Evolutionary Criminology and Cooperation: Retribution, Reciprocity, and Crime 3031362748, 9783031362743

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PALGRAVE’S FRONTIERS IN CRIMINOLOGY THEORY

Evolutionary Criminology and Cooperation Retribution, Reciprocity, and Crime

Evelyn Svingen

Palgrave’s Frontiers in Criminology Theory

Series Editors Matt DeLisi, Criminal Justice Studies, Iowa State University, Ames, IA, USA Alex R. Piquero, Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX, USA

Frontiers in Criminology Theory advances contemporary theory and research on two broad areas of criminological scholarship. The first focal area is on conceptual content areas that seek to explain the etiology and developmental course of antisocial behavior. The series conceptualizes antisocial behavior broadly to acknowledge and incorporate research from multiple disciplinary perspectives including criminology, developmental psychology, sociology, behavior genetics, social work, and related fields. Works in this focal area include book-length developments of extant theoretical ideas, edited volumes of leading research within a specific theoretical area (e.g., self-control theory, social learning theory, general strain theory, etc.), and, of course, new theoretical ideas on the causes and correlates of anti-social behavior. The second focal area encompasses the criminology theory of the juvenile justice system, criminal justice system, and allied social service providers. Like focal area one, the criminal justice system is conceptualized broadly to include multiple disciplinary perspectives that advance research on prevention, psychiatry, substance abuse treatment, correctional programming, and criminal justice policy. Works in this focal area include book-length developments of extant topics, edited volumes of leading topics in criminal justice, and, of course, new theoretical and conceptual approaches to the prevention, treatment, and management of criminal justice clients.

Evelyn Svingen

Evolutionary Criminology and Cooperation Retribution, Reciprocity, and Crime

Evelyn Svingen Department of Social Policy, Sociology and Criminology University of Birmingham Birmingham, UK

ISSN 2945-655X ISSN 2945-6568 (electronic) Palgrave’s Frontiers in Criminology Theory ISBN 978-3-031-36274-3 ISBN 978-3-031-36275-0 (eBook) https://doi.org/10.1007/978-3-031-36275-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover credit: Sabena Jane Blackbird/Alamy Stock Photo This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Foreword

The field of criminological research is expanding in many domains, using a multitude of methodological, epistemological, and theoretical approaches yet the development of criminological theorising on lawbreaking, one of the most traditional topics of criminology, remains an issue that divides and unites scholars. Today, criminological theorising remains a very huge challenge for a multitude of reasons. Some scholars have therefore argued that theoretical integration is the way forward while others remain faithful to the idea of competition between theories and a comparison of criminological theories. As criminology is often viewed as a field rather than a discipline, it is questionable whether such an enterprise of improving our understanding of the complex etiology of law-breaking will be successful and widely embraced in the field. After all, pluralism is highly valued in the social sciences and the contemporary focus on interdisciplinary integration only seems to make visible some enduring actual challenges theorists face: conceptual inflation and the differential interpretation of similar concepts across disciplines make the endeavor of theorising even harder.

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It is with this current state of the art in my mind that I highly appreciate the goal of this book. This book aims at presenting the power of evolutionary theorising as a tool not to replace theoretical approaches but to provide a solid backbone for criminological theories. Contemporary theories start from conflicting views on human nature and social order and therefore it has previously been argued by a number of scholars in the field of sociobiology, evolutionary psychology, evolutionary sociology, gene-culture coevolutionary theories, and cultural evolutionary theories that the framework of evolutionary theorising might contribute to a better understanding of human nature and thus contribute to apparently conflicting ideas on crime causation and the role of different mechanisms, operating at different levels. Evolutionary theorising can contribute in many ways to our understanding of not only law-breaking but also law-making and the societal reaction towards law-breaking. It is often thought that the theory of evolution by natural selection (and genetic drift, gene flow, sexual selection) from the perspective of immortal genes, kin selection, reciprocal altruism, indirect reciprocity, network reciprocity, and the highly disputed multi-level selection, but also the blooming field of gene-cultural coevolution is useful in understanding the hominisation process (the morphological and physiological and behavioural evolution of the lines of hominins until the one species that we are today). I disagree. Contemporary course on biological anthropology stresses that we cannot properly understand who we are and why we do the things we do without proper knowledge of where we came from. The blooming field of cooperation, altruism, and punishment in neighbouring disciplines shows that a Darwinian outlook may also be a useful tool for criminology. Once the common misunderstandings with regard to evolutionary theorising such as the boogeyman of social Darwinism, biological determinism, etc. are taken into account, ideological opposition is no longer warranted. Many misunderstandings on evolutionary theories could be avoided if the distinction between proximate and ultimate causes is properly made. This is not to say that I think that “consilience”, the dream of the recently deceased sociobiologist Edward Wilson, will replace all other frameworks in criminology. The initial fear of some scholars, that evolutionary biology (widely interpreted as the evolutionary study of the living world, thus also humans) would

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swallow all other disciplines that study human behaviour has proven to be wrong. However, criminology would in my view be better off with less theories than with more. Indeed, less is more, is my view. In this book Dr. Evelyn Svingen presents her views on evolutionary theorising in criminology and argues why the field can benefit from this framework. She further presents the results of an empirical study, guided by key concepts in contemporary evolutionary theorising: retribution, reciprocity, and crime. Her model, the RRM model, is by no means meant as a replacement for contemporary theories, but rather as a complement that can help to further develop theorising. It is based on theoretically sound ideas on (strong) reciprocity and how that may relate to what criminologist refer to as rule-breaking. Why people cooperate on such a large scale, our human ultrasociality, cannot be properly understood without such an overarching framework. Many of today’s challenges, like nepotism, clientelism, racism, violent extremism (and other expressions of ingroup sociality vs. outgroup competition), punitiveness and vigilantism, climate change, and difficulties of nations to widely cooperate can be more fully understood within such a broad interdisciplinary framework. This book provides an excellent example of the added value of interdisciplinary thinking in the social sciences. May, 2023

Prof. Dr. Lieven Pauwels Faculty of Law and Criminology Ghent University Ghent, Belgium

Preface

It was never my intention to go against the grain of mainstream criminological thinking. When I started my journey into Criminology as a field, I was blissfully unaware of the ideological, disciplinary, and political debates surrounding what should and shouldn’t be studied. Having completed my undergraduate education in Liberal Arts and Sciences, specialising among other things in Neuroscience and Political Philosophy, I was used to dealing with several disciplines. I also thought this is how everyone did things, especially in such a multidisciplinary field like Criminology. As a budding neuroscientist, I swiftly moved into biocriminology and found myself a supervisor that called herself a neurocriminologist. I did not have much of a clue that I was about to enter a very complex debate about the place of biological explanations in crime causation. It was never my intention to create a theory of crime. Through some general observation, I have come to believe that our world is organised through tit-for-tat and wanted to study reciprocal feelings and how they might influence crime propensities, in the same way we study guilt or

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shame. I was surprised when I found little to no literature on this in the field of criminology, but I remained undeterred. I found a whole body of literature on retaliation and reciprocity from the field of behavioural economics and decided that this will do. My ambitions were small, and the intentions were straightforward. However, as I was writing my PhD thesis, I’ve constantly been told that I must incorporate these tendencies into a criminological theory or model. Despite searching far and wide, I found nothing that could accommodate my ideas, and made a theory of my own. Thus began debates over whether what I write can be considered a “big-T” Theory, or a “smallt” theory, and from the Retribution and Reciprocity Theory (RRT), my focus of study became the Retribution and Reciprocity Model (RRM) to leave this debate. It was never my intention to challenge the thinking about where criminology is heading as a field. Having removed the word “theory” from what I was developing and testing, I was hoping that I could avoid having those conversations, until my PhD reviewers told me that I must. I couldn’t position my interests into a theory because there wasn’t one, and I couldn’t position my (small-t) theory into the existing subfield because that also did not exist. Every biocriminological book I read started with a long justification for its existence, and articles listed criminologists with career-ending consequences for writing the sort of things I write. While it is common in any field for researchers to have strong opinions about the best approaches to theory-building, I found that very few people around me believe in biosocial explanations of crime. From a colleague that stated, “I have given up on these ideas 20 years ago but talking to you makes me very happy that these thoughts are emerging again” to the colleagues that look at me blankly and say “but what’s the point of looking at the brain?” I slowly started to realise that I am, in fact, in an intellectual minority. Nevertheless, in my journey I also found many colleagues very excited to listen. Many colleagues admitted that they were not convinced at first, but they found my ideas thought-provoking and meaningful. Therefore, I hope to start this book not simply by introducing a model of crime, but also by explaining why biosocial criminology, and by extension evolutionary criminology, matters.

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It is still not my intention to revolutionise anything. Even though biocriminology is supposedly the next “paradigm shift” (Wright & Cullen, 2012) criminology needs, I simply believe that I am adding a new chapter to our ability to explain crime. I also believe that evolutionary criminology can be an invaluable tool, and that RRM has the potential to explain a lot about crime. Whether you like biological criminology or not, I really hope that everyone can and will be excited to engage with this book. The field will be better off if we start looking beyond the narrow constraints of our schools of thought and methodological norms. I believe that evolutionary criminology has a tremendous offering to the field and that my small-t theory, the Retribution and Reciprocity Model, is an excellent example of how it can be used. And if you are a sceptic of biocriminological thought, I would like to listen too. To quote an anonymous reviewer for this book proposal, “what a dark hole of ignorance we would be in if the scientific curmudgeons of old were silenced because their work is offending the sensitivities of some”. Birmingham, UK

Evelyn Svingen

Acknowledgements

I would like to thank Dr Kyle Treiber for helping me develop the means of testing the Retribution and Reciprocity Model, supervising the process of the model development, as well as sharing her expertise in biosocial criminology; Professor Lieven Pauwels for the very thoughtprovoking discussions about evolutionary criminology and etiology of crime; Professor P-O Wikstrom for pushing me to consider the theorybuilding aspects of the Retribution and Reciprocity Model; Professor Will Leggett for some very insightful dialogue about multidisciplinarity; Dr Teresa Baron for her philosophical expertise; to the reviewers and endorsers for their faith and support; and Dr Matthew Temple for absolutely everything. Some of this research has been funded by the Cambridge Trust International Scholarship and conducted at the University of Cambridge Institute of Criminology. The production of this book was supported by the University of Birmingham Department for Social Policy, Sociology, and Criminology.

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Praise for Evolutionary Criminology and Cooperation

“The book by Dr Svingen, Evolutionary Criminology and Cooperation: Retribution, Reciprocity, and Crime, constitutes a laudable effort to draw attention to the field of evolutionary criminology. This monograph is well-written, skillfully organized, comprehensive, and meticulously referenced. Dr Svingen has done a masterful job of synthesizing the literature on the concepts of retribution and reciprocity in explaining crime; and empirically tested the Retribution and Reciprocity Model. This book is unique in that it brings together the multidisciplinary knowledge from neuroscience, evolutionary biology, and behavioral economics. It is an invaluable reference for theorists, scholars, field practitioners, policymakers, and graduate students interested on this frequently misunderstood topic. Highly recommended!” —Heng Choon (Oliver) Chan, Ph.D., Associate Professor of Criminology, Department of Social Policy, Sociology, and Criminology, University of Birmingham, Birmingham, UK, Series Editor of The Wiley Series in the Psycho-Criminology of Crime, Mental Health, and the Law

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Praise for Evolutionary Criminology and Cooperation

“This book shows convincingly why criminology needs a basis in evolutionary theory—an understanding of who we are as human beings. Dr. Svingen proposes an exciting new general theory that links crime to fundamental questions about the origins of human cooperation, retribution, and reciprocity. She also presents remarkable results of empirical work designed to test the theory. This is a great achievement of innovative interdisciplinary scholarship.” —Professor Manuel Eisner, Wolfson Professor of Criminology, Director of the Institute of Criminology, University of Cambridge, UK, Director of Violence Research Centre “Why are reciprocity and retribution viral concepts for a truly interdisciplinary criminology that does not shy away from evolutionary theorizing? Read this book, as it challenges traditional views on crime and punishment.” —Professor Lieven Pauwels, Professor of Criminology, Department of Criminology, Criminal Law, and Social law, Ghent University, Universiteitstraat 4, Ghent, Belgium

Contents

1

Evolutionary Theory and Crime: How Evolutionary Criminology Can Help Us Solve Criminology’s Theoretical Crisis 1 Introduction 2 History of Evolutionary Criminology 2.1 The Revolutionary Beginnings 2.2 A Great Misunderstanding 3 Evolutionary Science as a Solution to the Theoretical Crisis in Criminology 3.1 Challenging Assumptions on Human Nature and Social Order 3.2 A New Level of Analysis 3.3 Mechanistic Explanations and Falsifiability 4 A Unifying Force of Evolutionary Criminology 5 Conclusion References

1 1 4 4 7 10 10 15 17 20 22 23

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Contents

Introducing the Retribution and Reciprocity Model: An Evolutionary Theory of Crime 1 Introduction 2 Why Study Retribution and Reciprocity 3 Evolution of Cooperation and Punishment 3.1 Evolutionary Advantage of Cooperation 3.2 The Role of Learning 3.3 The Learning of Punishment 3.4 Culture-Gene Coevolution 4 Retribution and Reciprocity 4.1 Explaining and Defining Reciprocity 4.2 The Role of Retributive Punishment 5 Retribution and Reciprocity Model 5.1 The Importance of RRM in Criminological Theory 6 Conclusion References

3 The Neurophysiology of the Retribution and Reciprocity Model: The Anatomy of Cooperation 1 Introduction 2 Background Evidence 2.1 Evidence for Prosociality and Reciprocity 2.2 Justice and Retribution 2.3 The Role of Learning and the Environment 3 Neurophysiological Mechanisms of Cooperation 3.1 Positive Reciprocity 3.2 Negative Reciprocity 3.3 Retribution 3.4 Genetic Factors 4 Integrated Retribution and Reciprocity Model 5 Conclusions References

27 27 30 35 37 42 45 46 49 49 53 55 59 63 65 79 79 82 83 90 96 98 100 104 109 112 115 119 122

Contents

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Retribution, Reciprocity, and Vignettes: Testing the Retribution and Reciprocity Model through Hypothetical Scenarios 1 Introduction 2 Methodology 2.1 Hypothetical Scenarios 2.2 Participants and Recruitment 3 Results 3.1 Descriptive Statistics 3.2 Scores and Measures 3.3 Demographics 3.4 Evaluating Tendencies 4 Discussion and Limitations 4.1 General Discussion 4.2 Difference Between Tendencies 4.3 Difference Between Crime Types 4.4 Overall Limitations 5 Conclusion References

143 143 147 148 158 162 162 164 165 170 179 179 182 184 185 186 188

Retribution, Reciprocity, and Crime: Using a Public Goods Game to Measure People’s Prosociality and Criminality 1 Introduction 2 Methodology 2.1 Sample and Procedures 2.2 The Decision-Making Game 2.3 Hypothetical Scenarios 2.4 Questionnaires 2.5 Summary 3 Results 3.1 Descriptive Statistics 3.2 Scores 3.3 Retribution, Reciprocity, and Crime 4 Discussion and Limitations 5 Conclusion References

191 191 194 195 196 199 200 202 204 205 210 213 223 225 228

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Lessons Learned: What We Know About Retribution, Reciprocity, and Crime 1 Introduction 2 Reintroducing RRM 3 Methods to Study RRM 4 What RRM Explains and Does not Explain 5 The Future of RRM: A Theory of Everything? 6 What We Have Learned References

Index

231 231 234 237 241 243 245 246 247

Abbreviations

ACC AI AVP BSA BSS CC CC Score CSS DLPFC DS DWA DWS ECU EQ fMRI GC Score GEG HTML IPR Scale IT

Anterior Cingulate Cortex Anterior Insula Vasopressin Bus Stop Answer Bus Stop Scenario Competitive Condition Combined Crime Score Cascading Style Sheets Dorsolateral Prefrontal Cortex Dorsal Striatum Dropped Wallet Answer Dropped Wallet Scenario Experiment Currency Unit Empathy Quotient Functional Magnetic Resonance Imaging General Crime Score Gift Exchange Game HyperText Markup Language Interpersonal Relationship Scale Isotocin

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Abbreviations

JS MAOA (-H/-L) NCC NQA NQS NR OXT PADS+ PBS PET PG PGG PHP PoE PR R RRM SAT SCR SQL SRC Score SRCQ SSQ STG tDCS TMS ToM TPJ TPP UG V Score VMPFC

JavaScript Monoamine Oxidase A (High/Low activity) Non-Competitive Condition Nightclub Queue Answer Nightclub Queue Scenario Negative Reciprocity Oxytocin Peterborough Adolescent and Young Adult Development Study Person at the Bar Scenario Position Emission Tomography Public Good Public Goods Game Hypertext Preprocessor Perceptions of Environment Positive Reciprocity Retribution Retribution and Reciprocity Model Situation Action Theory Skin Conductance Response Structured Query Language Self-Reported Criminality Score Self-Reported Criminality Questionnaire Social Support Quality Superior Temporal Gyrus Transcranial Direct-Current Stimulation Transcranial Magnetic Stimulation Theory of Mind Temporoparietal Junction Third Party Punisher Ultimatum Game Violence Score Ventromedial Prefrontal Cortex

List of Figures

Chapter 2 Fig. 1

The mechanism of work of the RRM

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Chapter 3 Fig. 1

The neurophysiological mechanism of RRM

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Chapter 4 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5

An example of a Bus Stop Scenario as presented to the participants An example of a Nightclub Queue Scenario as presented to the participants An example of a Dropped Wallet Scenario as presented to the participants A version of the Person at the Bar scenario as presented to the participants Box-plots for Age vs answers to hypothetical scenarios with 1 = crime and 0 = no crime. Top left: BSS, top right: NQS, bottom left: DWS, and bottom right: BPS

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List of Figures

Chapter 5 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6

Summary of overall behaviours in the Public Goods Game Histogram of Age distribution of the participants Histogram of the distribution of scores of Perceptions of the Environment questionnaire Histograms of scores for the three tendencies: Positive reciprocity, negative reciprocity, and Retribution Scatterplots of all three tendencies Box plot of retribution score against general crime score

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List of Tables

Chapter 4 Table Table Table Table Table Table Table Table Table

1 2 3 4 5 6 7 8 9

Table Table Table Table Table

10 11 12 13 14

Table 15 Table 16

The summary of all versions of hypothetical scenarios All versions of the Bus Stop Scenario (BSS) All versions of the Nightclub Queue Scenario (NQS) All versions of the Dropped Wallet Scenario (DWS) All versions of the Person at the Bar Scenario (PBS) Progress of the participants through the experiment Gender breakdown of violence scenario responses Gender breakdown for theft scenarios Regression analysis results for the role of age in answers to the hypothetical scenarios Chi2 analysis of violence scenarios and retribution Chi2 analysis of theft scenarios and retribution Chi2 analysis of negative reciprocity and violence scenarios Chi2 analysis of negative reciprocity and theft scenarios Chi2 analysis of negative reciprocity scenarios and answers to give the money back in the DWS Chi2 analysis of positive reciprocity and violence scenarios Chi2 analysis of positive reciprocity and theft scenarios

149 150 153 156 159 163 166 167 170 172 173 175 176 176 178 179

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List of Tables

Chapter 5 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 Table 11 Table 12 Table 13

All items of the perceptions of the environment questionnaire Summary of all the scores and measures used for this experiment Summary of free-riders per round of the PGG Summary of the self-reported crime Summary of all participants’ answers to the hypothetical scenarios Correlation of the tendencies Chi2 analysis of perceptions of the environment and the general crime score Chi2 analysis of negative reciprocity score against general crime score Chi2 analysis of negative reciprocity scores against scenario answers Chi2 analysis of positive reciprocity against overall crime score Chi2 analysis of perceptions of the environment against crime score Summary of regression models for perceptions of the environment against crime scores Logit regression models of perceptions of the environment against scenario responses

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1 Evolutionary Theory and Crime: How Evolutionary Criminology Can Help Us Solve Criminology’s Theoretical Crisis

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Introduction

Criminology is a field growing at a breathtaking speed. With more and more researchers identifying as criminologists, universities creating more courses on the subject, and thousands of new students embarking on a journey to explain and understand crime and the criminal justice system, it should be called an unprecedented success. Nevertheless, as their first wave of excitement fades, new students and researchers immediately hit a wall: multiple theories of causes of crime that appear to be speaking completely different languages. As Bruinsma (2016) famously reflected on the proliferation and fragmentation of crime causation models in criminology, more of us started to reflect on what the purpose of a criminological theory is, and what can be done to unite the field to develop better explanations of crime and criminality. In that light, it might appear counterintuitive that I am writing a book not only introducing a new theory of crime but also defending a field that some might not deem necessary. Evolutionary Criminology presents

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. Svingen, Evolutionary Criminology and Cooperation, Palgrave’s Frontiers in Criminology Theory, https://doi.org/10.1007/978-3-031-36275-0_1

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a road less travelled in the search for comprehensive models of explanations of crime, and perhaps for good reason. There is no biological theory that would explain all crime just as, I argue, there is no psychological or sociological explanation. There is no gene that intrinsically leads to crime, no mental health condition, or environment factor: the only thing we can look at is interactions and mechanisms, and for that, we need all the pieces of the puzzle. As we ambitiously build our Babylonian tower of criminological knowledge, we have to acknowledge the sheer complexity of this task and realise that every field and level of explanation has its place. Biological explanations have their place alongside psychological, sociological, and others. Our task as criminologists now is to both discover all possible levels of crime causation, but at the same time to test the theories proposed, to retire the theories that have been falsified, and to incorporate and integrate the explanations that do work. I posit that evolutionary theory is uniquely positioned to facilitate both of these aims. This book is about evolutionary criminology, which I define to be the study of crime-related mechanisms, naturally selected throughout human evolutionary history. As such, it falls under the umbrella of biosocial criminology (which I use interchangeably with biocriminology),1 which studies the biological contributions to the study of crime. Biosocial criminology had uneasy beginnings, starting with Lombroso’s infamous theorising, which was later used by the proponents of eugenics. Nevertheless, biological explanations moved a long way; in terms of genetics, neuroscience, and evolutionary theorising. I believe that all three branches of biocriminology have a place in criminological theory, even though in this book I focus on defending only one of them—evolutionary criminology. Criminology is a young field, and yet through the limited amount of time we’ve had to study crime and related aspects, we have come up with hundreds of theories of crime. Bruinsma (2016) cuts down the major explanations of crime to six major theory groups: anomie/

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The reason why I do it is because I don’t believe that there are many (if any) biocriminologists that would argue that there is a biological explanation of crime that does not have any environmental influences. As such, I consider ‘biosocial’ to be a simply more precise term for what biocriminologists do either way.

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strain, control, learning, labelling, rational choice, and social disorganisation. Some theories attempt to unite all explanations in one overarching account. Wilson (1998) referred to attempts to unify different methods by merging them together as consilience. That is a method that not everyone accepts, with some embracing it, and others not. However, at the root of the problem of criminology is that most of these theories offer correlations, or propensities, rather than actual mechanistic explanations of crime, prompting Thornberry (2012, p. 59) to say that “there are more and more theories, but not necessarily greater and greater explanatory power”. Bruinsma (2016) notes that “from a distance the discipline looks like a battlefield of masses of rival and conflicting ideas about the causes of crime”. It is hard to disagree with him, as textbooks are filled with competing explanations of crime, correlates, and untestable hypotheses. Some (Bruinsma, 2016; Wikström, 2011) insist on Karl Popper and Imre Lakatos’s ideas of falsifiability and testable hypotheses to sift through the existing explanations. They tell us that every theory has to be testable and has to be tested experimentally to determine its explanatory power and discarded if the data do not support the central hypotheses of the theory. As scientists, this is something that we should agree on, but in fact, most of the theories we use to attempt to explain crime are yet to be tested. In this chapter, I explore how evolutionary criminology can help criminological theorists in this enormous task of sifting through the fragmented field. Later in the book, I present a criminological theory, namely the Retribution and Reciprocity Model (RRM), as an example of how an evolutionary theory can be developed, studied, and empirically tested. RRM is not a general theory of crime and never will be—it requires incorporation into other explanations and development into something bigger than it is now. However, RRM is an example of how we can use evolutionary knowledge and frameworks to create a theory that can be tested and falsified. In this chapter, I do not develop or explain RRM—this particular chapter is devoted to showing what evolutionary criminology is, how it can contribute to our understanding of crime, and why it is an essential tool in our search for a way to sift through the myriad of existing

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theories of crime. I consider it to be an essential framing exercise to explain the theoretical and practical contribution of RRM to the theoretical debate and explanations of aetiology of crime. In this chapter, I argue that evolutionary theorising can help us sift through those theories without expensive and lengthy attempts to falsify all of them. Evolutionary Theory’s ability to unite, explain, and—most crucially—provide mechanisms and testable relationships between variables, may be indispensable for current criminology. Instead of using this book just to offer yet another theory, I explain how evolutionary science can help us untangle the predicament in which theoretical criminology finds itself today. I begin with the history of evolutionary theory and biocriminology, explaining their uneasy beginnings. I then proceed to talk about two important aspects in which evolutionary criminology can change the field as we know it: first, through the ability to sift through existing theories based on testing the underlying assumptions about human nature, and second, through its unique ability to create a unified theory that cuts across all three areas of criminological enquiry: rule making, rule breaking, and rule enforcement.

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History of Evolutionary Criminology

2.1

The Revolutionary Beginnings

Evolutionary science had a rocky start. In On the Origin of Species (1859), Charles Darwin famously argued that contemporary forms of life are the descendants of older forms of life.2 He explained that species had evolved from simpler organisms by natural selection acting upon the variability of populations. Natural selection was based on three main principles: the principle of variation, the principle of heredity, and the principle of selection, which to this day remain important mechanisms through which we understand many aspects within such diverting field such as 2 Darwin was not the only one to have discovered the principle of evolution by natural selection; Wallace came independently to the same conclusion.

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environmental science, conservation biology, human health, agriculture, and natural resource exploitation (Hendry et al., 2011). Darwin’s views were originally not well received, as they opposed the traditional (creationist) view that life on Earth was created by God, and that the Earth was very young. Bishop James Ussher calculated the Earth’s age, based on his reading of the holy scripture—he arrived at the figure 5600 years. Two centuries later, Darwin applied views of the evolution of life to humans. Although there was no conception of genetics at the time, and the fossil record was scarce, not to say absent, Darwin seemed to be right: contemporary humans were the descendants of early life forms. As a concept, evolution, which suggested life to have evolved over aeons, has always contradicted the dominant views of the church and the calculations of Bishop Ussher. Nevertheless, despite the rocky beginnings of going against the grain, evolutionary theory prevailed and shook the scientific community. Darwin offered an explanation for the evolution of life, through natural and sexual selection. Principles of evolution proliferated, and their applications led to a rapid growth of the field of evolutionary science. Later, genetic drift and gene flow were added to the mechanisms of evolution to create the modern synthesis of evolutionary theory and genetics. In time, the theory of natural selection became a go-to explanation of the origins and evolution of life, from which the concept of biosociology emerged. Biosociology studies the role of evolved biological factors (genetic, neural, hormonal, etc.) in different dimensions of social behaviour, as well as being concerned with the biosocial mechanisms of social phenomena and processes at both micro and macro levels.3 Biosocial criminology, more specifically, looks for the biological explanations of crime, and is sometimes referred to as biologically informed criminology. Evolutionary criminology is a part of bio(social)criminology, emphasising the evolutionary mechanisms that explain how humans evolved with mechanisms that lead to criminality. 3 Macro-level theories relate to large-scale issues and large groups of people, they explain the “big picture” of crime, across the world or across a society. Micro-level theories look at very specific relationships between individuals or small groups. They attempt to answer why some individuals are more likely than others to commit crime.

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One can ask oneself what such a macro-level perspective has to offer to a field of criminology, which is concerned with the three-pronged approach of explanations of why people break rules, why we have rules in the first place, and how we react as a society to rule breaking (Sutherland et al., 1992). These seemingly different research topics have surprisingly much in common if viewed through a Darwinian lens. In this chapter, I explain that the framework of modern evolutionary theorising can offer us insights we might not have had before. The study of the biological and cultural variation of the human species reveals much about human behaviour that is absent in traditional criminological inquiry. Evolutionary explanations fall under the umbrella of biosocial criminology, and biological explanations have had their differences with criminological research. Many of the misunderstandings are related to outdated and disproven theories (discussed at length in the section below), but it has to be said that early social scientists were, at least to some extent, influenced by Darwin. For instance, Robert Ezra Park (1921) and his account of the struggle for space in urban areas bear the influence of Darwinian thinking. George Herbert Mead (1934) accepted the biosocial man in his writings which would later become known as symbolist interactionism. These approaches used to be characterised as sociobiology, which later evolved into the younger disciplines of evolutionary psychology, behavioural ecology, and gene-culture coevolution. Biosocial explanations of crime have never had a smooth sailing, despite being accepted by some theorists. Nevertheless, evolutionary theorising is a perspective that is getting more traction in recent years. Biosocial criminologists added a chapter on the role of evolutionary processes in crime causation in their handbooks (Duntley & Shackelford, 2008; Walsh, 2010; Walsh & Beaver, 2009), and wrote a few books about evolutionary criminology (Durrant & Ward, 2015; Roach & Pease, 2013). This renewed interest and wider acceptance, I argue, is the most welcome and timely intervention that criminological theory needs. It is my goal in this chapter to outline some of the ways in which evolutionary criminology, and biosocial criminology, by extension is not just useful for our understanding of criminology, but is, in fact, indispensable.

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A Great Misunderstanding

As Charles Darwin revolutionised social thinking in the nineteenth century, his thoughts were applied to criminology in an infamous case of misunderstanding. Cesare Lombroso (Ferrero, 1911) theorised that people who are less evolved are more crime-prone, and that they can be identified by specific appearance features, or “stigmata”. This theory was central to the unease criminologists felt regarding biological, and especially evolutionary, explanations of crime. This unease is understandable, considering the consequences that can follow from misunderstandings about genetics and evolutionary explanations. However, these simplistic explanations remain what they are: a misunderstanding. We owe many of these misconceptions to the work of Herbert Spencer, who coined the phrase “the survival of the fittest”, and whose social Darwinism created a hostile vision of humans being only in competition with each other. However, Darwin wrote much more about cooperation than about competition. In fact, cooperation was viewed as a riddle in those days, but following Darwin, evolutionary biologists have contributed extensive work explaining the evolution of cooperation. The now-classic concepts of kin selection (Williams, 2018), inclusive fitness (Hamilton, 1963), reciprocal altruism (Trivers, 1971), indirect reciprocity (Alexander, 1987), strong reciprocity (Gintis, 2000), and network selection and multi-level selection (Nowak & Highfield, 2011), contributed significantly to our understanding of the evolution of cooperation and altruism. The sociobiology controversy continued, prompted by Wilson’s (1975) classic work on sociobiology, where he announced that the division between biology and social sciences no longer exists. Wilson was falsely accused of racism and genetic determinism. However, sociobiology continued to thrive (Alcock, 2001) and produced some explanations of crime that are still widely respected in criminology, such as Terrie Moffit’s (1993) studies of the neurophysiology of conduct disorders and the neurodevelopment of adolescents and its relation to the age-crime curve. Some scholars have lamented the lack of interest in biological explanations from within the field of mainstream criminology (Walsh & Ellis,

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2004). However, criminology remains cautious of biological—and especially genetic—explanations, possibly owing to their bad reputation tied to clumsy early theories that were used as justification for eugenics. The field of evolutionary theory moved on to produce more nuanced explanations, but the accusation that contemporary biology is racist remains alive in isolated circles, although if contemporary geneticists stress one thing, it is the fact that genes do not play a deterministic view (Sapolsky, 2017). Even though biosocial criminology has moved on as a field, many textbooks discussing the topic continue to describe biocriminology as an idea that justified eugenics and racism, gave us Nazism, and is ultimately a dangerous idea that should not be propagated (Pinker, 2003; Rafter, 2008). As a result, very few biocriminological papers are published in criminological journals (Wright et al., 2008) and even fewer such theories are taught at postgraduate or undergraduate level (Wright & Cullen, 2012). The pushback against earlier, and contemporary clumsier theories— such as the one suggested by Lombroso—is understandable; but evolutionary science has since gone on to produce more nuanced and detailed explanations, from which criminology could greatly benefit. Owing to the great misunderstandings on which these earlier scholars based their claims, and the accusations of racism and determinism these claims prompted, biosocial explanations of crime remain controversial. Nevertheless, there is nothing to suggest that biological explanations are any more fatalistic than sociological explanations. We have many correlates of crime that are considered mainstream and are accepted as explanations of crime. For example past abuse, perceived strains, poverty, or the neighbourhood in which the person lives. All these things are very hard to change, and yet we will not describe their relation to crime as fatalistic (often communicated as “deterministic”). We accept these findings and seek to find explanations, with the hope we can intervene to prevent people from falling into a life of crime. There is no reason to treat biosocial criminology differently. We have already established that the misnamed “warrior gene” (MAOA-L) simply makes people more susceptible to their environments, not more crimeprone (Sohrabi, 2015) and that there are no other genes directly linked

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to criminal propensity. In fact, if there is anything we have learned from looking at evolutionary theorising, it is that humans are unique in their ability to act against their instincts4 (Gardenfors, 2006). The other thing we have learned is that evolution thrives on individual differences— having differences allows us to fill different functions and as a result survive as a group (Buss, 2009). Variation is a vital principle of evolutionary theorising, and ability for rational reason is one of the most defining features of humanity. As a result, biosocial explanations are the opposite of fatalistic. The utility of biological explanations of crime had already been proven by the dual taxonomy theory. Terrie Moffitt (1993), in her article introducing the life-course persistent and adolescent-limited offenders, put forward a biosocial theory of crime but did not define it as such. As a result, her theory was embraced by mainstream criminology where other theories, calling themselves biocriminological, failed. However, the success of Moffitt’s dual taxonomy model demonstrates the added value that biocriminological approaches can bring to the field. Agnew (2011) recognised the need for biological explanations for the added element that they can give us. In the same way that Moffit’s theory explained the neurological development of adolescents, understanding any other processes would add another aspect to our understanding. Situational Action Theory (SAT) discusses at length the processes of moral emergence and the importance of understanding neurological mechanisms in order to understand the person’s morality and their decision-making process (Treiber, 2017). Biosocial criminology has a lot to add to our understanding of the explanations of crime. However, its contribution goes beyond simply adding another factor or simply a level of analysis.

4

Exercise free will if you must.

