsystem. The principle of reciprocity is valid on many levels of analysis and is thus also relevant for causal schemas of both physical and cognitive actions. Thus, the agent and object roles are functions of physical actors who mutually determine each other in a dyadic causal relationship. The same applies to the semiotic roles, signifier, interpretant and significance, which determine each other in a triadic causal relationship (which in semiotics is called a sign). The principle of reciprocity is an expression of the context dependence of functional ascriptions, which in the model building process is reflected in considering the functions of a subsystem as the background for the modelling of another subsystem in the foreground (and vice versa). On a larger scale the principle the reprocity is therefore also relevant for the functional relations between different technologies in SCPS. This is demonstrated in the analysis of control actions presented in Chap. 16.

15.7.2 Levels of Abstraction and Context of Use Another challenge in the use of the embedding forms is to establish a level of abstraction in determining action structures appropriate to a given application of the functional model. The problem is not in the embedding forms themselves, but in the determination of actors (for a given action type and embedding form) in each subsystem. The actors can be determined in many ways depending on the use of the model. The same applies to objectives which can be specified at several alternative levels of abstraction. The application of the forms of embedding in the design of SPCS will require a decomposition into subsystems (actors) that is consistent with the modular

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division of SCPS into plant, equipment and components and the related design and construction tasks. The division is conditional on the need for standardization and well-defined interfaces between subsystems supporting design by composition of part solutions. In applications of functional models for operator support in on-line diagnosis, the identification of actors should satisfy the need for detail in the determination of failure causes. In addition, the level of abstraction must also be adapted to the objectives of operation as they define the criteria for assessing failure consequences. If a functional model is used for on-line planning of counteractions, the level of abstraction should also be adapted to the objectives of operation of the plant and its subsystems, so that the functional model can be used to generate plans which are matching the given objectives and the available means of intervention. The levels of abstraction and their content will therefore be different depending on the application of the functional model. However, this dependency on the application is not a special feature of functional models. It is common to models based solely on physics as well.

15.8 The Designer and Artefact as an Action System Seeing the artefact as an embodiment of design intentions involves embedding of actions via sender and receiver roles. Sender and receiver roles are important for representations of chains of needs (ends) and satisfiers (means). They relate therefore to the relation between designer and the artefact. In the context of the practice triangle, the sender (in a causal interpretation) provides the object and the receiver is the consumer. In Sect. 12.4 it was shown that the sender and the receiver roles also have interpretations by intentions which are important for understanding the role of the designer. Relations between actions are in Greimas’ model captured by the sender and receiver roles, and the distinction between the two roles and the other roles agent, object, helper, and opponents, implies the setting of a boundary between what is considered external (sender and receiver) and internal to the action (agent object, helper and opponent). The boundary between external and internal in question has no immediate physical interpretation (such as spatial boundaries of artefact subsystems) but is abstract and reflects the structure of causal and intentional influences between actions. The interactions between technical artefacts and humans involve both types of influences. The ascription of sender and receiver roles to actors will not be explored in detail in the book but will be illustrated by the heat transfer system which was introduced in Chap. 1. Example: Heat transfer system. Consider the pump in the water circulation system which is used for cooling the heat source. The pump detached from its context of design or use, i.e. seen strictly as a physical object has no purpose.

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However, it is an embodiment of the designers intention to use water circulation for cooling purposes. Let it be clear what is meant when saying that an artefact is an embodiment of the designers intention. First of all, the intention is not an intrinsic physical property of the pump but is the mental state of its designer. The pump is a physical realization of the designer intention. The pump has accordingly a design purpose. The “in-order to motive” for the designer is “to circulate water” and “the because of motive” is that a pump is able to circulate water (under some circumstances not elaborated here) they are both mental states representing reasons for the designer’s decision to use the pump. When the pump is considered to be an embodiment of the designers intention it is ascribed the design function to “circulate the water”. Considering the action done by the pump i.e. the circulation of water, it is clear that the pump is the agent and the water is the object of the action. Furthermore, in order to make the pump work as intended it is selected to be able to compensate for friction in the pipes. Otherwise the friction would hinder the circulation of water. The friction mechanism can therefore be seen as an opponent which is overcomed by the pump (agent) in the causal interpretation (and a surfactant which reduces the friction would be considered as a helper). The opponent has no intentional interpretation since there is no intention to let the friction hinder the circulation. The friction is simply a physical fact which in the context of the pumping means that it lacks the ability to rotate freely.1 The sender and receiver roles in the pumping require more consideration: First consider the sender role in the intentional interpretation. It is here needed to identify an entity in the pumping system and its context of design and use which “request” the junction between the agent and the object (the circulation). The question is therefore what in the situation asks for the conjunction between the agent (the pump as a mover) and the object (the water as the moved) ? The request originates from a design intent derived from a decomposition of the design problem for the water circulation system (that the water is the object of circulation, and the designer has chosen a pump to serve as the means or agent of the circulation. In conclusion, the request is a consequence of the decomposition of the design problem, and could be seen as being made to a pump specialist by a member of the design team responsible for the overall design of circulation systems. The sender in the intentional interpretation is accordingly a member of the design team. In Greimas’ schema the design team would accordingly appear as an actor realising the sender role. Next consider the causal interpretation of the sender role. Here the causal influences of the sender on the receiver mediated by the juncture of the agent

1 Langkjær [8] makes a distinction between needs and lacks and explains that the designer provides

the means of satisfying needs which relate to a system having corresponding lacks. The point made is that the system does not need anything. It has lacks but only seen in the context of the needs adressed by the designer.

References

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and object should be identified. In the case of water circulation the causal factors influencing the receiver in the water circulation should be considered. The question is therefore what element in the water circulation situation is the receiver? According to the definition, the receiver is the element which benefits from the water circulation. Here the question is whether the water circulation is desirable in some sense for an actor. This means that intentions should be considered again, but this time from the perspective of the receiver, and how the receivers interest is served by the causal relations established by the junction (and requested by the sender). Depending on the design objective of the pump system, the consequence of the pump operations on the water could simply be its result i.e. that water is circulated, but it could also be a cooling effect brought about by the circulation. It is accordingly seen that, as was the case for the sender, the receivers benefit is intrinsic to a decomposition of the design problem and could be related to a member of the design team responsible for design of the cooling system. It is also seen that the causal interpretation of the sender (bringing about the cooling) is dependent on the receivers intention—to achieve cooling. It is seen from the heat transfer system example, that assignment of the sender and receiver roles to actors requires insight in the design process which conventionally is temporally separated from the situation of use as in the conventional system life cycle, but also in other more dynamic organization of the process of design and use. This separation has its roots in the embodiment relation between the artefact and the design intentions, which is not taken into account in Greimas’ schema but which can be investigated by analysis of the relations between roles and actors.

References 1. L. Althusser. For Marx. Verso, 2005. 2. T. Højrup. State, Culture and Life-Modes: The Foundations of Life-Mode Analysis. Routledge, 2018. 3. P. A. Brandt. “Historien, Friheten, Teksten -Noter om Praxis”. In: Hoften for Kritiska Studier 4.1-2 (1971), pp. 75–83. 4. S. E. Larsen. “Modelproblemer”. In: Exil 8.29 (1975), pp. 52–90. 5. L. Hebert. Tools for Text and Image Analysis. Tech. rep. Department de Lettres, Universite du Quebec a Rimourski, 2011. 6. M. Lind. Description of Composite Actions - Towards a Formalization of Safety Functions. Tech. rep. NKS-R(04)07/11 Barriers, Control and Management. Nordic Nuclear Safety Research, 2004. 7. H. Bergson. Creative Evolution. New York, USA: Dover Publications Inc., 1998, p. 407. 8. A. Langkjær. Contributions to a General Normology or Theory of Purpose Setting. Copenhagen: Dansk Videnskabs Forlag, 1961.

Chapter 16

Control Actions

This chapter analyses the functional relations between a control system and the process in a SCPS by applying the dyadic and triadic transformation types, the roles types and the theory of action systems developed in Chaps. 11, 13, and 15. The overall aim is to demonstrate applications of conceptual schemas for functional modelling of control systems and their interaction with the process under control.

16.1 Control as a Relation Between Two Objects In the SCPS shown in Fig. 2.1 the term control denotes an automated system which is provided by the designer for this explicit purpose and usually implemented by information technology. However, when control is seen as an action system, it is necessary to abstract from the technologies or the people i.e. the means used for control. The main reason to abstract from the technology used is that control, seen as a type of action, is performed by other SCPS subsystems as well. Thus, the operator has an important role in supervising the control systems and their interaction with the process, thus an important aspect of the design of the operation system is to allocate control functions between the operator and the automation. In addition, safety related protective control actions are also realized by so-called engineered safety systems where physical mechanisms are used to implement the actions without using information technology. Physical mechanisms are likewise used to implement so-called self controlling features in industrial processes without using instruments or electronic control devices (e.g. in nuclear boiling water reactors to stabilize the neutron flux by feedback mechanisms related to changes in the percentage of steam in the reactor cooling water). Furthermore, industries operating in environments with risk of explosions caused by ignition of gasses by electric sparks, sometimes use fluid mechanisms to implement control actions, which © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_16

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in other less risky industries would be implemented using electrotechnological solutions. It is therefore necessary to see control as a type of action which can be implemented by several different technologies. Another reason to avoid the strong coupling between function and technology is the traditional separation of control and safety systems as means of achieving the two overall process objectives; production quality and efficiency (control) and safety (prevention and protection). Control and safety systems are accordingly distinguished by the different objectives they serve in operating the process. However, from a safety perspective alone, control and safety systems should bee seen as two types of countermeasures or barriers against hazards. This ambiguity created by associating control systems with both production and safety objectives can be avoided by seeing control and protection/prevention as types of action distinguished by their causal and intentional aspects. The conceptual problems outlined above are caused by the tradition of seeing control systems in the context of an application or a technology. This is not a problem for the individual mechanical, electrical and chemical engineering disciplines involved in their design. They often define the means and ends of the artefacts they design within such contextual constraints. However, it is an obstacle when addressing design and operational issues related to the SCPS as a whole where subsystem interactions across boundaries between different technologies are important e.g. in diagnosis and management of faults and other abnormal operational situations.

16.1.1 The Nature of Control Both natural physical environments and technical artefacts like SCPS are in general dynamic objects because their states can change under the influence of internal or external disturbances (counteragents). They do so independently of possible interventions by control actions whose purpose is to reduce or eliminate the effect of the counteragents. The automated control systems also increases the reliability of operation by relieving the human operator from a stressful and therefore error prone task of coping with frequent disturbances.

16.1.1.1

Causal and Intentional Aspects

The objective of a physical action is to change the state of the process system (P) in the SCPS according to the designers intention. The action is a dyadic causal relation between an actor, the agent of the change, and another actor being the object of action which is undergoing a change or transformation of state. In a physical action the relation is unidirectional and the result of the action does not influence the intention which is in the mind of the designer. This means that if the action fails due to the influence of an opponent, it does not effect the execution or outcome of the action. The process system in SCPS which is based on dyadic causality

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implements such actions. Physical actions should therefore be distinguished from control actions which are interactive by involving a relation between a control agent, an object of control (being a physical action) and an opponent.1 A physical action can accordingly succeed or fail but the intended outcome has no direct significance for their definition. In contrast, control actions cannot be defined without reference to concepts of failure or success of the physical process i.e. to intentions. Weak and Strong Types of Control In all types of action, the agent actor is supposed to determine the process state resulting from the action. The opponent actor is in other words defeated and it could be argued that the agent actor is in control of the process by the ability to determine the state of the process. But this would only be control in a weak sense of the term because the ability to determine the outcome is only the first of three aspects of control (see also the distinction between weak and strong control presented by Rescher [1]). The second aspect of evaluation is that a control action is intended by being executed with an aim to achieve a desired state of the process. The third observational aspect of a control action is understood in the sense of monitoring the outcome. Feedback and Feedforward The definitions of weak and strong types of control correspond to the known distinction between feed-forward and feed-back control. In both cases the objective of the control actor is to determine the state of the controlled object so that it corresponds to the process designers intention.

16.1.1.2

The Meanings of Control

Apart from the distinction between feedback and feedforward control which distinguishes two different ways to implement a control action, the concept of control has also several meanings depending on its objective. In the context of process control engineering the following functions are distinguished: • • • •

to regulate—to maintain the current state of the process to steer—to bring the process into a new desired state to protect—to bring the process out of an undesired unsafe state to interlock—to prevent the process from entering an undesirable state

However, the concept of control has other meanings in a context of decision making practice: 1. 2. 3. 4. 5.

to observe or monitor to evaluate to intervene a combination of 1, 2, and 3 to command

1 The result or consequence of a physical action may cause a subsequent reaction which should not be confused with the type of interaction considered here where the interactions are concurrent.

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In the following it is shown that the different meanings of the control concept can be combined when control is represented as an action system combining physical and cognitive functions.

16.2 The Control Relation is Bi-directional In order to further the functional analysis of a control action it is in the following seen as a bi-directional relation between a control actor and a process actor as depicted in Fig. 16.1. The relation is bi-directional because there are causal and intentional dependencies in both directions. A temptation to only assign an agent role to the control actor and an object role to the process actors would accordingly be mistaken as this would only correspond to the meaning of control as intervention. Due to the bidirectionality of the control relation, it is more fitting to see control as an interaction between process and control actors. This clarification is required in order to be consistent with the action concepts presented in Chaps. 11, 12, and 15.

16.2.1 Three View Points on the Control Relation The bidirectional control relation shown in Fig. 16.1 can be described from three view points2 which each defines a separate context for ascription of functions to control and the process actors. According to the principles of reciprocity, each of the three view points include functional representations of both actors. A way to understand the meaning of the view points is to see them as focussing on three types of practice involved in the interaction between process and control actors. Fig. 16.1 The control relation as interactions between process and control systems

2 The three view points should not be confused with the overall functional perspectives for systems design introduced in Chap. 9. The three view points are subordinate to the control perspective.

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The practices associated with the three view points on the control relation are the following: • The process control view: Here the control relation is seen in the context of the practices of producing products from raw materials. The objectives of control described within this view are subordinate to the process objectives through a collaboration relation as mentioned in Chap. 15 and elaborated in more detail below. • The decision making view: The control relation is here seen in the context of practices of acquisition of process status information using sensors, evaluation of the status according the control objectives, and planning and executing interventions in the process using actuators. The associated physical and cognitive functions ascribed to the control and process actors in this view are means for realization of actions/functions within the process-control view. • The representation view: Here the control relation is seen in the context of the practices of representation, reasoning and computation involved in realizing the cognitive functions defined in the decision making view. The means-end relations between the three related practices and their associated view points are depicted in Fig. 16.2 and explored further in Chap. 19. Fig. 16.2 The functions in the three view points of the control relation are related through means-end relations

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Table 16.1 Correspondences between types of doing and control engineering terms

Doing producing p maintaining p destroying p suppressing p

Control steer regulate protect interlock

16.2.2 The Process Control View The function of a control system is here described by the intended effect of the control actors action on the process actors. The coupling of process and control actions within this view will be investigated further by using the theories of dyadic transformation and role types presented in Chaps. 11 and 12. Within this view a control action is described by its influence on the functions and objectives of the physical process by using dyadic causality. The description of the control relation within this view is accordingly independent of the means of control, which are described within the decision making view. The objective of control is to ensure that a physical action in the process is achieved according to the intention represented by the process objective i.e. to ensure that the effect of disturbances (the opponent) is eliminated. This is expressed in terms of actions systems as a collaboration between the process actors and the control actors.

16.2.2.1

Control and Dyadic Transformations

The dyadic nature of control actions within the process-control view can be illustrated by Table 16.1 showing the correspondence between types of doing and the control function types mentioned above. It is seen that the doings correspond one-by-one to four different types of control functions known in control systems engineering. The theory of dyadic transformation types provides in this way a theoretical explanation for both the necessity and the sufficiency of the four types of control actions (within the process-control view). The correspondences between control actions and doings also indicate the presence of an opponent (called a disturbance in usual control engineering terminology). Types of control corresponding to forbearing may be added to the list of control functions, but will not be considered here.3 The dyadic transformation types for doings and the extensions with descriptions proposed in Chap. 11 provide accordingly a formal foundation for the definition of control functions (as dyadic transformations) in the process control view.

3 Using forbearances in control are not only a theoretical possibility, they are actually used in some control strategies comprising interventions followed by forbearances e.g. in walking.

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However, the types of doing in Table 16.1 apply only with a minor modification. The question is how to interpret the proposition p for a control action? In the doing types discussed in Chap. 11, the proposition referred to the state of the product transformed. But when using the transformation types to formalize control actions the proposition refer to the intended transformation i.e. to the process objective. This can be formalized by introducing the process objective .P which can have the following possible values depending on the type of doing involved: • • • •

P P .P .P . .