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3

Evolutionary Science as a Solution to the Theoretical Crisis in Criminology

3.1

Challenging Assumptions on Human Nature and Social Order

Some of the issues criminologists are faced with when trying to communicate across disciplines include the different objects of research with which they are concerned. While psychologists tend to focus on individuals and their offending patterns, many sociologists study institutions or societies as a whole. It is unsurprising, as a result, that some theories become difficult to integrate with one another since they have different ontologies and attempt to explain different phenomena by appealing to different factors and frameworks. Nevertheless, the question of different levels of explanations is not the biggest issue in attempts to explain criminality. In the search for a unified criminology, Agnew (2011) raises an important question: what are the fundamental assumptions on which criminological theories are built? And, more importantly, why do mainstream criminologists not discuss those assumptions? Every theory of crime causation, implicitly or explicitly, starts from an assumption about human nature and/or the social order. Control theory views people as selfish (Akers, 1991), learning theories view people as blank slates (Akers, 2011), Situational Action Theory views people as rule-guided (Wikström et al., 2012). On a more macro level, Marxist or conflict criminologies foreground unequal material class relations, whereas a Durkhemian would start from an assumption of (at least latent) social interdependence and value consensus. This raises a number of issues. First, it causes a lack of coherent understanding. When a field exists on different levels of analysis, it is unlikely that theorists will be able to easily see how different pieces of information from a universal explanation. Additionally, different starting assumptions are also a likely cause of the fragmentation of criminology. It is very hard to connect the different explanations in one theory when the theories have a completely different starting assumptions. As a result, theorists tend to form “camps”

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and rarely interact with people who research starting with a different theoretical framework. Second, even if there is an in-principle willingness among different theoretical schools to cooperate, the fact is that many of their foundational assumptions are indeed mutually exclusive, and perhaps intrinsically impossible to reconcile. We often refer in general terms to “nature vs nurture” debates, where theories compete over whether crime is something that is environmental/learned or whether there are inherent criminal propensities. The answer, in my view, is “it’s both”, and it is likely that some criminologists would agree with that. Humans are born with some predetermined features, which change depending on their surroundings, and they also learn how to act in the world around them through the process of learning. However, we often get bogged down in the argument of which one is more important rather than see how the assumptions can be reconciled and fit into the same model. This points to the third issue posed by these differing assumptions: the impact on our ability to interpret empirical results to assess a theory’s explanatory power. If there are theories with fundamentally different assumptions about human nature, it becomes hard to understand how to interpret empirical results. The data might fit the proposed framework, but if the assumptions are mutually exclusive it might be a cause for re-evaluating what the data is telling us. Differing assumptions about human nature and social order lie at the heart of the theoretical crisis in criminology, and that is why evolutionary criminology is uniquely positioned to solve it. Wright and Cullen (2012) argued that biosocial criminology is able to bring about a paradigm shift to the field if allowed to thrive. Whilst a true paradigm shift in the Kuhnian sense is unlikely, the discipline-shifting potential of evolutionary criminology is vast. First, any attempt to self-censor as a discipline is doomed to end in a loss of knowledge. Second, it offers explanations at the level of analysis that we are rarely exploring as criminologists. Third, because evolutionary criminology has tools to help that other approaches do not. In the remainder of this chapter, I argue that biosocial criminology can help bring about a shift in the theoretical conundrum that our field found itself in.

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There are many benefits of bio(social)criminology as a whole, but I argue for evolutionary criminology more specifically, as a subset of biosocial theorising. The reason I believe that evolutionary criminology is the key to solving the criminological crisis is because of its focus: on the emergence of humans as a species and how we survived as a group. In short, one key object of study of biosocial criminology is human nature, which is precisely the subject of all the unfounded assumptions of criminological theories we discussed previously. Evolutionary criminology starts with the most important question: what is human nature? I consider this to be evolutionary criminology’s greatest strength, but it is also the cause for great antagonism, as this approach to theorising “confronts many sacred values in the social sciences generally and in criminology specifically” (Wright & Cullen, 2012). That is precisely why the field remains largely avoidant of biological explanations. The important thing to remember is that evolutionary criminology simply creates explanations and brings about mechanisms. Explaining something does not mean condoning it, in the same way, that the fact that some theories have been used to justify poor policy-making does not render those theories unjustified. Marxist ideologies were used to justify totalitarian regimes and genocides, and yet we do not shun Marxist criminologies as a result. Evolutionary criminology deserves the same willingness to engage scientific merit on its own terms. It has long been documented that criminological research falls prey to predominant ideological thinking (Walsh & Ellis, 2004), and criminologists’ self-identified political orientation predicts their view on the causes of crime almost perfectly: for example, political liberals favour explanations of crime that centre on material social inequality, while conservatives focus on culture and family dysfunction (Wright & Cullen, 2012). Needless to say, any science, especially one which concerns itself with such important themes as crime and responses to crime, cannot remain hostage to political orientations. Biological criminology receives little to no support from many criminologists, regardless of whether they are more right- or left-leaning (Wright & Cullen, 2012) despite being able to offer mechanistic explanations of crime that are otherwise so often lacking in mainstream criminology.

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A word often associated with biocriminology is determinism, used in a sense of fatalism, as I explained in the earlier section on the history of the field. Since evolutionary criminology argues that evolution has shaped our behavioural tendencies and mental capacities, as well as “hardwired” humans for some behaviours, some have rejected these views as fatalistic. However, if there is anything we have learned from studying evolutionary theory, it is that humans are unique in their ability to exercise free will. Despite all the hardwiring, instincts, and other biological; forces at hand, humans remain unique in their ability to forge their own destiny while still being adaptive to their environments. We could say that we are softwired, or wired to learn, with some things being easier to learn than other things. Evolutionary criminology might be deterministic in a sense that there are some behaviours that are predetermined by natural selection. Nevertheless, it is far from fatalistic. Biosocial explanations do not condemn any individuals with any characteristics to a life of crime—they simply provide explanations that can help with possible interventions. They offer us answers as to why people commit crime and what can be done to make crime less likely to happen, which does not contradict the understanding that humans possess free will. The other principle that lies at the core of evolutionary criminology is that variation is a rule, as without variation there would be no successful variation that would thrive. The reason that humans have persevered and proliferated as a species, dispersing to all corners of the world where many other species of animals would die, is because they (a) are able to adapt, and (b) possess different skills and abilities, and are therefore able to take on different perspectives and social roles. Evolutionary theory explains patterns and why we evolved to have those; however, it does not take away either the effects of the environment (in fact, it amplifies those!) nor individual differences and free will. Biological variation is an evolutionary product, and there are numerous studies that show a heritability5 to both social and antisocial 5 It is a statistic explaining differences between individuals in groups, and is sometimes also used in a flawed way-like when comparing ‘races’. Some contemporary biosocial criminologists are still using outdated frameworks. That is also a reason why biosocial criminology and evolutionary criminology is criticised.

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behaviour (Arseneault et al., 2003; Mason & Frick, 1994). Even more strongly hereditary is self-control, a trait that we find highly important in criminology (Friedman et al., 2008). Rejecting findings such as these for appearing “deterministic” would prove to be sub-optimal for both the development of criminological theory, and ultimately the people who we try to turn away from crime by creating interventions. As a society, we do not reject eyesight tests because they represent biological fatalism. Instead we do the opposite: we encourage more people to get their eyesight tested in order to benefit from eyeglasses and contact lenses. If biosocial criminology can help us identify any traits that may make people more crime-prone than others, that is something to be applauded as we can use that knowledge to develop better intervention and crime-prevention methods. We should treat biological explanations the same way we would treat explanations of past abuse or poverty: through a lens of using these explanations to create better interventions. In fact, in contrast to many other accepted criminological risk factors, such as poverty, past abuse, or a poor neighbourhood, biological effects are often reversible. The brain is a uniquely plastic organ (MateosAparicio & Rodríguez-Moreno, 2019) that is changing and growing until our mid-twenties, and timely intervention can stall or reverse any potential poor development caused by neglect or abuse. Psychiatry as a field developed an incredible number of medical interventions for conditions that cannot be healed naturally, improving people’s quality of life. Evolutionary criminology can help us explain the innate human instincts, how people survived as a group, and how we can create systems and societies that promote prosocial behaviours. People change and people are different, there are no explanations in biological sciences that can be called deterministic. The aim of much of science is to create interventions that lead to better crime-prevention outcomes. There is no reason why evolutionary criminology would be different.

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A New Level of Analysis

Evolutionary criminology will never replace social or psychological explanations of crime—it asks different questions and offers different levels of analysis. In 1963, Tinbergen proposed four levels of explanation of behaviour (as synthesised in Kapheim, 2019). This framework suggested four questions that should be asked of any animal (and by extension human) behaviour. These levels are the ultimate (evolutionary) explanations, (1) adaptive function and (2) phylogenetic history; and the proximate explanations, (3) underlying physiological processes, and (4) ontogenetic/developmental history. In simple terms: (1) why have we evolved the way we evolved? (2) how did this evolution happen? (3) what is the mechanism of this behaviour? and (4) how does it develop in an individual? Most criminological theories will likely deal with levels 3 and 4, and most of the time those theories will be separate. For example, developmental theories often just deal with level 4, whereas theories dealing with issues such as situational crime prevention would likely be at level 3. Looking at evolutionary science in criminology is essential in order to tap into level 1, and understanding the biological and neurophysiological mechanisms is essential for level 2. To bring the discussion back to RRM: this framework operates on all four levels. It offers an evolutionary explanation of why people display in various degrees both retributive and reciprocal tendencies, explains how these tendencies developed from cooperative behaviours, presents a neurophysiological mechanism (Chapter 3), and a model of development through engagement with social learning. As such, it is by itself a comprehensive model that encompasses various levels of human behaviour and aims to explain them. RRM maps out both the propensity and the action with the concepts of specific and general reciprocity. On the most fundamental level, we know that people will respond negatively to acts of hostility and positively to acts of kindness. As Chapter 4 tests, if the environment is manipulated in a certain way, making it more retributive or negatively reciprocal, more people would say that they would commit a crime in a

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hypothetical scenario. Therefore, looking at specific reciprocity or immediate actions allows us to explain why people would commit an act of crime. This chapter, however, is not about RRM. This merely serves as an explanation of how evolutionary science answers some questions that sociology does not pose. As a result, it can give us an additional level of explanation that makes our theories of crime deeper and more powerful. Evolutionary criminology will never replace sociological approaches, but will complement them. Criminology has not been shy of different levels of explanations. For example, life-course criminology is growing, expanding our knowledge of the different mechanisms of crime causation, starting with Moffit’s biosociological theory as well as Samson and Laub’s life-course theory (1992). In addition, we recognise intergenerational criminology as adding another dimension in which we can explain crime. Evolutionary criminology goes further beyond the intergenerational, explaining how humans evolved as a species. However, we are still not going far enough in incorporating all these different levels. Most theories, in fact, would remain on one level of Timbergen’s analysis, which leads to several problems. First, theories talk past one another. While some theories discuss personal emergence, such as learning theories, some theories describe action, such as those concerned with situational crime prevention or the environment. When theories exist on a different level of analysis, they cannot communicate with one another. Second, explanations that remain on one level become limited. As Timbergen pointed to the need for a detailed understanding of behaviour, biologists have universally adopted this model, which led to a better understanding of animal behaviour. Human behaviour is more complex than that of other animals, and hence is an even greater need to have a nuanced understanding of what we were hardwired to do, what we learned, or what we chose to do. Our behaviour is a constant competition between our instincts and impulses with our deliberate deliberation and rational thought (more on this model in Chapter 3), and hence we have to understand what impulses we evolved with in order to understand how they come to be learned, unlearned, and suppressed. Any

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theory that chooses not to engage with evolution as a level of analysis is doomed to ignore the ultimate explanations, remaining in the field of proximate explanations. As a result, evolutionary criminology offers a whole new level of analysis to add to any criminological theorising. As a minimum, that helps us come up with better explanations. However, it can go beyond that, by giving us the ability to come up with causal and mechanistic explanations that are at the moment lacking in criminological research.

3.3

Mechanistic Explanations and Falsifiability

There is a debate over when exactly criminology started, but, regardless of when it did, we have amassed a vast knowledge of the risk factors, or descriptors of people who commit crime, as demonstrated by the existence of the Handbook of Crime Correlates (Ellis et al., 2019). However, not enough attention is given to integrating those correlates and testing which have causal effects, and which are merely markers of something else (Wikström & Kroneberg, 2022). While there are many steps to be taken to ensure true theory falsification in criminology, integration of evolutionary explanations would be a helpful step forward. In contrast to most criminology, which starts with people who commit crime and then describes them, evolutionary criminology starts with mechanisms. Instead of making assumptions about human nature, evolutionary criminology collects evidence of how humans evolved to be, and why they come to act in a certain way. Therefore, any criminological theory that chooses to start from an evolutionary mechanism is already more likely to (a) start with an informed assumption about human nature, (b) have an array of causal mechanisms about human behaviour that could be tested. Evolutionary theory, as a result, will allow us to sift through the myriad of risk factors, or help to “separate the theoretical wheat from the chaff ” (Cullen et al., 2011, p. 2) by first identifying which assumptions about human behaviour are based on empirical knowledge and eliminating or adjusting those theories of crime that do not align with the data.

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In addition to helping us separate causal mechanisms from simple correlates, evolutionary criminology allows us to improve and add nuance to the already existing theories that do withstand empirical scrutiny. Evolutionary criminology offers a more nuanced definition of the existing theoretical assumptions and proposed mechanisms. Any action a person takes involves a decision-making process that is largely shaped by the way their brain developed as well as by the way evolution shaped it. While it is possible to measure behaviour alone and not get bogged down in the exact mechanism of how that behaviour came about, it leads to a poor theory, both in terms of explanatory power and in terms of falsifiability. In cognitive psychology, there is something called the “computer analogy”,6 which makes a distinction between “hardware” and “software”. As such, human biology is its hardware, with its predispositions and processing powers. The environment in which we found ourselves in and the lessons we learn are the “software”. For instance, humans are usually born with the ability to learn a language (hardware), but not with a language itself. Depending on which country they are born into and what language they are spoken, people then grow to be speaking different languages. Some would speak multiple languages! Some people are not able to speak a language, which could be due to severe brain damage (a hardware issue) or due to the fact that the person was never exposed to any human language (a software issue). It does not matter how powerful your computer is, but if you would not be able to play your favourite computer game without paying for it first. On the other hand, if you would like to play a state-of-the-art videogame but all you have access to is a basic machine from 2011, you will be disappointed. As a result of either of these issues, (either lack of hardwiring or a lack of proper software) the result is the same: you will not be able to play the video game. Of course, it would be a gross oversimplification to compare human development of criminological tendencies to this particular scenario, but it is exactly because criminal behaviour is so complicated that we cannot 6

The computer analogy is a controversial issue in the field since not all scientists agree that this is how the mind processes information, but I consider it a useful analogy for demonstrating my point.

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afford to ignore half the issue. As sociology tends to cover the aspects of “software”, we cannot ignore the question of “hardware”, which only biocriminology can answer. In fact, our neurophysiology is highly dependent on our environments as well. Project Prakash (http://www.ProjectPr akash.org/) shook the world of neuroscience about a decade ago teaching science about brain plasticity. The project started as a humanitarian mission to cure blindness in India, and they have treated many teenagers that were born blind, but whose conditions were nonetheless treatable. What they found is that the patients treated later in life were still unable to see properly. For example, they struggled with seeing colour and, more seriously, they could not separate objects from one another, because that is something that their brains never learned to do. Therefore, even though the patient’s vision was completely healthy, their brain was not trained to deal with this information properly. The fact that the brain atrophies the areas that are not in use is by no means new in neuroscience, but the groundbreaking effect of Project Prakash was to show that it is, in fact, possible to teach the brain to see again, even if the person in question has gained vision later in life. The way we act, the way we talk, and the things we find important or less important, are all reliant on our evolved preferences, which are a result of biology interacting with culture. Perceptions of the world go through our physiology and then we act starting from our brains and by moving our bodies. Understanding physiology is important,—as with the example of Project Prakash, which demonstrated that understanding brain development can help us treat (learned) blindness. Understanding how the brain works, how humans make decisions, and what predispositions we are born with a will, in a similar way, help us understand why people commit crime and how we can better help them desist from it.

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A Unifying Force of Evolutionary Criminology

So far, this book has been focused on discussing the theoretical fragmentation of crime causation theories. However, anyone engaged with criminology knows that criminology is much wider than that. Sutherland’s account of (sociological) criminology, lists three areas of enquiry: rule making, rule breaking, and rule enforcement. Indeed, apart from theories of crime causation, there is a wealth of research into why we criminalise some behaviours and not others, or into the police and prison forces, or into victimology and responses to crime. The issue is, as we have seen, that most criminologists that exist within those strands tend to not talk to one another much. There might exist some notable exceptions. However, theories of policing, or imprisonment, or law guidelines tend to not overlap with one another, and criminologists exist in a self-contained bubble of researchers who study things that are relevant to them. This leads to limitations within each subfield, as well as thinning of the pool of potential explanations of crime. Evolutionary theory has a unique ability to unify us along the lines of potential explanations of all three: crime causation, law-making, and law enforcement. As such, evolutionary criminology offers “A Theory of Everything”, and that is due to the fact that evolutionary criminology asks the fundamental questions about human nature and human societies that would be useful in all areas of inquiry. In this book, the Retribution and Reciprocity Model (the RRM) is presented as a theory of crime causation. The reason for this is simply because that is the only environment against which it has been tested. In the most simplified form, RRM suggests that we on average tend to (a) respond to kindness with kindness, (b) with hostility to hostility, and (c) have a general knack for punishing the violation of a social norm. These evolved preferences or motives are generally not studied by criminological theories. This is relevant for explaining crime causation and, as the rest of the book shows, however, it is also relevant for all the other strands of criminology.

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On the law enforcement side, it is clear that any discussion of cooperation with the police or with courts is reliant on the ideas of reciprocity and trust. In fact, these ideas already exist within legitimacy theory (Bottoms & Tankebe, 2012) and could be further expanded on with mechanisms that evolutionary criminology provides. There also exists a discussion within prison research about the staff–resident relations that affect how the people in prison perceive their incarceration. On the law-making side of criminology, the ideas of Retribution and Reciprocity become even more evident. Retribution lies at the heart of the court system of Western liberal democracies and plays a central role in penological debates. RRM would be able to offer insights about why retribution is important to us, how to best achieve it, and why we consider some acts to be more serious than others. RRM is only one example of a potential “theory of everything”. While this book does not go into much detail over what could be achieved by RRM to unify the three prongs of criminology. It further exemplifies the possibilities for the theoretical and practical advances evolutionary criminology can bring to the study of the etiology of crime. Evolutionary criminology has the potential to create unified frameworks on which other theories can be built. It can bring into conversation criminologists from all across the discipline to help us create more nuanced, inclusive, and integrated theories of criminal behaviour. For example, labelling theory can have a mechanistic explanation in the evolutionary concepts of sexual and social selection (Tomasello, 2016) of how we make decisions based on choosing a reliable partner to cooperate with. Police brutality could have an explanation in the study of evolved hierarchy (Boehm & Boehm, 2009) and how we use various forms of status symbols to exercise our hierarchical structures. Strain theories could benefit from an explanation of endocrine systems and stress and how they influence our decision-making. Situational crime prevention can be improved by further thinking about the environments that humans have evolved to adapt to and improving our cities to promote the prosocial and cooperative behaviours that we evolved for. Racism has its evolutionary roots in the ingroup-outgroup syndrome (Cliquet, 2010). Gender-based violence has great explanations

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in Darwinian feminism (Vandermassen, 2005) which can be further studied. Evolutionary science has long developed mechanisms and explanations for many aspects that we try to study, be it in law-making, law-breaking, or law enforcement, and through looking at these explanations we can begin to develop overarching and powerful theories that can significantly advance the theory of criminology.

5

Conclusion

In science, we do not invent causes and mechanisms; we discover them. As criminologists, we have picked one of the most diverse, complicated, ideologically loaded, and contested areas for research. Therefore, the theories that attempt to explain crime, its causes, and its consequences, should be equally robust, integrated, and nuanced. Integrating yet another field does not sound like a way to make our explanations simpler; however, it might just be the necessary step. Evolutionary criminology explains many aspects that biologically uninformed criminology does not. As criminology is a multidisciplinary field, we cannot afford to ignore these observations. Instead of arguing over which general approach is better, we should acknowledge all theories and explanations presented and judge them on their individual merit of being able to explain criminal behaviour and beyond. Nevertheless, as I explained in this chapter, the contribution of evolutionary criminology could go way beyond simply adding a different factor to consider or a level of analysis. Through its focus on human nature and mechanistic explanations, evolutionary criminology can help us sift through the existing theories, unify when needed, and streamline explanations of the theories that do withstand empirical scrutiny. That is the goal of consilience. In addition, it can help facilitate further discussion of how different theories add a different level of explanations and how different explanations can be united to create more nuanced, overarching explanations. Criminology is a multidisciplinary science, and it should be treated as such. As criminologists, we cannot afford to shy away from particular

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fields for fear of it being mistreated, as all fields are necessary to add another level of explanation and help us sift through the myriad of explanations of crime. Evolutionary criminology will never replace sociology or psychology, but it offers unique tools to both sift through existing theories as well as unite and expand them to create a better, more well-rounded, and analytically facing criminology. There is a lot of scope for developing evolutionary frameworks. In this book, I present one of them. However, for these models to be accepted, we as a field need to be open-minded enough to accept them for the potential for a shift in the discipline that is long overdue. In the next chapter, having presented the theoretical discussion within which the framework sits, I introduce the Retribution and Reciprocity Model (RRM).

References Agnew, R. (2011). Toward a unified criminology. New York University Press. Akers, R. L. (1991). Self-control as a general theory of crime. Journal of Quantitative Criminology, 7 (2), 201–211. Akers, R. L. (2011). Social learning and social structure: A general theory of crime and deviance. Transaction Publishers. Alexander, R. D. (1987). The biology of moral systems. Aldine De Gruyter. Alcock, J. (2001). The triumph of sociobiology. Oxford University Press. Arseneault, L., Moffitt, T. E., Caspi, A., Taylor, A., Rijsdijk, F. V., Jaffee, S. R., … & Measelle, J. R. (2003). Strong genetic effects on cross-situational antisocial behaviour among 5-year-old children according to mothers, teachers, examiner-observers, and twins’ self-reports. Journal of Child Psychology and Psychiatry, 44 (6), 832–848. Boehm, C., & Boehm, C. (2009). Hierarchy in the forest: The evolution of egalitarian behavior. Harvard University Press. Bottoms, A., & Tankebe, J. (2012). Beyond procedural justice: A dialogic approach to legitimacy in criminal justice. Journal of Criminal Law & Criminology, 102, 119.

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Bruinsma, G. (2016). Proliferation of crime causation theories in an era of fragmentation: Reflections on the current state of criminological theory. European Journal of Criminology, 13(6), 659–676. Buss, D. M. (2009). How can evolutionary psychology successfully explain personality and individual differences? Perspectives on Psychological Science, 4 (4), 359–366. Cliquet, R. L. (2010). Biosocial interactions in modernisation. Masaryk University Press. Cullen, F. T., Wright, J., & Blevins, K. (Eds.). (2011). Taking stock: The status of criminological theory (Vol. 1). Transaction Publishers. Duntley, J. D., & Shackelford, T. K. (2008). Darwinian foundations of crime and law. Aggression and Violent Behavior, 13(5), 373–382. Durrant, R., & Ward, T. (2015). Evolutionary criminology: Towards a comprehensive explanation of crime. Academic Press. Ellis, L., Farrington, D. P., & Hoskin, A. W. (2019). Handbook of crime correlates. Academic Press. Ferrero, G. L. (1911). Criminal man, according to the classification of Cesare Lombroso. Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008). Individual differences in executive functions are almost entirely genetic in origin. Journal of Experimental Psychology: General, 137 (2), 201. Gardenfors, P. (2006). How homo became sapiens: On the evolution of thinking. Oxford University Press. Gintis, H. (2000). Strong reciprocity and human sociality. Journal of Theoretical Biology, 206 (2), 169–179. Hamilton, W. D. (1963). The evolution of altruistic behavior. The American Naturalist, 97 (896), 354–356. Hendry, A. P., Kinnison, M. T., Heino, M., Day, T., Smith, T. B., Fitt, G., … & Carroll, S. P. (2011). Evolutionary principles and their practical application. Evolutionary Applications, 4 (2), 159–183. Kapheim, K. M. (2019). Synthesis of Tinbergen’s four questions and the future of sociogenomics. Behavioral Ecology and Sociobiology, 73(1), 186. Mason, D. A., & Frick, P. J. (1994). The heritability of antisocial behavior: A meta-analysis of twin and adoption studies. Journal of Psychopathology and Behavioral Assessment, 16 (4), 301–323. Mateos-Aparicio, P., & Rodríguez-Moreno, A. (2019). The impact of studying brain plasticity. Frontiers in Cellular Neuroscience, 13, 66.

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Mead, G. H. (1934).Mind, self, and society (Vol. 111). University of Chicago press. Moffitt, T. E. (1993). The neuropsychology of conduct disorder. Development and Psychopathology, 5 (1–2), 135–151. Nowak, M. A., & Highfield, R. (2011). Supercooperators. Canongate. Park, R. E. (1921). Sociology and the social sciences: The social organism and the collective mind. American Journal of Sociology, 27 (1), 1–21. Pinker, S. (2003). The blank slate: The modern denial of human nature. Penguin. Rafter, N. (2008). The criminal brain: Understanding biological theories of crime. NYU Press. Roach, J., & Pease, K. (2013). Evolution and crime. Routledge. Sampson, R. J., & Laub, J. H. (1992). Crime and deviance in the life course. Annual Review of Sociology, 18(1), 63–84. Sapolsky, R. M. (2017). Behave: The biology of humans at our best and worst. Penguin. Sohrabi, S. (2015). The criminal gene: The link between MAOA and aggression. In BMC proceedings (Vol. 9, No. 1, pp. 1–1). BioMed Central. Sutherland, E. H., Cressey, D. R., & Luckenbill, D. F. (1992). Principles of criminology. Altamira Press. Thornberry, T. P. (2012). Criminological theory. The future of criminology, 46– 54. Tomasello, M. (2016). A natural history of human morality. Harvard University Press. Treiber, K. (2017). Biosocial criminology and models of criminal decision making. In W. Bernasco, J.-L. Van Gelder, & H. Elffers (Eds.), The Oxford handbook of offender decision making (pp. 87–120). Oxford University Press. Trivers, R. L. (1971). The evolution of reciprocal altruism. The Quarterly Review of Biology, 46 (1), 35–57. Vandermassen, G. (2005). Who’s afraid of Charles Darwin?: debating feminism and evolutionary theory. Rowman & Littlefield Publishers. Walsh, A. (2010). Social class and crime: A biosocial approach (Vol. 9). Routledge. Walsh, A., & Beaver, K. M. (2009). Biosocial criminology. Springer. Walsh, A., & Ellis, L. (2004). Ideology: Criminology’s Achilles’ heel? Quarterly Journal of Ideology, 27 (1/2), 1–25. Wikström, P. O. H. (2011). Does everything matter?: addressing the problem of causation and explanation in the study of crime. In When crime appears (pp. 53–72). Routledge.

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Wikström, P. O. H., & Kroneberg, C. (2022). Analytic criminology: Mechanisms and methods in the explanation of crime and its causes. Annual Review of Criminology, 5, 179–203. Wikström, P. O. H., Oberwittler, D., Treiber, K., & Hardie, B. (2012). Breaking rules: The social and situational dynamics of young people’s urban crime. OUP Oxford. Williams, G. C. (2018). Adaptation and natural selection: A critique of some current evolutionary thought (Vol. 61). Princeton university press. Wilson, E. O. (1975). Some central problems of sociobiology. Social Science Information, 14 (6), 5–18. Wilson, E. O. (1998). Consilience among the great branches of learning. Daedalus, 127 (1), 131–149. Wright, J. P., Beaver, K. M., DeLisi, M., Vaughn, M. G., Boisvert, D., & Vaske, J. (2008). Lombroso’s legacy: The miseducation of criminologists. Journal of Criminal Justice Education, 19 (3), 325–338. Wright, J. P., & Cullen, F. T. (2012). The future of biosocial criminology: Beyond scholars’ professional ideology. Journal of Contemporary Criminal Justice, 28(3), 237–253.

2 Introducing the Retribution and Reciprocity Model: An Evolutionary Theory of Crime

1

Introduction

“We live in a society” is a sentence often overused to the point of becoming a running joke. Nevertheless, that does not make it less true. Where there is a group of people there is always politics and there is always crime. Societies create rules, some of which are codified in law, and there are always people who break those rules, and some of those rules we call “crime”. Therefore, crime cannot be understood in isolation from the societal forces that surround every aspect of our life. Crime is always a response to something, and that “something” is not always direct provocation. Any activity is embedded into a setting, be it the physical environment or the other people who decide on what the rules of that setting are. As a result, if we want to understand crime, we need to understand many more aspects of society than simply the exact moment at which the crime happened. Criminology is a multidisciplinary science, and as criminologists we take pride in that despite the complexities it might cause in the field. Crime is a complex behaviour, embedded in many processes, from the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. Svingen, Evolutionary Criminology and Cooperation, Palgrave’s Frontiers in Criminology Theory, https://doi.org/10.1007/978-3-031-36275-0_2

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genetic predisposition or B12 deficiencies (Dommisse, 1991) to the larger societal processes and culture (Karstedt, 2001). Therefore, if we are to explain crime, we should cast our net wide for possible influences. In addition, we should aim to look into the mechanisms that drive human behaviour as a whole, not just in the aspect of criminal activity. That is precisely what I aim to do in this book, by bringing together the knowledge from neuroscience, evolutionary biology, and behavioural economics to shed light on one of the most unlikely explanatory mechanisms of crime: cooperation. Humans are unique cooperators: we exchange resources, help one another at a time of need, and also choose to enforce the rules. Most of these behaviours are so deeply embedded into us that we do not even think about them. Two of such behaviours are retribution and reciprocity. An eye for an eye is a belief shared among most cultures around the world and as such formed the basis of our criminal justice policy in the form of the Hammurabi code (Hogan & Henley, 1970). Reciprocal and retributive behaviours guide most human interactions and have a lot of explanatory power in evolutionary science. In this book, I use retribution and reciprocity to help explain crime. I posit that since these behaviours form the basis for cooperation and human interaction in general, they are also useful in explaining rule breaking behaviours. There are some theories of criminology that touch upon these aspects; however, in this chapter, I aim to introduce retribution and reciprocity as a coherent mechanism of crime that could be incorporated into further theories to help explain why some people may choose to commit crime. A mechanistic understanding of criminology has been argued to be essential for the central activity of any science: theory construction and falsification (Proctor & Niemeyer, 2020). In criminology, we have amassed a significant amount of knowledge about what correlates with crime, but much less about what causes crime. A mechanism is a process that connects the cause and effect and brings about said effect (Wikström, 2007). That means that a link between a dependent and an independent variable should not simply be an association, but the independent variable should be able to lead to the (crime) outcome.

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In this book, I describe the process by which crime happens via a mechanism and test the various aspects of the proposed model to gather empirical evidence in support of its assumptions. Not only do I seek to study the association of retribution and reciprocity to criminal behaviour, but I seek to describe a mechanism of how this association happens on the action level (forthcoming in this chapter) and the neuroscientific mechanism of retributive and reciprocal tendencies (Chapter 3). In this book, I gain inspiration from sciences not often associated with criminology to develop a new tool that can help us explain crime. I develop, present, test, and examine my assumptions to present a theory of retribution and reciprocity, called the Retribution and Reciprocity Model (RRM). In the previous chapter, I explained the importance of examining evolutionary criminology and the benefit these frameworks can bring. In this chapter, I introduce a framework. I explain the reasoning behind looking at retribution and reciprocity as well as present RRM and how it works. In Chapter 3, I look at the neurobiological mechanisms of RRM to both support its validity in explaining cooperative behaviours and discuss a possible mechanism of these behaviours. In Chapter 4, I use hypothetical scenarios to connect RRM to criminal behaviour and to test the main assumptions. In Chapter 5 I present the tool I developed by adapting some methods existing in behavioural economics and game theory that helps us examine not only RRM itself but also to measure the individual differences and go in-depth into our understanding of how this tool can help us understand criminal behaviours. As a whole, this book forms a coherent overview of the justification, methodology, and evidence for using retribution and reciprocity in order to enhance our understanding of crime. This chapter presents the background information on retribution and reciprocity by explaining how it became such a fundamental part of society, starting with a description of the evolutionary processes that have led to cooperative tendencies. The subsequent section outlines the concepts of retribution and reciprocity, defines them, and explains their impact on cooperation. The fifth section brings that knowledge together to present RRM and its action framework as a testable theory. The

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concluding section of this chapter summarises the reasons for and benefits of studying RRM and further introduces the structure of how I studied it for this book.

2

Why Study Retribution and Reciprocity

The ability to cooperate, communicate, and coordinate allowed humans to persist as a species despite being neither the strongest nor the fastest and having many other vulnerabilities. Through many years of evolution, our decision-making has adapted to a necessity to stay with a group (van Vugt & Hart, 2004) by making us able to recognise the social norms of that group and abide by them (Fehr & Fischbacher, 2004). Even though human survival no longer directly depends on many of these behaviours, many of them are ingrained in us from birth. For example, we are capable of expressing empathy (Carr et al., 2003; Decety & Jackson, 2004), recognising facial expressions (Izard, 1994), following another person’s gaze (Driver et al., 1999; Farroni et al., 2002), and following the majority as a means of social conformity (Berns et al., 2005). Cooperation is deeply etched in the human brain; every community creates social rules and punishes the violators, directly or indirectly. As society evolved in its complexity, these rules were codified, and punishment systems emerged to form the basis of criminal justice systems as we know them today (Parekh, 2003). These criminal justice systems share remarkable similarities despite cultural differences between societies. In most places, offences like homicide and theft are generally unacceptable. But even more specifically, it is generally not acceptable for individuals to act in a wholly selfish manner, and members of society are expected to act in a way that contributes to the functioning of the whole group (Berthoz et al., 2002; Bowles & Gintis, 1998; Fehr et al., 2002). In other words, the expectation is to cooperate. For millions of years, society imposed harsh punishments for not following these expected norms of cooperation, such as ostracising some of their members from the group thus rendering them unable to survive. As a result, natural selection in humans tended not to benefit the strongest or the fastest necessarily, but instead, those who were most able to work for the benefit of the

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entire group and hence be accepted by it (Axelrod & Hamilton, 1981). In practice, this instilled survival behaviours in humans that are, as a result, largely hardwired into our brains, such as learning by imitation and feeling empathy (De Waal, 2008). These innate tendencies tell us to run when other individuals run even if they cannot see the predator, aid in learning which berries should not be eaten by simply trusting the other group members and imitating one another in learning how to use tools or behave towards our elders. Features such as these facilitated the success of humans as a species, formed the basis of our society, and affected every aspect of our lives. They are etched into the genes and brains of every individual and play a role regardless of their environment and the situations they find themselves in (Curry et al., 2019). These evolutionary predispositions must be considered when explaining human behaviour, even when people show non-cooperative acts, such as committing a crime. As such, crime is not that different from other things that we do—most people commit a crime at some point in their lives (Farrington et al., 2014). Crime most certainly cannot be understood separately from all the other social processes that occur every minute of every day. Many social processes make crime more or less likely to occur, and there are a myriad of factors that influence crime propensity, including environmental features and personality traits. In this book, I add another chapter of research that advances our knowledge of criminology. Of those factors, I focus on reciprocity and retribution. Reciprocity forms the basis of cooperation: without the certainty that something positive will follow their good deeds, people would be unlikely to share and help one another. The expectation of reciprocity from others in the group means that all group members must cooperate to benefit from the cooperation of others. However, as in many social contracts, there is always a possibility to free-ride.1 The tendency to punish the violator of a social norm is a natural consequence of the need to cooperate, a tendency I call “retribution”. Without the expectation of punishment from potential social norm violators, many more people would choose to free-ride. Even when 1

The free-rider problem refers to somebody benefiting from public goods or resources of communal nature without paying for them.

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punishing the violator of a social norm is costly, many people choose to do so for the potential benefit of cooperation that it would bring (Fehr & Gächter, 2002). As such, I argue that crime is embedded in reciprocity and retribution. I posit that sometimes crime itself may be an attempt by a person to punish a perceived injustice imposed on them by society or a specific person and serve as a retributive act. An evident example is the activity of vigilante groups that take justice into their own hands by punishing offenders without legal authority. In other cases, someone might be deterred from committing a crime by their positively reciprocal feelings, such as a reluctance to steal from a person who has been nice to them. Conversely, someone might be encouraged to commit a crime in response to a hostile act towards them, eliciting negatively reciprocal feelings, such as punching someone who has previously hit them. These tendencies (retribution and reciprocity) can be both motivating and constraining factors and as such, they cannot be overlooked if we wish to understand crime. It is generally accepted that most people do not act in a self-interested manner but are guided by the social norms and the considerations of what is just and fair according to their moral compass (Henrich et al., 2010; Spitzer et al., 2007). The concept of fairness is relatively hard to study because of the ambiguity of what is meant by “just” or “fair”.2 Within the field of political philosophy, one might find hundreds of different ways to distribute welfare, all of which would be considered “fair”. However, fairness appears to be essential for understanding not only crime but also other human motivations. For example, research shows that workplace theft increases following a cut in wages but reduces again when the reason for cuts is thoroughly explained, and no inequality of wages is perceived (Bewley, 1998; Blinder & Choi, 1989; Greenberg, 1990). Some researchers argue that the sense of fairness makes people support social norms (Fon & Parisi, 2005), and different neurological 2 If one opens a textbook for political philosophy, one will find a myriad of theories of fairness and a whole field of retributive justice. Albeit interesting to get into, the debate over what fairness means would not enhance the argument I am making. For the purposes of this book no particular theory of fairness or justness is relevant. What is important is the individual’s idea of what fair is and the fact that they care about it.

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pathways activate when the actions of others are perceived to be fair or unfair (Singer et al., 2006). Criminologists have not overlooked society and perceptions of fairness. Social Control Theory explains crime through weak links to the community, described as weak social bonds (Hirschi, 2002). Strain Theory suggests that people who experience strains that cause negative emotions might cope by using criminal strategies (Agnew, 2005). Defiance Theory suggests that people re-offend when a punishment they received is perceived as unfair or disrespectful (Sherman, 1993). Growing up in a deprived neighbourhood is found to be a risk factor for offending, suggesting a link between feeling worse off and adopting criminal strategies (Welsh & Farrington, 2007). Together, these theories suggest that society, and perceptions of it by the offenders, matter when trying to explain criminality and that criminal behaviour may be reciprocal and retributive. Crime by definition violates a social norm; however, not all social norm violations are crimes in the legal sense. For this research, however, any social norm violation was considered to be caused by the same mechanism, whether it is forbidden by law or not (Wikström et al., 2006). Group behaviour, as mentioned above, typically punishes norm violators in a retributive manner by ostracising them. It proves to be a powerful punishment since people are afraid of losing social ties necessary for survival (Hirshleifer & Rasmusen, 1989; Knez & Simester, 2001). Humans have a remarkable ability to detect cheating on a social contract, despite not having a general-purpose ability to see rules violations (Cosmides & Tooby, 1992), which indicates the importance of social norms compliance. In summary, humans are rule-guided (Ostrom, 2014), and reciprocity and retribution are essential rules by which society functions (Elster, 2000). Nevertheless, even theories discussing the role of society and the environment in the criminal decision-making processes rarely mention retribution or reciprocity. I argue in this book that many of the observations found by criminology that indicate criminality through the environment can be explained through the lens of reciprocity and retribution. RRM would argue that weak bonds do not cause crime but

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influence reciprocal feelings. People with weak social ties or who experience strains might feel the need to lash out against society because they believe that they have gotten nothing out of it. In contrast, people with strong social ties might encounter opportunities to commit a crime but would not act on those opportunities because of the tendency to reciprocate positively towards the society that has been fair and supportive of them. Without strong social ties, there is no reciprocal motive to stop a person from committing a crime. Strain itself might reduce positive reciprocal tendencies and heighten retributive tendencies. Therefore, RRM could add to the understanding and the explanatory power of many of the already existing theories of crime. RRM could also help explain individual differences when some people commit crimes and others do not, despite experiencing identical strains or having similar social bonds. This discrepancy might be attributed to the fact that some people, who grew up in a good environment and felt society’s support, might reciprocate by not resorting to criminal coping strategies. On the other hand, others who think humanity has mistreated them might feel the need to seek retribution and hence may adopt a criminal coping strategy. RRM would suggest that retribution can help explain why some people choose to re-offend and some choose not to. Defiance theory presents evidence that unemployed people are more likely to re-offend if they are incarcerated, whereas employed people become less likely to re-offend after incarceration (Sherman, 1993). RRM offers a new detail by stating that unemployed people would feel negative reciprocity and retribution. People who are employed tend to have higher degrees of trust in the society around them, feel more protected and supported (Westholm & Niemi, 1986), and hence feel the need to reciprocate positively. This book does not endorse any particular theory of crime. Instead, it introduces RRM as a valuable tool for other theories to use and integrate. However, it does rely on some assumptions that not all theories share. For one, it sees crime as an interaction of individual propensities and the environment. Some environments are more criminogenic than others, just as some people are more prone to committing crimes. When

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a person with a higher propensity ends up in a more criminogenic environment, the likelihood of crime dramatically increases (Wikström et al., 2006). Explaining crime through the interaction of perceptions of the environment and the combination of reciprocal and retributive tendencies seems to make intuitive sense, is backed by evidence from many fields (discussed below) and carries great explanatory power to fill some gaps in current knowledge. Crime, retribution, and reciprocity are all a response to the environment by definition. The actual environment is quite important, but it is not the exact focus of this book. RRM’s main focus lies in the people’s crime propensity that depends on their tendencies towards retribution and reciprocity and how they have learned to perceive the world around them.

3

Evolution of Cooperation and Punishment

Though scientists from many fields study “cooperation”, the term is used to describe a range of different behaviours. For the purposes of RRM, I define cooperation as a situation in which an individual incurs a cost to provide a benefit to another person or group. That definition commonly appears in game theory3 experiments and allows us to study cooperation more broadly to include everyday occurrences and behaviours in experimental settings. Therefore, cooperation comes in two forms: helping a group at a price and the costly punishment of defectors (noncooperators). A cost can be paying money, enduring discomfort, or even simply investing time in a task. It is a broad definition that encompasses many tendencies involving various everyday occurrences in any human society. Cooperation is a fundamental part of human society. Examples include paying tuition fees for one’s children, volunteering for social projects, deciding to put effort into recycling, or simply stopping to give lost strangers directions. It occurs between family members, friends, 3 Game Theory is the study of mathematical models of strategic interaction among decisionmakers of various levels of rationality.

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strangers, in pairs, small groups, or big groups. Since most human interactions incur a cost to help another person, cooperation becomes an important concept when trying to explain human societies. Creatures of every level of organisation cooperate to survive. From the earliest bacteria feeding their neighbours nitrogen (Brockhurst et al., 2010; Czárán & Hoekstra, 2009) and meerkats risking their lives to protect a communal nest (Clutton-Brock et al., 2001) to humans helping one another after natural disasters (De Alessi, 1975). However, the range and extent to which humans cooperate is rarely observed in the animal world (Johnson & Earle, 2000), apart from some highly cooperative groups like ants and bees. Humans’ astonishing ability to collaborate has made them into “supreme cooperators” (Nowak & Highfield, 2011, p. xiv) and allowed them to flourish. However, a lot of our cooperative behaviours are so small and mundane, we do not even realise that they are part of the same mechanism. For example, we cooperate by keeping our voices down in libraries, standing in a queue in an orderly fashion, or putting our garbage into bins instead of throwing it on the streets. These are behaviours that we unfailingly do every day and rely on extensively, but even our closest relatives the chimpanzees would not be able to comply with any of those behaviours. This ability to cooperate differentiates our society on a multitude of levels (Stevens & Hauser, 2004). As with many factors that affect the large majority of the population, we must first understand its origins to understand the origins of cooperation. Is there a neurological mechanism hardwired into the human brain, is it passed on from generation to generation in the process of teaching and learning, or is cooperation simply an outcome of rational deliberation? As with most features of human behaviour, it is likely to be a combination of all of these factors. The most likely explanation is that the ability for learning by imitation as well as for cooperation posited an evolutionary advantage for the members of the group, which predisposed humans for certain behaviours, which we then learned from our cultural surroundings. Four main theories currently offer an explanation as to why cooperation was naturally selected for: kin selection (Hamilton, 1964); reciprocal

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altruism (Trivers, 1971); indirect reciprocity (Alexander, 1986; Nowak & Sigmund, 1998); and costly signalling (Gintis et al., 2001; Zahavi, 1975). Kin selection explains cooperation as helping individuals with similar genes and hence putting your genes forward to pass them on to the next generation. Reciprocal altruism describes cooperation as a way of ensuring that somebody will repay kindness with kindness, i.e., an investment for the future. Finally, indirect reciprocity and costly signalling assume that a person cooperating with others is building themselves a reputation to ensure that other group members cooperate with them in the future. All four of these theories work on the assumption that cooperation within the group was selected for because it benefitted everyone in the group and allowed group members to put their genes forward. Numerous Game Theory experiments (Bowles & Gintis, 2002; Boyd et al., 2003; Fehr & Fischbacher, 2003, 2004; Fehr & Gächter, 2002; Fehr & Henrich, 2003; Fehr & Rockenbach, 2004; Fehr et al., 2002; Gintis, 2000; Gintis et al., 2003) have tested cooperation in various conditions, and in all settings, participants show considerable degrees of cooperation. As there is much evidence that cooperation yielded advantageous results, evolution likely favoured people with strong innate tendencies to display such behaviour.

3.1

Evolutionary Advantage of Cooperation

There are many moral behaviours and patterns of behaviour hardwired into humans from birth (Hauser et al., 2007). It is believed that humans are predisposed to many of these tendencies because they offer a significant evolutionary advantage. Many of these behaviours are perceived as widespread solutions to prevalent problems of survival (Evans & Levinson, 2009). For example, most societies consider truthtelling a virtue, which is likely to be a consequence of humans relying on accurate information to survive, and hence needing to trust one another (Churchland, 2006). Similarly, humans are hardwired to trust one another by default, which was necessary to avoid predators (Churchland, 2011). Although not all of the features we have are the most

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efficient mechanism for putting our genes forward (Galis et al., 2001); other features, primarily behavioural, are likely to have stayed with us because they provided an evolutionary advantage. In this book, I posit that cooperation is one of those behaviours. Nevertheless, cooperation has always been a puzzle. How, in the world of eat-or-be-eaten, did cooperation become evolutionarily advantageous? When a bacterium shares nutrients with its neighbours, it has less for itself. Similarly, when a person contributes money to a charity, they cannot spend it on themselves. When a starving wolf gives the last of her food to her cubs, she risks dying from starvation. Works on evolution are saturated with terms like “struggle”, “competition”, and “survival of the fittest”: how does cooperation come into this? Even when studying contemporary societies, we usually hear about the Prisoner’s Dilemma4 and the cost of defecting. In Game Theory, we talk about Pareto Optimal point, which is the most beneficial for most groups and requires cooperation, versus the Nash Equilibrium, a less optimal strategy for which people opt out when they decide to defect rather than cooperate.5 Pareto Optimal often means trusting other group members to cooperate, which people often do not and hence they go with the more costly Nash Equilibrium.6 The standard economic theory views humans as utility maximisers, choosing to behave in a way that helps them achieve their goals most quickly and cheaply. “Altruism”, or unconditional kindness, does not seem to have a place in that logic.

4 Prisoner’s Dilemma is a famous Game Theory experiment in which two members of a criminal organisation that cannot communicate with one another are arrested and imprisoned. The people are then given two options: either to betray the other person and walk free while the other person serves a long sentence; or to stay silent and take a lesser charge. If both of them stay silent, they will both serve a lesser charge, but if both betray one another both serve a longer sentence. Most people in these circumstances would choose to betray even though it offers a worse outcome than both staying silent because they don’t believe the other person. 5 In the Prisoner’s Dilemma, both staying silent would be a Pareto Optimal, while betraying one another would be a Nash Equilibrium. 6 I’ll explain this as an example of two housemates cleaning the house. Both would benefit from a clean home and that requires cleaning. The pareto optimal would be for both housemates to do the cleaning and then to both benefit from the cleanliness of the house. However, it is possible for one person to benefit from the second housemate doing all the work and them simply enjoying the outcome. Therefore, the Nash equilibrium often ends up being that none of the housemates do any cleaning and hence lose out on the clean home.

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One of the most prominent forces in evolutionary theorising is sexual selection that argues that it is the male competition for potential mates that is a driving selection force (Dale et al., 2007). Therefore, since acquiring female partners is a zero-sum game, species where males cooperate to reproduce have long puzzled evolutionary biologists. Nevertheless, newer research shows us that competition and cooperation are closely intertwined (Díaz-Muñoz et al., 2014). Darwin (1871) himself outlined that sexual selection is nuanced and not as linear as males competing with one another, and, as Díaz-Muñoz et al. outline, there are numerous examples of species that participate in sexual cooperation. The reason why they do it is because cooperation often helps competition with the other males. There is an argument to be made that it is precisely these self-centred motives that allowed social cooperation to arise. Axelrod (1984) tested the evolutionary principles by organising a complex tournament in which several computer programs played against one another in repeated Prisoner’s Dilemma environments in which they had to share resources with one another and accumulate points. The most successful programs were rewarded with offspring (more versions of themselves), and those who did badly were killed off. Researchers worldwide submitted their programs that played in the tournament against one another tirelessly, and the one that got the most points won. Contrary to expectations, the winner of the computerised competition was a simple four-line program that used a tit-for-tat strategy. Put simply, it cooperated when it was cooperated with, and it defected in response to defection. Even more simply, it always repeated the co-player’s previous move. In his further analysis of what ensured success in the tournament, the author concluded that the programs more likely to win were the ones that were “nice”, i.e., the ones that never defected first. This simple experiment shows that a tit-for-tat strategy that allows for cooperation is the one that is likely to reap the most benefit. As such, it would be easy to conclude that it is expected that those that apply tit-for-tat strategies in real life would have an evolutionary advantage. However, the experiment was held in an artificial environment of programs playing against one another, lacking many real-world variables

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that make us human. Humans are not utility maximisers with excellent skills for reading the environment: they make mistakes, suffer mood swings, and misunderstand situations. In summary, humans produce a lot of noise. Even infrequent errors can have devastating consequences on the outcome of the interaction between programs by triggering an endless cycle of retaliation. As a result of these shortcomings, new types of computer models emerged, taking human error into account (Nowak & Sigmund, 2004). The new types of tournaments incorporated noise and forgiveness. In the past, thousands of generations of computer simulations have shown the triumph of tit-for-tat strategies, but in these noise-sensitive tournaments, the results were quite different. Every simulation started with a state of chaos, where programs were allocated strategies at random and, out of that, the Always Defect strategy always emerged with an early lead. For around a hundred generations, Always Defect dominated the tournament. At this point, a minority of Tit-for-Tat programs that previously barely clung to existence emerged and reversed direction. As soon as the Always Defect programs were left with nobody to exploit, they quickly died off against the reciprocal cooperators, Tit-for-Tats. In the end, the whole pool of programs consisted entirely of reciprocal cooperators. However, as the simulation progressed further, Tit-for-Tats lost to social programs that were more forgiving than the initial Tit-for-Tats. That program was the Generous Tit-for-Tat, which was programmed to never defect in response to cooperation, but sometimes cooperated in response to defection. That programming allowed them to prevail in the simulation in which mistakes happen. By the end, Generous programs wiped out Tit-for-Tat programs and managed to protect themselves against defectors. That created a population in which everyone uniformly cooperated and hence multiplied. However, as a result of mutations, Always Defect strategies emerged once more and conquered the population, but they never managed to wipe out all the other programs, and the cycle started once again (Nowak & Sigmund, 2004). What does this mean? Tit-for-Tat strategies seem most successful in a world of rational point maximisers that make no mistakes. They also seem to be highly successful in a world where errors are taken into account but lose to the Generous Tit-for-Tat’s more forgiving strategy.

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Cooperation is more advantageous even in noisy environments; it makes sense that cooperative individuals would be more likely to pass on their genes and survive. However, perhaps the most interesting feature of these results is their cyclical nature; the chaos with which it starts, the prevalence of Always Defectors in the first stages, the emergence of Titfor-Tats, then the victory of Generous Tit-for-Tat’s programmes, and the following re-emergence of the defectors. It is this cyclical nature that explains interpersonal differences—why some choose to cooperate more readily than others and why some choose to opt out only for certain types of reciprocity. Evolution led to us having natural tendencies to cooperate due to the apparent evolutionary benefit that allowed cooperators to pass on their genes. However, it is also the hunt for resources that makes it possible for some people to remain non-cooperative since when everyone cooperates it might be temporarily beneficial to defect. Nevertheless, there is a reason why most people remain cooperative: it increases survival fitness (Eisler & Levine, 2002). Experiments involving testing the theories of sexual selection also indicated that females prefer males who are cooperative. An experiment showing male students photos of women revealed that the levels of cooperation (or “altruism” as used in the paper) increased as the women got more attractive (Bhogal et al., 2016). These findings support the hypothesis that cooperation is also a sexually selectable trait. As a result of the survival benefits of cooperation, biological mechanisms had to develop that reward cooperation. There is a limited pool of neuroimaging evidence of the association, but some areas connected to cooperation have been identified. Participants playing cooperation games were found to have activation in their medial prefrontal cortex (McCabe et al., 2001), others in the ventromedial prefrontal cortex and the ventral striatum (Rilling et al., 2002) as well as the anterior cingulate cortex (Gallagher et al., 2002), all regions belonging to the prefrontal cortex. Generally, the prefrontal cortex is believed to be involved in the mediation of executive functions, i.e., planning, working memory, self-control. Hence, its activation may show that participants are rationally deliberating the relative benefits of cooperation versus defection. Studies tend to show activation in regions specifically involved in reward

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processing: the nucleus accumbens, the caudate nucleus, ventromedial frontal/orbitofrontal cortex, and rostral anterior cingulate cortex (King-Casas et al., 2005; Rilling et al., 2002). The medial orbitofrontal cortex is specifically essential for situations where participants cooperate (Decety et al., 2004), and it is known as the area crucial for goal-directed behaviour and motivational control (Tremblay & Schultz, 1999). Evidence is consistent in showing that humans have a reward system that favours cooperation and more of that evidence is presented and detailed further in Chapter 3 of this book. This chapter, in contrast, turns to a different issue. Even though evolutionary advantage can explain why most people are predisposed to cooperate and even suggests a theory for explaining the individual differences, it does not fully explain why some people are more cooperative than others.

3.2

The Role of Learning

In the same way, as people are born with the ability to learn the language but not the ability to speak (Chomsky, 1959), people are born with certain cooperative predispositions, but not with rules for cooperation themselves. Children and adults observe the behaviour of others around them and do their best to imitate them, in a process known as “learning by imitation”. In an experiment (Bryan, 1971), children were invited to play a game of bowling, after which they had a chance to contribute to a charity out of their winnings, which would mean that they will have less money to spend on toys and sweets in the future. Therefore, any child’s contribution would be costly for them and purely altruistic, as they know that they will get nothing out of it. Children are also left alone in the room and instructed that they are not observed by anyone, which means that they cannot act to impress others. Will the child contribute to a charity? There is no rational reason to do that and no possible benefit to be derived from it. Nevertheless, children donate to charities. How much and to what charities can be easily manipulated.

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Findings replicated through several research groups and hundreds of children aged 6–11 produced strong evidence that children generally contributed around a quarter of their winnings to charities (Henrich & Henrich, 2007). When children were presented with a charity-donating human model to imitate, their donations increased. The more the model donated, the more children donated as well. If the model failed to donate, children contributed less than the “no model” condition (Bryan, 1971; Grusec et al., 1978). Having a model present was much more effective than instructing children to contribute (Bryan & Walbek, 1970; Grusec et al., 1978). Not only did children imitate the act of donation, but they also imitated the order of putting the contributions into different charities (Rosenhan & White, 1967). The effect of learning from the model lasted for several months and extended to slightly different contexts (Rice & Grusec, 1975; Rushton, 1975). Social learning underpins everything that we tend to call culture or custom: children observe adults and imitate their behaviour. By the process of natural selection, individuals most able to learn and imitate others were more likely to survive (Boyd & Richerson, 1988). They avoided poisonous plants by not eating the plants the group did not eat, saved themselves from an attack by predators by hiding the same way their group did, and avoided being ostracised and dying alone by following the social norms. As such, evolution favoured those genetically predisposed to social learning. A wide range of human behaviours that are often believed to be purely cultural, such as taboos, greetings, or food, have an essential genetic and evolutionary component: they require specific neurophysiological and genetic machinery to facilitate the learning of these complex patterns (Henrich & Henrich, 2007). This is proposed in the Dual Inheritance Theory as the idea of culturegene coevolution, in which genetically evolved adaptations influence culture; and cultural traditions, in turn, change the selective environments in which genes develop, making human biology adapt to culture (Henrich & McElreath, 2007). Since individuals learn by imitation, they must choose who to imitate. However, acquiring new information costs time, and learning the wrong habit may be dangerous. In determining whom to imitate, individuals

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rely on their measure of prestige and success (Henrich & Gil-White, 2001). As they do not know which precise practices lead to that person’s success, individuals tend to imitate others on a wide range of traits, relevant or not (Hendrich & Hendrich, 2007). Laboratory experiments have shown that people imitated other participant’s economic strategies (Kroll & Levy, 1992; Pingle, 1995), beliefs (Offerman & Sonnemans, 1998; Offerman et al., 2002), and social interaction (Apesteguia et al., 2010). In these studies, individuals preferred to imitate others who were perceived to be most successful, even when the perceived success was not relevant to the task at hand (Ritchie & Phares, 1969; Ryckman et al., 1972). Outside of laboratory conditions, similar trends are observed, such as in the diffusion of innovatio7 (Rogers, 1995), jaywalking (Mullen et al., 1990), the transmission of dialect (Labov, 1990), and even suicide (Booth, 1999; Jonas, 1992; Stack, 1982). Apart from “learning from the best”, humans also tend to imitate the behaviour of the majority, in socalled conformist transmission (Boyd & Richerson, 1982; Kameda & Nakanishi, 2002; Muthukrishna et al., 2016). Therefore, what we learn from our surroundings often depends on who we are surrounded by, and who of those are the most successful people or what behaviour most people around us exhibited. The behaviours we imitate, including those relating to cooperation and punishment, often become entrenched into our reward systems as habit. As such, habits reflect the social learning of what we regard as right and wrong (Churchland, 2011; Graybiel, 2008). The pain of being shunned and the pleasure of belonging shape our behaviours so that we can exercise cooperation without thinking about it. As neurological evidence consistently shows that we constantly reward ourselves for cooperative behaviour (Tabibnia & Lieberman, 2007), it is likely that once we learn to cooperate with others, we are unlikely to stop; thus, cooperation becomes the default behaviour. However, as explained earlier, cooperation consists of two elements: sharing with others and supporting them; and costly punishment that ensures others’ long-term cooperation. 7 Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread.

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The Learning of Punishment

Even though habitually we might be more inclined to cooperate, rational deliberation may still lead us to the path of defection. Great benefits are drawn from cooperation, which ensures cooperators spread their genes. However, even greater benefits could be drawn by a free-rider abusing others’ cooperation, and that should have led to their genes proliferating. However, this is not observed in any of the societies. The reason for that is that free-riding is strongly deterred by other cooperators (Trivers, 1971). In our societies, we create whole criminal justice systems that are built upon the idea of punishing the offender and isolating them from society, in part to deter others from committing crimes. Therefore, punishment is an example of a cooperative tendency. These punishing mechanisms are also found in many other vertebrate societies and are similar to the ones observed in humans (Clutton-Brock, 2002). For example, non-cooperative behaviours are punished by rhesus monkeys (Hauser, 1992) and coyotes (Bekoff, 2004). That is unsurprising; once emotions that incentivise altruistic and cooperative behaviours evolved, cooperators would be vulnerable to exploitation by non-cooperative members. Therefore, a mechanism exists to protect the cooperators: a means of punishing defectors (Trivers, 1971). That mechanism is believed not only to be beneficial in a twoperson interaction but is also thought to be relevant on a group level (Alexander, 1986; Nowak & Sigmund, 1998). This means that people punish those who have wronged them personally and also tend to punish those who have violated the whole group to which an individual belongs, protecting the entire group against defectors. As such, human cooperation can be best summarised as “be nice but punish”. We reward others for norm-abiding behaviours, but equally, as importantly, we punish them for social norm violations (Sober & Wilson, 2011). Evidence from both ethnographic (Boehm et al., 1993) and laboratory (Fehr & Gächter, 2002; Ostrom et al., 1992) studies shows that people tend to punish non-cooperators, even in one-shot games. Although punishing a violator of a social norm may be costly for oneself, it is believed that the effect of deterring others from cheating later leads to a

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group benefit, as well as individual benefit (Sethi & Somanathan, 1996). As such, punishment—even costly punishment—should be beneficial for the group and is essential for resource-sharing. This explains how punishment would become hardwired into humans the same way cooperation is, and why people punish even in conditions where no future benefit could be derived. Simulation studies showed that groups in which more group members retaliated against the defectors were more likely to survive than non-punishing groups. The reason for this is that punishment encourages cooperation, and cooperative groups are more likely to survive than non-cooperative ones (Boyd et al., 2003). A neurological mechanism for punishment exists that was most likely passed on through the process of natural selection. More specifically, punishment feels rewarding for people, so they get conditioned to using it more. Studies involving punishment in economic games found activation in the brain’s dorsal striatum (De Quervain et al., 2004). The striatum is a crucial part of reward-related neural circuits, evidence supported by both human (Delgado et al., 2003, 2004; Knutson et al., 2000; Martin-Soelch et al., 2001) and primate (Schultz & Romo, 1988) studies. More importantly, it is suggested that the striatum is implicated in detecting the rewards that follow a decision (O’Doherty, 2004). Therefore, punishment is likely to provide relief or satisfaction to people even though they might incur a cost due to that punishment. All humans have predispositions for cooperation and punishment; however, there is much more to human behaviour than genes and neurological mechanisms. Our environments also play a role, and a vital role at that.

3.4

Culture-Gene Coevolution

As evidenced by the previous sections, both biology and cultural influences are playing a role in turning humans into cooperators. Our genetic predisposition heavily influences our culture and the environment. Those cultural outcomes, in turn, create a specific environment in which genes further evolve. The prime example of this interaction is the ability of certain groups to metabolise lactose. In most primates, lactase, an enzyme

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that helps break down lactose, is only found in infancy. Later, when the need for the digestion of milk disappears, lactase disappears (McCracken, 1971). However, some regions, such as northern Europe and pastoral Africa, began adopting a culturally transmissive practice of milking large cattle, which led to natural selection preferring people that could retain lactase through adults over the ones who could not. This was happening because in situations of food scarcity, people who could get nutrients from lactose were more likely to survive and hence passed on their genes (Bersaglieri et al., 2004; Mulcare et al., 2004). In other places, such as the Middle East and China, milk was more likely to have been turned into cheese and yoghurt, for which lactase was not necessary, and hence there was no natural selection of adults that could maintain lactase (BejaPereira et al., 2003). In the future, societies that have not developed the ability to absorb lactose will likely avoid drinking milk, thus making the ability to retain lactase unnecessary. Cooperation in societies develops following a similar pattern in a process theorised by the dual inheritance theory (Boyd & Richerson, 1988), first mentioned in the previous subsection of this book. Human behaviour is influenced both by the culture in which people grow up and their genes. For example, it is found that pathogen prevalence can predict collective (as opposed to individualistic) values in society. That means that cultures with a higher prevalence of deadly infectious diseases were much more likely to endorse collective values, probably due to a disease-preventing and -combating nature of collective action (Fincher et al., 2008). That shows that groups respond to their environment and form a culture that is most likely to help them survive, and in the case of being exposed to illnesses, those groups put more emphasis on helping the group. More evidence of the dual inheritance theory comes from studying the serotonin transporter gene (SLC6A4), which regulates serotonergic (5HTT) neurotransmission. 5-HTT has a region known as 5-HTTLPR, which can either contain a long (L) or a short (S) allele, which results in different levels of 5-HTT expression (Hariri, 2009; Lesch et al., 1996). Individuals carrying an S allele have a lower 5-HTT expression, and as a result are much more prone to negative emotion, including increased amygdala reactivity (Hariri et al., 2002; Munafò et al., 2008), heightened

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anxiety (Sen et al., 2004), negative bias and heightened risk of depression (Caspi et al., 2002; Taylor et al., 2006; Uher & McGuffin, 2008). Population genetic studies show that in some areas, such as East Asia, 70–80% of the sample is an S allele carrier, whereas in Europe, the percentage is typically 40–50% (Chiao & Blizinsky, 2010; Gelernter et al., 1997). That evidence would suggest that East Asian societies should have a higher prevalence of depressive and anxiety disorders than Europeans, however, that is not the case. Studies have consistently reported a lower prevalence of anxiety and mood disorders than in Western populations (Chiao & Blizinsky, 2010; Sorel, 2010). Culture-gene coevolution explains this contradiction by positing that cultural norms are adaptive. Therefore, values of collectivism that preserve social harmony over individualism were adopted in East Asian societies as an environment protecting from stress and reducing the risks of developing depression since the individuals are more likely to have the S allele (Chiao & Blizinsky, 2010). As a result, the relationship between the S allele of 5-HTTLPR and cultural values of collectivism-individualism was so strong that the former served as a sole predictor for the latter (Chiao & Blizinsky, 2010). This evidence shows often human social norms can be viewed as an outcome of culture-gene coevolution. Cooperation, being one of those norms, survived and amplified in humans following this exact mechanism. Along with the theories of kin selection, reciprocity, costly signalling, and indirect reciprocity, another idea was put forward: group selection theory, which supposes that there are competitions for survival within a group and between groups. That means that it is not the best individual who is likely to survive and pass on their genes, but the best group (Wilson, 1975). When it comes to humans, cultural differences between groups are much larger than genetic ones (Bell et al., 2009; Chudek & Henrich, 2011). Group competition favours those groups with stable social norms that ensure their group members’ long-term prosperity and success (Boyd & Richerson, 1990; Henrich, 2004). These processes have shaped the environment in which the genes further developed: the genes that allowed the bearers to rapidly identify the social norms and adhere to them received an advantage (Chudek & Henrich, 2011). Since cooperation is a strategy that benefits most group members,

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cooperating groups are more likely to survive. Within those groups, the individuals with genes for identifying and supporting cooperation are more likely to prosper. Since the most successful individuals tend to be imitated in the first place, they are likely to spread their norms and genes for cooperation even further. All these findings show us how cooperative behaviours evolved, and retribution and reciprocity are likely to have evolved by exactly the same mechanism, considering how prevalent these behaviours are. Therefore, the next section defines, grounds, and analyses our tendencies towards retribution and reciprocity before bringing them together into one model that explains crime, the RRM.

4

Retribution and Reciprocity

4.1

Explaining and Defining Reciprocity

We live in a reciprocating world. We smile when someone smiles at us; prepare costly Christmas gifts when expecting to receive a present; invite people to dine with us when they invited us before. Reciprocity has long been a potential solution for the puzzle that is cooperation (Trivers, 1971). By applying tit-for-tat strategies, humans self-select to associate with other cooperators, far more so than animals do (Hammerstein, 2003). Even among non-kin and in the absence of long-term interactions, people have been shown to be reciprocal, despite not always having any possibility of profiting from the interactions in the future, such as in one-shot games (Leimar & Hammerstein, 2001; Wedekind & Milinski, 2000). Reciprocity has even been shown to be maintained with defectors in the group (Nowak & Sigmund, 1998). In the same way as cooperation, reciprocity is not unique to humans. Vampire bats have been shown to share blood but appear to be much more likely to share blood with those who shared with them before (Wilkinson, 1990). Impala antelopes were much more likely to groom an antelope that groomed them earlier (Mooring & Hart, 1997). Members of olive baboon groups come to the rescue of a group member that has previously helped them (Packer, 1977). Toque macaques help in

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future conflicts macaques that previously helped tend to their wounds (Ratnayeke, 1994). Although examples from both animals and humans are numerous, humans take it to a completely different level from even the most closely related animal. In the simplest of terms, reciprocity is responding positively to positive acts (positive reciprocity) and negatively to hostile acts (negative reciprocity). However, for this book and RRM in general, reciprocity only looks at the past deeds and does not concern itself with the rational deliberation of what benefits current cooperation might deliver in the future. As such, reciprocity requires recognition of the partner, the memory of the outcome of their previous interaction, and an analytical capacity to figure out how to reciprocate. In summary, it requires more advanced cognitive ability than regular cooperation, which we can observe in, for instance, bacteria. There are many types of reciprocity, and the previous sections explained in detail that most people do act in a reciprocal manner. Nevertheless, as with any human behaviour, there will always be individual differences with some people showing more reciprocity than others.

4.1.1 Direct and General Reciprocity Two types of reciprocity can be observed: direct and general. Direct reciprocity relates to the experience of personal contact with specific individuals or groups. It refers to the situations in which one person has a particular reputation in the eyes of the reciprocator, which is acted upon accordingly. Therefore, it depends on an ongoing set of interactions between a pair of people. For example, if person A punched person B, person B would have direct negative reciprocity towards that person, as their behaviour is something they have experienced first-hand and respond to that person directly. The same reasoning applies if person A lent person B money in the past: person B would be expected to lend person A some money if person A were to experience financial peril in the future. The extent and nature of direct reciprocity are different depending on the context and the social group (Fiske, 1991). The reasons for that

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are twofold: (1) cultural evolution happens faster and is more flexible than genetic evolution, allowing us to learn different rules for different situations (Rogers, 1995); (2) evolution offers custom-fit solutions to specific problems, and sometimes reciprocity fits better than at other times (Henrich & Henrich, 2007). Direct reciprocity is calibrated to specific situations, environments, and acts. General reciprocity, in contrast, refers to situations where people do not have direct contact to guide their behaviour. Instead, they rely on their general expectations of the environment (including negative biases), the aggregate of past experiences, cultural expectations of how people should behave in these situations, etc. For example, if people were asked to donate to a charity, they would choose to contribute based on their general understanding of the expectations in similar situations and their perceptions of that specific charity rather than their past experiences with that organisation.