= p is = p is = p is = p is

produced maintained destroyed suppressed

The control functions and corresponding objectives are then the following (compare with Table 11.12 for doings): The control types shown in Table 16.2 are promotive doings. But as mentioned in Chap. 11 doings can be distinguished in promotive and opposive doings (see Fig. 11.7). Applying this distinction to control actions the complete set of control functions can be shown as Fig. 16.3. Table 16.2 Control functions and corresponding objectives

Function producing .P maintaining .P destroying .P suppressing .P

Fig. 16.3 Control functions in the process-control viewpoint

Objective is produced .P is maintained .P is destroyed .P is suppressed .P

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Note that whereas the promotive control types have interpretations of direct relevance for control engineering it is a question for further investigation whether interpretations of similar relevance for practice can be found for the opposive types. Such actions would probably be described as sabotage or disruptions rather than control actions. The control functions and their two associated descriptions shown Fig. 16.3 explains why control actions sometimes can be described as preventive measures i.e. as a countermeasures or barriers. A typical example of this apparent ambiguity is when the action of a car driver is described as “keeping the car running on the road”, which also can be described as “preventing the car from driving off the road”. The two descriptions refer to the same observable behaviour but the question is which of the descriptions should be taken as the proper one? The answer depends on the intention of the agent i.e. the in-order-motive. Control as Transformation of a Possible Failure into Success The meaning of the control functions in Table 16.2 are explained by two examples in Fig. 16.4. Here the control functions are represented as transitions been states of failure and success of two physical actions producing p and maintaining p by using transformation graphs (see Appendix A). Representation as an Action System The intentions of the helper and the agent in Greimas’ schema has a particular interest for the analysis of the collaboration relation between control and process action or functions because the agent and the helper by definition share intentions. Furthermore, if the helper is not participating in the action the agent will fail in achieving his intention as illustrated above. In addition, the process objective cannot be met without the participation of the control agent. The relation between the agent and the helper is therefore similar to the relation between a process agent and a control agent. This is exactly an expression of the relation of sharing intentions between an agent and a collaborator. Actually, it is the purpose of the control to ensure that the process objective is achieved i.e. to help the process agent. Control as help should accordingly be seen as a special type of means for achieving the process objectives or bringing about success.

16.2.3 The Decision Making View Within the decision making view, triadic causality is used to represent the behavioural preferences or values of the control actor and the associated significance of events or changes of state of the process actors i.e. the cognitive functions described in Chap. 13. The bi-directional control relations between control and process actors are as shown in Fig. 16.5 represented by the dispositions offered by the actors in the context of acts of observation, evaluation and intervention. The control functions are within this view accordingly defined by triadic causal relations whose overall objective is defined within the process control view.

16.2 The Control Relation is Bi-directional

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Fig. 16.4 Control as a dyadic transformation of process actions producing p and maintaining p from failure to success. The failure of the (process) agent to achieve the process objective due to the influence of opponent1 is eliminated by the control agent

Fig. 16.5 Control in the decision making view is seen as a bidirectional relation between the process and control actors involving triadic causal relations

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The control action is decomposed into cognitive functions representing stages of execution of the action. The decomposition follows Morris’ [2] distinctions between three agent preferences with associated dimensions of significations and value relations (detachment, dependence and dominance). The decomposition of control into subtasks is, as mentioned in Chap. 13 well known to engineering. But Morris’ semiotic analysis provides a theoretical basis for the decomposition based on knowledge of values which is implicit or tacitly assumed in the design of any control action. The decision making view describes accordingly the means provided by the control system designer (the cognitive functions) to achieve the intended effect on the process (the end). It is important to note the distinct categorical difference between the result of the action (the object state) and the intention (a state of the agent’s mind). The object state is a (physical) property of the object under control whereas the intention is a reference to the future or current state i.e. represents information about it. This means that the object state and the intention only can be related through observation, (where a representation (the signification) of the physical state (the signifier) is created by using an instrument and associated procedure of measurement (the interpretant)). Similarly, there is a categorical difference between an intention as a state of mind and the physical state obtained by intervening in the world.

16.2.3.1

Control as a System of Cognitive Actions

The decomposition of a control action into cognitive functions and their combination into an action system can be illustrated with the heat transfer system example shown in Fig. 16.6. The example was presented in Fig. 7.2 but is here extended with information about the process variables and signals involved in control of the physical heat transfer process. Example: Control of the heat transfer system The control of the heat transfer system (Fig. 16.6) are realized by two controllers CON1 and CON2 connected in a so-called cascade and associated instrumentation TM1 and FM1. The functions of the control and instrumentation are based on several triadic causal mechanisms: • sensor TM1 (interpreter) converts temperature T (signifier) into a temperature measurement a (signification) • controller CON2 (interpretant) generates a command b (signification) from a and the temperature set-point Tref (signifiers) • the command output b of CON2 is the set-point (signifier) to CON1 (interpretant) • sensor FM1 (interpretant) converts the coolant flow rate F (signifier) into a flow measurement c (signification)

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Fig. 16.6 The heat transfer system with indication of process variables and signals

• the controller CON1 (interpretant) generates a command d (signification) from c and the set-point b for the coolant flow rate F (signifiers) • the command output d from CON1 to the pump (agent) determine the pump speed W The conversions involve interpretations and therefore triadic causal relations (see Chap. 6). However, in existing engineering practice control systems are described by using signal processing terms. This means that the meaning i.e. the information carried by the signal is implicit. The functions of the control system is accordingly described by the means used rather than by the purposes it has in the process or in the coordination with other control systems (e.g. the communication between CON1 and CON2). These purposes can only be expressed by interpretation of the signals i.e. by reference to their meaning. The analysis of the example as an action system including the cognitive functions is shown in Fig. 16.7. The diagram of cognitive functions presented in Fig. 16.7 can be mapped into a traditional signal block diagram connecting process variables (Fig. 16.8). The mapping is done by ignoring the interpretants and associated actors in the diagram and only including the associated dispositions represented by mathematical functions. However, the semantic aspects of the cognitive functions (the roles and actors) are essential in a means-end analysis of causes and consequences of failures in the control system (see also the discussions in Chap. 7 about functions as variable mappings and the use of causal schemas for reasoning about failures in Chap. 15).

Fig. 16.7 Cognitive functions and their relations to physical functions through the instrumentation in control of the heat exchange system

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Fig. 16.8 A diagram shown relations between process variables in the example. The diagram can be derived from Fig. 16.7 by mapping the cognitive functions into mathematical functions between variables

16.2.4 The Representation View The third view point describes the means of representation and reasoning which are relevant for realization of cognitive functions within the second view by humans or implemented in software. This view point is relevant for the applications of functional modelling in the design of knowledge bases for artificial cognitive agents. As an example of the content of the representation view Lind [3] presents an analysis of the architecture of model based reasoning systems based on Rasmussen’s decision ladder presented in Chap. 13. The representation view will not be investigated further in this book.

16.3 Challenges in Representing Control Functions Functional modelling of processes which includes control systems as subsystems constitutes a particular modelling challenge because the intentional structure of such systems is more complex than simple physical artefacts because they rely on both dyadic and triadic causal mechanisms. Processes including control systems can only be understood by assuming that their behaviour is determined by goals and objectives, which are defined by constraints which are not only physical but also cognitive i.e. only can be explained by reference to intentions such as representations of possible future anticipated situations (set-points) or conditions. With the technologies currently available goals and objectives of control systems are

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defined by human designers, and artefacts including the control subsystems are in this perspective only complex causal and intentional objects which are embodiments of designer intentions.

References 1. N. Rescher. “The Concept of Control”. In: Essays in Philosophical Analysis. Ed. by N. Rescher. Pittsburgh, USA: University of Pittsburgh Press, 1969. Chap. VII. 2. C. Morris. Signification and Significance. Cambridge: The MIT Press, 1964. 3. M. Lind. “Decision Models and the Design of Knowledge Based Systems”. In: Human Decision Making in Process Environments. Ed. by E. Hollnagel, G. Manzini and D. Woods. Springer Verlag, 1984.

Part V

Means and Ends

The purposes of this part are to clarify the meaning of the means-end relation and to show how the concepts of function and action presented in Parts III and IV can be integrated into a means-end framework. Chapter 17 develops the concept of a means-end relation which was introduced in Chap. 6 and shows how means-end structures can be constructed. It is shown that the means-end structure can be used to define concepts of operation which usually are used informally in engineering practice. Chapter 18 focuses on the ends of the means-end relation and introduces the distinction between goals and objectives and their dependence on values. The chapter includes also a clarification of the distinction between objectives and functions, and a summary of different kinds of means which has been identified. Chapter 19 presents a framework for functional modelling of technical artefacts based on the developed foundations.

Chapter 17

The Means-End Relation

This chapter introduces concepts of means and ends which are generic i.e. apply for all domains of design and operation. The means-end relation and its use in creating structures is introduced. Means-end structures describe dependencies between means and ends and are used to break down activities into subordinate actions. They determine inner relations between actions in a system and define the constraints between the outcome of an action and its preconditions.

17.1 The Relation The means-end relation was introduced in Chap. 6 as an interpretation of the causeeffect relation of particular relevance for modelling the interactions between humans and technical artefacts. A distinction is made in Chap. 6 between singular and general causation but here it is only seen as a relation of general causation due to its relevance for reasoning about means-end relations and for the definition of functions proposed in Chap. 7 based on dispositions. The concepts of means and ends have many meanings in their common use. An end can for example be a state to be obtained or could be the performance of an action, and a means could be a physical object (a tool), an action or a procedure. Furthermore, an item which is a means for an end in one context can, in another context be considered to be an end. Being a means or an end is accordingly not a inherent property of an item or a situation but depends on how it enters into a context or practice. The means-end relation was represented in Chap. 6 as a vertex connecting two nodes as shown in Fig. 17.1. The means and the ends are the terminals of the relation, and the nodes P and Q themselves are therefore neither means nor ends in themselves, but become so by being related through the means-end relation. The relation is abstract and represents generic properties which are common to more © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_17

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Fig. 17.1 A means-end relation between two items P and Q

specific and expressive types of means-end relations. The means-end relation can be seen as having four aspects related to the distinction between objectives, functions, dispositions and structure, with associated distinctions between different types of means-end relations. These aspects will be explored in Chap. 18.