4.1.2 Positive and Negative Reciprocity Positive reciprocity is a tendency to respond positively to an act of kindness even if a person is harming themself by doing so. It is important to emphasise that there is no direct and obvious benefit from a reciprocal action, only losses. A great example is an experiment in which a smiling waitress got more tips, and the number of tips increased as the smile became wider (Tidd & Lockard, 1978). The customers were not regulars: there was no material or social gain to be achieved; the only motivation was to respond to kindness with kindness. Other examples include economic experiments that mimicked the wage market. Employees put in the amount of effort proportional to the salary they received even though their pay would not have changed even if they put no effort into their task (Fehr et al., 1993). That means that the employees responded with kindness to the employers setting up high salaries even if they did not need to. However, when in the same setting the employer received the opportunity to reward the workers for good performance and punish them for inadequate performance, hence removing the act of kindness the employees were reciprocating, the overall level of effort decreased

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dramatically (Fehr & Gächter, 2000b). As a society, we largely acknowledge the tendency to act reciprocally; that is why, for instance, charities include a small present when asking for donations; or companies make use of free samples as a marketing tactic (Reno et al., 1993). It is essential to make a distinction between positive reciprocity and altruism. Generally, altruism is often defined in behavioural economics as behaviour that benefits others at a personal cost (Fehr & Fischbacher, 2003). That definition does not fit with my framework, since it is close to how I define cooperation in general and lacks specificity. I view altruism as unconditional kindness, meaning that the person would always respond positively to any act, be it hostile, positive, neutral, or even unprovoked. As such, it is different from positive reciprocity. Positive reciprocity occurs in response to previous acts of kindness, but altruism is unconditional kindness. Altruistic types would always try to help other members of the group, whereas positively reciprocal types adapt their behaviour and only respond with kindness to the kindness of others. Experiments show that, in general, people adjust their behaviour. For example, suppose they end up in a group where the others contribute less. In that case, they will start contributing less, reflecting a tit-for-tat tendency (Sonnemans et al., 1999), meaning that the majority of the sample of those experiments are not altruistic. Negative reciprocity is a tendency to respond negatively to an act of hostility and is only relevant as a reaction to an action directed towards the responder. As with positive reciprocity, no direct and obvious benefit can be gained from said reaction. One of the great examples is people playing the ultimatum game, in which one player has some resources to share between himself and the second player. The second player can either accept or reject the offer, which would result in both getting nothing. However, when players are presented with unfair offers, they almost always choose to punish the offeror by rejecting the offer, even if that would mean losing their income (Camerer & Thaler, 1995; Güth et al., 1982). The robust results of over a hundred trials show that a tendency to punish a player for an unfair offer is stronger than the incentive to maximise one’s gain. Even though one might assume that positive and negative reciprocity could both be just called reciprocity, they are fundamentally different

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behaviours (Fehr & Gächter, 2000b) which tend to manifest differently within an individual (Hoffman et al., 1998), and can be tested using different methods (Fehr & Schmidt, 2006). That means that there is a difference in levels within an individual, and one does not imply the other, so if a person is highly positively reciprocal, it does not mean that they would be negatively reciprocal to the same extent. Numerous game theory experiments (Fehr & Gächter, 2000b) showed that people who are likely to punish the people who wronged them are not necessarily also the ones to reward the people who were nice to them. Even though we are used to thinking about reciprocity in terms of tit-for-tat, it is not necessarily as simple as it sounds, and positive reciprocity and negative reciprocity should be considered as different behaviours. It is important to distinguish negative reciprocity and retribution, however, as explained below.

4.2

The Role of Retributive Punishment

Direct reciprocity alone cannot explain cooperative behaviours (Henrich & Henrich, 2007). Other factors such as reputation are likely to affect the responder’s willingness to cooperate. For example, in a “helping game” where players were randomly assigned a donor or receiver role, participants were much more likely to donate money to a participant they were told helped others (Engelmann & Fischbacher, 2009). Another aspect that emerged was that participants were much more likely to donate when their donation score was visible than when it was not (Seinen & Schram, 2006), meaning that people decide whether to cooperate based on whether that would enhance their reputation. This finding seems to be consistent with the behaviour in many societies, commonly termed “costly signalling behaviour” (Bird et al., 2001; Hawkes & Bliege Bird, 2002). That makes sense since having a reputation as a cooperator reduces the chances of being punished in the future. So, reputation is essential, but why? As I derived from previous sections, social norms, including the social norm of reciprocity, result from the co-evolution of biological mechanisms and social learning. From the perspective of social learning,

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cultural norms and punishment both play a role: cultural norms allow for learning by imitation, and punishment (or retribution) provides deterrence to potential defectors. Retribution serves as a driving force in learning cooperation. It does so by reinforcing the social norm and threatening a potential norm violator with ostracisation in the case of failing to comply. Despite some conflicting evidence, research suggests that having punishment in the group promotes cooperation (for a meta-analysis, see Balliet et al., 2011). As a general rule, the effectiveness of punishment increases if it is seen as promoting the group’s interests instead of a selfish interest (Mulder & Nelissen, 2010), and people are willing to pay more to punish wrongdoers (Egas & Riedl, 2008). Retribution is a tendency to punish the violator of a social norm even if said punishment is harmful to the punisher themself. For instance, when a company implemented wage cuts perceived as unjustified, the employees harmed the employer even if it harmed themselves, even when their wages remained unchanged (Akerlof & Yellen, 1990). In contrast to negative reciprocity, retribution, as I define it for this book, is punishment directed towards an individual for the future benefit of the group gains when everybody is compliant. We can observe retributive tendencies in so-called Third Party Punisher games in which a group is supposed to contribute to social good. However, the participants still have the possibility to free-ride. Participants were punished for not contributing even if said punishment was costly for the punisher and when the punisher was not even participating in the game and thus had nothing to gain from it (Charness & Rabin, 2005; Fehr & Fischbacher, 2004). For a behaviour to be retributive, other than negatively reciprocal, it has to respond to an action directed against the group or another person, but not against the actor themself. Most literature that studies the tendency I choose to call “retribution” refer to it as “retaliation” (Fehr & Gächter, 2002); however, I argue in this book that it is not the best term to use. There are several reasons for this, primarily connected to the everyday use of the two words. First, retribution is more commonly understood as a fairness-driven action utilised for establishing justice. In contrast, retaliation is understood as a

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response to a hostile act, which does not necessarily need to be proportionate to the action itself or be fairness-driven. As such, retaliation in its meaning is much closer to “negative reciprocity”, bringing more confusion to an already confusing field. Since retribution is both more accurate in describing the tendency and less likely to be confused with negative reciprocity, this is the term I use in this book, even though some other researchers use the word retaliation to describe the same concept. The area of the brain most associated with retribution is the dorsal striatum (DS). Using functional magnetic resonance imaging (fMRI), researchers found activation in the DS when participants were punishing defectors in the economic game by reducing their payoff, although no activation was found there when the punishment was only symbolic (Dominique et al., 2004; Krämer et al., 2007). Similarly, the DS was not activated in another experiment in which participants passively observed another person being punished for unfair behaviour (Singer et al., 2006). This suggests that retribution is likely to be a separate neurological process to negative reciprocity and general perceptions of injustice, which means that there is evidence to believe that these concepts should be studied separately.

5

Retribution and Reciprocity Model

It can be concluded that retribution, positive reciprocity, and negative reciprocity are separate concepts and not part of the same tendency, even though all fall under the umbrella term of cooperation. Now that we have a definition and an understanding of these concepts, it is time to bring them together into a model that will help us predict and understand crime. For that, another important factor needs to be added to the model: the role of the (perceived) environment. The Retribution and Reciprocity Model (RRM) identifies new concepts that can help to explain criminal behaviour and the framework of their interaction. These factors are: perceptions of the environment (PoE), positive reciprocity (PR), negative reciprocity (NR), retribution (R), and initial motivations. In short, the theory suggests that when presented with an incentive to commit a crime (of which

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retribution could be one), a person enters a reciprocity-environment interaction process. Positive perceptions of the environment interacting with positively reciprocal tendencies usually lead to crime not being committed. In contrast, negative perceptions of the environment interacting with negatively reciprocal tendencies make crime more likely to occur (Fig. 1). Even though one might assume that positive and negative reciprocity could both be just called reciprocity, they are fundamentally different behaviours (Fehr & Gächter, 2000b) which tend to manifest differently within an individual (Hoffman et al., 1998), and can be tested using different methods (Fehr & Schmidt, 2006). That means that there is a difference in levels within an individual, and one does not imply the other, so if a person is highly positively reciprocal, it does not mean that they would be negatively reciprocal to the same extent. Two types of reciprocity can be observed: direct and general. Direct reciprocity relates to the direct experience of personal contact with specific individuals or groups. General reciprocity, in contrast, refers

Positive

Not Likely

Negative Reciprocity

Crime Outcome

Positive Reciprocity

Retribution Negative

Fig. 1 The mechanism of work of the RRM

Likely

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to situations where people do not have direct contact to guide their behaviour. Instead, they rely on their general expectations of the environment (including negative biases), or the aggregate of past experiences. For example, an individual could have an overall negative experience of the police force based on the stories they hear from friends (general reciprocity), but have a good relationship with the one police officer that showed kindness and helped them on an occasion (direct reciprocity). In short, the theory suggests that when presented with an incentive to commit a crime, a person is influenced by the interaction of their perceptions of the environment and their reciprocal and retributive tendencies. Positive perceptions of the environment interacting with positively reciprocal tendencies usually lead to crime not being committed. In contrast, negative perceptions of the environment interacting with negatively reciprocal and retributive tendencies make crime more likely to occur (Fig. 1). Positive and negative perceptions of the environment are hard to quantify, although not impossible. PoE refers to the persons’ perceptions of places and circumstances. It could refer to a specific person, neighbourhood, or even an entire country. For the sake of this theory, “positive” refers to when a person feels they have been treated fairly, helped and supported by their environment, and feel they are getting something out of it. In contrast, “negative” means the opposite, that people feel that they were treated unfairly and being exploited. Perceptions of the environment are not binary but exist on a spectrum and are a continuous variable. As a result, the score can also be neutral. Perceptions of the environment are directly related to general reciprocity, as they affect the way the person perceives the world in general and what expectations they have of the social norms and environment around them. Perceptions of the environment are involved in social learning and observations of the world: people derive social norms from how people around them behave and apply them accordingly. By definition, reciprocity always means a response to something: therefore, only positive acts can trigger positively reciprocal responses, and only negative acts can trigger negative reciprocity. The same works for the perceptions of the environment. We all form our opinions about the world around us through past experiences and learning from the

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people around us, and that shapes our understanding of the world around us. PoE are important in and of themselves. For instance, a person is much less likely to assault a stranger who just smiled at them than one who has shouted at them. A teenager is much less likely to draw graffiti on the wall of a shop that has always given them a discount than a shop where the cashiers are rude to them (Fisher & Baron, 1982; Kahan, 2002). In general terms, people’s perceptions about their environment informs what they think is acceptable or not in a particular situation and shapes their expectations. For example, if a person thinks that everyone around them steals things from one another, they would be more likely to steal themselves. Even in trivial situations like someone stepping on their foot, each person may interpret the situation differently. A person with a negative perception of the environment, in which they believe the world is working against them, might lash out and think the person that stepped on their foot did so on purpose. On the other hand, a person who has a very positive view of the environment is more likely to believe that the situation was an accident. As such, some people are much more susceptible to the positivity or negativity of their environment than others, and there are many factors that feed into that variation; some people are more reactive or sensitive to their environments than others. I posit with RRM that the reason for this is the interaction of PoE with positively and negatively reciprocal tendencies. Since reciprocity is a response to something, it cannot be separated from an environment and a specific situation. In order to invoke positively reciprocal mechanisms, one has first to perceive an act of kindness. To invoke negatively reciprocal tendencies, one must recognise something as a hostile act towards them. That means that different levels of retributive and reciprocal tendencies would result in different sensitivities to the environments. Non-reciprocal types would most likely not be significantly influenced by the positivity or negativity of the environment. They would be likely to judge the situation solely by the initial motivation. For example, people scoring high on NR scores and low on PR scores would be very hard to deter from committing a crime by simply doing something nice

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to them; however, they would be easily pushed into committing a crime by doing something hostile towards them. There are, of course, other individual factors and features of temperament that might influence crime propensity, such as impulsivity or negative emotionality. However, those factors are not included in this model for their multitude and complexity. There are also, of course, the immediate environmental factors, such as the presence of witnesses and availability of suitable victims, which also fall outside of the scope of this model, since its focus is on simply determining the role of retribution and reciprocity in crime causation. In a similar vein, initial motivations cannot be overlooked as they might trigger a person to commit a crime. Initial motivations are various aspects that have motivated a person to commit a crime, e.g., peer pressure, or financial gain. It is the goaldirected preference for a behaviour-event contingency that involves an interaction between situational stimuli and personal preferences (Heckhausen & Heckhausen, 2008). Defining and examining these motivations is outside of the scope of this book. Still, they play an essential role in the crime causation process. This model is not the whole story of why crime happens—other factors interplay, such as rational deliberation, specific situational characteristics, or other initial motivations (temptations/provocations). Albeit important, these factors are outside the scope of this book but can be integrated in future research. This book presents the importance of retribution and reciprocity on their own and how they may interplay with one another. Furthermore, it evaluates their significance and how much these factors can explain. Only knowing that we may move towards integrating and comparing RRM with other theories of crime, but that job lies within the directions for future research.

5.1

The Importance of RRM in Criminological Theory

In line with the first chapter of this book, I find it essential to identify the space that this theory takes in criminological theorising.

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RRM is a theory that aims to depict a mechanism of criminal behaviour that explains crime through the lens of retribution and reciprocity and identifies the processes that lead to crime outcomes. However, it is important to understand that in social science, explanations are not always simple. Over the years, criminologists have identified many correlates of crime, from B12 deficiencies to global cultures. All of these correlates, however, exist on different levels. It raises a very important question of what the point of looking at evolutionary theory is at all in criminology. The answer is rather simple: it lets us test the basic assumptions of existing theories of crime. Every theory of crime comes with (a) a definition or understanding of crime, and (b) some assumption about human nature, be it that people are rational (rational choice theories), greedy (self-control theories), or rule-guided (situational action theory). Some theories make these assumptions more explicit than others, however, it is these assumptions that often form the basis of a theory. As such, it becomes imperative to test this assumption about human nature in order to test and falsify the theory. Looking at evolutionary science in criminology can therefore give us answers to the question of what human nature is like and what fundamental behavioural tendencies people have. For example, RRM brings together evidence that people are cooperative, reciprocal, and retributive. If there is a general theory of crime that relies on the assumptions about human nature that do not fit that view (i.e. if the theory is that people are inherently self-centred and only care about maximising their own benefit), that theory should be rejected as a theory of crime or reformulated to fit the evolutionary knowledge. However, RRM can offer more insights than simply serving as a benchmark for theory selection and falsification. RRM is in itself an evolutionary theory of crime, albeit not a complete one. As such, it offers its own explanation of why crime happens, and it does it on a lower level than most. In 1963, Tinbergen proposed four levels of explanation of behaviour (as synthesised in Kapheim, 2019). This framework suggested that there are four questions that should be asked of any animal behaviour (which he believes were also applicable to human behaviour). These questions

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can also be understood as levels of analysis, stating that any behaviour should be explained at four levels: the ultimate (evolutionary) explanations, (1) adaptive function, (2) phylogenetic history; and the proximate explanations, (3) underlying physiological processes, and (4) ontogenetic/developmental history. In simple terms, it is (1) why have we evolved the way we evolved, (2) how did this evolution happen, (3) what is the mechanism of this behaviour, and (4) how does it develop in an individual. As such, most criminological theories will likely deal with levels 3 and 4 and most of the time those theories would be separate. For example, developmental theories often just deal with level 4, whereas theories dealing with issues such as situational crime prevention would likely be at level 3. Looking at evolutionary science in criminology is essential in order to tap into level 1, and understanding the biological and neurophysiological mechanisms is essential for level 2. RRM operates on all four levels. It offers an evolutionary explanation of why people are retributive and reciprocal, explains how these tendencies developed from cooperative behaviours, presents a neurophysiological mechanism (Chapter 3), and a model of development through engagement with social learning. As such, it is by itself a comprehensive model that encompasses various levels of human behaviour and hence explains them. It maps out both the propensity and the action with the concepts of specific and general reciprocity. On the most fundamental level, we know that people will respond negatively to acts of hostility and positively to acts of kindness. As Chapter 4 tests, if the environment is manipulated in a certain way, making it more retributive or negatively reciprocal, more people would say that they would commit a crime in a hypothetical scenario. Therefore, looking at specific reciprocity or immediate actions allows us to explain why people would commit an act of crime. On the other hand, the whole model of individual tendencies and the social learning in which they develop is a developmental model of propensity. The model states that people who are more negatively reciprocal and retributive are also more likely to commit a crime than people who are more positively reciprocal. As such, RRM can model both the action and the propensity.

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RRM, as any evolutionary theories, can be complementary with other theories of crime. An evolutionary mechanism such as RRM can provide the necessary foundation for a general theory of crime, for instance, in testing the validity of an underlying assumption. RRM as a model exists independently from the general theories of crime, but could serve as a supporting cog in the different mechanisms. For example, RRM would argue that weak bonds do not cause crime but influence reciprocal feelings. People with weak social ties or who experience strains might feel the need to lash out against society because they believe that they have gotten nothing out of it. In contrast, people with strong social ties might encounter opportunities to commit a crime but would not act on those opportunities because of the tendency to reciprocate positively towards the society that has been fair and supportive of them. Without strong social ties, there is no reciprocal motive to stop a person from committing a crime. Strain itself might reduce positive reciprocal tendencies and heighten retributive tendencies. Therefore, RRM could add to the understanding and the explanatory power of many of the already existing theories of crime. RRM could also help explain individual differences when some people commit crimes, and others do not, despite experiencing identical strains or having similar social bonds. This discrepancy might be attributed to the fact that some people, who grew up in a good environment and felt society’s support, might reciprocate by not resorting to criminal coping strategies. But, on the other hand, others who think humanity has mistreated them might feel the need to seek retribution and hence may adopt a criminal coping strategy. RRM would suggest that retribution can help explain why some people choose to re-offend and some choose not to. Defiance theory presents evidence that unemployed people are more likely to re-offend if they are incarcerated, whereas employed people become less likely to re-offend after incarceration (Sherman, 1993). RRM offers a new detail by stating that unemployed people would feel negative reciprocity and retribution. People who are employed tend to have higher degrees of trust in the society around them, feel more protected and supported (Westholm & Niemi, 1986), and hence feel the need to reciprocate positively.

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63

Conclusion

This chapter set out to develop and present a model of a crime that is based on the evolutionary mechanisms for cooperation. As such, it does just that, by first outlining the reasons for looking at retribution and reciprocity, then by defining them, and then by combining all the important factors into the Retribution and Reciprocity Model (RRM) that is then further developed, examined, and tested in the next chapters of this book. Even though criminologists tend to concern themselves with the relatively rare event of committing a crime, it is essential to look at how humans organise their lives if we are to understand crime. Cooperation may be understood as a defining feature of human society, and hence understanding it might shed more light on crime as an act of non-cooperation. Although cooperation does not seem like an obvious consequence of the “survival of the fittest” understanding of evolution, numerous mathematical models have shown that cooperation is often the most rational course of action in the long run that allows for most individual advantage. To a degree, cooperative tendencies are believed to be hardwired into humans, as cooperators were more likely to pass on their genes to future generations, as such providing an evolutionary advantage. However, despite some behaviour being hardwired into our biology, our physiology does not fully determine it; social learning also plays a crucial role in the development of cooperative tendencies. People emulate the majority and the people they perceive as successful. As a result, what we learn affects how our brains develop and rewire during our lives, presenting a case of culture-gene coevolution that allows for cooperative and punitive tendencies to arise. Looking at cooperation is vital in criminology, as it plays such an essential role in human lives in general. As such a fundamental foundation of human behaviours, it follows that cooperation plays a role in crime. A lot of acts of crime can be understood as a classic case of defection against society: acting in a selfish manner that damages the group. However, other acts of crime could be understood as acts of cooperation, such as gang crime.

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I argue that the most important aspects of cooperation are those of reciprocity and retribution. To test the role of these tendencies in crime, I organise them into a Retribution and Reciprocity Model (RRM). RRM suggests that people possess different levels of negatively reciprocal, positively reciprocal, and retributive tendencies. These, in turn, interact with the individual’s perceptions of the environment and elicit a response resulting in them either committing or not committing a crime. Most chapters of this book are devoted to presenting, explaining, and testing that theory. The next chapter, Chapter 3, dwells on the neuroscientific basis of retributive and reciprocal mechanisms. It explains the biology behind the claims that RRM makes and demonstrates how biology shapes human behaviour, especially in settings that may lead to crime. First, this chapter collates and synthesises existing knowledge in neurobiology. Then, it offers suggestions for experiments that could be designed to test the neuroscientific basis of RRM. Chapter 4 details preliminary evidence for RRM that supports the claim that crime can be predicted by manipulating the factors of the particular situations that relate to reciprocity and retribution. Hypothetical scenarios are used to manipulate specific characteristics of the situations in which every participant is invited to imagine themselves. The participants are given slightly different versions of each scenario in which the environment is manipulated to observe whether that would elicit different levels of reciprocal or retributive responses. Chapter 5 measures the individual differences in retributive and reciprocal tendencies by using a Public Goods game, in which participants played a simple online cooperation game. The game also aimed to measure perceptions of the environment using a purpose-built inventory and measures of self-reported crime and additional hypothetical scenarios. The experiment allows for the analysis of the impact of different levels of retributive and reciprocal tendencies on crime and the effects on the environment.

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In sum, the chapters to follow offer a detailed presentation of RRM and examine its effectiveness in predicting crime in general and explaining individual differences.

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3 The Neurophysiology of the Retribution and Reciprocity Model: The Anatomy of Cooperation

1

Introduction

Many scientists have attempted to pinpoint what, if anything, makes humans different from other animals. For example, we create elaborate language, use complex technologies, and persevere in all regions of the planet, from the coldest to the hottest, through our ability to learn, adapt, and invent. However, many other apes possess skills similar to us: they understand how events relate to one another and can identify individuals in their group (Tomasello & Herrmann, 2010). When compared to a group of two-year-old human children, apes did not underperform in a battery of tasks relating to the physical world: dealing with space, quantities, and causality (Herrmann et al., 2007). This indicates that humans do not have a unique set of working memory or intelligence in the physical world. Some like to celebrate humanity for the ability to exercise self-control, suppress initial instincts, and make rational decisions (Herrmann et al., 2015). The current state of science suggests that evolution did not select for perfect self-control (Hayden, 2019), and it is unmerited to claim © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. Svingen, Evolutionary Criminology and Cooperation, Palgrave’s Frontiers in Criminology Theory, https://doi.org/10.1007/978-3-031-36275-0_3

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that human behaviour is devoid of emotional decisions. Sometimes we help strangers without asking for anything in return or lash out at a stranger that insulted us and face dire consequences as a result. Retribution and reciprocity, among others, are our natural responses to the events around us and might not always be rational choices. The experiment of comparing other great apes to human children showed that the children were performing much better in intention-reading, social learning, and communication (Herrmann et al., 2007). These are the skills that are, above all, needed for cooperation, demonstrating that our ability to cooperate and operate as a group is what makes humans so distinct and so successful as a species. The Retribution and Reciprocity Model (RRM) is based on the belief that people are hardwired towards cooperation, and incidentally, that it is also something that they excel at. While the second chapter of this book presents the evolutionary evidence for RRM, in this chapter, I aim to investigate the machinery and discuss the neurophysiological mechanisms that make us act in a retributive and reciprocal manner. As much as we might like to think of ourselves as above animalistic instincts, we generally overestimate our ability to make purely rational decisions. Everything that happens to us is processed by the brain, and every action we take must be made through the movement of our muscles attached to the skeleton. There are not many ways to influence the world around us without at least some sort of movement. What does it mean? It means that if our personalities, experiences, and thoughts are the “software” of the computing and feeling machines that human beings are, then our bodies and, most importantly, our brains are the “hardware”, limiting our memory capacity, learning, and attention span.1 The world around us influences us with every encounter. Every new connection that the brain has to make can be thought of as programs that get installed, deleted, or updated into our software every second of every day. Therefore, looking only at one aspect of human behaviour is of as little help as looking at the type of hard drive of a laptop and hoping that that will tell us something about whether the said laptop has Skype installed. 1

The brain-computer analogy in cognitive psychology is a rather old one that is often criticised for oversimplification of the way the brain works (Notterman, 2000). Nevertheless, I find it useful to explain the mechanism even if it might suffer from lack of nuance.

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In the same way, looking at the number of tabs open in Chrome will tell us nothing about whether the speakers are working or not. RRM is a model that is designed to avoid this mistake of looking at only one aspect by addressing both the behaviour and the biological mechanisms. As discussed in the previous chapter, aeons of evolution have shaped humans, equipping them with the tools to deal with the world’s complexities. The concept of survival of the fittest ensured that our nervous systems and brains have evolved with the idea of maximising survival and procreation. As such, a lot of behaviours that were beneficial for survival and passing on our genes became hardwired into us and influenced how we behave. In the process of social and biological coevolution, our everyday encounters change how our bodies function: they create new patterns in our dendrite connections and influence specific genes’ expression (Gual & Norgaard, 2010). As a result, they can quite literally change our brain structure over time (Kalia, 2008). The way we perceive the world, however, is also largely shaped by the way our brains operate (Demuth et al., 2016): past experiences tell us whether we should or should not trust a stranger; a mental health disorder may paralyse us with fear in the middle of a routine task; seeing an angry face may make our blood rush and cause adrenaline to be secreted. As a result, certain innate predispositions which are “hardwired” into our behaviour may make us behave in a certain way before we even think about it. As established in the previous chapter, we show a lot of prosocial behaviours naturally and without thinking. We smile when someone smiles at us, we cringe when we see another person in pain, even if it is a fictional character, and we feel rage in the face of injustice. These behaviours make us human, make our society function, and they are part of the overarching concept of cooperation. Retribution and reciprocity are tendencies towards which we have a predisposition from birth, mainly because they were evolutionarily advantageous. Those predispositions get strengthened increasingly through everyday encounters, social learning, experience, and rational deliberations. Thus, there is powerful machinery that predisposes humans towards reciprocal and retaliatory behaviours, the analysis of which is the purpose of this chapter.

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Understanding biological mechanisms of cooperation is essential. It enhances our knowledge of these behaviours and helps us learn more about what drives these responses and how deeply entrenched they are in our nature. For example, studying the neurophysiology of retribution and reciprocity can help us understand whether these behaviours are inevitable or just a feature of the culture around us. In addition, it can also demonstrate whether it is even worth studying positive reciprocity, negative reciprocity, and retribution separately or whether they have the same mechanisms and hence can be studied as one. Through looking at the biological mechanisms of RRM, this chapter serves three primary purposes: first, it adds to evidence RRM as a valid theory that could help explain crime; second, it untangles some of the debates and contradictory evidence that exist in the fields of neuroscience and evolutionary psychology to bring several pieces of opposing or just seemingly unconnected evidence together; and third, it creates testable hypotheses that can be looked at in the future by psychologists and neuroscientists alike to answer more questions about the interrelation between those tendencies. This chapter is organised into three main sections: the first outlines the initial evidence of how hardwired our tendencies towards punishment and cooperation are. The second section discusses the existing evidence on the neurobiological mechanisms of retribution and reciprocity. And finally, the last section summarises the findings in one unified mechanism, which is designed to provide the underlying mechanism for the work of RRM. This knowledge will contribute to the understanding and future testing of RRM and other theories that rely on or could make use of these mechanisms.

2

Background Evidence

RRM relies on several pivotal assumptions: that people are prosocial and willing to cooperate; that they can be empathetic to what others are feeling and, in turn, care about those emotions; and that they are motivated to repay kindness with kindness at the same time as hostility with hostility. Thus, this section is divided into three parts: the first deals with

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the evidence for feelings of empathy, prosociality, and reciprocity (that I conclude are highly interlinked); the second outlines the evidence for our desire for justice and retribution; and the third explains the role of social learning and the environments. There is much debate around the origin of our cooperative tendencies and whether they result from rational deliberation, social and cultural learning, or whether these tendencies are hardwired into us. As such, it is essential to discuss and evaluate the evidence available to determine where retribution and reciprocity originate. I posit that a lot of these behaviours are, in fact, innate for their clear evolutionary advantage; however, their expression is heavily influenced by the environments we find ourselves in and the things we learn from them. This section outlines the initial evidence for the validity of RRM as a theory before going into the second section that details a neurophysiological mechanism for it.

2.1

Evidence for Prosociality and Reciprocity

Most people are not entirely self-centred and show a multitude of different prosocial tendencies. We enjoy spending time around friends and family, give each other birthday presents, and help those in need (Churchland, 2011). There are many reasons to believe that much of this behaviour is learned and not innate. We see people’s behaviours change across the generations and differ between cultures. Examples of concepts that were normalised in the past but are considered appalling and unacceptable in the present include slavery and wife-beating. Moreover, cultural differences in customs show us that people are relatively flexible in their moral behaviours and beliefs of what is acceptable and what is not. That, however, does not mean that a lot of our tendencies to be prosocial are a product of nurture more than nature. On the contrary, many of these behaviours appear to be hardwired, as evidenced by the study of young children. Infant experiments are fundamental in the nature vs nurture debates as babies typically do not have enough time to learn much about their

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environments and behave closely to their genetic predisposition. Moreover, they are not concerned with others’ perceptions of them and do not behave in any way that can be “expected” of them since they are yet to understand what expectation is. Therefore, when young children and toddlers show a particular behaviour, there is a high likelihood that that behaviour is innate. Evidence demonstrates that infants are drawn to and engage in social interaction from birth (Trevarthen & Aitken, 2001) and, in fact, even before birth (Castiello et al., 2010). Typically, developing infants show their unquestionable capacity and motivation to connect with and share emotions with others (e.g., Rochat, 2009; Trevarthen & Aitken, 2001). For example, they like the sound of human voices and the look of people’s faces (Bloom, 2013). Not only do they want to engage with other people, but they also want to be prosocial. For example, children share toys and food (Dahl, 2015) and comfort others in distress by their first birthday (Svetlova et al., 2010). At the age of 12 months, infants showed a preference for people who were friendly to others instead of hindering them (van de Vondervoort & Hamlin, 2018), reflecting that children also learn to make sociomoral judgements very early into their development. Even before children develop Theory of Mind (ToM2 ) abilities, early signs of empathic concern can be observed before the second year of life and are distinct from personal distress (Decety & Michalska, 2012; Jensen et al., 2014; Zahn-Waxler & Radke-Yarrow, 1990). By early in the second year of life, infants can behave altruistically by helping others without being asked, sometimes even at a cost to themselves (Warneken & Tomasello, 2009). Even though we know that infants can be aggressive and unruly to deal with, there is much evidence that children are naturally inclined to connect with others and be nice to them (Churchland, 2011). It makes evolutionary sense; we need to be able to feel what others feel in order to survive. In fact, we consider the inability to feel empathy 2

The ability to understand that other people have different intentions, desires and beliefs to one’s own. Essentially, it is about perspective taking and is believed to show in children at around four years of age, but some more recent debates suggest it might be happening even earlier than that.

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or any other deficits in responding to emotional stimuli as something abnormal or even a disorder (Miville et al., 2006). Therefore, even though media might be filled with images of psychopaths3 that succeed in business or other areas of life, psychopathy would not lead to success from an evolutionary perspective. For example, psychopaths were found to be insensitive to the expression of fear (Marsh & Cardinale, 2012; Marsh et al., 2011), and without recognising fear in others, it is more likely that those individuals would fall prey to a predator or some other danger. As a result, most people are not psychopaths because it does not make much sense in terms of survival to be one. On the contrary, most people are empathetic and social, and they show those tendencies before the first year of age (Dunfield et al., 2011). Children show tendencies towards cooperation from a very young age; for example, they start helping others. In an experiment (Martin & Olson, 2013), an adult played with three-year-olds and asked them to help them with a task, such as giving them a cup to pour some water in. If the cup was intact, the children happily gave the cup to the adult. However, if the cup was not fit for purpose (for example, broken), the children would ignore the request and bring the adult some other cup. That demonstrates that children were motivated by trying to help the adult rather than just following a request. That is not only a prosocial behaviour but also a highly cooperative one that the children have not yet had time to learn, indicating even further that it’s a hardwired tendency (Hamlin, 2013). In the second part of their first year of life, children start to share spontaneously, and the degree of sharing only increases into the second year (Brownell et al., 2009). However, although children happily share a lot with family members, they are much less likely to share with strangers (Bloom, 2013). That is to be expected, considering that adults are not that keen to share with strangers either unless they get to know them. In experimental settings, children appear to be much kinder and willing to share once they have established some sort of reciprocal relationship 3

There is a lot of debate around what psychopathy means and whether it exists in the first place (Arrigo & Shipley, 2001). While it is used here to make a certain point, defining psychopathy/ sociopathy, albeit interesting and relevant for the field of criminology, is not the aim of this book.

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with the adult they are planning to be working with. Without a preexperiment “warm-up” in which children get a chance to get to know the adults, the rate of children helping in the experiment drops by about half (Barragan & Dweck, 2014). If these conclusions demonstrate anything, it is that prosociality is a complex behaviour even when it emerges early in life. Prosociality, however, is a backbone to understanding the mechanism of RRM since it is precisely these behaviours that serve as a base for any future cooperation, and as such, for the emergence of retribution and reciprocity. As it happens, there is a significant argument to be made that most of our prosocial tendencies depend largely on empathy (Decety et al., 2016). Sensitivity to others’ distress coupled with caring for their welfare lies at the base of any cooperative tendencies; therefore, it is vital to understand it.

2.1.1 Empathy Empathy is broadly defined as our ability to understand other people’s feelings (Gallese, 2003), and there are many everyday examples where people show their profound ability to engage in such behaviours. For instance, they stand up for their friends if someone insults them, get upset when something bad happens to their loved ones, and donate to charities. We feel a whole array of emotions when we think about others being wronged or hurt, even if those are fictional characters in books or movies. Humans are moral animals, with moral sentiments defined as feelings related to the interest and welfare of others (Haidt, 2003). The more we identify with the victim of wrongdoing, the angrier we feel about the situation (Yzerbyt et al., 2003); that is to say, the more we empathise with the victim. Empathy and prosociality seem to be very strongly interlinked. Empathy can motivate prosocial and caregiving behaviours, inhibit aggression, and facilitate cooperation (Decety et al., 2016; Hay, 2009; Zahn-Waxler et al., 1992). Empathic concern is associated with prosocial behaviour in both children (Davidov et al., 2013; Williams et al.,

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2014) and adults (Batson, 2009; Miller et al., 2015). In children, affective and cognitive empathy observed in the first year of life systematically predicted subsequent levels of prosocial behaviour during the second year (Davidov et al., 2013). The opposite is also true; sociable temperament has been linked to greater empathic concern in the first years of life (Batson, 2012; Light & Zahn-Waxler, 2011). Most of the empathic capacity is innate and not learned, as supported by longitudinal studies that have found either no or a small increase in empathic concern with age (Knafo et al., 2008; Light et al., 2009; Roth-Hanania et al., 2011; Vaish et al., 2009; Volbrecht et al., 2007; Zahn-Waxler et al., 1992); this is in contrast to the more substantial age increases found for cognitive empathy and prosocial behaviour. Furthermore, empathy is evolutionarily advantageous: affective signalling between group members increased caregiving, promoted defence against predators, and facilitated bonding between group members (Decety et al., 2016; Nowak, 2006). Therefore, it is only reasonable for us to have evolved with empathetic and prosocial tendencies. Numerous experiments were designed, correlating the Empathy Quotient (EQ), a recognised psychometric measure for empathy (Billington et al., 2007), with various prosocial tendencies. For example, higher scores on EQ are strongly correlated with differences in volunteering and charitable donations (Davis et al., 1999). In behavioural economics games, it was discovered that EQ affected the decision to share resources with the others during a dictator game (Ben-Ner et al., 2004) and affected the measure of trustworthiness in a trust game (BenNer & Halldorsson, 2010). Pelligra (2011) found a strong association between EQ and patterns of restitution, such as reciprocity, and Singer and Fehr (2005) found that people with higher EQ scores are most likely to exhibit altruistic behaviours. We all know that people are capable of predicting the actions of others. In psychology, it is often studied as Theory of Mind (ToM). The idea that we are capable of ToM lies in the foundation of Game Theory. In ToM, two aspects are recognised as part of this ability: cognitive (i.e., mentalising and rationally explaining the actions of others) and affective (emotionally driven ability to anticipate and share emotion with others;

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Singer & Lamm, 2009). There is a lot of neuroscientific evidence that supports that the process of empathy is automatic. Studies show an emotional network that automatically activates in response to others’ pain (Shamay-Tsoory, 2011) and even in the cases when a particular group has been dehumanised (Fiske, 2009). In an fMRI study (Singer et al., 2004), participants were shown or asked to imagine people experiencing certain emotional states. The participants elicited brain activity that corresponded to feeling that state. They also concluded that this response does not require any form of judgement of others’ feelings and is entirely automatic. However, the same study also finds an incredible heterogeneity between individuals in their ability to empathise. Many reasons are theorised to be responsible for the variation in empathy levels, starting from face expression recognition (Olderbak & Wilhelm, 2017) to brain structure (Banissy et al., 2012). It seems that even though the ability to empathise is found within all humans without specific disorders, the extent to which they do varies. This variation is significant for understanding other behaviours, more specifically reciprocity.