17.2 Means-End Structure The means-end relation can be used to create means-end structures, and two main principles are proposed for this purpose. According to the first principle, means and ends can be connected into chains. Following the second principle, means and ends can be connected by many to many mappings. These two principles are generic and rooted in a common sense understanding of means-end concepts. They remain valid within a functional modelling framework and will be interpreted in Chap. 19 in the development of a generic artefact model.

17.2.1 Chains of Means and Ends Means and ends can be connected into chains as depicted in Fig. 17.2 whereby an end becomes the means for another end. This principle is well known in the design of plans of action where the outcome (end) of an action (e.g. R in Fig. 17.2) is a precondition for the execution of another action (S). The action R producing the precondition becomes in this way a means for realisation of S (the end). Apparently, such a chain can be extended without limits in both directions because it is always possible to identify further ends and more primary means. The connection of means and ends in chains raises therefore a fundamental question regarding the existence of ultimate means and ends. Chains of means and ends are related to causal chains, and the causal relation suffers also from a similar problem of infinite regress because more primary causes for an event and further consequences can always be found (see Chap. 6). These problems of infinite regress were addressed by Hegel’s [2] concept of praxis which sees teleology and causality in the context of practical action (see also Højrup [1]). According to Hegel the means-end relation is an abstraction of a more complex praxis schema including two relations of causality and teleology connecting the agent and the object of action (see also Chap. 6). In a context of design and operation of engineering systems, means will always be selected from within a set of possible options given by the situation and not derived from more

17.2 Means-End Structure

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Fig. 17.2 A chain of means and ends

primary means (causes). Furthermore the effect of applying a means in a situation will always be defined in relation to the result intended and not to more ultimate ends. The lengths and delimitations of the chains of means and ends (and cause and effect) will accordingly be limited in practice by what is considered relevant for the problem of design or operation at hand. Example: Pump operation and maintenance. An operator in a process plant will see the pump as the primary means for transport of water. A person responsible for equipment maintenance would rather see the windings of the motor and other parts of the pump as the primary means of water transport because his task is to ensure proper working conditions of the pump. Note that it cannot be seen from the means-end chain in Fig. 17.2 if the same agent and object are involved in all links in the chain. The chain can actually represent the collective result of a community of agents which interact according to a plan which produces the end T. Action systems provides a more comprehensive representation of the roles that the different agents and objects play in achieving the ends by the association of actors and related roles to the means-end relation (see Chap. 15). The principle used for connecting means and ends in chains given here, does not capture situations where the action of an agent directed towards a certain end E1 has another consequence or side effect which is not relevant to the end E1 to be achieved, but is a means for another agent to achieve a different end E2 . The graph notation used for representing means-end structures could be extended to allow the representation of such situations but will not be attempted here.

17.2.2 Aggregation and Decomposition The means-end relation is transitive i.e. if P is a means to the end Q, which again is a means to another end R, then P is a means to the end R. The transitivity of the relation therefore enables aggregation of a chain of means and ends as illustrated in Fig. 17.3. The figure shows also that a chain of means and ends may be decomposed by introducing intermediary nodes and relations. Aggregation and decomposition of a chain of means and ends can be seen as two types of abstraction; when decomposing the parts are emphasized and the whole is ignored, and when aggregating the parts are ignored and the whole is emphasized.

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Fig. 17.3 Chains of means and ends can be decomposed and aggregated

The transitivity of the means-end relation is a desirable property in modelling because it allows changes in the level of abstraction by ignoring details which are irrelevant for the problem at hand. However, for the same reason, it can also be difficult to identify implicit hidden links and nodes in a chain and the underlying semantic structures related to the praxis schema. Decomposition of a chain should therefore be supported by an analysis of the underlying action systems.

17.2.3 Many-to-Many Mappings Above the means and ends were connected in chains by a single relation. This is a simplification since means and ends are often related by many-to-many mappings. In the following two mapping directions are distinguished corresponding to the teleological and the causal aspect of the means-end relation.

17.2.3.1

Mapping from Ends to Means

An end can often be realized by several or alternative means. This mapping from ends to means can be expressed more formally by extending the vertex representing the means-end relation with an AND/OR graph notation as shown in Fig. 17.4. The AND/OR branches can be seen as a decomposition structure for the ends and should be read from the end (round dot) towards the means (square dot). The AND/OR combinations of the means at the square nodes are accordingly derived from the teleological aspect of the means-end relation i.e. the end determines or constrains the combination of means.

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Fig. 17.4 Means-end relations can be represented by AND/OR graphs Fig. 17.5 Diagrammatic representation of the sharing and exclusive use of means

17.2.3.2

Mapping from Means to Ends

A means can in some situations be used to realize several ends or it can only be used for one end at a time. These relations can be depicted as shown in Fig. 17.5 by extending the means-end structures with sharing/arbitration nodes. The branches in these sharing and arbitration structures, which should be read from the means (square dots) towards to the ends (round dots), represent causal constraints i.e. the effects or the results (ends) which can be obtained at the same time by the same means (cause). Here the means determine or constrain the combination of ends as expressed by the sharing and arbitration relations. Means and ends are therefore connected by many-to-many mappings as depicted by an example in Fig. 17.6.

17.2.4 Modes A means-end structure like the one shown in Fig. 17.6 defines different exclusive ways to realize the ends Q1 , Q2 and Q3 by means of P1 , P2 , P3 and P4 . Each realization comprises a mode and can be represented by a means-end structure without OR nodes as shown in Fig. 17.7.

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Fig. 17.6 Diagrammatic representation of many-to-many mappings between means and ends

Fig. 17.7 Diagrammatic representation of three possible modes derived from the means-end structure in Fig. 17.6

17.2.5 Loops of Means and Ends The creation of chains of means and ends in principle also allow the connection of means and ends in loop structures as shown in Fig. 17.8a. However, accomplishment of the end here determines the existence of the means which therefore cannot realize the end. Apparently this seems to be an impossible situation due to the temporal constraint of the causal relation, that the cause must precede the effect. How can a means then contribute to an end which again is a means for itself? This leads to a contradiction both teleologically and causally. It may therefore be concluded that such self referring loops in means-end structures are impossible. However, loops are possible if an auxiliary means is introduced as depicted in Fig. 17.8b. The logical problem about the temporal precedence of the means and the end is solved by letting the auxiliary means P1 realize the end Q which can then can serve as the means when established. More complex means-end structures including loops are obviously possible as exemplified in Fig. 17.8c. Example: Start-up of air-gas burner. The loop structure shown in Fig. 17.8b is often used in process control to resolve start-up problems. An example is the operation of a gas/air burner. Here the burner is a means for gas combustion, but the gas cannot burn without first being ignited. Since the burner cannot do it by itself it is necessary to use an auxiliary means to turn on the burner flame.

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Fig. 17.8 Means-end loops Fig. 17.9 Means-end loop for the burner process

When the combustion first has been established by the flame it can support itself i.e. it becomes a means for the ends of the burner. The corresponding looping means-end structure is depicted in Fig. 17.9. The burner example illustrates the relevance of means-end analysis for the design of control strategies. The control logic implied by the means-end loop could be expressed by using logic or one of the specialized languages used by industry (such as Petri Nets). The means-end analysis reveals the intentions or meanings underlying the logic and is therefore suitable as a tool for conceptual design and for communication of control strategies to an operator.1 1 The

distinction between general and singular causality introduced in Chap. 6 has implications for means-end analysis which will not be investigated here. Usually the control schemes expressed by e.g. Petri Nets in control engineering are based on singular causality connecting specific events with control actions. It is an open question how means-end analysis of the process based on generic causality (dispositions) can inform the design of control strategies.

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The above example also illustrates some of the difficulties of approaching means-end analysis in an informal “intuitive” way i.e. without being supported by functional modelling concepts. The reader may for example have wondered how it is decided to label the nodes representing the means and ends. The label “burner” is a noun referring to a physical object whereas the label “ignition” is a verb referring to a function. Furthermore, why is “ignition” used instead of “ignitor” which refers to a physical object. Does it really make a difference what kind of word is put in the nodes? This problem is solved by functional modelling through the distinction between the actor (the physical object) and the transformation and the roles involved (ignition is transformation of work into heat, ignitor is the agent, and the object is the spark). The relation between means-end concepts and the concepts of function and actor is elaborated in Chap. 18.