2.1.2 Reciprocity There is a strong link between empathy and prosociality, which directly relates to the feelings of reciprocity. A study asking people to play an investment game (Pelligra, 2011) found that scoring higher on the empathy questionnaire made participants more norm-compliant and willing to feel that they need to repay the trust and behave reciprocally. Indeed, other experiments (Fong, 2007) found that people who are more willing to empathise with and help others were also more likely to act in a reciprocal manner. That is to be expected and does relate to the concept of perceptions of the environment: you need to be able to perceive kindness to respond with kindness. However, that would only relate to positive reciprocity, and it is also important to discuss responding with hostility to hostility. There is ample evidence of people’s tendencies towards negative reciprocity in various situations. For example, employees are less likely to

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steal from their workplace if they feel that they are being treated well (Chen & Sandino, 2012), and they are less likely to attempt to avoid tax if they believe that the tax system is fair4 (Niesiob˛edzka, 2014). Bowles and Gintis (2000) posit that our support for welfare state policies is deeply entrenched in the notions of reciprocity. They argue that the reason that people might want to contribute to the welfare state is that they believe that people on the receiving end of it are contributing to society themselves. On the other hand, as soon as that belief is turned and more impoverished people, for example, are perceived as too lazy to earn money, the willingness to contribute decreases dramatically. In economic games, people tend to reward one another where possible. For example, in a Gift Exchange Game (GEG), the Proposer offers money to the Responder, which can be interpreted as a wage payment. The Responder can either accept or reject the wage. In case of a rejection, both parties get no payoff, but if the offer is accepted, the Responder can make a costly effort that will result in a higher profit to the Proposer. For the most cost-effective strategy, the Responder should always choose the minimum effort level. However, that is not always the case. Many studies have been conducted (Charness, 2000; Falk et al., 1999; Fehr et al., 1993, 1998; Fehr & Falk, 1999; Hannan et al., 2002) and all concluded that the mean effort is positively related to the offered wage, meaning that the more generous the offer is, the more the Responder is willing to give back reciprocally. All of the above evidence suggests that there is much evidence for people’s tendencies towards empathy, reciprocity, and prosociality. That offers support to one half of the basic assumptions of RRM that people tend to respond with kindness to kindness. However, for the sake of the model, the opposite should also be true: people should also react with unkindness to unkindness in a principle of an eye for an eye, which lay in the foundation of many criminal justice policies for so long.

4 Which does not mean that it is objectively fair, but this judgement remains in the eye of the beholder.

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Justice and Retribution

2.2.1 Evidence for Justice Concerns Justice has historically been a fundamental concern in all social organisations, and it is deeply rooted in human nature (Decety & Wheatley, 2015). We are sensitive to fairness from a very young age and are generally motivated to strive for justice and to avoid injustice (Hamlin, 2014; Jensen et al., 2014; Schmidt & Sommerville, 2011); think about what would happen if a child received fewer presents than their sibling for Christmas. It is not easy to define what justice means, but people do not like being treated worse than their counterparts on the most fundamental level. The perceived discrepancy between what a person expects, and the actual outcome causes severe distress in experiment subjects (Törnblom & Vermunt, 2012; Vermunt & Steensma, 2005), as evidenced by a study among workers that shows that unfair treatment by their boss leads to destructive behaviours among subordinates, such as theft or destroying equipment (Starlicki & Folger, 1997). Reciprocity is strongly interconnected with our understanding of fairness.5 Children have a strong preference for equality and show it as early as 16 months (Geraci & Surian, 2011; Hamlin et al., 2007; Schmidt & Sommerville, 2011). The tendency towards equality is so strong that many children will opt to discard their resources to end up with an equal amount, such as in an experiment by Shaw and Olson (2014) where children were asked to share five erasers between themselves and another child, and they chose to throw one away to allow for equal distribution. In another experiment, 16-month-old children were shown a puppet show in which a lion and a bear were distributing resources between a donkey and a cow. The lion shared all the resources equally, and the bear gave one animal everything and nothing to the other. When asked which of the animals was good and which one was bad, the children named the 5 Fairness is an interesting topic and a concept to define, as many literary works in the field of political philosophy and especially distributive justice can testify. I would like to stay out of this debate, instead of saying that people in general have a preference for “fairness”, even though they have different (and often conflicting even within one individuals) views on what fairness is.

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fair divider the good one (Geraci & Surian, 2011). However, some other concepts are still more important than equality. When asked to clear the toys away in the same study, the children are then given five erasers to distribute between two people. By default, they choose to give two to each and then toss the fifth one away. However, when they are instructed that one of the children did more work than the other, they immediately change their answer to giving more to the person who did more work (Sloane et al., 2012), demonstrating that children also care for other forms of justice and that they do take into account the surrounding circumstances. Of course, children can be selfish too on occasion, as any parent knows, but most of them in the previously discussed experiments show a general tendency towards fairness and equality. We see that behaviour evidenced in economic games, most notably the Ultimatum Game (UG), in which a pair of subjects has to agree on how to divide a sum of money. The Responder can choose to either accept or reject the offer, and the Proposer makes the offer. In case of rejection of the proposal, both parties receive nothing. Hundreds of experiments have shown that even though it would always be beneficial for the Responder to receive any sum of money greater than zero, that usually is not the case. Proposals offering the Responder less than 20% of the available surplus are rejected with a probability of 0.4 to 0.6 (Fehr & Schmidt, 2001). That means that people often respond to unfair offers by punishing the Proposer even though they also lose the money. This behaviour continued even when the stakes were very high (Cameron, 1999; Slonim & Roth, 1998). There is, of course, individual variation in how people react, with many participants in these experiments still opting for an unfair reward against receiving nothing. However, even when participants choose to accept unfair offers, they tend to be still outraged by unfair treatment but show more ability to exercise self-control (Kirk et al., 2011). Some people care for justice more than others, and some are willing to pay for it while others are not. However, perceptions of injustice remain essential. Additionally, the drive for justice is not only present when people feel wronged themselves. Most people also want other people to be treated fairly, and they are driven to adhere to principles of justice themselves

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(Baumert et al., 2013). There is ample evidence that even in the Third Party Punishment (TPP) scenario, where the participants observe the interaction and their outcomes are not affected by it, they still elect to inflict costly punishment when they perceive an injustice being done (Fehr & Schmidt, 2001). That concern for injustice against others plays a fundamental role in distinguishing between negative reciprocity and retribution in RRM. If negative reciprocity is responding to a hostile act by others, retribution is precisely the tendency for third-party injustice perception. However, self-centred concerns also play a role. Even though people are concerned with justice for others, they still react more strongly to the injustices done to themselves. Behaviours in economic decision-making tasks differ based on whether one is the victim or the observer of injustice, such that unfair third-party offers are punished less than unfair offers made to oneself (FeldmanHall et al., 2014). Nevertheless, evidence still suggests that humans react to an injustice being done, whether to themselves or others, and they often respond by punishing the violator.

2.2.2 Punishment A desire to punish wrongdoers is a natural and widespread sentiment, from a bar brawl that results from someone insulting another to the foundation of retributive justice and imprisonment, as well as the capital punishment debate. The appetite for revenge exists within most people. Even though most societies choose to avoid the culture of vigilantism and decide to leave the matters of justice to the police, people are still prone to behaviours like nasty gossip or snarky emails. Retaliation is a natural inclination, and even if we choose very subtle ways of punishing others ourselves, we take great pleasure in seeing revenge happen in books and movies and sympathise with it. However, we are not only interested in punishing those who wronged us. People also have the appetite for third party punishment or, as I call it, retribution; that is, as I explained earlier, punishment of the people who have done something wrong but did not harm us directly. For example, people on sex offender lists often end up stalked and abused to the point

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of having to move town or losing their jobs (Cubellis et al., 2019). In China, there is a “human flesh search engine” phenomenon, which is an attempt by some people to use the crowd-sourcing features of the internet to identify wrongdoers, such as corrupt officials (Cheong & Gong, 2010). Our desire to punish is so strong that when one participant of the famous show Great British Bake-Off supposedly “sabotaged” another participant’s Baked Alaska, people have been calling for her disqualification or even arrest, stalked her on social media and ultimately, the participant did not come back to the show. Punishments are necessary for the functioning of our modern societies. In behavioural economics, studying human cooperation is often done through a Public Goods Game (PGG). In a PGG, the participants are asked to choose between contributing money to a Public Good or keeping it for themselves; the Public Good then multiples everything that has been put into it and shares it equally between all participants. It is designed as a metaphor for our society’s existing public goods systems, such as paying taxes to fund roads and state schools. The public good benefits everyone, but everyone needs to pay for it. Of course, the most beneficial strategy is for everyone to contribute the maximum amount and then get the maximum rewards. However, a mechanism for defection emerges. Naturally, the best individual outcome is for everyone else to contribute the money but them, making sure that they are getting the money from the Public Good but do not have to spend their own money on it. Therefore, in any game, some players inevitably try to contribute less to benefit more individually. That, in turn, makes the other players contribute less since they do not want to be taken advantage of. In the end, as the players progress through the rounds, they contribute less and less and hence get less and less money from the Public Good. Therefore, complete free-riding is a dominant strategy of most players in most Public Goods games. However, that dynamic changes entirely when the possibility of punishment is introduced in the game, with contributions rising on average by 58% of the endowment (Fehr & Gächter, 2000b). Findings like these further demonstrate why the process of natural selection would select for behaviours such as these.

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Powerful motives drive the need to punish. Even when punishing another player in the game is costly, people are willing to do so to ensure cooperation (Ostrom et al., 1992). Of course, there would be material incentives to doing so in the game: by punishing a defector, a player can ensure that everyone contributes to the Public Good and that the whole group profits. However, the need to punish runs stronger than that. Even in treatments where all the players played only one round of the game and punishments would carry absolutely no material benefits to any participants, many still opted for costly punishment, at least 80% of players were punished at least once (Fehr & Fischbacher, 2004; Fehr & Gächter, 2000b). We find a lot of evidence for that in our everyday life. A lot of societies tend to use a criminal justice system based on the punishment of the wrongdoer. Of course, there are many real-life Public Goods, such as taxation systems. For example, we enjoy having good roads built, but we all need to contribute some money to the government in taxes for that to happen. If anyone chooses to free-ride, the government may choose to fine or even imprison them as punishment to ensure cooperation. However, for thousands of years before the appearance of state-administered punishment institutions, humans relied on personal sanctions to cooperate (Fehr et al., 2002; Fehr & Gächter, 2002; Richerson et al., 2003). Many studies show a strong link between third-party sanctions and negative emotions towards the norm violators (Darley & Pittman, 2003; Fehr & Fischbacher, 2004; Seymour et al., 2007). For example, some experiments studied the feelings of anger (by manipulating responsibility for sanctioning) and guilt (by manipulating responsibility for sanctioning) concerning punishment. They found the relationship between anger, guilt, and punishment quite strong, showing that inhibiting either one of them reduced punishment (Nelissen & Zeelenberg, 2009). Others link punishment to envy, arguing that some participants only feel the need to reduce inequality if the other participants are better off (van de Ven et al., 2009). Nevertheless, even though a tendency to punish is strong, some people are significantly more likely to punish wrongdoers than others. Therefore, even when discussing the results from Ultimatum Games, we need

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to remember that there was always a relatively large proportion of the participants that acted in a self-centred manner as per theories of gain maximisation. There are significant individual differences in how people perceive injustice and react when witnessing unfairness (Schmitt et al., 2010; Vermunt, 2014). Most stable individual differences are in sensitivity to justice issues (Baumert & Schmitt, 2009; Schmitt et al., 1995, 2010), as in whether people perceive something as unfair or not. Justice sensitivity proves to be one of the driving factors in predicting prosocial behaviour, such as sharing as measured by the Ultimatum Game (Edele et al., 2013). Justice sensitivity reflects an individual’s concern for justice. It is an important predictor of justice-related emotion and behaviour (Baumert et al., 2013). While relatively stable, it develops over time and is susceptible in predictable ways to previous justice-related experiences (Wijn & Bos, 2010). That means that concerns for justice not only change over time but also reflect our experiences. A theoretical framework that accounts for the hypothesised link between justice concerns and selfrelated concerns can be derived from the notion that interpersonal suspiciousness might be the psychological manifestation of a “cheater detection” module that can be seen as an evolutionarily stable strategy aimed at keeping a social “tit-for-tat” strategy in balance (Axelrod, 1981; Cosmides & Tooby, 1992). There is evidence that there are other factors that predict our sensitivity to injustice. For example, individual differences in cognitive empathy and empathic concern predict sensitivity to justice for others and the endorsement of moral rules. Empathy is a multifaceted construct used to account for the capacity to share and understand the thoughts and feelings of others. For individuals scoring high on justice sensitivity, perceiving injustice provides a strong motivation to avoid injustice or restore justice (Decety & Yoder, 2016). Empathy makes people notice injustices more frequently and feel compelled to act to prevent future injustices from happening (Bondü & Elsner, 2015). In the previous section of this chapter, I explained how empathy plays a role in predicting people’s reciprocal tendencies. This evidence suggests

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that empathy is vital for positive reciprocity and potentially for negative reciprocity and retribution, rendering it a critical component of the model. However, other factors on top of empathy are important for RRM, and those factors are the role of the environment and learning.

2.3

The Role of Learning and the Environment

People behave differently depending on what circumstances they find themselves in. In criminology, we generally recognise that to be the case. For example, we study the idea of environmental criminology (Brantingham & Brantingham, 1993); evaluate neighbourhoods and the concepts of collective efficacy (Sampson et al., 1997); or delve into the methods of situational crime prevention (Clarke, 1980). We spend our time trying to design cities in such a way to decrease the number of criminogenic environments, learn how to plan against crime (Landman & Liebermann, 2005) and design hot-spot analysis (Block & Block, 1995). We may disagree on the exact extent to which our surroundings play a role or the exact mechanism, but the idea that crime does not happen in a vacuum remains. Spatial differences, of course, are found in more areas than crime prevention and are prevalent in most of the things we do. For example, there are significant differences in the way people play the Ultimatum Game, part of which is of course accounted for by the individual differences. However, cross-cultural research found that participants of different nationalities led to differing results, with a wide contrast of people rejecting the offers, which means that these behaviours are to some extent influenced by culture (Roth et al., 1991). The most vivid example is the Ultimatum Game that was played with students in the West Bank who were much more likely to reject an offer when written in Hebrew as opposed to Arabic (Schubert & Lambsdorff, 2014). That shows that in a conflict-ridden environment, discrimination exists against the Israelis compared to the Palestinians, which would not have been the case if there was no war. But even in other cases, such as Russia versus Switzerland, we find significant differences in the willingness to reward and punish in economic games (Gächter & Herrmann, 2009).

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We can also observe that people from collectivist cultures are much better at understanding someone else’s perspective (in almost all Theory of Mind tasks) and are more likely to blame people for violating the social norm (Sapolsky, 2017). This is because people learn social norms and behaviours from others in the process of cultural learning (Tomasello et al., 2005). This process of cultural learning leads people to accept similar values and beliefs, and among others are ideas about rewarding and punishing others for their behaviours (Henrich & Henrich, 2007). Moreover, behaviours of retribution and reciprocity are strongly shaped by the local social norms about what constitutes appropriate behaviour and reaction to a violation (Henrich & Henrich, 2007; O’Gorman et al., 2008). Studies on deceit (Batson et al., 1997, 1999) and bystander intervention (Gollwitzer et al., 2005) demonstrate how strongly moral behaviour depends on the situational context. Indeed, people’s reciprocal behaviours also change depending on their immediate environments. For example, people are more likely to be reciprocal if their familiarity and trust in the other party increases (Sánchez-Franco & Roldán, 2015). It also changes depending on who the players think they were playing with, with reciprocal behaviours diminishing once the participants realise they are playing with a computer (Mahmoodi et al., 2018). Moreover, people tend to change their behaviours depending on their general view of human nature (Gollwitzer et al., 2005). Elias (1969, in Vermunt, 2014) introduces a concept of social configuration. He suggests that the actions of individuals lead to changes in social make-up. These changes are unplanned and therefore unpredictable but lead to changes in behaviour, such as table manners, greeting rituals, or attitudes towards strangers and cooperation. That means that people themselves change the way the world around them is, while the world is changing them simultaneously. This interaction lies at the core of understanding our tendencies towards retribution and reciprocity. We respond to what we think the norms are, and by responding, we support and create social norms that influence others. As a result, we can conclude that the evidence from the fields of evolutionary psychology and child development points us in the direction of

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seeing cooperation as an innate tendency, which is then further developed by various forms of learning. Our ability to have empathy makes us respond with kindness to kindness, and the desire to punish the wrongdoers supports the creation and the existence of social norms that humans do not like to violate. Cooperation is a behaviour selected for by natural selection that allows us to survive by working as a group. As a result, these behaviours have robust neurophysiological underpinning. The evidence for this claim, together with the neurobiological mechanisms for retributive and reciprocal behaviours, is described in the next section.

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Neurophysiological Mechanisms of Cooperation

As much as some of us would like to believe that we are dominated by rational thought, humans are emotional beings. Even in cases in which we hope to detach our primal emotional systems from our decisionmaking, we often cannot. Numerous studies on moral behaviours found that when we are asked to make a moral judgement, we activate brain areas responsible for emotional processing (Greene et al., 2001, 2004; Heekeren et al., 2003; Moll et al., 2002). Moreover, manipulating people’s affective states in an experiment can also alter people’s moral judgements (Valdesolo & DeSteno, 2006; Wheatley & Haidt, 2005). It is unsurprising, then, that cooperation is also something that is heavily influenced by our immediate reactions. Since cooperation is evolutionarily advantageous, some mechanisms have evolved that allow us to ensure it, and a lot of these mechanisms rely on our emotional processes in the brain. This section is designed to outline the neurophysiological evidence for all the concepts discussed in the previous section before bringing them together under one mechanism explaining RRM in the following section. I do that starting from the concept that underlies the tendencies of retribution and reciprocity: empathy. When individuals perceive another person’s emotion, they often experience a similar emotion themselves (e.g., Decety & Meyer, 2008; Eisenberg et al., 2006).

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A popular explanation for empathy has long been the “mirror neurons”, a group of cells found in the premotor cortex of the brain around 30 years ago (Rizzolatti, 2005). The idea is that the mirror neurons fire when we perceive someone experiencing something, which makes us experience the same thing, i.e. empathise, and this discovery caused a lot of stir in the field of neuroscience. However, the importance of mirror neurons should not be overestimated, and proof of their existence is yet to be fully validated (Pascolo, 2013). A meta-analysis examining 51 studies that tackled the question of empathy and mirror neurons found that a lot of these studies were contradictory and in the majority, there was no real relationship between the two (Bekkali et al., 2021). That finding is not that surprising. Initially, mirror neurons have been found in the brains of rhesus macaques. However, even though macaques are perfectly able to feel empathy, their feelings are not as deep and complex as the feelings of humans. Moreover, all humans possess mirror neurons within their brains, but some individuals are more empathetic and compassionate than others. Therefore, the issue of the neuroscience of empathy requires a more complex approach than that. This ability to feel what another is feeling is thought to result from an overlap in brain circuits; exposure to another’s emotion activates some of the same neural mechanisms involved when the self experiences that emotion (Decety & Meyer, 2008; Preston & de Waal, 2002; Singer, 2006). For example, as we see someone else experience pain, activation in the bilateral anterior insula (AI) and the rostral anterior cingulate cortex (ACC) can be observed (Morrison et al., 2007; Singer & Fehr, 2005). That is the same circuitry that activates when we ourselves experience pain. That means that when we see the pain of others, our brain makes us feel it too. One does not need to be close to the other person to experience empathy. The same circuitry activates for strangers and imaginary persons, such as book characters (Jackson et al., 2005; Morrison et al., 2004). However, we usually do not get confused between our experiences and others. For example, when reading a good book, we do not close it down and believe that we have experienced everything the characters experienced. Moreover, reading about a favourite character’s misadventures

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might be unpleasant, but we will never experience pain as vividly as if it happened to us. The ability to distinguish implicitly between self and others likely occurs because parts of the neural network do not overlap when processing similar experiences of self and others; some neural pathways are activated only (or faster) when the self is directly involved (Decety & Meyer, 2008; Singer, 2006). Empathy is very complex, but at the same time, it relies on some very basic mechanisms since being able to understand and share others’ emotional states was so critical for our survival. Decety and Svetlova (2012) studied the neuroscience of empathy and found that the areas involved are the brainstem, the amygdala, hypothalamus, basal ganglia, insula, and orbitofrontal cortex. They also found that these areas develop gradually as children start growing up but start to play a role in empathic behaviours from birth or even before. The involvement of areas such as the amygdala and the brainstem indeed demonstrates that empathy has deep evolutionary and neurological underpinnings. Therefore, it is unsurprising that the tendencies of retribution and reciprocity are related to empathy. Nevertheless, when looking separately at the neurophysiology of Retribution, Positive Reciprocity, and Negative Reciprocity, it appears that a lot of the areas involved do not necessarily match.

3.1

Positive Reciprocity

Positive Reciprocity is built upon the idea of responding with kindness to kindness. That is not a behaviour unknown to monkeys or even to bacteria, but the way we engage in reciprocity is a behaviour unique to humans (Melis & Semmann, 2010) and requires high social cognition (Brosnan et al., 2010). Hence, it heavily relies on the areas responsible not necessarily for empathy but the Theory of Mind (ToM) to discern people’s motives and react accordingly. ToM, as explained earlier, is our ability to infer others’ internal states that develop in infancy and develop into adulthood and a study by Schug et al. (2016) found that the development of ToM predicts participants’ positively reciprocal actions in a decision-making game in preschool children. The brain

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regions associated with ToM include the superior temporal gyrus (STS), the temporoparietal junction (TPJ), the medial prefrontal cortex, and sometimes the precuneus, and the amygdala (Gallagher & Frith, 2003). Unsurprisingly, there is some overlap between activity associated with ToM and activity associated with positive reciprocity. You need to be able to understand people’s intentions, social beliefs, and personality traits. Studies found that the area associated with understanding other people’s goals and intentions is the temporoparietal junction (TPJ). The TPJ is an area that integrates information from outside and inside of the body and plays a crucial role in ToM in general. However, it is mainly responsible for immediate and short-term reactions. The area that is responsible for the more enduring information regarding the dispositions of others and social norms and scripts is the medial prefrontal cortex (MPFC; van Overwalle, 2009), known to play a role in the cognitive evaluation of morality. Numerous MRI studies found that cooperation of any kind requires prefrontal cortex (PFC) involvement that inhibits the desire for an initial reward in lieu of future benefit (delayed gratification; McCabe et al., 2001). That is unsurprising since the PFC, as the newest area of the brain to evolve, is heavily involved in complex cognitive behaviours. Therefore, social cognition seems to be an interaction of understanding the long-term goals and social norms on the more abstract level (guided by the MPFC) and the immediate reactions to other’s goals and intentions (guided by the TPJ). However, when studied more closely, fMRI studies (Mitchell, 2009; van Overwalle, 2009) suggest that the TPJ is only active when a participant is on the receiving end of reciprocity, not when engaging in reciprocal behaviours themselves. In contrast, when the player decides to reciprocate, only the MPFC is being activated, more specifically, the ventromedial part of the PFC (van den Bos et al., 2011; Rolls, 2000). That means that even though there are some studies that suggest that the TPJ might be important in reciprocity, it does not guide people’s behaviours when they make a decision to reciprocate. The MPFC, however, does. The ventromedial prefrontal cortex (VMPFC) plays a pivotal role in caregiving behaviours, empathic concern, and moral decision-making (Decety & Cowell, 2014; Parsons et al., 2013), and was strongly linked

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to following social norms (Baumgartner et al., 2011). There is a lot of evidence that suggests that the VMPFC plays a critical role in moral judgements. Early neurological damage (before five years of age) of this region leads patients to endorse significantly more self-serving judgments that break moral rules or inflict harm on others later in life (Decety & Yoder, 2016). In an experiment (Koenigs et al., 2007) involving subjects with lesions to their VMPFC, the subjects were asked to make decisions on moral dilemmas such as the trolley problem. In normal subjects, the utilitarian decision to sacrifice one person to save a few causes a strong emotional aversion. However, subjects with the VMPFC damage were much more willing to make those utilitarian decisions. Therefore, VMPFC seems to play a role in the affective/emotional way we make the decisions, but not in the rational/conscious system. However, more systems than only emotions are involved in our behaviour. The system most associated with emotional regulation is the limbic system. There is a lot of debate about what areas are involved in the limbic system, but the main suspects are the hypothalamus, the amygdala, the thalamus, and the hippocampus (Morgane et al., 2005). Few studies exist that study the role of the limbic system in retribution and reciprocity, possibly because there are a lot of areas involved which makes it hard to study all of them together in an experimental setting. It is difficult to include the limbic system in the model, considering it governs a lot of functions that can at times be conflicting. There is little scientific support that would suggest that any of these areas are heavily involved in positive reciprocity. On the other hand, the VMPFC appears to be more involved in positive reciprocity. Unfortunately, most of the papers coming close to the topic of responding to kindness with kindness are studies of gratitude, which is a related concept to positive reciprocity but not the same thing (Fox et al., 2015). However, the evidence that does exist supports the role of the VMPFC in positive reciprocity in the way I define it. For example, a study (Wang et al., 2017) using transcranial Direct-Current Stimulation (tDCS) involved a gift exchange game. It showed that anodal stimulation of the VMPFC (increasing activity) led to an increased effort from the workers. Disrupting the working of the

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VMPFC further reduced the amount of effort put into work. That shows that the role of the VMPFC is quite essential. Interestingly, the VMPFC is only activated as a response to the fair offers and not to the unfair offers (Feng et al., 2015), suggesting that the VMPFC reacts positively to a reward and does not quite regulate the reaction to a lack of reward. Another region of the PFC that is thought to have a role in positive reciprocal feelings is the dorsolateral prefrontal cortex (DLPFC). The DLPFC is involved in regulating empathetic feelings involved in relationships with other individuals (Balconi & Bortolotti, 2012) and was found to be activated when participants exchanged gifts with one another (Balconi et al., 2019). In general, the DLPFC is involved in the regulation of thought and action and the cognitive processes that affect the mental maths of calculating the benefit (Dehaene et al., 2004). As such, it is rather difficult to find any complex behaviour in which the DLPFC would not be involved, and in this case, it starts to lose its meaning. As such, there are no studies to my knowledge that concluded that the DLPFC plays an essential role in positively reciprocal behaviours. Therefore, it is reasonable to assume that the area we are mostly thinking about when studying reciprocity is the VMPFC, the area responsible for social behaviour and emotional regulation. Some studies find the activation in the anterior cingulate cortex (ACC) when participants reciprocate (van den Bos et al., 2009). The ACC is involved in many high-level functions such as reward anticipation and morality; hence it makes sense that it is important. However, it becomes hard to analyse when it comes to neuroimaging studies, especially with the VMPFC, since the ACC is structurally part of the MPFC6 (but not necessarily part of the VMPFC). Nevertheless, studies (Knoch et al., 2006; Rilling et al., 2007) suggest that the ACC becomes quite important when it comes to cooperation when the reward is low, suggesting that it plays an important role in overriding selfish motives to defect. Therefore, all the findings suggest that the most important areas of the brain associated and responsible for positive reciprocity are the VMPFC 6

There is, in fact, very little agreement when it comes to the PFC on the exact borders of brain areas since there are no obvious structures. Therefore, when two areas are adjacent to one another there is no way of knowing for sure where one starts and the other begins.

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and the ACC. These areas are structurally and functionally quite proximal. However, Vassena et al. (2014) demonstrated that these areas are, in fact, quite distinct and contribute different things to emotional processing and moral decision-making. They found that the ACC is connected to intentionality and was not active when the participant was responding to the allocation of resources that happened at random, while the VMPFC reacted to all events that required reward processing, random or otherwise. They also found that the ACC was more active in high-risk low-reward conditions, and that the VMPFC mainly reacted to the positive events, whereas the ACC reacted to negative events (i.e., losing resources as a result of unfair allocation). Therefore, both of these areas should be included and treated separately. An interesting finding, however, is that both of these areas are responsible for median-order emotional processing. That means that these areas take input from the lower order areas such as the amygdala, and then send the information off for higher order processing in the prefrontal areas responsible for rational thought. Therefore, they both take the feelings of initial emotional response and a desire for reward, and then make us make decisions that would maximise our rewards. That makes positive reciprocity a strong and interesting behaviour to study. The question remains whether negative reciprocity uses the same areas of the brain. The answer is no, albeit some overlap exists.

3.2

Negative Reciprocity

Negative reciprocity, it turns out, is a very complicated behaviour that involves many areas of the brain, all competing against one another within various circuitries. Negative reciprocity involves a strong emotional response which is then regulated by some higher cognition brain areas, and both of these mechanisms are explained and presented in this section, separated into three different subsections. First, I explain the emotional response, then I present the higher order cognition elements, and finally I bring them both together in a dual-process model.

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3.2.1 Role of the Emotions Despite the initial urge to assume that positive reciprocity and negative reciprocity use the same systems, there is very little support that they do. For example, the same study that found ToM to be important in positive reciprocity (Schug et al., 2016) also found that ToM plays no role in negative reciprocity. However, some similarity exists. In a similar vein as positive reciprocity, negatively reciprocal feelings also rely heavily on emotional responses. We do not like being exploited or cheated, and any injustice we experience often elicits a strong emotional response. There is a strong relationship between negative emotions and the need to negatively reciprocate. In a study that measured participant’s skin conductance response (SCR) as an index of emotional arousal, Van’t Wout et al. (2006) found that increased SCR predicted participants rejecting unfair offers in an Ultimatum Game. In a different study (Harlé & Sanfey, 2007), the participants’ emotional states were manipulated prior to the game. Those presented with sad movies prior to the game were more likely to reject unfair offers than those who watched neutral or happy movies, demonstrating that the emotions we feel play a big role in our decision-making to punish a hostile act. Therefore, it is not surprising that fMRI studies show activation of the amygdala, the area responsible for affective experiences, when the participants were cheated on during a cooperation game (Sakaiya et al., 2013). The amygdala has previously been linked to responding to intense social stimuli (Krämer et al., 2007), involving a powerful emotion (Phan et al., 2006) and is related to both social (Olsson & Ochsner, 2008) and moral cognition (Moll et al., 2005). However, amygdala activation occurs in many other situations that elicit an aggressive emotional response and is not specific to the negative reciprocal behaviours. Therefore, it is unlikely to be a very useful area to study when examining the mechanism for negative reciprocity specifically. Studies (Hollmann et al., 2011; Sanfey et al., 2003) found that the activation of the Anterior Insula (AI) predicted rejection of unfair offers in the Ultimatum Game. AI is associated with negative emotions in general, such as pain and distress (Derbyshire et al., 1997; Iadarola et al., 1998), hunger (Denton et al., 1999), and autonomic arousal (Critchley

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et al., 2000). Emotion-based disgust can also cause AI activation (Sanfey et al., 2003), which might be the reaction to being presented with an unfair offer. In an fMRI study, Sanfey et al. found that people with higher AI activation were more likely to reject unfair offers. Moreover, higher AI activation within an individual also predicted whether an offer presented would be accepted or rejected. It is not entirely clear what role AI activation plays. Some papers suggest that unfair treatment elicits negative emotions (Rilling et al., 2008; Sanfey et al., 2003). Others argue that it is not necessarily the negative emotion that is important but the violation of a social norm in general, and the AI activation plays a role in the decision to reject a social norm violation (Civai et al., 2012, Corradi-Dell’Acqua et al., 2013). There is a possibility that it is both since neither are mutually exclusive. Nevertheless, it is clear that the AI responds to perceived unfairness and urges us to retaliate by punishing the violator. However, there are opposing mechanisms that prevent us from doing so indiscriminately. Therefore, even though we have a mechanism of AI activation that makes us lash out, there are other mechanisms at play.

3.2.2 Impulse Suppression In a similar light as positive reciprocity, studies find that the ACC is activated in negative reciprocity scenarios as well. A meta-analysis (Gabay et al., 2014) found that it is one of the most robust brain activations across all the studies on the subject. The ACC activation is generally explained as monitoring a conflict between emotional and cognitive motivations (Baumgartner et al., 2011; Sanfey et al., 2003). It is also not entirely clear what the exact role of the ACC is in this situation. A potential suggestion is that the ACC is responsible for detecting a norm violation (Chang & Sanfey, 2013; Güro˘glu et al., 2010; White et al., 2013) and top-down inhibition of initial responses to reject (Etkin et al., 2011), possibly coming from the AI. In general, two areas of the brain typically engage when individuals make decisions when there is a conflict between the social norm and the personal interest: the anterior cingulate cortex (the ACC) and the right

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dorsolateral prefrontal cortex (rDLPFC; Spitzer et al., 2007). It is speculated that the primary job of these areas is to suppress selfish motives and urge us to cooperate even when it is costly. Even though evidence suggests that different areas of the brain activate when dealing with varying motives for reciprocity, the ACC and the rDLPFC were found to be consistently activated in any reciprocal scenario when the benefit to participants was low (van den Bos et al., 2009). However, a different explanation emerged that introduces the ACC and the rDLPFC not as the two areas responsible for the same thing, but mainly as competing with one another (Sanfey et al., 2003). In an Ultimatum Game (UG) involving an fMRI scan, the areas that were the first to light up in response to unfair offers were the ACC and the DLPFC (Sanfey et al., 2003). Moreover, the study also showed that the activation was much greater when playing against human subjects as opposed to computers, and was also sensitive to the degree of unfairness. The other area that also activated at the same time as the ACC was the AI. Sanfey et al. (2003) suggested that it might be that the ACC and the AI are activating together as an impulsive response to injustice. Then the rDLPFC is the area responsible for suppressing that initial impulse, making these areas compete against one another. A study using an EEG scan reported activation in the DLPFC during a reciprocal exchange in the Prisoner’s Dilemma game (Fallani et al., 2010). The DLPFC is an area that is linked to reward circuitry but is also responsible for regulating social cognition. While the left side of the DLPFC is mainly related to the learning and integration of information (Bunge et al., 2009), the right side is more closely linked to norm compliance (Spitzer et al., 2007) and hence more relevant to negative reciprocity. Servaas et al. (2015) found that the frontal regions of the brain were strongly associated with accepting unfair offers. DLPFC activation seems to be a consistent factor in almost all games in all conditions where unfairness is involved (Grecucci et al., 2013). This area of the brain that is involved with higher processing and inhibition may be responsible for focusing on the task at hand, i.e. maximising the monetary outcome of the experiment. An unfair offer is more difficult to accept; hence higher

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demands have to be placed to suppress the emotional desire to reject the said offer. Knoch et al. (2006) tested the idea that rDLPFC influences the tendency towards negative reciprocity in a Prisoner Dilemma experiment. Since the DLPFC is located in the outer regions of the brain, it is possible to use low-frequency Transcranial magnetic stimulation (TMS) to temporarily disrupt the working of that area and see how it affects behaviour. It is further supported by the studies that used disruptive magnetic stimulation of the DLPFC and found that they could make participants change their strategy when playing decision-making games (Knoch et al., 2006; Van’t Wout et al., 2005). Furthermore, when the rDLPFC was disrupted, it increased the acceptance of offers by 34%, suggesting that the rDLPFC is responsible for suppressing a selfish motive of monetary gain and plays a crucial role in punishment to enforce a social norm. Studies found that disruption does not affect people’s fairness-related judgements. The participants would still judge the offers as unfair, but the disruption affected the fairness-related behaviour, making them more likely to accept anyway (Fehr & Camerer, 2007). However, although DLPFC is always activated when presented with unfair offers, the activation does not necessarily correlate with acceptance rates (Sanfey et al., 2003). That means that even though there is an activation in that area, it is not sufficient to predict behaviour. However, in this chapter I aim to make sense of the vast and often conflicting evidence that exists on this topic, therefore, the way in which all of this evidence would make sense is in a dual-processes model.

3.2.3 Dual-Process Model All in all, a lot of areas of the brain seem to be involved in negative reciprocity, but there is still a lack of agreement over the exact process. Negative reciprocity appears to be a combination of some initial impulses and the overriding mechanisms, which aligns with the way most human behaviour works. Rejected offers have greater AI and ACC activations than DLPFC activation and accepted offers have it the other way around,

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concluding that the negatively retributive behaviour results in competition between the areas. Therefore, what we observe can be summarised into a dual-process model. The AI, the ACC, and the rDLPFC seem to activate simultaneously and compete for the result (Sanfey et al., 2003). In this model, the AI activates in response to a perceived unfairness, which causes us to have an impulse to respond with unkindness to unkindness. However, the ACC notices that conflict and regulates it on par with the rDLPFC that overrides those initial tendencies. The ACC tends to override the tendency to punish in favour of accepting to maximise the monetary income, as opposed to the rDLPFC that overrides the selfish motive to make monetary gains and opts to enforce a social norm instead even if it is costly for us. The harder it is to override the tendency the more activation is observed in the area. Negative reciprocity is by far not the only behaviour that involves conflict between several brain areas, and therefore a dual-process model is not completely unique to it. However, it has a potential to explain a lot of existing observations as well as explain the existing individual differences in punishment. People might always feel the urge to punish injustice, but at the same time they often choose not to even if they do feel that initial emotional response. That means that sometimes the urge is not strong enough, making it easier to suppress. Other times, however, the urge is so strong that the higher order processing areas are not able to regulate that reaction and hence that results in negatively reciprocal action. This posits the question whether retribution would share the same complexity as negative reciprocity.

3.3

Retribution

It is reasonable to assume that the same brain areas responsible for negative reciprocity are also responsible for retribution. After all, both tendencies involve a response to unfairness and some idea of punishment (Eisenberger et al., 2004). Therefore, we should expect the same variation of the AI, ACC, and rDLPFC activation. However, imaging studies that study this relationship find that not to be the case. The relationship

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between retribution and negative reciprocity is less close than one would expect. For instance, other animals such as chimpanzees, show only negative reciprocity but do not show a tendency towards retribution (Riedl et al., 2012), which already suggests that these processes are distinct from one another. This view is also supported by neuroscientific studies. A study that compared negative reciprocity and retribution showed notably more activation in the ACC in the negative reciprocity scenario as opposed to retribution (Strobel et al., 2011), suggesting that the ACC does not seem to play an essential role in retribution, in contrast to the important role it plays in negative reciprocity. The DLPFC does not seem to play as much of a role either as it is not activated much in the third-party scenarios (Knoch et al., 2006), possibly because there is not as much thinking about the goal outcome as in negatively reciprocal behaviours. There is, however, some evidence of the DLPFC involvement in retribution. In a study by Buckholtz et al. (2008), participants were scanned as they were assessing the scenarios in which a person was engaging in various activities starting from noncriminal and finishing with severe criminal activities such as rape or murder. The study targeted two parts of third-party decision-making: determining responsibility and assigning appropriate punishment magnitude. In comparing the Responsibility and Diminished Responsibility scenarios, activation in the rDLPFC was found much more active in the Responsibility scenario. No effect of condition was found in the left DLPFC, suggesting that punishment-related prefrontal activation is confined to the right hemisphere. That might suggest that the rDLPFC may play some role in retribution. In the previous section, I described how disruptions of the rDLPFC made the participants less negatively reciprocal (Knoch et al., 2006; Van’t Wout et al., 2005). However, the disruption made no difference on their third party punishment behaviours when the participants were told they were playing against computers and not actual people (Knoch et al., 2006). Therefore, combined with the study by Buckholtz et al. (2008), it can be concluded that rDLPFC is only activated when criminal responsibility can be assigned to the perpetrator. However, even though these areas seem to be active at some point during the experiments, these

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activations do not seem to correlate with decisions to punish or not to punish and are therefore not sufficient at explaining this behaviour (Strobel et al., 2011). Therefore, we can conclude that neither the ACC nor the DLPFC plays a determining role in retribution despite being responsible for negative reciprocity. Studies that examined the differences in the magnitude of punishment found that the areas most active are the ones responsible for affective processing: namely the amygdala, medial prefrontal cortex, and the posterior cingulate cortex. These areas have been consistently linked to emotional and social processing in many other studies (Amodio & Frith, 2006; Lieberman, 2007). This shows that there is a link between affect and motivation for punishment in a third-party context. That could be both anger about unfair offers or enjoyment of revenge. There is, therefore, one area that ties negative reciprocity and retribution together. The AI activates in third party punishment scenarios as well as direct negative reciprocity scenarios (Civai et al., 2012; CorradiDell’Acqua et al., 2013; Strobel et al., 2011), which leads to a conclusion that it is an area that is responsible for fairness-related emotion in general. This conclusion is in line with other studies suggesting that the AI responds to norm violations in many different scenarios (Güro˘glu et al., 2010). Therefore, we can conclude that our emotional response to perceived injustice stems from the same area whether it involves the subject or the third party, but the other mechanisms are different. We tend to derive satisfaction from punishing the violators of a social norm.7 Therefore, it may be expected that the areas implicated in reward processing would be involved, such as the Dorsal Striatum (DS). DS is an area of the brain responsible for processing rewards resulting from the decision, and neuroscientific evidence suggests that it plays a significant role when the participants believe their trust is being abused (O’Doherty, 2004). Numerous studies using positron-emission tomography (PET; de Quervain et al., 2004) and fMRI (Singer et al., 2006) show a very strong activation in the DS in Prisoner Dilemma (PD) games as well as other experiments (Krämer et al., 2007). Greater activation of the DS was associated with more severe punishments (Seymour et al., 2007), leading 7

Which is why we say “revenge is sweet”.

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to a conclusion that the DS is responsible for the presence or absence of punishment and the level making this a critical area for explaining retributive behaviours. It is important to note that although we do derive reward from a punishment, there is a difference in brain activation depending on the motives. When it comes to so-called “anti-social punishment”, i.e. the tendency to spend their resources to punish cooperative or fair behaviours, the activation of the brain is entirely different from the so-called “altruistic punishment” (Nikiforakis, 2008). Even though the punishment mechanism is the same, anti-social punishment tends to mainly activate the TPJ and does not concern the other areas much (Gerfo et al., 2019). This finding suggests that there is something specific about punishing the violator of a social norm that makes us feel good about ourselves. As we can conclude from the findings in this section, it is the DS and the AI that give us this particular satisfaction. Interestingly, the desire for retribution does not seem concerned with the more frontal regions of the brain responsible for top-down processing, instead concentrating on the more primal areas of the brain, such as the AI and the DS. This suggests that our rejection of social norm violations is very much innate and harder to be unlearned even though social learning still plays a central role in teaching us what those social norms are.

3.4

Genetic Factors

At the beginning of this book, I discussed evolution and how we evolved to be cooperative as this was the most successful strategy. And yet, I did not elaborate on the very core of the term “hardwired”, i.e. the genetic component of reciprocity and retribution. There are several reasons for this, starting from it not being the main focus of the chapter and finishing with saying that there is not much data to make any firm conclusions as to how exactly these mechanisms work. First, however, I will outline the main suspects to expand the model a bit further.

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The neurons in the brain cells communicate via hormones, neurotransmitters, and other endocrine players.8 These hormones are coded by the genes that we hold, and therefore the genetic differences between people can lead to different hormones and their levels being produced in people. We already know that mutations in specific genes that affect the production of a particular hormone could lead to disease. These include Huntington’s disease, Parkinson’s disease, Alzheimer’s, and many more. In addition, we know that the neuroendocrine pathways regulated by hormones are critical for the evolution of cooperative behaviours. For instance, the tendency to reject unfair offers was found to be higher in males with high testosterone than in males with lower testosterone (Burnham, 2007). However, little is known about contributions to reciprocity from a molecular genetic perspective, even though twin studies suggest that cooperative behaviour is inherited (Cesarini et al., 2008). However, some papers do examine the importance of hormones. When it comes to cooperation, studies find many hormones that are important in explaining behaviour, such as oxytocin (OXT; Anacker & Beery, 2013; Madden et al., 2011), vasopressin (AVP; Ebstein et al., 2010), isotocin (IT), dopamine and serotonin (Kasper et al., 2017). Stimulation of dopamine receptors increased cooperative behaviours in fish (Messias et al., 2016) and an increase in oxytocin in humans led to more cooperation and trust (de Dreu, 2012). Experimentally increasing OXT or its homolog IT led to a rise in cooperative behaviours and a decrease in aggression in meerkats (Madden & Clutton-Brock, 2011). In an experiment (Rilling et al., 2012) using Prisoners Dilemma, intranasal OXT and VAT were administered, making the participants more positively reciprocal. However, making inferences about the role of a specific hormone is difficult. For example, even though the OXT administration made meerkats more cooperative, it made no difference to mice (Harrison et al., 2016). In humans, even though OXT increased cooperation 8 Both hormones and neurotransmitters are responsible for how cells communicate, with the main difference being that hormones are secreted by glands and go into the bloodstream to find their targets, whereas neurotransmitters are released straight into synaptic cleft and are responsible for neurons talking directly to one another. For the purposes of this book there is no reason to go further into this than I already have.

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within the groups, it increased competition between the groups (de Dreu, 2012). Moreover, there appears to be a difference in how OXT affects behaviour between men and women (Gao et al., 2016). Studies on serotonin, dopamine, and other hormones are no less confusing and contradictory (Walter et al., 2011). As a result, even though the importance and the genetic component in explaining retributive and reciprocal behaviours are evident, not enough is known to merit their inclusion in the model of RRM as it stands. Therefore, even though the integrated model aims to encompass these hormones, it does not include them for the lack of a direct causal relationship. This may change for future research as scientific inquiry improves our knowledge on the role different hormones play. However, there is one gene that is included in the model. In criminology, one of the genes that has received the most attention is the so-called “warrior gene”. This is the gene that is involved with the production of monoamine oxidase A (MAOA gene). It was previously thought to be connected to aggression; however, it turned out to make people more susceptible to their environments (Goldman & Rosser, 2014; Widom & Brzustowicz, 2006). This seems to be because when people have a low-activity form of the MAOA gene (MAOA-L) and are raised in adverse environments, they are more aggressive than both those carrying the higher-activity form (MAOA-H) with the same maltreatment condition or the same low-activity form with no maltreatment (Caspi et al., 2002; Champagne & Curley, 2005; Foley et al., 2004). In contrast to the studies on dopamine or OXT, the studies on MAOA-L seem to be somewhat more consistent and relevant. There could be a connection between MAOA and RRM. Since a person scoring exceptionally high on positive and negative reciprocity would also be highly susceptible to their environment, there is likely a connection to social learning. Moreover, studies consistently show that individuals with MAOA-L tend to be more retributive than their MAOA-H counterparts, which is explained through emotional hypersensitivity (Eisenberger et al., 2007; McDermott et al., 2009). Other studies using functional near-infrared spectroscopy (fNIRS) have demonstrated that individuals with MAOA-L have also exhibited heightened activity in the DLPFC (Ernst et al., 2013), which suggests a further connection

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between MAOA and retribution. Therefore, MAOA could be connected to both social learning and the working of the DLPFC. As such, however, there is only enough evidence of a relationship between the MAOA and retribution. There does not seem to be much connection between MAOA and positive or negative reciprocity. As a result, there seem to be many genetic factors that could influence our tendencies towards retribution and reciprocity, but none of them seem to have a direct link yet. The only gene that could be directly related to the model is MAOA-L which would affect how heavily we are influenced by our environments, but that influence so far seems to only be limited to retributive behaviours.

4

Integrated Retribution and Reciprocity Model

The previous section outlined all the relevant findings and examined the evidence of what neurological and genetic mechanisms and systems might be relevant in explaining our retributive and reciprocal tendencies. What is important at this stage is to bring all of that evidence together into one model that can demonstrate what systems are responsible for RRM. This section serves to help us understand how all of the individual systems interact with one another to form the neuroscientific basis for RRM. Many mechanisms interact in this model—environmental, genetic, evolutionary, sociological, and neurophysiological factors. In general, the biology of this decision-making process cannot be overlooked. Even though positive reciprocity, negative reciprocity, and retribution seem to be very similar behaviours, they use different networks and activate different brain areas, although there is some overlap. Importantly, these behaviours together seem to involve all orders of processing, from the lowest order emotional responses to the highest order rational deliberations. Judgement of morality is a high-order function; therefore, it is not surprising that there is a lot of involvement of the frontal regions of the brain. Children were found to be more likely to accept unfair offers

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in the ultimatum game than adults. The propensity for rejecting and punishing unfairness increases steadily with age into adulthood, which might be associated with the development of the prefrontal cortex (PFC), and more specifically, the right dorsolateral prefrontal cortex (rDLPFC; Murnighan & Saxon, 1998). Transcranial Magnetic Stimulation (TMS) studies show that when rDLPFC is disrupted, negatively reciprocal tendencies decrease (Knoch et al., 2006). The participants still perceived the unfair offers as unfair but were more reluctant to reject them than the control (Knoch et al., 2006; Van’t Wout et al., 2005). However, the rDLPFC seems only to play a role when it comes to negative reciprocity. Positive reciprocity relies on a different frontal area, the ventromedial prefrontal cortex (VMPFC). A study (Wang et al., 2017) using transcranial Direct-Current Stimulation (tDCS) used a gift exchange game. It showed that anodal stimulation of the VMPFC (that increases the activity in the area) led to an increased effort from the workers. Despite the differences, both negative and positive reciprocity rely on the activation of the anterior cingulate cortex (ACC). The ACC activation is typically explained as monitoring a conflict between emotional and cognitive motivations (Baumgartner et al., 2011; Sanfey et al., 2003), which makes sense in both cases. For example, there is a conflict between playing to maximise the monetary outcome of the Game Theory experiment and the emotional response to either rewarding or punishing other participants’ behaviours, and the ACC is responsible for regulating this conflict. In contrast, the area most associated with retribution is the dorsal striatum (DS). Using the functional magnetic resonance imaging (fMRI), researchers found the activation in the DS when people were punishing the deflectors in the economic game by reducing their payoff, although no activation was found there when the punishment was only symbolic (Dominique et al., 2004; Krämer et al., 2007). The same area does not activate when a person only passively observes another person being punished for unfair behaviour (Singer et al., 2006). That means that retribution is likely to be a separate process from both negative reciprocity and general perceptions of injustice.

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It is easy to assume that retribution and negative reciprocity involve the same mechanism as many researchers use these concepts interchangeably (Eisenberger et al., 2004). Evolutionary psychologists argue that the reason for having negative reciprocity is because we have learned that retribution can bring us necessary results through social learning. In realising that retribution can bring the desirable consequences of others complying with social norms, we learn to overapply that rule even in situations where no future benefit can be derived (Camerer & Thaler, 1995). However, even though it has been argued that the mechanism is the same, neuroscientific evidence seems to contradict that view, as different brain areas seem to be responsible for those behaviours. For instance, negative reciprocity does not involve the activation of the DS. There is, however, some overlap. Both negative reciprocity and retribution tend to involve activation in the anterior insula (AI). Some papers suggest that unfair treatment elicits negative emotions regulated by the AI (Rilling et al., 2008; Sanfey et al., 2003), and others argue that the AI responds to a social norm violation (Civai et al., 2012; CorradiDell’Acqua et al., 2013). In any case, whether the unfair act is directed towards the self or others, AI activation is observed. Whether retribution and negative reciprocity are learned together or not, science suggests that both of them are learned behaviours (Rosenthal & Zimmerman, 2014). In the same way, in which we are born with an ability to learn a language but not to speak (Chomsky, 1959), we might be born with an ability to learn to be reciprocal and retributive, but not with these motivations themselves. People might be learning these behaviours because punishing a violator of a social norm and responding to kindness with kindness yields future gains (Camerer & Thaler, 1995). That would mean that depending on the environment in which an individual grew up, they are likely to exhibit different amounts of reciprocal and retributive tendencies. If this assumption were supported, it has the potential to explain variations in reciprocity within societies, noted by multicultural research in the ultimatum game (Roth et al., 1991). Other factors, including upbringing, are also expected to play a role. Previous research shows that the nature of child-parent interactions strongly influences how an individual operates within society in the future (Isley et al., 1999; Parke et al., 1992). Therefore, all of these

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tendencies, no matter which areas are involved, rely to some degree on social learning and are influenced by the environment around them. Numerous genetic components could play a role, such as the production of dopamine or oxytocin. However, there is not much evidence and agreement to say whether they would play an essential role in RRM or not. However, only one gene, the MAOA-L gene, is so far included in the model. It has been shown to influence people’s retributive tendencies (Eisenberger et al., 2007; McDermott et al., 2009) and make the DLPFC more active (Ernst et al., 2013). The role of MAOA-L in aggression and punishment is not fully understood, but it is believed that this gene makes people more susceptible to their environments, hence why it is included in the model as affecting social learning. Altogether, these factors point towards a rather complicated interactive model of reciprocal and retributive tendencies. It is not within the scope of this book to thoroughly examine this mechanism; that task lies with future research. The model is summarised in the figure (Fig. 1):

Fig. 1 The neurophysiological mechanism of RRM

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The figure brings together all the findings in this chapter, showing which brain areas interact with which in order to explain different tendencies. In addition, it demonstrates the dual-process model of negative reciprocity as well as the role of social learning and the MAOA-L gene. As the final model of the neurophysiology of RRM, it creates a testable model that should be examined in the future in order to find evidence for these theoretical findings.

5

Conclusions

Human behaviour is complex and requires many tools even to begin attempting to explain it. Some behaviours are more difficult than others, but it can be safely concluded that crime is one of the hardest to explain. Many factors influence our behaviours, starting from free will and past experiences to the very simplistic genetic components and the environments surrounding us. Trying to understand human behaviour without looking at biology is challenging. Despite how complex and nuanced humans are, all behaviours begin with simple processes of cells transcribing our genes and neurons communicating with one another using neurotransmitters. RRM is designed to shed light on how our cooperative tendencies affect crime propensity. Cooperation plays a critical role in our society and is arguably the most important behaviour that helps us as humans persevere in life where other species have failed. I also posit that the most critical tendencies which lead to cooperation are those towards retribution and reciprocity, and hence they are at the centre of the explanatory model. However, retribution and reciprocity are hardwired tendencies that date way back. Numerous evolutionary theories explain how or why those tendencies were beneficial and how they are carried on from generation after generation. Still, one fact remains: we all show these tendencies, albeit at varying levels. There is a lot of evidence for prosociality and empathy that we show from a very young age. For example, babies show a desire to be around other humans and help them and share even where there is no benefit for themselves. Even very young children have strong preferences for equality

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and are no strangers to the idea of justice or fairness. That includes, among other things, the desire to punish the people who have wronged them when they think they have not been treated fairly. Some of this behaviour is, of course, learned. However, since there is so much evidence that we show those tendencies before we even start to speak, there is an argument to be made that some of those behaviours are “automatic” or hardwired. Often, we do not choose to be angry over an issue; we simply are, and sometimes, we cannot even explain to ourselves why we are so affected. Moreover, at times even when we want to act differently, we cannot. There is extensive evidence from numerous economic games that shows that even when we lose money over punishing a violator of a social norm or rewarding the other players for being cooperative, we still do so. This chapter discussed the mechanisms that are responsible for us showing these behaviours. It aimed to unpack the contradictory evidence of what areas of the brain might be responsible for our desire for retribution and reciprocity to understand better what drives this behaviour. Of course, there is still a long way to explain what exactly makes us react to injustice, but as a result of this chapter, some understanding starts to emerge as to why we react the way we do. In addition, the most important conclusion of this chapter might be proving that most of our behaviours towards cooperation are not the outcome of rational thought but rather a consequence of an evolutionary mechanism that allowed us to persevere as a species. Therefore, these behaviours cannot be studied without looking at the biological mechanisms. As such, human behaviour cannot be separated from our bodies since they govern everything we do. It is the neurotransmitters that pass the signals between the neurons, and ultimately it is our brain area activation that gives us that immediate reaction on which we act. Most of our behaviour is not a result of deliberate action. Therefore, studying those automatic responses is very important if we want to understand the majority of our decision-making. If RRM is to be a helpful tool in helping explain crime, it should have a biological mechanism embedded in it; first, for explaining how it works, and second for the reason of adding validity to the existence of this model.

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This chapter discussed which areas of the brain activate when people exhibit three tendencies: positive reciprocity, negative reciprocity, and retribution. Even though there is some overlap, there are also a lot of differences. Positive reciprocity mainly relies on the ventromedial prefrontal cortex (VMPFC) and the anterior cingulate cortex (ACC). In contrast, even though negative reciprocity also relies on the activation of the ACC, most of the decision of whether to exhibit the behaviour ends up being a competing activation of the right dorsolateral prefrontal cortex (rDLPFC) and the anterior insula (AI). The same anterior insula activates when it comes to retribution, signalling that the AI responds to the perception of injustice. In addition to the AI, the dorsal striatum (DS) is also found to be active in retributive scenarios. Some genetic components were discussed as important for this model, such as dopamine, oxytocin, and others. However, not many of them were found to have a direct role in RRM. It might be possible to design future studies that would study the role of various hormones on reciprocal and retributive tendencies, but it would not be possible to find the answer to this question in this book. However, one gene was included in the model, the infamous Monoamine oxidase A (MAOA) gene, also known as the “warrior gene”, which I theorise to play a role in retribution. There are, of course, other factors that influence our behaviour. While studying biology is important, it is but one factor. As such, it is always important to look at the environment as anything we ever do is, to some extent, just a response to what is happening around us. It is always essential to look at social learning because even though there is evidence that some behaviours we show are hardwired, the way we choose to react largely depends on what we think is an appropriate response in this reaction. Therefore, RRM is a model that presents neurophysiological evidence to support its validity but also recognises the need to study the environment in which people find themselves. Unfortunately, there are still many unanswered questions as to how some of the connections work, and there is some contradictory evidence that should be resolved. Testing the model’s neurophysiological mechanisms lies beyond the scope of this book. Future research should look more closely into some of the debates outlined in the body of this chapter

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and spend more time studying these tendencies together in one experiment to see with more clarity how the interactions work. However, despite the gaps in knowledge, RRM already has an existing framework that can be tested further. Now that the framework is outlined, the following chapter focuses on testing the RRM itself to see if it carries any explanatory power that helps explain and predict crime.

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4 Retribution, Reciprocity, and Vignettes: Testing the Retribution and Reciprocity Model through Hypothetical Scenarios

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Introduction

People do not exist in a vacuum but instead are continuously shaped by the world around them as well as other people. Aeons of evolution in which we were forced to adapt to the world in order to survive made us very good at adapting, otherwise as a species we would not have evolved this far. As such, building on the evidence from the previous chapter, it is easy to conclude that almost every human action is a reaction to the environment, be it a physical construct or a situation, such as an interaction with other people. Nevertheless, many theories of criminology focus on one of the two: either what makes a person more crime-prone than others (such as the self-control theory; Gottfredson & Hirschi, 2017) or labelling theory (Becker, 2018), or what makes a setting more or less criminogenic (broken windows theory; Wilson & Kelling, 2015). There are theories, of course, that incorporate the two, most notably the Situational Action Theory (SAT) that operates with an idea of crime

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. Svingen, Evolutionary Criminology and Cooperation, Palgrave’s Frontiers in Criminology Theory, https://doi.org/10.1007/978-3-031-36275-0_4

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as an interaction between an individual and their setting (Wikström et al., 2012). It is, indeed, important to look at both if one is to understand crime, because there are many factors that lead to the occurrence of a crime, both environmental and individual, that should not be overlooked. The Retribution and Reciprocity Model (RRM) takes both into account as well, realising that crime is always a response to the environment. People come into the setting with their own motivations, tendencies (including the ones towards retribution and reciprocity), and perceptions of the environment, and then respond to events around them. In this chapter I aim to find evidence for the underlying statement of RRM that people are reciprocal and retributive and that these behaviours are relevant to our study and understanding of crime. For that purpose, I make use of hypothetical scenarios: a small vignette in which the participants are presented with a situation and then asked how they would react. By manipulating the exact circumstances of the situation, I can bring out certain reciprocal and retributive feelings, and that in turn can lead to a different outcome from the participant. By doing that we can learn many things about the nature of crime. Specifically, about what might motivate people towards committing a crime or, on the other hand, what might prevent them from committing one. The basic assumption or RRM is that people are more likely to commit a crime against a person that was aggressive or unfair towards them or others rather than against a person who was nice to them. This is the basic premise of reciprocity. Reciprocity is a behaviour we observe frequently in everyday life and in a lot of aspects of our interaction with the world this behaviour is assumed. In fact, market research shows that many companies use marketing strategies which exploit reciprocity from the potential buyer. For example, when companies choose to give gifts to potential customers, the customers are more likely to buy their product (Friedman & Herskovitz, 1990) as failure to do so would result in feelings of guilt and causes them to keep buying the merchant’s products (Dahl et al., 2005). On the other hand, when the consumers feel wronged, they go to extreme lengths to retaliate against the companies, e.g. creating websites

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such as walmartsucks.com1 and presentations about bad hotel stays or repaying everything in very small coins to make transactions as painful and costly as possible for the retailer (Funches et al., 2009). Revenge is a behaviour so fundamental that it was first described by Aristotle in 384–323 B.C., and is as such a well-recognised motive for crime. It has previously been studied as a motive for genocide and war atrocities (Balcells, 2010; Hinton, 1998), school shootings (Levin & Madfis, 2009), and serious crimes such as rape and murder (Mann & Hollin, 2007). However, albeit many criminologists understand the importance of motives of anger and retaliation, not many of them study them in a systematic manner and separate all of these behaviours, such as separating negative reciprocity and retribution. Moreover, very few researchers look at the other side of reciprocity—the positive aspect of it that can explain why crime is less likely to happen, rather than explain it as a motive. In this chapter, I study retribution, negative reciprocity, and positive reciprocity in order to see what role they play in crime causation. I do that by giving the participants hypothetical scenarios2 in which I manipulate the environment in such a way to make it more positive (to get a positive reciprocity reaction) or negative (to get a negatively reciprocal or a retributive reaction) and then examine how their responses change. The assumption is that the more negatively reciprocal or retributive the scenario will be, the more likely the participants will be to say that they would commit a crime. On the contrary, the more positively reciprocal the scenario is, the less likely the participants will be to say they would commit a crime. If that turns out to be the behaviour we observe, then the evidence supports the fundamental assumptions of RRM. The previous chapter of this book examined the neurophysiological processes that lead to people becoming more or less reciprocal and retributive; this chapter is the first step towards looking at the environment.

1 Which became such a popular public forum for attacking Walmart that Walmart itself felt threatened by it and asked to take it down. 2 Also known as randomised scenarios, vignettes, and factorial objects. The factors being manipulated in these scenarios are usually referred to as dimensions.

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There are many ways to study the environment and there are a variety of scales that can be used for that purpose. Some people study culture and habits on societal level (Guerette & Freilich, 2016), others focus on neighbourhoods (Sampson, 2004), and some on features of the settings such as situational crime prevention measures (Clarke, 2013). All of these are important to look at when trying to explain crime. However, in this experiment the focus is on the individual setting. The overall neighbourhoods and the environment that people grew up in influence their propensities in many ways. Nevertheless, it is the interaction of the person and a very specific setting that matters, as a person considering committing a crime in a pub, for example, is unlikely to be influenced by what is going on two roads away. RRM posits that crime is always a response to the environment, as something needs to trigger reciprocal feelings. If reciprocity is about responding with kindness to kindness and with hostility to hostility, people need to experience the kindness or the hostility to respond to it. In this experiment I put people in different settings by experimentally manipulating the circumstances of the hypothetical scenarios. There are no measures of individual propensities or otherwise coming into this experiment, which makes it possible to isolate the role the immediate environment plays. Most importantly, hypothetical scenarios allow us to examine the basic assumption of RRM, that people will be more likely to commit a crime as a response to hostility and less likely to commit a crime in response to kindness. By carefully manipulating the features of the scenarios we can establish a link between the setting and the outcome of criminal behaviour. A “crime” is a vague word since we all know that different crimes are committed for very different motivations; however, the basic assumption of RRM is that all crimes will be predicted by it, since reciprocity and retribution are so fundamental to our society. In order to study the various facets of crime, the scenarios are split into “violence” scenarios that ask how likely the participant is to push or punch someone and “theft” scenarios that ask how likely they are to steal money from another person. There are many more types of crime and situations that lead to crime, but these scenarios have been selected as a starting point that encompasses a large number of criminal incidents in our daily lives.

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As a result, the outcomes of this study should not only answer the question of whether there is experimental support for the basic assumptions of RRM, but also help to shed light onto the question of whether it explains both of these types of crime at all and whether one is explained better than the other. That, in turn, will give us even more tools to apply the results of this research and a basis to understand what role the immediate environment plays and how it can be taken further to study the role of individual differences. The following section of this chapter outlines the methodology used and designed for this experiment, including explaining the hypothetical scenarios and the sample. The third section presents the results of the experiment, and the fourth section discusses those results as well as the limitations of the findings. The fifth section offers a summary and a conclusion before bringing us to chapter five of this book, which builds upon the findings of this chapter to measure people’s individual propensities.

2

Methodology

The purpose of this experiment is to examine whether retribution and reciprocity can help us predict crime, and the method used for it is quantitative in nature. This experiment uses hypothetical scenarios (explained in detail in Section 2.1) as a means to experimentally manipulate settings of the environment that elicit retributive and reciprocal feelings within the participants. The aim of this experiment is to identify patterns within RRM: if the basic assumptions of the model are correct, then by manipulating the circumstances of the scenarios it should be possible to make people report that they are either more or less likely to commit a crime. That would also establish a relationship between certain features of the environment pertaining to retribution, reciprocity, and willingness of the participants to commit a crime.

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Hypothetical Scenarios

Hypothetical scenarios are a simple tool that asks the participants to put themselves in a certain situation and report how they would respond to it. For the study of criminology, it is important to put people in situations in which they would commit a crime, but that is difficult to achieve in real life both from a resource perspective and from an ethical point of view. In hypothetical scenarios, however, the participants are only asked to imagine themselves in certain situations, which makes it both easy and safe to study. Hypothetical scenarios have been widely used in criminology on topics ranging from white-collar crime to sexual assault (Bachman et al., 1992; Bouffard, 2007; Exum, 2002; Piquero et al., 2005; Tibbetts, 1999). Despite some criticism that hypothetical scenarios ignore the role of drugs and alcohol as well as do not differentiate between “hot” and “cold” mental states, studies (summarised in Exum & Bouffard, 2010) found that self-reported intentions to offend indeed approximate real-world offending. In this research, the answers to hypothetical scenarios are used as an outcome variable. That means that they are used to analyse whether manipulating reciprocal and retributional characteristics of the scenario can yield different answers from the participants. In this experiment, all three tendencies (positive reciprocity, negative reciprocity, and retribution) were manipulated, and for each scenario, participants were asked how likely they would be to react in a certain way and were given four options. By manipulation I mean changing the circumstances of the scenario. By varying the situation of the scenario ever so slightly, I attempt to elicit a certain reaction from the participants. There are, of course, many factors that influence crime, and it is very hard to isolate one factor completely from the manipulation in the scenario, especially when it comes to something as hard to quantify as reciprocity and retribution. A number of things in the way the scenario is presented could influence the participants and their answers. However, if the fundamental assumptions of RRM are correct, it should be possible to observe a trend. Overall, there were two main crime types that I studied in this experiment: theft and violence, each represented by two situations. The

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Table 1 The summary of all versions of hypothetical scenarios BSS (1)

Baseline Low Medium High

NQS (2)

DWS (3)

PBS (4)

NR

PR

R

NR

PR

R

NR

PR

R

NR

PR

R

1.0.0 1.1.1 1.1.2 1.1.3

1.0.0 1.2.1 1.2.2 1.2.3

1.3.0 1.3.1 1.3.2 1.3.3

2.0.0 2.1.1 2.1.2 2.1.3

2.0.0 2.2.1 2.2.2 2.2.3

2.3.0 2.3.1 2.3.2 2.3.3

3.0.0 3.1.1 3.1.2 3.1.3

3.0.0 3.2.1 3.2.2 3.2.3

3.3.0 3.3.1 3.3.2 3.3.3

4.0.0 4.1.1 4.1.2 4.1.3

4.0.0 4.2.1 4.2.2 4.2.3

4.3.0 4.3.1 4.3.2 4.3.3

violence scenarios are: (1) the Bus Stop Scenario (BSS) and (2) the Nightclub Queue Scenario (NQS), and the theft scenarios are (3) Dropped Wallet Scenario (DWS) with the (4) Person at the Bar Scenario (PBS). The scenarios varied in what tendency they tested for (PR, NR, or R) and also in the level of intensity (low to high). The variations were equally likely to be allocated to be shown to the participant. The full breakdown of all the versions of the scenarios is shown below (Table 1). There are 11 variations per scenario and 44 options in total. Each participant received four scenarios, two for each tendency in both crime types. The gender of the characters in a scenario matched the gender that participants chose as the one they identified with most on the first page of the questionnaire. Every participant had four options to choose from when asked how likely they are to respond to the situations in a certain way. BSS and NQS had the same options, however, the options for DWS and PBS were slightly different. Below are the examples of scenarios and how exactly the situation was manipulated.