17.2.6 Hierarchies and Heterarchies More complex means-end structures can be created by combining the many-tomany mappings described above with the chaining of means and ends. These structures will in general be forming heterarchies (a heterarchy is a hierarchy with more than one end i.e. with several top nodes). An example of a heterarchy is shown in Fig. 17.10.

Fig. 17.10 A heterarchy of means and ends

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17.3 Countermeasures and Hazards The concepts of means and ends and the associated causal and teleological relations are necessary but not sufficient for functional modelling of SCPS. It is relevant for describing the means and ends of accomplishments (see the distinction between action types in Chap. 10), but situations incurring some sort of hazard or risk is not considered to be the end of an action. Such situations are rather to be seen as not-ends and are avoided. Furthermore the actions or things used to avoid or suppress hazards are usually called countermeasures. This relation between a hazard and its associated countermeasures has a teleological aspect. It has also a causal aspect because the effect of the countermeasure (the cause) is that actualization of the hazard is avoided. There is accordingly a counterfactual relation between the actualization of the hazard and the countermeasure, since the hazard would be realised if the countermeasure was not used.2 Ends are situations to be achieved (targets) or to be avoided (hazards). However, even though targets and hazards belong to the set of possible future situations they do not make up the whole set. Future situations also include situations which the agent does not care about because they are neither desirable (the ends) nor undesirable (the hazards). This means that the hazards cannot be seen as the logical complement of the targets, since this set, in addition to the hazards, would include situations to which the agent does not assign any value. A countermeasure-hazard relation is accordingly different from a means-end relation. They do not combine into chains in the same way. Using the graph notation shown in Fig. 17.11 for a countermeasure-hazard relation, the three examples in Fig. 17.12 illustrates how the two types of relation can be combined in meansend/countermeasure-hazard structure, including basic transitivity rules.

17.4 Means and Ends in Context The distinction between means and ends (and countermeasures and hazards) can be applied in any context of action. Within design and operation of industrial artefacts three overall contexts can be distinguished: process design, process operation, and process control. Within these contexts the following interpretations of means and ends can be made: Fig. 17.11 The countermeasure-hazard relation

2 See

also the analysis of causal schemas for accomplishments and avoidances in Chap. 15.

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Fig. 17.12 Combining means and countermeasures into structures

• In the context of process design two interpretations are possible – the ends are goals and objectives of a design activity (e.g. considering sustainability, reliability, safety, and economy), and the means of design are the theories, tools and practices (e.g. design patterns) applied to achieve these design objectives. The ends are related to both accomplishments (sustainability reliability economy) and avoidances (safety) – the ends are goals and objectives of the production process and are related to both accomplishments and avoidances (physical barriers). The means and countermeasures of the process are the equipment and the procedures (recipe) used for production. The production process is here seen as a system of actions. • In the context of operation the ends are goals and objectives of using the plant resources provided by the process designers (process equipment and automated controls) and used by the human operator to optimize production (accomplishments) and to deal with unpredictable upsets and disturbances (avoidances). The means and countermeasures are the degrees of freedom and associated equipment made available for this purpose. • In the context of control the ends are goals and objectives of the automated regulation and steering functions (accomplishments), and to protection functions

References

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(avoidances). The means and countermeasures of control are the equipment (sensors and actuators), and procedures (algorithms) provided by the automation designers to be used for detection, interpretation, evaluation of events, protection and for intervention in the process.

References 1. T. Højrup. State, Culture and Life-Modes: The Foundations of Life-Mode Analysis. Routledge, 2018. 2. G. W. F. Hegel. Science of Logic. Humanity Books, New York, 1969.

Chapter 18

Ends, Means and Functions

The purpose of this chapter is to clarify the distinction between goals and objectives which can be considered as different types of ends. Unfortunately they are often confused or seen as synonyms. There is therefore a need for distinctions for the foundations of functional modelling. Furthermore a distinction between different types of means is presented which is summarizing results obtained in Chaps. 10 and 15.

18.1 Types of Ends On an overall level, an end is either a physical or an abstract object, that has an intrinsic value. It is defined as a statement of something that somebody desires to achieve. An end is accordingly an expression of an intention (desire) which should be accomplished by somebody and refers therefore to the future. Goals and objectives are ends which are different in scope along several dimensions: • Goals are broader than objectives in the sense that goals are general intentions and are not sufficiently specific to be directly measurable. Objectives are narrow and are set for certain specific tasks • Goals are general while objectives are specific. Goals are just general intentions towards the attainment of something while objectives are precise actions for accomplishment of a specific task. • Goals may be intangible while objectives ought to be tangible. Goals may be directed at achieving non-measurable things while objectives may be targeted at getting measurable things or tasks. • Both have a certain time frame. Goals usually have a longer time-frame than objectives. Objectives are usually precise targets set for a short term. Goals may be set for a longer term but many objectives may be set within that goal. • Goals may or may not be measured, but in most cases objectives are measurable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_18

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A simple way to make the distinction is to say that goals are value oriented whereas objectives are oriented towards the means. Keeney [1] proposes in accordance with this two different types of objectives; fundamental objectives and means-objectives. Fundamental objectives are the goals and do not refer directly to actions but to values. The means-objectives relate to the objective of the actions and the means used for its realization. Rescher [2] explains the relation between values and goals as follows: Having a certain value is obviously different from having a certain goal and certain preferences. But, of course, the goal one adopts or the preference one has are reflections of, and indicators for, one’s values. The connection between values and goals is not straightforward. Usually we can make very plausible presumptive inferences from values to goals (if someone “values money” its acquisition is presumably a goal of his.) But knowing he has a certain goal, we may be quite uninformed about the operative values (e.g., if his goal is “making money” we cannot say what his values are)

The distinction between values, goals and objectives is illustrated below by a concrete example of a technical artefact. Example: A central heating system. A central heating system is standard in many houses (or it was in the soon gone ages using fossil fuels for heating). Its purpose is to produce heat by the burning of fuel (e.g. gas or oil). The heat produced by the burner is exchanged with a coolant (usually water) which is circulated through a radiator back to the boiler. The radiator exchange heat with the environment (the living rooms of the house). The pump is lubricated by a subsystem composed of a pump, some piping and an oil tank. It is seen that two values are guiding the operation of the central heating system, one related to the comfort of the user and the other to the overall cost of operation. The associated goals which are listed are general requirements to the operation. They reflect the values but not the way of operating i.e. the means to be used which are addressed by more tangible and measurable objectives. Note that the description of the goals and objectives in the example does not have a propositional form as they should (see below). The descriptions are actually referring to functions but are good examples of the descriptions made when people are asked for specification of goals and objectives. Goals and objectives can be organized into tree structures of subordination as exemplified by the goal-objective tree shown in Fig. 18.1. A subordination relation has a correspondence to a means-end relation between the actions satisfying the goals and objectives (satisfaction of the subordinate goal G1.1 is a means for satisfying G1 ). Goals and objectives can, apart from these general distinctions, also be categorized according to context or practices. Thus in design and operation of SCPS a distinction between goals and objectives of production, safety and economy is commonly made. The objectives mentioned in the example are production related. Objectives are most relevant to means-end analysis and will therefore be discussed in more detail in the following.

18.1 Types of Ends

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Fig. 18.1 Operational goals and objectives of a central heating system

18.1.1 Heckhausen’s Types of Objectives The definitions given above specify only overall features of objectives and their relations. However, a more detailed specification is needed for the purpose of functional modeling. An objective, for example, can refer to an action at several stages of realization which need to be distinguished. An objective can also specify either the result of an action or how it is achieved. Consider the objective “maintain the boiler temperature at 200 degrees”. Here “the boiler temperature is at 200 degrees” is the result intended of the action. The “maintenance” of the state of

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affairs refers to how the result is achieved (by eliminating disturbances) and thereby includes performance aspects of the action which is not specified in the result. Sometimes however, objectives are specifying both the result and how it should be achieved. Heckhausen et al. [3] proposes three types of objective addressing different stages of realization of an action. An objective can specify: • • • •

the performance of the action (the doing to be done) the result of the action (the result to be achieved) a consequence of the action (what it should bring about) a condition for another action (e.g. enablement)

Example: Opening a window The objective can here be the “opening” as a performance (the doing), it can be that “the window is open” (the result), it can be that “fresh air enters the room” (a consequence) or it can be to enable somebody to climb out of the window (a condition for another action). These four objectives can each be associated with observable behavior and interpreted according to the intention of the actor using the distinction between doing and bringing-about mentioned in Chap. 10.

18.1.2 Describing Objectives As mentioned in Chap. 7 objectives are often described by using verb clauses like: “to make money”, “to produce electricity”, “to maintain boiler temperature at 200 degrees”, “to prevent explosions” etc. But according to Achinstein [4] they should be described in a propositional form in order to distinguish them from functions i.e.: • • • •

money is made electricity is produced boiler temperature at 200 degrees is maintained explosion is prevented

These examples can be formulated using the general forms for promotive and opposive doings presented in Chap. 11. Objectives for promotive doings (accomplishments) can be expressed using the forms in Table 18.1. Here the propositions p or .¬p represent targets i.e. desirable situations. Objectives for opposive doings (avoidances) can be expressed using the forms in Table 18.2. Here the propositions p or .¬p represent hazardous i.e. undesirable situations.

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Table 18.1 Promotive doings and objectives

Doing producing p maintaining p producing .¬p maintaining .¬p

Table 18.2 Opposive doings and objectives

Doing destroying p suppressing p destroying .¬p suppressing .¬p

Objective p is produced p is maintained .¬p is produced .¬p is maintained Objective is destroyed .p is suppressed .¬p is destroyed .¬p is suppressed .p

18.2 Types of Means The objective types can be used to formalize means-end analysis. In a similar way a distinction between types of means can contribute to making the analysis more expressive. A distinction between different types of means can be obtained by combining the distinction between doing and bringing about presented in Chap. 10 with the analysis made by Achinstein (op.cit.) of the relations between objectives, functions and means.

18.2.1 Achinstein’s Analysis Achinstein (op. cit.) summarizes the conceptual relations between objectives, function and means as follows: If X’s function is to “do y”, then that “y is done” is an objective for which X is a means.

The relations between objectives functions and means implied in this definition are represented in Fig. 18.2. Functions and Bringing About In Fig. 18.2 functions were defined as doings. This is suitable for many situations. However we need also to represent situations where the objective is not that some transformation is done, but is to bring about a situation by intentionally causing something else to happen (see also Chap. 10). Danto [5] emphasizes this important distinction between doing something and to bring it about. The conceptual relations between functions, doings, means and ends (objectives) involved in bringing about are shown in Fig. 18.3. It can be concluded that a doing is a means for its consequence if it is intended.

306 Fig. 18.2 Relations between objectives, functions and means according to Achinstein [4]

Fig. 18.3 Bringing about Z by doing Y

18 Ends, Means and Functions

References

307

18.2.2 Summary A means can accordingly be a physical object being the actor for a role in a doing H1 . It is a means due to its dispositions for change. However, the actor can also be another doing H2 whose consequence is a role in H1 . The doing is a means because one of its consequences is intended. It is therefore called a functional means in Fig. 18.3 in order to distinguish it from the physical. In a cognitive action the means are actors for the signifier and the interpretant. It can also be another cognitive action. These relations can be expressed by action systems through the principles for embedding presented in Chap. 15.

References 1. R. L. Keeney. Value-Focused Thinking. London, England: Harvard University Press, 1992, p. 416. 2. N. Rescher. Introduction to Value Theory. Lanham, USA: University Press of America, 1982, p. 191. 3. H. Heckhausen and J. Kuhl. “From Wishes to Action: The Dead Ends and Short Cuts on the Long Way to Action”. In: Goal Directed Behavior: The Concept of Action in Psychology. Ed. by M. Frese and J. Sabini. Hillsdale, New Jersey: Lawrence Erlbaum, 1985. 4. P. Achinstein. The Nature of Explanation. Oxford: Oxford University Press, 1983. 5. A. C. Danto. Analytical Philosophy of Action. Cambridge: Cambridge University Press, 1973.

Chapter 19

A Functional Modelling Framework

The purpose of this chapter is to integrate the findings of the book into a framework for functional modelling of technical artefacts, with a particular emphasis on modelling SCPS. The integration is based on the principle that a technical artefact should be seen as a system of actions (Chap. 1), and the fundamental distinction and interdependence of concepts of action and the means-end relation which can be expressed as follows: an action is the realization of an intention obtained by actualizing the potential of some means for change (see Chap. 10). In addition to these overall principles and conceptual clarifications the integration is based on the following insights presented in the book: 1. dispositions, functions, transformations, roles and objectives discussed in Chaps. 6, 7, 11, and 18 are separate aspects of the means-end relation 2. physical and cognitive actions are based on different types of causality and should be distinguished (see Chap. 6) 3. actions can be embedded through actors and roles and be organized into sequences and into chains of means-end relations (see Chap. 15) 4. control actions are combinations of cognitive and physical actions connecting means-end chains of control and means-end chains of process actions (see Chap. 16) These aspects will be discussed below with a focus on the means-end relation and illustrated with selected examples. The discussion of item 3 can be seen as complementing the results presented in Chap. 15.

19.1 From Actors and Doings to Values The means-end relation can be seen as a bridge from actors and doings to values provided by functions, objectives, and goals. These different aspects of the means© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4_19

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Fig. 19.1 From actors and doings to values through functions and objectives

end relation which are shown in Fig. 19.1, connect the means for action (dispositions of actors and doings) with the potentials and opportunities available for action (the roles and the transformation) and the objectives to be achieved. The aspects are therefore organized according to phases of actualization when using some means to realize an objective and goal i.e. connecting the past with present and future. The ordering of the aspects of the means-end relation as shown in Fig. 19.1 should not be seen as forming a hierarchy. That would imply that the aspects were ordered according to a principle of subordination, which is not the case. As indicated, the ordering is fundamentally related to the distinction between the potential and the actualization of an action and is therefore of a temporal nature.

19.1.1 Physical and Cognitive Actions Figure 19.2 presents an expansion of Fig. 19.1 obtained by decomposing the function in physical and cognitive actions into their two basic constituents, the transformation and the roles involved. Note that the two diagrams in Fig. 19.2 are complementary to the diagrams in Fig. 15.3 which includes information about the causal directions which is not shown in Fig. 19.2. This means that the embedded actions shown in this chapter have counterparts in Chap. 15 where the focus is on the causal relations rather than the relation between functions (transformations and roles) and objectives.

19.2 Embedded Actions in a Means-End Perspective

311

Fig. 19.2 The means-end relation of physical and cognitive actions expanded with transformations, roles, dispositions and actors (compare with Fig. 15.3 where the focus is on causality)

19.2 Embedded Actions in a Means-End Perspective Chapter 15 showed how actions could be embedded through actors and roles. The forms of embedding represented the different ways actions could be combined through causal relations. Similar forms of embedding can be formulated on a the basis of the means-end relation since it has a causal interpretation. However, as mentioned in Chap. 6, the means-end relation has also a teleological interpretation. This means that when actions are embedded in a means-end perspective, the focus is on the teleological aspect. In this way the forms of embedding presented in this chapter are complementary to the forms presented in Chap. 15. In the following forms of embedding based on the means-end relation are presented. One of the keys to modelling means-end relations between actions is the distinction between different types of objectives which was presented in Chap. 18. Another key is to see an actor as a consequence of an action. Finally, the third key is the distinction introduced in Chap. 15 between two types of helper roles (support and collaboration). The following five categories of objectives of an action are considered: 1. 2. 3. 4. 5.

to achieve a state produced by the action/function to transform a state or serve a role to enable/disable another function or role (support) to produce an actor having a role in another function to control the state of another function (collaboration)

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19.2.1 Action Sequences A sequence of two physical actions H1 and H2 sharing an object actor is shown in Fig. 19.3. Note that the diagram does not show the temporal sequence which is represented by the causal direction in the complementary action systems diagram in Fig. 15.4a. A sequence of two cognitive actions H1 and H2 sharing an actor is shown in Fig. 19.4. The diagram shows indirectly the temporal sequence through the meaning of the two roles signifier and signification (the signification in H1 must appear before it can be signifier in H2 ). The causal direction is shown explicit in the complementary action systems diagram in Fig. 15.11a.

Fig. 19.3 Sequence of two physical actions H1 and H2

19.2 Embedded Actions in a Means-End Perspective

313

Fig. 19.4 Sequence of two cognitive actions H1 and H2

19.2.2 Means-End Chains A means-end chain of two physical actions are depicted in Fig. 19.5. Here the action H2 is the means for the agent actor in action H1 . The complementary action system diagram is shown in Fig. 15.4c. Another example of a means-end chain of two physical actions is depicted in Fig. 19.6. Here the action H2 bring about the agent in action H1 . The complementary action system diagram is shown in Fig. 15.4c. A third example of a means-end chain of two physical actions similar to Fig. 19.6 is where the action H2 bring about the object in action H1 . The complementary action system diagram for this case is shown in Fig. 15.4b. Means-end chains of arbitrary depth representing means-end hierarchies can be created using the forms presented above (including other possible forms which are not presented here).

19.2.3 Support An action can support another action as mentioned in Chap. 15. This can be depicted using the diagrams in Fig. 19.2 as shown in Fig. 19.7. It is here assumed that the support is for the agent and not for the object which would require a distinction which are not directly expressed here. The complementary action system diagrams are shown in Fig. 15.5. Chains of support of arbitrary depth (included in means-end hierarchies) can be created using the forms presented in Fig. 19.7.