2.1.1 Bus Stop Scenario The Bus Stop Scenario (BSS) offers a situation in which a person is standing at the bus stop and then gets bumped into (or sees someone else being bumped into in the case of retribution scenarios), but the exact circumstances change depending on what is being tested. They all start with “you are standing at the bus stop, when suddenly a woman you don’t know…” and then they differ from there. The differences are summarised in the table below (Table 2): The participants were presented with one of these versions and then were given four options of action to which they had to respond how

[1.0.0] … bumps right into you and hurts your arm

[1.1.1] … bumps right into you and hurts your arm. The woman does not offer an apology, mumbles something in an angry voice, and tries to push past you

[1.1.2] … bumps right into you and hurts your arm. The woman does not offer an apology, angrily demands that you move out of her way and calls you an idiot

Baseline

Low

Medium

Negative reciprocity

BSS

[1.2.2] … bumps right into you and hurts your arm. The woman sincerely apologises and tries to get out of your way

[1.2.1] … bumps right into you and hurts your arm. The woman mutters an apology and tries to get out of your way

[1.0.0] … bumps right into you and hurts your arm

Positive reciprocity

Table 2 All versions of the Bus Stop Scenario (BSS)

[1.3.0] … bumps into another person at the bus stop and hurts their arm [1.3.1] … bumps into another person at the bus stop and hurts their arm. The woman does not offer an apology, mumbles something in an angry voice, and tries to continue her journey [1.3.2] … bumps into another person at the bus stop and hurts their arm. The woman does not offer an apology and angrily demands that the person she bumped into moves out of her way and calls them an idiot

Retribution

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High

[1.1.3] … bumps right into you and hurts your arm. The woman does not offer an apology; instead she angrily swears at you and pushes you away to clear her own way

Negative reciprocity

BSS [1.2.3] … bumps right into you and hurts your arm. The woman sincerely apologises, offers her seat as a compensation for her clumsiness, and tries to get out of your way

Positive reciprocity

[1.3.3] … bumps into another person at the bus stop and hurts their arm. The woman does not offer an apology, instead she angrily swears at the person she bumped into and pushes them away to clear her own way

Retribution

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Fig. 1 An example of a Bus Stop Scenario as presented to the participants

likely they were to do any of them. In all the versions, the basic act of being bumped into is what is designed to trigger a response, but many factors about the situation are manipulated, such as what the woman says (whether she is being apologetic or insulting) or does (walks away or becomes aggressive). As such, the more hostility the woman shows, the more we expect the participants to respond with hostility as well by pushing or punishing her. However, the more kindness she shows, the more kindness we expect in return from the participants (Fig. 1).

2.1.2 Nightclub Queue Scenario The Nightclub Queue Scenario is the second scenario after BSS that measures the participant’s willingness to commit a violent crime. In contrast to BSS, NQS offers more provocation, and it is expected that the participants will be more inclined to respond with “likely” or “very likely” to this scenario rather than to the BSS, as observed in previous research in which they were used (Wikström et al., 2012). They all start with “You are walking by a nightclub queue, when a woman you don’t know…” and then they differ from there. The differences are summarised in the table below (Table 3): The basic premise is the same in all the versions: you are walking past a nightclub queue when someone jumps in front of you accusing you of queue jumping. In the retribution and negative reciprocity scenarios she pushes the person being accused, but in the positive reciprocity scenario she is being apologetic and offers an explanation for her actions. The reason why this scenario is attracting more crime in general, is because

[2.1.2] … jumps in front of you and accuses you of queue jumping and pushes you so hard you fall and hurt your leg

Medium

Low

[2.0.0] … jumps in front of you and accuses you of queue jumping [2.1.1] … jumps in front of you and accuses you of queue jumping and pushes you so hard you almost fall

Baseline

Negative reciprocity

NQS [2.0.0] … jumps in front of you and accuses you of queue jumping [2.2.1] … jumps in front of you and accuses you of queue jumping. She sounds apologetic and tries to explain that queue jumping would be unfair to others [2.2.2] … jumps in front of you and accuses you of queue jumping. She apologises several times for the intrusion and tries to explain that queue jumping would be unfair to others

Positive reciprocity

Table 3 All versions of the Nightclub Queue Scenario (NQS) Retribution

(continued)

[2.3.2] … jumps in front of a person walking next to you and accuses her of queue jumping. The woman then pushes that person so hard she almost falls

[2.3.0] … jumps in front of a person walking next to you and accuses her of queue jumping [2.3.1] … jumps in front of a person walking next to you and accuses her of queue jumping. The woman then pushes that person

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High

[2.1.3] … jumps in front of you and accuses you of queue jumping. She pushes you so hard you fall and she then calls you a clumsy idiot

Negative reciprocity

NQS

Table 3 (continued)

[2.2.3] … jumps in front of you and accuses you of queue jumping. She apologises several times and tries to explain that queue jumping would be unfair to others. Meanwhile, she notices that you have dropped your phone on the floor and hands it back to you

Positive reciprocity

[2.3.3] … jumps in front of a person walking next to you and accuses her of queue jumping. The woman then pushes that person so hard she falls and hurts her leg and then calls her a clumsy idiot

Retribution

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Fig. 2 An example of a Nightclub Queue Scenario as presented to the participants

in all R and NR versions apart from baseline the person gets pushed, which means that there is a higher level of provocation in general. The options that were presented to the participants were the same as in the BSS scenario (Fig. 2).

2.1.3 Dropped Wallet Scenario In order to test for theft, the dropped wallet scenario offers a situation in which a person interacts with others on the street, and then drops their wallet on the ground. Importantly, in theft scenarios, the baseline is the same for all tendencies, including retribution. They all start with “You are walking down the street, when…” and then they differ from there. The differences are summarised in the table below (Table 4): The scenario offers a situation in which a person drops their wallet on the ground and the participants are then asked if they would pick it up or not. What changes is what we know about the person itself and what they did before the event to prompt people to want to pick up the wallet or to give it back instead. In some scenarios (NR and R) the person is rude and aggressive, whereas in the positive reciprocity scenarios they are helpful and polite. The options for answers are also slightly different to the violence scenarios, to reflect the different situations that the participants are in (Fig. 3).

[3.1.2] … a woman you don’t know bumps into you and steps on your foot. She does not offer an apology and angrily demands that you move out of her way. She continues walking, but you see her drop her wallet, notice money sticking out of it

Medium

Low

[3.0.0] … you see a woman drop her wallet; and you notice there is money sticking out of it [3.1.1] … a woman you don’t know bumps into you and steps on your foot. She continues walking, but you see her drop her wallet, and notice money sticking out of it

Baseline

Negative reciprocity

DWS [3.0.0] … you see a woman drop her wallet; and you notice there is money sticking out of it [3.2.1] … a woman you don’t know bumps into you and steps on your foot. The woman mutters an apology and tries to get out of the way, but you see her drop her wallet, and notice money sticking out of it [3.2.2] … a woman you don’t know bumps into you and steps on your foot. The woman apologises sincerely and tries to get out of the way, but you see her drop her wallet, and notice money sticking out of it

Positive reciprocity

Table 4 All versions of the Dropped Wallet Scenario (DWS)

[3.3.2] … a woman you don’t know bumps into another person and steps on that person’s foot. The woman not offer an apology and angrily demands that the person she bumped into moves out of her way. You then see she drop her wallet, and notice money sticking out of it

[3.0.0] … you see a woman drop her wallet; and you notice there is money sticking out of it [3.3.1] … a woman you don’t know bumps into another person and steps on that person’s foot. You see she drop her wallet, and notice money sticking out of it

Retribution

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High

DWS

[3.1.3] … a woman you don’t know bumps into you and steps on your foot. She does not offer an apology, instead she angrily swears at you and pushes you away to clear her own way. She continues walking, but you see her drop her wallet, and notice money sticking out of it

Negative reciprocity [3.2.3] … a woman you don’t know bumps into you and steps on your foot. The woman apologises greatly and offers you tissues to clean your shoe, but you see her drop her wallet, and notice money sticking out of it

Positive reciprocity

[3.3.3] … a woman you don’t know bumps into another person and steps on that person’s foot. The woman does not offer an apology, instead she angrily swears at the person she bumped into and pushes them away to clear her own way. You then see she drop her wallet, and notice money sticking out of it

Retribution

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Fig. 3 An example of a Dropped Wallet Scenario as presented to the participants

2.1.4 Person at the Bar Scenario The Person at the Bar Scenario is created alongside the DWS because it is asking the participants whether they would steal the money sticking out of somebody’s pocket, which is a much more serious act than picking up somebody’s wallet. They all start with: “You walk into the bar and see a woman you don’t know sitting there”. and then change from there (Table 5). In all the versions of the scenario, the participant is asked to imagine walking into a bar and then sees a person at the bar with money sticking out of her pocket and is then being asked how likely they would be to steal that money. As such, the ease with which a person might be able to take the money does not change, but what does change is what the person at the bar did before that. They were either hostile by swearing at you or the bartender, or they were being polite and helpful. The options, therefore, differ slightly from the previous scenario, but are very similar nevertheless (Fig. 4).

2.2

Participants and Recruitment

The questionnaire was created and distributed via Qualtrics and there was no particular group of people that were targeted for this research. In fact, the aim was to recruit as many people as possible of different ages and nationalities in order to make sure that the findings of this study are

[4.0.0] … the woman has money sticking out of her pocket

[4.1.1] … the woman gives you a dismissive look and laughs disrespectfully as you pass by. The woman has money sticking out of her pocket

[4.1.2] … the woman gives you a dismissive look and swears loudly at you as you pass by. The woman has money sticking out of her pocket

Baseline

Low

Medium

Negative reciprocity

PBS

[4.2.2] … the woman smiles at you, moves away so it is easier for you to pass through, and mentions that you have dropped your prone. The woman has money sticking out of her pocket

[4.2.1] … the woman smiles at you and moves aside to make it easier for you to pass through. The woman has money sticking out of her pocket

[4.0.0] … the woman has money sticking out of her pocket

Positive reciprocity

Table 5 All versions of the Person at the Bar Scenario (PBS)

(continued)

[4.0.0] …. the woman has money sticking out of her pocket [4.3.1] … the woman gives the bartender a dismissive look and smiles as the bartender accidentally drops a glass. The woman has money sticking out of her pocket [4.3.2] … the woman openly makes a rude comment about the bartender and laughs when the bartender accidentally drops a glass. The woman has money sticking out of her pocket

Retribution

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High

[4.1.3] … the woman gives you a dismissive look and swears loudly at you as you pass by. When you do not respond, she sticks her foot out so you trip, pretending it was not her. The woman has money sticking out of her pocket

Negative reciprocity

PBS

Table 5 (continued)

[4.2.3] …. the woman smiles at you, moves away so it is easier for you to pass through, and then rushes to the door to pick up the phone you dropped earlier to hand it back to you. The woman has money sticking out of her pocket

Positive reciprocity

[4.3.3] … the woman openly makes a rude comment about the bartender and drops her glass purposefully on the floor, so it breaks and blames the bartender for it. The woman has money sticking out of her pocket

Retribution

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Fig. 4 A version of the Person at the Bar scenario as presented to the participants

as easy to generalise as possible. The basic assumptions of RRM assume that every person is following the same mechanisms with the tendencies towards retribution and reciprocity, and hence the important aspect of this research is getting enough variation rather than controlling for any particular features. The call for participants was distributed through social media as well as recruitment websites and university newsletters and included a link that people could follow in their own time. The first page of the questionnaire asked participants to consent to the use of their data for this particular research and informed them that they could drop out at any time during the process without any consequences. No identifiable data was collected apart from age, gender, and nationality. These data were collected in order to control for variables that we know matter in criminology and to see how they would interact with the findings. All the data is stored in a password-protected folder on a password-protected laptop and is not distributed to anyone. As an incentive for participation, three Amazon vouchers were offered as a raffle. Email addresses provided for entry into the raffle were not associated with the questionnaire in order to ensure the participants’ anonymity in terms of their responses. This research was approved by the Institute of Criminology Ethics Board.

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Results

The hypothesis for this research is that there is a relationship between the independent variable of the version of the scenario and the dependent variable of the number of people who would say they are likely to commit a crime in the hypothetical scenarios. I will be comparing distributions of people who say they would commit a crime in hypothetical scenarios between all versions of the scenarios to see if the distribution is different. If it is, we can reject the null hypothesis that there is no change and embrace the alternative hypothesis that changing the retributive and reciprocal setting of the environment affects the outcome of the number of people who would commit a crime in the scenarios.

3.1

Descriptive Statistics

A grand total of 1002 participants took part in the experiment. Two people were excluded for not progressing far enough in the questionnaire to yield any useful data to analyse. From the remaining 1000, various degrees of progress through the questionnaire were achieved with 31 participants completing as little as 17% of the full form. That means that those participants responded to the first scenario and did not proceed further. They were still included in the analysis since there is still some data that could be analysed from them. Of the 1000 participants, 89.8% finished the whole questionnaire. The full breakdown of how far the participants progressed in the questionnaire is presented in the table below (Table 6): Some participant’s nationalities were excluded because their values were “aryan” (1), “metis” (1), “cosmopolitan” (1) and “asian” (2), which are not accurate nationalities. From the remaining participants, 52 different nationalities were represented. Despite the fact that this research does not look directly into the impact of nationality on crime, having such a diverse group in the sample makes the outcomes of the study more universally applicable. Since RRM is designed based on the assumption that a lot of reciprocal and retributive tendencies are hardwired, it is useful to have a sample that involves so many cultural contexts. This

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Table 6 Progress of the participants through the experiment Progress

Frequency

per cent (%)

Cumulative (%)

17 29 40 51 63 76 87 98 100 Total

31 18 21 5 6 4 3 14 898 1000

3.1 1.8 2.1 0.5 0.6 0.4 0.3 1.4 89.8 100

3.1 4.9 7.0 7.5 8.1 8.5 8.8 10.2 100

degree of diversity will add validity to the conclusions about the wider applicability of the model. Overall, 872 (87.2%) of the participants were female and the remaining 128 (12.8%) were male with ages spanning from 14 to 64 (mean: 22.8, SD: 5.6). Even though the number of women far outweighs the men (probably indicating a general trend towards women being more willing to participate in research; Smith, 2008), there are still more than enough men that have participated to provide the necessary variety to make conclusions about the role of gender. There is an argument to be made that men and women have different levels of empathy or other behaviours such as reciprocity, but in this particular sample the variation is wide enough to capture the right levels to study in both genders. In addition, making gender inferences is not the primary purpose of this research, the fundamental assumptions of RRM should stand regardless of gender identity. From the remaining 1000 participants, six were excluded for responding “very likely” to mutually exclusive options on the questionnaire or behaving in an otherwise chaotic manner that showed they were not taking the experiment seriously. However, the remaining group is still large enough so that there is enough variation for every scenario.

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Scores and Measures

In order to analyse the importance of reciprocity and retribution in criminal decision-making, the answers to the scenarios are compared to one another by intensity. For the answers of “very unlikely” and “unlikely” the individual score is “0” since that outcome means no crime, for “likely” it is “1” and for “very likely” it is “2”. There were four options available as answers to the scenario, albeit not all of them are included in the analysis. For the study of violence, we are interested in the answers that say how likely the participant is to punch or shove the other person, whereas for explaining theft the more interesting answer for the analysis is how likely they are to pick up a dropped wallet (DWS) or steal money sticking out of the woman’s pocket (PBS). The other options were included in order to make sure that the participants do not feel like committing a crime is the only option in that scenario. Moreover, for the study of positive reciprocity, it is interesting to see how likely the participants would be to respond with kindness to kindness (i.e., smile or pass the wallet back). In the cases of negative reciprocity and retribution, the scores are expected to get higher as the intensity rises. In the case of positive reciprocity, the opposite is expected: the likelihood of crime should decrease as the reciprocity rises, reflecting PR’s role in preventing crime. For the sake of analysis, for most scores, the values were merged into a binary variable with scores of “0” for no crime and “1” for crime. That was done in order to have fewer cells and hence obtain more observations within each cell. For violence scenarios, the answers for “push” and “punch” options were merged into one (the “violence” score), so that “0” stands for neither and “1” stands for either of the two. For the theft scenarios the “steal” and “pick up” options were a simple “0” and “1”. Doing that not only helps to get more observations per cell, but also ensures that it is possible to run additional analyses such as regressions. The tendencies were not measured in any other particular way rather than looking at the intensity of the scenario. Therefore, in order to analyse the relationship between the tendencies and crime propensity we need to compare the scenario version to the crime outcome.

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3.3

165

Demographics

The purpose of this experiment is to test whether reciprocity and retribution play a role, and all the additional data that was collected for the purposes of this research, such as nationality, age, and gender are mainly there in order to make sure there is enough variation to draw conclusions about the applicability of RRM to the general population. However, there is a lot of research to suggest that there are many factors, such as people’s age and gender, as well as cultural heritage (in this case nationality as a proxy for that) that may influence crime causation. Moreover, the same factors might influence people’s reciprocal and retributive feelings. Chapter two of this book already outlined the importance of learning and the surroundings in the understanding of retribution and reciprocity. That means that whether a person grew up in an individualistic or a collectivist culture, for example, would play a role. There are other factors that might influence retributive and reciprocal feelings. Previous research (Wilkowski et al., 2012) found that there are significant gender differences in the feelings of revenge and that revenge might be a moderator for gender differences in aggression. Another experiment (Bereby-Meyer & Fiks, 2013) showed that age may influence a decision to reject unfair offers by comparing kindergartners to sixthgraders. There are many explanations for this, starting with socialisation and social learning to brain development. Answering that question is not the purpose of this book, but it is important to look at those factors here to see if there is anything important that could help us make sense of the data that comes later. As a result, even though gender, age, and nationality do not play a direct role in the model, I consider it important to analyse how these factors influence participant’s reporting that they would commit a crime. Since these factors might influence reciprocal and retributive feelings, it is important to see if there are any important trends in the scenarios before analysing retribution and reciprocity themselves.

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3.3.1 Gender There is a lot of evidence that suggests that most people who engage in crime are male, and the explanations for why men commit more crime than women are varied and range from biological explanations such as testosterone (Aromäki et al., 1999; Vermeersch et al., 2008) to social learning (Burton & Meezan, 2004) such as the way men have been socialised when growing up. Solving this problem is outside of the scope of this book, even though it is still useful to look at in order to see how much crime variation in the scenarios is explained by gender. Even though we already know to expect more crime being committed by men rather than women, it is interesting to see what the relationship would be specifically in the retributive and reciprocal environments. Moreover, it is important to see the exact distribution before the future analysis of the tendencies and crime. The same variables of theft and violence described in the above section are tabulated against the categorical gender number to reveal a relationship in the table below (Table 7). Unsurprisingly considering what we already know about gender and crime, in this experiment more men said they would commit a crime of violence (push or shove someone) than women. However, the differences are not as dramatic as they could have been with the differences of only Table 7

Gender breakdown of violence scenario responses

BSS violence No Female 747 Male 97 Total 844 Pearson chi2 (1) = 4.0; p

(85.8%) (78.9%) (84.9%) = 0.045

Yes

Total

124 (14.2%) 26 (21.1%) 150 (15.1%)

871 (100%) 123 (100%) 994 (100%)

Yes

Total

182 (21.2%) 46 (38.3%) 228 (23.3%)

859 (100%) 120 (100%) 979 (100%)

NQS violence No Female 677 Male 74 Total 751 Pearson chi2 (1) = 17.33;

(78.8%) (61.7%) (76.7%) p < 0.001

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Table 8 Gender breakdown for theft scenarios DWS steal No Female 806 (93.9%) Male 104 (85.3%) Total 910 (92.9%) Pearson chi2 (1) = 12.17; p < 0.001

Yes

Total

52 (6.1%) 18 (14.8%) 70 (7.1%)

858 (100%) 122 (100%) 980 (100%)

PBS steal No Female 759 (95.2%) Male 101 (92.7%) Total 751 (76.7%) Pearson chi2 (1) = 1.32; p = 0.251

Yes

Total

38 (4.8%) 8 (7.3%) 228 (23.3%)

797 (100%) 109 (100%) 906 (100%)

7% for BSS and 17% for NQS. The p-values are low, demonstrating that gender does play a role, but the effect is not noteworthy (Table 8). Observations for theft follow the same pattern with men being more likely to commit a crime than women, although the findings in PBS are less definitive than the ones in the violence scenarios or indeed DWS. This shows that men are more likely than women to commit theft as well as violence, but the findings are still not pointing to a drastic difference between the two. We can conclude that gender does play a role, but that role is not significant enough to include in the model. One of the reasons is that this study focuses on finding out the impact of retribution and reciprocity. While gender could influence the levels of retribution and reciprocity (Wilkowski et al., 2012), it serves the role of the cause and hence not of the central interest to this book. However, since there might be a relationship between gender, the tendencies, and crime, it is important to explore that relationship further. This issue is explored in chapter five of this book, since the experiment presented there is better designed for answering these questions.

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3.3.2 Nationality As with gender, the main purpose of collecting data about nationality is to make sure there is enough variation to make general observations. In terms of RRM, there are in fact ways in which nationality can influence the way we respond to the environment, as suggested by a whole body of research into collectivist vs. individualistic cultures (Tyson & Hubert, 2003). We know for reasons outlined in chapter three that people respond to the social rules around them and act accordingly, and the country in which the person grew up would influence the norms and behaviour that person learned. Nevertheless, in this particular experiment, nationality did not play a significant role. The chi2 p-values were too high to assume there is any explanatory power in looking at nationality for either BSS (Pearson chi2 (83) = 69.4610; p = 0.856) or NQS (Pearson chi2 (82) = 68.4956; p = 0.857) or DWS (Pearson chi2 (83) = 58.915; p = 0.979). These findings are not unexpected. There is no reason why nationality would be a good proxy for the social norms that the person learned. Mainly because within one country and even one city, there will be many societies with varying norms and rules which cannot be grouped under one umbrella. Specifically, when it comes to retribution and reciprocity it is much more important what the person encountered when they were growing up from their families and then in the future from their closest friends. Studying people’s attitudes towards the environment can shed light on explaining crime and predicting it but doing it through the medium of nationality does not yield sufficient results, as shown by the disappointing p-values. As a result, nationality is a data point that will be collected for diversity purposes but will not be included in the analysis for RRM. Since RRM aims to be universally applicable, it is important to recruit participants from as many backgrounds as possible in order to have the ability to generalise.

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3.3.3 Age Age is a frequently discussed variable in criminology from a life-course perspective. A common trajectory sees people commit crimes in their younger years, between the age of 15 and 19, but then desist in later life (Farrington, 2003). When looking at crime as an aggregate, the fact that people seem more crime-prone in certain ages stands out despite the fact that we know that people follow different trajectories. Nevertheless, in terms of the average for this experiment, age did not seem to play an important role as shown in the graphs below (Fig. 5).

Fig. 5 Box-plots for Age vs answers to hypothetical scenarios with 1 = crime and 0 = no crime. Top left: BSS, top right: NQS, bottom left: DWS, and bottom right: BPS

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Table 9 Regression analysis results for the role of age in answers to the hypothetical scenarios Age

BSS violence

NQS violence

DWS steal

PBS steal

N = 994 LR(1) = 1.92 p = 0.17 R2 = 0.0023

N = 979 LR(1) = 2.21 p = 0.137 R2 < 0.01

N = 980 LR(1) = 0.04 p = 0.845 R2 < 0.01

N = 906 LR(1) = 2.15 p = 0.14 R2 = 0.01

The box plots for all four scenarios show that the averages are very similar for both “crime” and “no crime” groups with both averaging around 22 from the general participant’s ages spanning from 14 to 64 (mean: 22.8, SD: 5.6). The averages for the “crime” group tend to be slightly lower, but not enough to be statistically significant. The “no crime” group also has significantly more older outliers that might both increase the average while also indicating that the older population is less likely to say they would commit a crime. In order to test whether age plays a role in determining the likelihood of committing a crime, I ran a logistical regression with age as an independent variable and the measures that were already used in the above analyses as dependent variables, presented below (Table 9). All in all, all four regressions show that responses do not vary significantly by age in this experiment and hence can be omitted from future models. All the p-values are comfortably above 0.5 and the odds ratios are very high, suggesting that we can comfortably reject the null hypothesis that age plays an important role in predicting participant’s responses in all four scenarios. With that analysis in mind, it is evident that there is no real need to include any of those factors in the future analysis, but it is important instead to focus on retribution and reciprocity themselves.

3.4

Evaluating Tendencies

Since the goal of this chapter is to study if feelings of reciprocity and retribution play a role in crime causation, in the following section I explore the answers that the participants have given in all of the scenarios. The expectation is that as the intensity of the situation rises, that is to say

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as the need to respond in a negatively reciprocal or retributive manner rises, more people would say that they would punch another person or steal the money. As positively reciprocal tendencies rise, fewer people would say they are likely to commit a crime. For the purposes of that comparison, I am looking at comparing counts of crime across those versions. In this section, I analyse all of the tendencies separately.

3.4.1 Retribution The idea behind retribution is that people will react negatively and aggressively towards a violation of a social norm even if it is not directed against them specifically. The scenarios are written in such a way that the participant is observing a situation happening to someone else and then has an option to intervene either by pushing or punching the person in violence versions or be more inclined to steal their money in the theft versions.

Violence For the violence score (BSS and NQS), all participants were given four options of behaviour, they were asked how likely they are to: (a) apologise to the person, (b) smile at the person, (c) push the person, or (d) punch the person. The answers that provide a crime score are push or punch. In order to make sure there are more observations to be able to run a more accurate analysis, the answers for “push” and “punch” have been united into a violence score with a “0” when the participant said they would do neither and a “1” when they said they would do either. The outcomes are then tabulated to be compared (Table 10). The results in the tables are quite conclusive with both p-values below 0.05. The trend is clear that as a general rule, as the intensity of the scenario rises, the more participants report that they would commit a crime and that willingness rises in a linear fashion. Although the jump from baseline of NQS is not as extreme as in BSS, both scenarios

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Table 10

Chi2 analysis of violence scenarios and retribution

BSS violence BSS Ret

No

0 88 (96.7%) 1 73 (85.9%) 2 66 (81.5%) 3 82 (81%) Total 309 (86.3%) Pearson chi2 (3) = 12.18; p = 0.007

Yes

Total

3 (3.3%) 12 (14.2%) 15 (18.5%) 19 (18.8%) 49 (13.7%)

91 85 81 101 358

(100%) (100%) (100%) (100%) (100%)

NQS violence NQS Ret

No

0 75 (88.2%) 1 75 (80.7%) 2 60 (80%) 3 56 (67.5%) Total 266 (79.2%) Pearson chi2 (3) = 11.28; p = 0.010

Yes

Total

10 (11.8%) 18 (19.4%) 15 (20%) 27 (32.5%) 70 (32.5%)

85 93 75 83 336

(100%) (100%) (100%) (100%) (100%)

show a difference from the baseline, with the percentage of participants reporting a crime increasing sixfold in BSS and threefold in the NQS. There are several observations to be made, one is that there seems to be more criminogenic responses in the NQS than the BSS. This is consistent with previous research (Wikström et al., 2012) and might be due to the fact that NQS is marginally more extreme in its nature and involves more provocation. Nevertheless, despite more numbers, the trend remains linear in nature and reasonably strong. The second observation is that the cumulative percentages of violence are rising quite gradually with the intensity of retribution rising, reflecting the gradual nature of retributive feelings. The differences in the numbers observed are not very high, perhaps indicating that more observations could be collected in order to make concrete conclusions about the nature of the relationship between the variables.

Theft The theft scenarios are the DWS and the PBS. The answer options for those were different and ranged from positive things such as smiling at

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Table 11

Chi2 analysis of theft scenarios and retribution

DWS violence DWS Ret

No

0 95 (97.9%) 1 83 (91.2%) 2 81 (90.0%) 3 61 (75.3%) Total 320 (89.1%) Pearson chi2 (3) = 24.23 p < 0.001

Yes

Total

2 (2.1%) 8 (8.8%) 9 (10.0%) 20 (24.7%) 39 (10.9%)

97 91 90 81 359

(100%) (100%) (100%) (100%) (100%)

DWS violence DWS Ret

No

0 90 (98.9%) 1 87 (96.7%) 2 74 (91.4%) 3 66 (88.0%) Total 317 (94.0%) Pearson chi2 (3) = 10.91 p = 0.012

Yes

Total

1 (1.1%) 3 (3.3%) 7 (8.6%) 9 (12.0%) 20 (5.9%)

91 90 81 75 337

(100%) (100%) (100%) (100%) (100%)

people and handing their money back to them to negative things such as insulting them. Nevertheless, for the purposes of this analysis we are only interested in knowing whether the participants would either take the wallet (DWS) or take the money from someone’s pocket (PBS), with the latter being a more extreme version of the crime. That difference is needed in order to establish whether RRM applies to both types of crime and whether we will see any difference in what the participants are reporting. The results are presented below (Table 11). In both these scenarios the data align well and show a very clear trend; the findings are supported by statistically significant p-values as well. DWS picked up a lot more crime than PBS, which is not surprising considering that picking up someone’s wallet lying on the ground is a lot less serious than physically taking someone’s money sticking out of their pocket. Nevertheless, it is very encouraging to see that the findings are consistent with the assumptions of RRM, and that retribution plays a role in both situations and can predict crime with more risk as well as a crime with less risk.

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3.4.2 Negative Reciprocity The results we expect from negative reciprocity are the same as retribution. As the intensity goes up, so should the number of people that said they would commit a certain crime. Negative reciprocity involves a direct interaction with the participant, so the participant responds to a negative act directed towards themselves rather than to a third party. As previously, the data is split into violence and theft versions and is tabulated for a comparison and study of the importance of the version intensity.

Violence Negative Reciprocity scenarios use the same options given to the participants as retribution, and we are interested in how likely they would be to push or punch somebody. The results are then combined to the Violence Score the same way they were for Retribution analysis with the expectation to see a similar trend of violence rising as the intensity of the scenarios rises (Table 12). The data shows that there is a lot more crime being picked up in the negative reciprocity than in retribution scenarios, which is suggesting that negative reciprocity might be a more powerful tendency in crime causation than retribution. In the NQS as much as 47% of participants were willing to either push or punch someone or both, which is a big number especially compared to 32% in the retribution scenarios. That means that the data is once again consistent with the basic assumptions of RRM. Both scenarios share the predicted pattern of more people willing to commit a crime as the intensity of the scenario rises, which further supports the hypothesis that negative reciprocity plays a role in crime causation. Moreover, in both cases the willingness to commit a crime by the participants increases by a significant amount (more than doubles in the case of BSS and more than quadruples for NQS), which suggests that negative reciprocity might be an important explanation of violent crime.

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Table 12

Chi2 analysis of negative reciprocity and violence scenarios

BSS violence BSS NR

No

0 71 (83.5%) 1 76 (78.4%) 2 71 (74.7%) 3 58 (65.9%) Total 276 (75.6%) Pearson chi2 (3) = 7.82 p = 0.050

Yes

Total

14 21 24 30 89

85 97 95 88 365

(16.5%) (21.7%) (25.3%) (34.0%) (24.4%)

(100%) (100%) (100%) (100%) (100%)

NQS violence NQS NR

No

0 75 1 47 2 54 3 61 Total 237 Pearson chi2 (3) = 31.47

Yes (89.3%) (58.8%) (60.0%) (52.6%) (64.1%) p < 0.001

9 33 36 55 133

Total (10.7%) (41.3%) (40.0%) (47.4%) (35.9%)

84 80 90 116 370

(100%) (100%) (100%) (100%) (100%)

Theft Even though the results for theft are expected to be similar between retribution and reciprocity, this appears to not be the case. In fact, although the distribution seems to be in line with the theory, the tests failed to find statistically significant results. Since the numbers seem to be showing the same consistent trend that was shown in the violence scenarios, it is likely that that problem was caused by not having a high enough number of participants and hence it might be worth repeating the same experiment with bigger numbers in order to collect enough data to accept or reject the hypothesis (Table 13). In general, the amount of crime picked up on the negative reciprocity scenarios is lower than on the retribution scenarios, suggesting that maybe retribution plays a more important role in theft. Furthermore, this may suggest that perhaps retribution and reciprocity do not play as significant a role in theft cases. Even then, they still made a difference. The numbers are not large enough to lead to any significant values, but the trend is clear suggesting that there is more that should be investigated further with a larger pool of participants.

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Table 13

Chi2 analysis of negative reciprocity and theft scenarios

PBS steal PBS NR

No

0 90 (98.9%) 1 90 (95.7%) 2 97 (94.2%) 3 77 (90.6%) Total 354 (94.9%) Pearson chi2 (3) = 6.53 p = 0.088

Yes

Total

1 4 6 8 19

91 94 103 85 373

(1.1%) (4.3%) (5.8%) (9.4%) (5.1%)

(100%) (100%) (100%) (100%) (100%)

DWS steal DWS NR

No

0 90 (97.8%) 1 79 (94.1%) 2 90 (89.1%) 3 82 (91.1%) Total 341 (92.9%) Pearson chi2 (3) = 6.20 p = 0.102

Yes

Total

2 (2.2%) 5 (5.9%) 11 (10.9%) 8 (8.9%) 26 (7.1%)

92 84 101 90 367

(100%) (100%) (100%) (100%) (100%)

Table 14 Chi2 analysis of negative reciprocity scenarios and answers to give the money back in the DWS DWS give back DWS NR

No

0 1 (1.1%) 1 9 (10.7%) 2 18 (17.5%) 3 28 (31.1%) Total 56 (15.9%) Pearson chi2 (3) = 33.66; p < 0.001

Yes

Total

91 (98.9%) 75 (89.3%) 85 (82.5%) 62 (68.9%) 313 (84.8%)

92 (100%) 84 (100%) 103 (100%) 90 (100%) 373 (100%)

Nevertheless, there are more variables that could be looked into in order to study the impact of negative reciprocity on theft. Looking into who would take the wallet is one way of studying this relationship. Another way is asking how many people would, for example, give the wallet back (Table 14). In this table the picture is much clearer because there are many more observations and the results are statistically significant. The percentage of people who would return the dropped wallet to their owner decreases dramatically as the intensity of the scenario rises. This indicates that some

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sort of response to the social norm violation still occurs, just not necessarily resulting in the crime of theft. People are much more willing to give the money back to the people who are kind to them rather than the people who are hostile to them. As such, that still indicates some support for the hypotheses of RRM. Therefore, negative reciprocity may still aid us in understanding theft, albeit not in the most obvious way.

3.4.3 Positive Reciprocity In contrast to retribution and negative reciprocity, positive reciprocity has the opposite effect. As the intensity rises, we expect people to commit less crime. The thinking behind positive reciprocity is that people will respond with kindness to kindness, therefore they will be less inclined to commit a crime if the person in the scenario was kind to them. As a result, as the intensity of the scenario rises, we expect the number of people who report that they would commit a crime decrease.