314

Fig. 19.5 H2 is a means for the agent actor in H1

19 A Functional Modelling Framework

19.2 Embedded Actions in a Means-End Perspective

Fig. 19.6 H2 bring about the agent role in H1

Fig. 19.7 H2 bring about the support role in H1

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19.2.4 Control As explained in Chap. 16, functional modelling of control actions involve three view points. This is depicted in Fig. 19.8 showing that the functions at the different views are connected by means-end relations. Only the process-control and the decisionmaking views are considered in the following. It will be shown how the embedding forms can be used to represent control functions. In the process control view, the functions of control are seen in the context of the process and its operations. This means that the control action is represented by its consequences in the process. Within this view the control relation is accordingly between two physical actions H2 and H1 where the former control the latter. In the decision making view, the functions are seen in the context of the decision making processes involved in control i.e. by a cognitive action H3 which, as shown in Fig. 19.8, is the means for realizing the agent actor in the physical action H2 . This relationship can be represented as H3 bringing about the agent of H2 . The functions in the two views and their means-end relation can be depicted using the diagrams in Fig. 19.2 and is shown in Fig. 19.9. The complementary action system diagrams are shown in Figs. 15.7 and 15.13. Means-end chains of control of arbitrary depth can be created using the forms presented above (included in means-end hierarchies).

Fig. 19.8 The functions in the three view points on the control relation are related as means and ends

19.3 Summary

317

Fig. 19.9 Representing means and ends of control. H2 bring about the collaboration role in H1 . The actor of H2 is brought about by means of H3

19.3 Summary This chapter has shown how means-end concepts can be combined with concepts of action, function, disposition, and actor into a framework for functional modelling of technical artefacts. It should be noted that the examples given for demonstration is not a complete set of possible combinations. Other combinations are possible as shown in Chap. 15. The examples shown are all means-end representations of action structures included in accomplishments. Action embeddings related to avoidances which are mentioned in Chap. 15 are accordingly not considered in spite of their importance for modelling technical artefacts SCPS. The present chapter concludes the analysis of the four contextual layers depicted in Fig. 1.1 which was presented in Chaps. 6, 7, 10, 11, 12, and 17. The layers represent different contexts where relations between functions, actions and means-and ends can be formulated. The layers and their relations offer principles for organizing

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the cognitive process involved in functional modelling and can accordingly serve as a foundations for a modelling methodology. The following levels of analysis have been investigated: 1. 2. 3. 4.

analysis by means-end relations with an emphasis on teleological interpretations analysis by actions systems with an emphasis on causality identification of transformation and roles types in physical and cognitive actions analysis of the relation between structure, dispositions, and causality

The four levels of analysis are involved in functional modelling of SCPS and can be addressed in the modelling process either top-down (1 to 4), bottom up (4 to 1) or in a process of inquiry involving an interaction between the four levels of analysis. The choice of modelling strategy will be highly dependent on the purpose of the functional model such as the level of abstraction required for solving the particular design or operational problem considered.

Appendix A

Dyadic Transformation Graphs

The purpose of this appendix is to introduce the graphs used to represent the dyadic transformations in Chaps. 11 and 16.

A.1 Change Graphs A change is a transition between situations as mentioned in Chap. 11, and the schema representing dyadic transformations is derived from the schemas representing elementary changes shown in Table A.1. The proposition p in the schema divides the set of situations W into two subsets . .Wp and .W¬p = W \ Wp containing the situations for which the truth function .ϕ(w, p) is true (.Wp ) and false (.W¬p ) respectively. With this division of W into subsets the elementary changes can be represented as transitions between .Wp and .W¬p as shown in Fig. A.1.

A.2 Transformation Graphs Dyadic transformations can be represented by extending the the change graphs into transformation graphs. The extensions capture two essential aspects of actions. The first aspect is that actions are directed either away from an actual or towards a future situation (or both). It must therefore be indicated which of these situations are in focus by the agent. The second aspect is the causal aspect of an action which implies that an action is distinguished from a change by being defined with respect to a counterfactual situation. This means that the specification of an action also includes the situation that would be obtained if the action was not done in addition to the initial and the resulting situation which characterizes a change. It wil be shown © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4

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320 Table A.1 The elementary changes

A Dyadic Transformation Graphs Schema .¬pTp pTp .pT ¬p .¬pT ¬p

Description p happens p remains p disappears p remains absent

Fig. A.1 Graphs of the elementary changes

Fig. A.2 Transformation graphs for elementary interventions

below how these two aspects of an action can be represented in transformation graphs and how they can be used to represent doings. Graphs for Interventions The transformation graphs in Fig. A.2 show how interventions can be represented using transformation graphs. In the case of interventions there is no intentional focus to be indicated. Graphs for Doings and Forbearances An example is used to introduce the notation of transformation graphs for doings and forbearances. Consider the doing described by the expression producing p. This doing is directed towards a set of situations .Wp which makes p true. The counterfactual condition .¬p defines a set of situations .W \ Wp that would occur if the action was not done. These two aspects of the action are expressed in a transformation graph by nodes representing

A

Dyadic Transformation Graphs

321

the situations and directed links (arrows) representing actual and hypothetical transitions. The situation(s) in focus .Wp is indicated by grey colour and connected with the initial situation(s) .W \ Wp by a black straight arrow indicating the actual transition between situation(s) caused by the doing. In cases where the situation remains after the action a circular grey arrow pointing back on the situation node is used. In the example is used a circular grey arrow to indicate that the initial situation would remain if the action was not done (the counterfactual aspect). In other cases where the counterfactual situation is not the same as the initial situation a grey straight arrow is used. Transformation graphs representing the doing producing p and the logically equivalent but semantically distinct action destroying .¬p are shown in Fig. A.3. Transformation graphs for all the elementary doings and forbearances introduced in Chap. 11 are shown in Fig. A.4.

Fig. A.3 Transformation graphs for producing p and destroying .¬p

Fig. A.4 Transformation graphs for doings and forbearances

Index

A Abstraction hierarchy, 61, 174 Accident Fukushima, 28, 43, 63 Three Mile Island, 2, 38, 71 Accomplishment, 186 promoting, 204 Achinstein, P., 134, 153, 210, 305 Action aspect, 181 actors, 182 modality, 182 rationality, 183 setting, 183 type, 182 cycle, 229 phases, 235 practical, 8 stages, 227 states, 241 system, 15, 23, 181, 247 type, 181 bringing-about, 188, 208 doing, 188 forbearing, 187, 206 letting, 187 making, 187 Affordance, 115 Aggregation, 291 Althusser, L., 248 Anscombe, G.E.M., 182, 184, 187 Aristotle, 4, 63 Artificial intelligence (AI) agents, 22, 30, 99, 118

Autonomous robots, 17 Autonomous systems, 77, 99 Autonomy, 145 Avoidance, 40, 186 opposing, 204

B Bainbridge, L., 2, 38 Barrier, 41 Baxter, G., 2 Belief-desire-intention (BDI) architecture, 60, 232 Bergson, H., 197, 259 Bernejo-Alonso, J., 77 Borgo, S., 68 Brandt, P.A., 248 Bremond, C., 233 Brewer, W.F., 90 Bringing about, 158 Browning, D., 139 Bruce, B., 214 Bunge, M., 108, 184 Bussmann, S., 99

C Cacciabue, P.C., 45 Carrara, M., 68 Cases, 214 Causal complex, 110 field, 110 path, 110

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 M. Lind, Foundations for Functional Modeling of Technical Artefacts, Design Research Foundations, https://doi.org/10.1007/978-3-031-45918-4

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324 Causal (cont.) relation dyadic, 106, 129, 170 triadic, 112, 129 roles, 106 Causality, 120, 121, 310 general, 109, 121 notions, 117 recipies, 118 singular, 109, 121 triadic, 163 Causation general, 289 Causes and conditions, 142 Chain means-end, 312 Chandler, D., 114 Chandrasekharan, B., 64, 66, 137, 153 Change description, 195 graph, 196 schema, 195 Chen, L.W., 77 Cheon, S.W., 77 Cognitive agent, 163 Cognitive System Engineering (CSE), 39, 59, 164, 174 Collingwood, R.G., 104, 117, 142, 219 Complexity, 56 design, 2 operation, 2 Conant, R.C., 23 Conceptual schema, 90, 122 Context, 6 layers, 9 Contextual control model, 231 Control relation, 313 action system, 276 Corcoran, W.R., 40, 43 Counterfactual, 192 Countermeasures, 297 Cummings, C.E., 2 Cummings, G.E., 38

D Dahlstrand, F., 75 Danto, A.C., 188, 209, 214, 305 Decision ladder, 60, 230 Decomposition, 55, 291 control volumes, 140 functions and objectives, 134 spatial structure, 137 temporal structure, 139

Index Deely, J., 112, 225 Defense in depth, 41 DeKleer, J., 66 Del Frate, L., 148 Deng, Y.-M., 146 Dennett, D.C., 98 Description objective, 304 Dewey, J., vi, 89, 226 Disposition, 107 causal power, 107 failure, 108 function, 135 liability, 107 Doing, 153, 201 opposive, 205 promotive, 205 Dowe, P., 112, 140 Duncker, K., 46 Dysfunction, 142

E Elementary changes, 195 Elementary interventions, 197 Elementary lettings, 200 ElMaraghy, W., 4 Embedding, 249, 310 Emmet, D., 148 Enclave, 234 End goal, 301 objective, 302 Entanglement, 7 Erden, M.S., 4, 64 Everett, J.O., 140 Example air-gas burner, 258 control, 47, 294 causal roles, 240 central heating system, 302 electric heater, 138 Fukushima accident, 236 heat transfer system, 74, 131, 155, 269, 282 water circulation, 189 opening a window, 304 operation of a valve, 189 power system, 147 pump function, 136, 244 operation and maintenance, 291 start of motor, 239 stone age artisan, 185

Index Explanation causal, 133 functional, 133

F Facts brute, 94 social, 94 Failure, 148, 157, 252 Fang, M., 73 Far, B.H., 64 Fillmore, C.J., 214 Framework interpretation, 7, 91 biological, 8 natural, 8, 94 social, 8, 95 Francis, B.A., 23 Franke, D.W., 66 Freeman, E., 112 Freeman, P., 66 Function action, 185 biological, 128 block diagram FBD, 68 chemical processes, 4 cognitive, 227 control, 4 design, 155 device-centric, 137 engineering practice, 4 environment-centric, 137 of functional model, 130, 164 hierarchy, 146 latent, 142, 160 manifest, 142, 160 objective, 134 role, 141, 186 safety, 4 service, 155 structure, 137, 169 transformation, 141, 186 use, 155 variable mapping, 156, 283 Functional fixation, 46

G Garbacz, P., 68 Gasking, D., 118 Gibson, J.J., 116 Goal Tree Success Tree (GTST), 77 Goffman, E., 7, 92 Gofuku, A., 75

325 Gouldner, A.W., 145 Greimas, A.J., 214, 220, 249, 280

H Habermas, J., 95 Haddon, W., 43, 236 Harré, R., 107, 140, 182 Hazards, 297 Heat transfer system, 170 Hebert, L., 215, 219, 250 Heckhausen, H., 304, 311 Hegel, G.W.F., 291 Hempel, C.G., 149 Hermeneutics, 99 Heussen, K., 73 Hierarchy function, 130 means-end, 130, 313 control, 316 support, 313 Hirtz, J., 64 Højrup, T., 121, 248, 291 Hollnagel, E., 39, 43, 231 Hubka, V., 4 Human-technology relation alterity, 164 background, 163 embodiment, 21, 161, 165, 269 embodiment externalization, 166 embodiment inclusion, 166 hermeneutic, 164 Hume, D., 104 Husserl, E., 162

I IAEA, 27, 41, 63 IDEF0, 68 IEC standard 61131, 34 61508, 43 Ihde, D., 29, 161 Illari, P., 104 Intelligent systems, 30 Intentional systems, 98 Intentions control systems, 134 extrinsic, 134 intrinsic, 134 Internal model principle, 23 Inter-subjective, 94, 95 Intervention, 187

326 Irehvije, R., 77 Iwasaki, Y., 66

J Jalashgar, A., 77 Jennings, R., 22, 99 Johnson, M., 104 Joint cognitive systems, 39, 174 Jørgensen, S.B., 25, 38, 71 Josephson, J.R., 137

K Kairos, 237 Kang, H.G., 75 Keeney, R.L., 302 Kepner, C.H., 60 Keuneke, A.M., 66 Kirchhubel, D., 73 Kitamura, Y., 67 Knowledge tacit, 90, 157 values, 282 Kotarbinsky, T., 186 Kroes, P., 65, 161 Kronos, 237 Kuhl, J., 304

L Lakoff, G., 104 Langkjær, A., 134, 271 Larsen, M.N., 73 Larsen, S.E., 248 Larsson, J.E., 75 Layer of protection analysis (LOPA), 41 Leaver, E.W., 25, 67 Levels abstraction, 145 automation, 29, 166 specification, 145 Lifeworld, 95 Lind, M., 32, 73, 176, 210, 283 List, C., 104 Luckman, T., 97 Luria, A.R., 137

M Machine learning, 5, 22 Mackie, J.L., 110, 142 Marca, D.A., 68 Mead, G.H., 227

Index Means-end analysis, 23 heterarchy, 296 hierarchy, 296 loop, 294 relation, 120 arbitration, 293 sharing, 293 Menzies, P., 104 Merleau-Ponty, M., 162 Merton, R.K., 142, 160 Minsky, M., 90 Mitchell, W.J., 15 Modarres, M., 77 Model based reasoning, 130 Model building, 130 Modes, 293 Modes of signifying icon, 114 index, 114 symbol, 114 Molnar, G., 107 Morris, C., 112, 115, 227, 282 Motive because-of, 46, 117, 184, 207 in-order-to, 46, 117, 184, 205 Multilevel Flow Modelling (MFM), 73, 176 Mumford, S., 107, 108, 135 Myers, W.T., 139

N Nakamura, G.V., 90 Narrative, 233 atom, 234 Neural networks, 5 NIST, 68 Nordvik, J., 77 Norman, D.A., 229 Nöth, W., 114

O Objectivity, 94, 95 Öhman, B., 75 Ontology, 139 Operator training, 166 Opponent, 187 Opposing, 204

P Pahl, G., 67 Parasuraman, R., 29

Index Pearl, J., 104 Pedersen, S.A., 110 Peirce, C.S., 112 Petersen, J., 41, 73, 110, 236 Peterson, J.L., 34 Phenomena-based building blocks (PBB), 38 Phenomenology human-technology, 163 Piping and instrumentation diagram, 32 Pirus, D., 71 Polanyi, M., 90, 94 Polkinghorne, D.E., 214 Practice, 248, 276 prevention, 249 production, 249 Practice schema, 248 Praxis, 291 Problematic situation, 227 Problem framing, 89 Process flow sheet, 32 Process philosophy, 105, 139 Promoting, 204 Propp, V., 215 Purpose, 184 of acting, 184 of object of action, 185

R Rasmussen, J., 22, 39, 59, 61, 70, 73, 110, 174, 230, 283 Reciprocity, 8, 169, 170 control relation, 276 dispositions, 108, 239 functions, 144, 239, 268 roles, 113, 216 Reliability diversity, 40 redundancy, 40 Rescher, N., 99, 105, 139, 172, 182, 302 Robbins, M.C, 43 Rodriquez, M., 77 Role, 141 causal, 107 helper collaboration, 258, 278 support, 256 structure, 141 Rolf, B., 90 Rosenbluth, A., 99 Rosenman, M.A., 54 Rosen, R., 88 Ross, D.T., 68 Rossing, N., 73

327 Russel, B., 119 Russo, F., 104 S SADT, 68 Safety critical situations, 22 Safety functions, 40 Safety systems passive, 28 Sanz, R., 77 Schema change, 192 transformation, 193 Schön, D.A., 7, 89 Schrenk, L.P., 60 Schutz, A., 95, 97, 117, 182, 184, 201, 226 Searle, J.R., 67, 93, 95, 99, 118, 149 Sembugamoorthy, V., 66 Semiosis function, 129 model based reasoning, 129 model building, 129 Semiotic triangle, 112 Sharfstein, B.-A., 6, 7 Sheridan, T., 29, 166 Sign, 112 complex, 129 Signal, 114 Simondon, G., 96 Simon, H.A., 25, 93, 153 Situations, 194 Sklet, S., 43 Smith, J.E., 96, 237 Smith, R., 64 Socio-Cyber-Physical Systems (SCPS), 2, 4, 17 value chain, 27 Song, M., 73, 75 Speech acts, 99, 118 Standard IEC 61131, 34 Suh, N.P., 4 System-centered-design, 36 System control diagram, 34 Szykman, S., 68 T Tanner, M.C., 66 Teleology, 4, 120, 121, 310 Time cronos, 96 kairos, 96

328 Tomiyama, T., 67 Toyoshima, F., 116 Transformation, 141 dyadic, 191 Transient occurance become, 105 becoming, 113 change, 105 changing, 113 Tregoe, B.B., 60 Trost, W.A., 43 Types means, 304 objectives, 302 U Umeda, Y., 67 Umwelt, 22, 116 Us, T., 73 V Validation, 149 Values, 94, 95 VanEck, D., 64

Index Van Paassen, M.M., 75 Vermaas, P.E., 65, 68 Vicente, K.J., 39 View points control relation, 276 Von Uexküll, J., 22, 116 Von Wright, G.H., 182, 192

W Wide, S., 117 Wiener, N., vi Willey, R.J., 41 Wooldridge, M., 67 Wright, L., 132, 133 Wu, J., 73

Y Yang, M., 75 Yoshikawa, H., 75

Z Zhang, X., 73