Violence Due to the nature of what positive reciprocity implies, instead of studying a reaction to something we are studying a lack of a reaction to the need to respond to something. The numbers for baseline scenarios are already relatively low since most people do not tend to report that they would commit a crime. However, the numbers are supposed to decrease even further as the intensity rises, in line with the hypothesis that people will be less likely to commit a crime because of an urge to respond with kindness to kindness (Table 15). The data shows a reduction in crime as the intensity of positive reciprocity is rising, exactly according to the expectation. This trend shows support for the main assumptions of RRM which suggests that positive reciprocity does indeed serve as a protective factor against committing a crime. Interestingly, in this case it was the BSS that got the statistically significant result, and the NQS did not. This is most likely due to the fact that the NQS still involves a significant level of provocation

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Table 15

Chi2 analysis of positive reciprocity and violence scenarios

BSS violence BSS PR 0 1 2 3 Total Pearson chi2 (3) =

No 71 (83.5%) 83 (94.3%) 88 (93.6%) 89 (100%) 331 (93.0%) 18.64; p < 0.001

Yes

Total

14 (16.5%) 5 (5.7%) 6 (6.4%) 0 (0.0%) 25 (7.0%)

85 (100%) 88 (100%) 94 (100%) 89 (100%) 356 (100%)

NQS violence NQS PR

No

0 75 (89.3%) 1 71 (91.0%) 2 83 (94.3%) 3 77 (98.7%) Total 331 (93.0%) Pearson chi2 (3) = 6.61; p = 0.085

Yes

Total

9 (10.7%) 7 (9.0%) 5 (5.7%) 1 (1.3%) 25 (7.0%)

84 (100%) 78 (100%) 88 (100%) 78 (100%) 328 (100%)

that prompts a certain response even in the high-intensity version of the scenario. Since there are more reasons for why people would or would not commit a crime than just retribution or reciprocity, those would interfere. Nevertheless, the data still aligns in the right direction for both scenarios and suggests that people are less likely to report that they would commit a crime when they experience positively reciprocal feelings towards someone. As a general observation, not many people in this scenario said they will commit a crime, so potentially the way to get statistically significant results could be simply down to collecting more observations since it was always going to be the scenario which collects least observations. Unfortunately, the same problem persists into the theft scenarios.

Theft The ongoing problem with theft scenarios is that all of the versions in general seem to not pick up much crime. On top of that, positive reciprocity itself does not pick up much either for the nature of it. Therefore,

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Chi2 analysis of positive reciprocity and theft scenarios

Table 16

DWS steal BSS PR

No

0 88 (95.7%) 1 70 (95.9%) 2 87 (98.9%) 3 94 (100%) Total 339 (97.7%) Pearson chi2 (3) = 5.51; p = 0.138

Yes

Total

4 3 1 0 8

92 (100%) 73 (100%) 88 (100%) 94 (100%) 347 (100%)

(4.4%) (4.1%) (1.1%) (0.0%) (2.3%)

NQS steal NQS PR

No

0 86 (94.5%) 1 104 (97.2%) 2 90 (98.9%) 3 89 (100%) Total 369 (97.6%) Pearson chi2 (3) = 6.69; p = 0.082

Yes

Total

5 3 1 0 9

91 (100%) 107 (100%) 91 (100%) 89 (100%) 378 (100%)

(5.5%) (2.8%) (1.1%) (0.0%) (2.4%)

even when the data seems to suggest a certain relationship, there are usually not enough observations to draw a conclusive result. However, the observations still show a consistent trend, which suggests that if the same experiment is repeated with more observations, we are likely to see the same consistent trend with statistically significant results (Table 16).

4

Discussion and Limitations

4.1

General Discussion

The main question I set out to answer is whether RRM is a promising mechanism to look at when it comes to crime. That is, whether it predicts crime outcomes. In this experiment, the outcome is whether the participants would say that they are likely to commit a crime. The independent variables are the three tendencies: positive reciprocity, negative reciprocity, and retribution. They were manipulated by the use of hypothetical scenarios and led to a noticeable change in the amount of people who reported they would commit a crime. Hence, the overall conclusion

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of the results above is that RRM, indeed, predicts crime. However, the relationship or indeed the conclusion is complex. The main assumption of RRM is that people act in a reciprocal and retributive manner, and by manipulating the scenarios in such a way as to elicit positively reciprocal, negatively reciprocal, and retributive tendencies I came to an outcome of resulting in participants becoming more likely to report that they would commit a crime (retribution or negative reciprocity) or less likely to commit a crime (positive reciprocity) for both theft and violence scenarios. The sheer number of the scenarios that were written for this purpose makes it possible not only to see the changes from the baseline, but also the gradual increment at which the number of crime-related answers happens. Additionally, having four main groups of scenarios—the BSS, the NQS, the BPS, and the DWS helped demonstrate that these findings seem to be generalisable to many different circumstances and types of crime. Most of the results are statistically significant apart from a few exceptions; moreover, the percentages for some scenarios were very high (such as the 47% that were observed in an NQS scenario). That means that the core assertions of RRM are supported by evidence and the overall mechanism should be studied more closely in order to understand criminality better. Going forward, criminology could benefit from spending more time looking into those tendencies and potentially integrating them into other theories of crime in order to enhance our understanding of crime in an effort to develop better prevention strategies. Where the results were not statistically significant, the distribution was still following the prediction with the percentage of people who said they would commit a crime increasing as the intensity of negative reciprocity or retribution rose and falling as the intensity of positive reciprocity rose. An interesting and important observation is that the number of people who are willing to commit a crime rises steadily through the intensity of the scenarios instead of presenting a sharp increase from the baseline once the trigger in the environment is present. That shows that willingness to commit a crime is not a matter of simply having something to respond to but can also be quantified to an extent. That becomes important when

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analysing the environments and perceptions of an environment in particular with situations not being simply good or bad but presented as a scale. From a methodological perspective, hypothetical scenarios appear to be a good way to study the initial evidence for RRM as they seem to be able to pick up enough crime in order to analyse it. Some are more effective than others, such as NQS picking up way more than BSS, reflecting different situations; however all in all, every one of the four scenarios were fit for purpose of picking up enough participants that reported they would commit a crime and being used to analyse the role of retribution and reciprocity. Moreover, the way in which they were written with a gradual increase in intensity allowed for a good distribution of the data showing a response to a changing environment. Therefore, hypothetical scenarios appear to be a useful method for the questions that were asked in this chapter. Nevertheless, as with any behaviour that can be studied using hypothetical scenarios, it is very hard to isolate retribution and reciprocity exactly, and there may be other confounding variables that play a role. I already discussed the significance of extra provocation in the NQS versus the BSS situation, but that is not the only factor that could play a role. The tendencies of retribution and reciprocity are not easy to quantify and isolate, and there are many other factors that are influenced by the change of the description, such as how scary the other person seems or how different settings affect the social norms of the situations. Despite these difficulties, there is no reason to dismiss the role that retribution and reciprocity played, considering that all the changes to every scenario in every situation resulted in the predicted outcome. Consequently, by the end of this chapter it is possible to conclude that there is empirical support for the basic assumptions of RRM, and that they will be tested further in the next chapter of this book. Other factors were discussed, such as age, gender, and nationality, but neither should be consequential for the final models. We know from previous research, discussed earlier in this chapter as well as in chapter three, that both age and nationality might play a role in crime propensity as well as retributive feelings. Despite that, neither age nor nationality yielded any significant results in affecting answers when it comes to crime

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in this research. Age observations might have suffered from the distribution with the majority of the participants being between the ages of 18 and 30 and with only rare outliers falling outside of that range. For the sake of making a valid conclusion about age, more observations might be necessary outside of the medium range of the current sample, but that once again falls outside of the scope of this book. Despite the fact that gender did play a role with more males saying they would commit a crime than females, the difference in percentages is not large enough to justify including it in the model. The data on nationality, age, and gender should still be collected, but mainly for the purposes of diversity and representation, rather than for adding an additional explanatory variable or an interaction. Moreover, there is still a lot to be said about the role of the causes. There are a lot of reasons that make males more crime-prone than females, ranging from biological to societal, and there are also a lot of reasons why these factors might affect retributive and reciprocal feelings. Nevertheless, the results of this experiment do not show a significant influence that gender might have. Gender might influence retributive and reciprocal feelings in the first place, and that would be an interesting direction to take for future research, but it is not useful for this book. As a result, we can conclude that changing the retributive and reciprocal features of the environment changed how many people were likely to report they would commit a crime. That offers empirical support to the basic assumptions of RRM. The next question to be asked is whether some tendencies explain crime better than others.

4.2

Difference Between Tendencies

The answer to this question is not obvious, considering that negative reciprocity and retribution have a very different effect than positive reciprocity. It is much easier to see an increase of crime as the intensity of the scenarios increases, but it is much harder to see how much crime decreases considering that the numbers of crime in baseline scenarios is quite low. It is fair to say that all tendencies have an effect in explaining crime and that all of them are promising and hence should not be

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dropped from this model, but the direct comparison might be harder to achieve. Nevertheless, in three out of four scenarios, the highest level of intensity of positive reciprocity reduced the amount of crime to 0, and in the remaining NQS it reduced it to “1”, which is understandable considering that NQS always records more crime. As such, positive reciprocity plays a very important role considering it can decrease people’s reports of willingness to commit a crime to zero in this experiment. However, it also has a very low amount of crime in the baseline scenarios, making it harder to draw conclusions from. Perhaps, repeating this experiment with higher numbers would yield more results. Alternatively, it is also possible to write scenarios that involve more criminogenic settings and situations in order to capture more reported crime in the baseline scenarios. Comparisons between retribution and negative reciprocity are easier because in both the percentage increase from the baseline can be considered. Both show an increase in crime as the intensity is rising, which is a predictable outcome, albeit the tendencies seem to explain different types of crime better. When it comes to violence, negative reciprocity seems to play a more important role. In the NQS scenario, retribution showed a maximum increase from 11.8 to 32.5%, whereas for negative reciprocity it ranged from 10.7 to 47.4%. That demonstrates that perhaps for the crime of violence the provocation against the individual themselves may be of greater import than the violation of a social norm. Nevertheless, retribution also showed a big increase, alongside negative reciprocity, therefore it evidently also explains violent crime quite well. For the theft scenarios the opposite is true, and retribution explains more variation than negative reciprocity. For DWS, that showed a bigger change, the percentage of people who said they would steal the wallet went from 1.1 to 9.4%. In retribution scenarios, the percentage went from 2 to 24.7%, which is a big difference, and a significant increase. There is a strong argument to be made that different elements of RRM play a different role in explaining different types of crime. In conclusion, all three tendencies are important in the understanding of crime, but retribution seems to explain theft better than negative reciprocity, and the opposite is true for violence scenarios. Positive reciprocity, on the other hand, affects both types of crime, leading to a big

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reduction in the number of people who report they would commit a crime. Nevertheless, it is not the only difference between the crime types.

4.3

Difference Between Crime Types

Theft and violence are rather general terms for all the types of crime there are. Of course, for the sake of simplicity and testing the overall effectiveness of RRM, splitting all crime into two types is a good starting point. However, it is entirely possible that there are some crimes that RRM explains better than others. This is logical considering all the different variations of situations, individual motivations, and other factors that influence crime causation. What can be observed from this particular experiment is that all in all RRM might work better for violence than for theft. The highest increase from baseline that can be observed for violence is going from 10.7 to 47.4% in the negative reciprocity version of NQS versus from 2 to 24.7% in the retribution version of the DWS. All in all, a much lower percentage of people reported that they would commit theft in the baseline scenarios than in violence scenarios, which is an interesting observation but makes it hard to compare the effect. This book does not focus on answering the question of why more people would rather punch someone than take the wallet on the ground, therefore it will remain a question unanswered. An explanation that I see is that stealing a wallet feels less morally justifiable than violence, since punching someone might be seen as a justified response to a provocation. There are a lot of other components that interplay in the scenarios, and, as I indicated earlier, it is difficult to completely isolate the effect of retribution and reciprocity from other factors. It might be worth rewording some of the theft scenarios to capture more variation. Nevertheless, for both types of crime, there is a significant change in responses from the participants. That suggests that RRM might be useful in explaining various types of crime, at the very least it can explain assault and minor theft, which are the more common types of crime, possibly because they have fewer serious consequences. There were not enough scenarios in order to test whether RRM explains all crime, but this is

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a starting point which suggests that it might be useful to test RRM in different scenarios and with different people since similar patterns can be observed for all the different types of crime. All in all, the evidence analysed and discussed in this chapter is promising for the development of RRM, however, as with any research, there are a few drawbacks.

4.4

Overall Limitations

Despite the fact that 1000 participants sound like a big number, due to the nature of randomisation and the fact that most people normally do not commit a crime or say that they would commit a crime, some scenarios (especially theft) were lacking the numbers in order to come to a firm conclusion about the effect that a tendency has on participants’ responses. It might be worth running these experiments again with more participants to properly study the effect of retribution and reciprocity on theft. Hypothetical scenarios are useful to test this model and they do pick up enough numbers to write the analysis, but they might require a bigger participant pool. That is also true for studying positive reciprocity, which by the nature of what I am trying to capture will not be picking much crime. Perhaps an important point to mention is that based on these results, despite the fact that they support RRM as a useful mechanism to look at to explain crime, that most people would still choose not to commit a crime even when the intensity was at the highest. This is especially true for theft scenarios. That means that despite appearing to be useful, RRM is not sufficient to explain why some people commit a crime and others do not. However, RRM never claimed to be a general theory of crime and of course there are many other aspects to look at to predict it better. As mentioned in the previous section of this thesis, some people are inherently non-reciprocal or retributive, which means that the environment will not affect them at all. It is hard to estimate what proportion of the population falls within that category, but that is something I study in chapter five and it could shed more light on the question of exactly how much crime RRM explains.

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Despite the fact that hypothetical scenarios are a useful way of predicting whether people will actually commit a crime in real life, it’s only a proxy for behaviour. Simply saying that one would commit a crime does not mean that they would and vice versa. However, since morality is a good predictor of crime, it is still important information to consider. This experiment did not make any use of the measures of self-reported crime in the past to look into actual behaviour, but it did not need to for the purpose of answering the questions presented. Manipulating the actual environment as effectively as was done in the hypothetical scenarios would prove to be very hard if not impossible, therefore the answers to hypothetical scenarios are the closest proxy there is to actual behaviour.

5

Conclusion

The purpose of this chapter is to examine the preliminary evidence for RRM and to establish whether it can contribute something to our understanding of crime. Almost everything that underlies human behaviour can be explained and understood in terms of reciprocity and retribution, and therefore it makes intuitive sense to see whether thinking about human behaviour in these terms will enhance our understanding of crime causation. For the purposes of this endeavour, I designed an experiment that involved the use of hypothetical scenarios in which all participants were given a number of situations and then asked how likely they would be to react in a certain way. The options ranged from smiling at someone to punching them and stealing their money. All in all the experiment involves four main scenarios (two for violence, two for theft), each of which has a baseline version and rising gradations of positive reciprocity, negative reciprocity, and retribution. If the predictions for RRM are correct, as the intensity of positive reciprocity scenarios rises, fewer people should say that they would commit a crime. On the other hand, the opposite should happen for retribution and negative reciprocity with the amount of potential crime increasing as the intensity rises.

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Just over a thousand participants were recruited for the study in which they were incentivised to participate using a raffle for an Amazon voucher. The participants were from various nationalities and ages and could participate online from their homes. The questionnaire was presented using Qualtrics and randomised the versions of the scenario, so every version would be given to the participants approximately equal amount of times. Considering every participant received four scenarios, every version had around 80–90 participants. Chi2 analyses that compared the distribution of observations of participants that said they would commit a crime versus the intensity of the scenarios found preliminary evidence for RRM. For both theft and violence variations, the number of participants that said they would commit a crime increased as negative reciprocity and retribution scenarios intensity rose, the opposite was true for positive reciprocity, which is consistent with the predictions of the theory. Most of the findings were statistically significant and showed a significant increase. Nevertheless, despite the fact that the evidence aligns, even in the highest intensity variations most people would still choose not to commit a crime. That is normal and found in many experiments across the field, so it simply points to the fact that RRM is not a general theory of crime and does not explain all variation, but it also does not have to, considering it can be integrated with many other theories of crime and used to enhance our knowledge and understanding of crime causation. Despite the fact that the evidence found in this chapter is only preliminary, it can already tell us a lot about the circumstances in which people may choose to commit a crime. Some scenarios, especially for theft and positive reciprocity, were lacking the numbers to make any firm conclusions about their relationship even though the data aligned in the correct way. Potentially, this experiment should be rerun with higher numbers in order to make sure enough variation is captured in order to run the necessary analysis. Nevertheless, even with the current numbers, it is quite clear that there is enough evidence to suggest that RRM is useful to study and use in the future. However, simply looking at overall trends might not be very useful in order to understand why an individual might commit a crime. We

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know from the previous chapter that there are individual differences between people of how reciprocal and retributive they actually are, and while manipulating the environment to see how willingness to commit a crime changes is interesting and important in order to test RRM, much more can be achieved by looking into how people’s individual differences towards these tendencies can explain crime. Precisely to that, this book turns with the next chapter.

References Aromäki, A. S., Lindman, R. E., & Eriksson, C. P. (1999). Testosterone, aggressiveness, and antisocial personality. Aggressive Behavior: Official Journal of the International Society for Research on Aggression, 25 (2), 113–123. Bachman, R., Paternoster, R., & Ward, S. (1992). The rationality of sexual offending: Testing a deterrence/rational choice conception of sexual assault. Law and Society Review, 343–372. Balcells, L. (2010). Rivalry and revenge: Violence against civilians in conventional civil wars. International Studies Quarterly, 54 (2), 291–313. Becker, H. S. (2018). Labelling theory reconsidered 1. In Deviance and social control (pp. 41–66). Routledge. Bereby-Meyer, Y., & Fiks, S. (2013). Changes in negative reciprocity as a function of age. Journal of Behavioral Decision Making, 26 (4), 397–403. Bouffard, J. A. (2007). Predicting differences in the perceived relevance of crime’s costs and benefits in a test of rational choice theory. International Journal of Offender Therapy and Comparative Criminology, 51(4), 461–485. Burton, D. L., & Meezan, W. (2004). Revisiting recent research on social learning theory as an etiological proposition for sexually abusive male adolescents. Journal of Evidence-Based Social Work, 1(1), 41–80. Clarke, R. V. (2013). Situational crime prevention. In Environmental criminology and crime analysis (pp. 200–216). Willan. Dahl, D. W., Honea, H., & Manchanda, R. V. (2005). Three Rs of interpersonal consumer guilt: Relationship, reciprocity, reparation. Journal of Consumer Psychology, 15 (4), 307–315.

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Exum, M. L. (2002). The application and robustness of the rational choice perspective in the study of intoxicated and angry intentions to aggress. Criminology, 40 (4), 933–966. Exum, M. L., & Bouffard, J. A. (2010). Testing theories of criminal decision making: Some empirical questions about hypothetical scenarios. In Handbook of quantitative criminology (pp. 581–594). Springer. Farrington, D. P. (2003). Key results from the first forty years of the Cambridge study in delinquent development. In Taking stock of delinquency (pp. 137– 183). Springer. Friedman, H. H., & Herskovitz, P. J. (1990). The effect of a gift-upon-entry on sales: Reciprocity in a retailing context. American Journal of Business, 5 (1), 49–50. Funches, V., Markley, M., & Davis, L. (2009). Reprisal, retribution and requital: Investigating customer retaliation. Journal of Business Research, 62(2), 231–238. Gottfredson, M. R., & Hirschi, T. (2017). Self-control and opportunity. In Control theories of crime and delinquency (pp. 5–20). Routledge. Guerette, R. T., & Freilich, J. D. (2016). Migration, culture conflict, crime and terrorism. Routledge. Hinton, A. L. (1998). Why did you kill?: The Cambodian genocide and the dark side of face and honor. The Journal of Asian Studies, 57 (1), 93–122. Levin, J., & Madfis, E. (2009). Mass murder at school and cumulative strain: A sequential model. American Behavioral Scientist, 52(9), 1227–1245. Mann, R. E., & Hollin, C. R. (2007). Sexual offenders’ explanations for their offending. Journal of Sexual Aggression, 13(1), 3–9. Piquero, A. R., Fagan, J., Mulvey, E. P., Steinberg, L., & Odgers, C. (2005). Developmental trajectories of legal socialization among serious adolescent offenders. The Journal of Criminal Law & Criminology, 96 (1), 267. Sampson, R. J. (2004). Networks and neighbourhoods. Demos Collection, 155– 166. Smith, W. G. (2008). Does gender influence online survey participation? A record-linkage analysis of university faculty online survey response behavior.Online submission. Tibbetts, S. G. (1999). Differences between women and men regarding decisions to commit test cheating. Research in Higher Education, 40 (3), 323–342. Tyson, G. A., & Hubert, C. J. (2003). Cultural differences in adolescents’ perceptions of the seriousness of delinquent behaviours. Psychiatry, Psychology and Law, 10 (2), 316–323.

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Vermeersch, H., T’sjoen, G., Kaufman, J. M., & Vincke, J. (2008). The role of testosterone in aggressive and non-aggressive risk-taking in adolescent boys. Hormones and Behavior, 53(3), 463–471. Wilkowski, B. M., Hartung, C. M., Crowe, S. E., & Chai, C. A. (2012). Men don’t just get mad; they get even: Revenge but not anger mediates gender differences in physical aggression. Journal of Research in Personality, 46 (5), 546–555. Wikström, P. O. H., Oberwittler, D., Treiber, K., & Hardie, B. (2012). Breaking rules: The social and situational dynamics of young people’s urban crime. OUP Oxford. Wilson, J. Q., & Kelling, G. L. (2015). Broken windows. In The city reader (pp. 303–313). Routledge.

5 Retribution, Reciprocity, and Crime: Using a Public Goods Game to Measure People’s Prosociality and Criminality

1

Introduction

This book has several goals: present and develop a new reciprocity and retribution-based model of crime (RRM), introduce the most useful methods in order to study that model, and demonstrate the evidence that the theory has merit. The first chapter introduced the merits of evolutionary criminology as a framework; the second chapter develops a model; the third chapter outlines the mechanism and evidence for said model; while the fourth chapter tests the model and offers preliminary evidence in support of it. The aim of this chapter is to develop and adapt a more complex method in order to not only study RRM deeper but also to measure the individual differences between participants to enhance our understanding of the scope of RRM as well as our knowledge about why people commit crime. Crime is a difficult behaviour to explain and study for the sheer complexity of all the factors that play into that decision-making. Many scientists choose to investigate the overall trends of society and see what factors affect the likelihood of crimes occurring, others choose to focus © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. Svingen, Evolutionary Criminology and Cooperation, Palgrave’s Frontiers in Criminology Theory, https://doi.org/10.1007/978-3-031-36275-0_5

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on the individual in question. Each approach has its strengths and weaknesses, but the main question is why some people commit crime when others do not and why the same person chooses to commit crime in some circumstances but not others. There is a balance to be achieved when looking at evidence from the overall trends versus the behaviours of individuals, and that is true for RRM. In the previous chapter of this book, we learned that generally people are indeed retributive and reciprocal and that they respond to their environment in a negative way when the world has been bad to them and in a positive way when it has been good to them. In more specific terms, people are more likely to choose to commit a crime when they are responding to a negative event and may choose not to commit a crime when they are responding to a positive event. That evidence is consistent with the basic assumptions of RRM, but that would be a rather simplistic way of looking at it. After all, the majority of participants chose not to commit a crime even in the most high-intensity scenarios. Overall tendencies do not tell us enough about crime causation and neither are they enough to say that RRM is a useful tool for the study of criminology specifically for that reason. While chapter three analysed in depth why the tendencies of retribution and reciprocity are present in most people and were, in fact, detrimental for human survival as a species, it also indicated that there will be differences in the level and the ways in which those tendencies are expressed. Some people will be more reciprocal retributive than others and there are many reasons for why that would be the case. However, in order to fully examine RRM as a mechanism of crime causation, individual differences remain an important question to be addressed. This chapter poses the question of why some people commit crime and why some people choose not to by using a method adapted from the emerging field of behavioural economics. Currently, there is a lot of research concerning human motivations in social interactions that is coming from the field of criminology; including prosociality (Henrich et al., 2015), gratitude (Bartlett et al., 2012), altruism (Li et al., 2019) and many others. In fact, it is behavioural economics that first introduced the concepts of reciprocity and revenge as important factors in people’s everyday decision-making processes (Fehr & Gächter, 2000).

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Therefore, it is only suitable to use the same method in order to study the importance of retribution and reciprocity to crime causation. Unfortunately, the models introduced by behavioural economists are not easily applicable to criminal behaviour and suffer from being incomplete. Criminological researchers, as well as behavioural economists tend to make sweeping statements about the human condition by either assuming that all people are rational, or that they are reciprocal, or that they are willing to support a social norm. Those sweeping statements in economics tend to be based on game theory experiments in which the majority of participants have exhibited a certain behaviour, despite the fact that some of these results could be explained by other factors. The findings from chapter four also demonstrate that there is an overall trend that can explain crime. However, sweeping statements should be supported by looking into individual trends, especially since chapter four shows that the majority of people still chose not to commit crime. In this chapter I aim to adapt a method widely used by behavioural economists to study reciprocity (Fehr & Gächter, 2000) called the Public Goods Game (PGG) in order to do something different: to measure people’s individual differences in tendencies towards retribution and reciprocity and to use them in order to help us explain crime. Moreover, this chapter adds the idea of people’s subjective perceptions of the environment to the model. Perceptions of the environment are important even without the interaction with retribution and reciprocity. It is reasonable to assume that a person is much less likely to assault a stranger who has just smiled at them than a stranger who has shouted at them; a teenager is much less likely to draw a graffiti on a wall of the shop that has always given them a discount than on a wall of a shop where the shop assistants are rude to them. However, some people are much more susceptible to the positivity or negativity of their environment than others. RRM claims that the reason for this is the interaction with positively and negatively reciprocal tendencies. Reciprocity and retribution cannot be separated from the environment or a specific situation, since by definition reciprocity and retribution are a response to an event. However, people perceive their environment and the events that happen to them differently.

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A person who perceives the world to be inherently unfair and unkind is more likely to interpret a random act in a negative way and a person that perceives the world as an inherently nice place is more likely to perceive even the negative acts of others as accidents or jokes. Therefore, it is hard to study ideas of retribution and reciprocity, especially when trying to measure the individual differences, without tapping into the way the individuals in question perceive the world. As a result, in addition to introducing an improved and adapted decision-making experiment, this chapter also introduces a new inventory for Perceptions of the Environment (PoE). Both the Public Goods game and the Perceptions of the Environment questionnaire were piloted previously (Svingen, 2017) and showed great potential to capture variation and scope in capturing individual differences. Therefore, in this book the use of this method will give us some definitive answers on how much merit RRM presents and whether it is a framework that will be useful to study and develop further.

2

Methodology

Methodology employed for this research is largely quantitative. It quantifies negatively reciprocal, positively reciprocal, and retaliatory tendencies within an individual, as well as measures their perception of the environment. The tendencies are measured using a game theory experiment, a decision-making game, commonly referred to as the Public Goods Game (PGG), which in this particular variation consists of three parts. In the three sections the participants can demonstrate various behaviours that are then interpreted with a framework of reciprocity and retribution to create the scores. Perceptions of the Environment (PoE) are quantified through a specifically developed inventory. All of the quantitative measures produce a continuous score which is relatively easy to analyse and interpret. The dependent variable, crime, is measured through two methods: a self-reported crime questionnaire that asks the participants about crimes they have committed in the last year and the hypothetical scenarios that

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present the participants with a situation before being asked how likely they would be to commit a crime in that particular circumstance. This research was approved by the Cambridge University Institute of Criminology Ethics Board.

2.1

Sample and Procedures

Participants were recruited through various mailing lists, participant search groups, and personal conversations. There were no exclusion criteria apart from being able to understand English and being over 16 years of age. Participants were incentivised to take part in the experiment by being entered in a prize draw for a £100 Amazon voucher and were informed that they could drop out of the experiment at any time without it affecting their chances of winning. In behavioural economics there is a lot of discussion on whether participants should be offered competitive payments to incentivise them to pay more attention in the game. However, in this version the participants’ winnings were not dependent on their decisions within the game. That is in line both with previous research (Hoffman et al., 1995; Slonim & Roth, 1998) and results of the pilot study (Svingen, 2017) that compared the two conditions (competitive vs. noncompetitive payment) and found no difference between the results of the two. Therefore, all participants were assigned to identical conditions for the sake of fairness and offering everyone the same chance of winning for participating in the experiment. After the participants were recruited, they were invited to take part in a group decision-making game, and if the participants agreed to participate, they were assigned a specific time slot at which they had to be online. They were informed that they had to log in at a specific time to create the atmosphere of them playing with other players. In fact, they were playing with bots that mimicked the behaviours of other players. At the designated time the participants were sent the link to the website where the game was hosted, as well as a randomly generated six digitand-letter identifier that they could use to log into the game to keep it anonymous. The first page that appeared after the login page was the

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consent form. The website did not allow participants to continue unless they gave their consent to participate in the experiment and for their data to be used for research. After agreeing they were given a demographic questionnaire asking for their age, gender, and nationality. The next questionnaire was the Perceptions of the Environment (PoE) inventory created for this research, followed by three hypothetical scenarios very similar to those used in chapter four that presented the situation and then asked how likely the participant was to commit a crime (theft and/or assault). Following the scenarios, the participants received the instructions for the decision-making game. Participants had to pass four comprehension questions in order to continue. The questions served two purposes: to make sure the participants fully understood the instructions, and to make the participants feel confident about the game. After the instructions, they were invited to participate in a practice round to familiarise themselves with the interface. After the practice round the game started as outlined in more detail in the next section. Once the decision was made, the message “please wait for the other participants to connect” appeared in order to reinforce the idea that the participants were playing with real people. The game ended after three rounds and participants were given a questionnaire on self-reported criminality. After the criminality questionnaire they proceeded to an experiment evaluation questionnaire, where they were asked if they understood the instructions, what they felt the goal of the experiment was, and whether they had any other comments. Winners of the vouchers were chosen using a random number generator and received their prize via email. Their emails were then deleted in order to protect their personal details.

2.2

The Decision-Making Game

Reciprocity and retribution are measured using a decision-making game, the so-called Public Goods Game (PGG), which has been widely used in behavioural economics to test precisely these behaviours (Fehr & Gächter, 2000). Despite the fact that there is some open-source software

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for conducting these experiments, such as oTree or z-tree, coding for this experiment was done from scratch. The reason for this is that none of the software could account for the specific tasks the game was supposed to achieve. For instance, z-tree would require participants to play on computers where the program would have to be pre-installed, thus preventing them from playing from home which would have reduced the sample size. Using oTree, on the other hand, would limit the possibilities for creating bots that pretend to be human as well as make it harder to add specific additions to the game (such as combining the Dictator Game with the PG in part three). For this study to work efficiently the whole experiment had to be a website. As a result, the main language for writing this game was PHP: Hypertext Preprocessor, combined with HyperText Markup Language (HTML) to make it run online, styled with Cascading Style Sheets (CSS), and made functional with JavaScript (JS). The database it connects to for data management is coded using Structured Query Language (SQL). In order to make the game accessible on any device (phone, tablet, etc.), Bootstrap is also used. Therefore, this experiment could be accessed from any device at any time as long as the participants had the right login details and access to the internet. The data was then passed onto a secure server on a password-protected computer. There are several versions of the PGG that already exist, but a new version was created for the purposes of this experiment. The overall rules of the game were simple: several people had the same starting amount of Experimental Currency Unit (ECU), which they could either keep for themselves or invest into a Public Good (PG). The PG then multiplied the ECU invested into it and shared them equally among all participants. In the version of the game created for this book, there were four players that started with 20 ECU each, and PG doubled everything that was invested into it. Most of the versions of the game used a different formula, which gives everyone 0.4 for every token they invest. However, those studies also tend to use econometrics students as their participants, who are familiar with these games and the calculations used for them; therefore, it was decided to create this version of the game with rules as simple and accessible as possible.

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There were three parts to this game with three rounds in each: in the first part players played by contributing to the PG. In the second part punishments and rewards were introduced. Every participant could either award or take money away from any other player in the game. However, in doing so they would incur personal costs, as they had to pay half of the total of rewards and punishments they wish to inflict on others; i.e. if one wished to reward one player for 6 ECU and punish another for 12 ECU they would have to pay 9 ECU from their own pocket. In the third round of the game only three players continued the game, and one person became the Third Party Punisher (TPP). TPP did not benefit from the PG, nor could they contribute to it; TPP only received 20 ECU at the start and had no gains in the game itself. However, they could still reward and punish other players following the same rules as in the previous part, while others could not reward or punish one another. In this part every participant became TPP and their role was to observe other players play against one another while not making any profit from the game themselves. Despite the fact that players were made to feel as if they were playing with real people, they were playing against the program, which simulated real participants. These simulations were highly adaptive to the behaviours of the actual participants, with their contributions and punishments being calculated from the contributions of the players in the previous rounds. The reason for not using real participants was to test how people operated in a specific environment and responded to the same stimuli, whereas playing in a group would cause different group dynamics in every game, making it harder to analyse individual differences. In the first part every simulation contributed to the PG, whereas in the following parts one free-rider emerged which was designed to test people’s retaliatory and negatively reciprocal behaviours towards it. Positive Reciprocity (PR) was measured as the sum of contributions and rewards in the game, as first introduced by Fehr and Gächter (2000). One can argue that the real PR only happened in the last round, where the players could not expect to gain any future profit from cooperation. However, one-shot games, in which the players know that they will be matched with a different person next time they are playing the game,

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versus a long-shot game, in which people are matched for prolonged periods of time, show the same results (Gächter & Falk, 2002). Negative reciprocity (NR) was measured by the punishments in the second part of the game. Retribution (R) was measured by the punishments in the third part of the game.

2.3

Hypothetical Scenarios

A self-reported criminality questionnaire can be used to correlate the measured behaviours to crime in general; however, these do not measure crime propensity at the same time as the experiment takes place. Hypothetical scenarios provide another alternative to assessing whether people differ in their tendency to see crime as an option. Out of the four scenarios that were used in chapter four, three were selected to be used here as an explanatory variable. The three scenarios are taken from The Peterborough Adolescent and Young Adult Development Study (PADS+; Wikström et al., 2012). They present three situations: the Bus Stop Scenario (BSS), the Nightclub Queue Scenario (NQS), and the Dropped Wallet Scenario (DWS). BSS presents a situation where a person approached the respondent at the bus stop and pushed them. NQS presents a situation where the respondent is passing a nightclub queue, when a stranger accuses them of queue jumping and pushes them so hard that the respondent falls and hurts their arm. Both BSS and NQS ask how likely the respondent would be to punch that person. DWS presents a situation in which the respondent sits at the library and sees a stranger drop a wallet which contains £100 and then asks how likely the respondent would be to keep the money. Scenarios were gender-specific, matching the gender of the respondent. Options were a 4-point Likert scale ranging from “very unlikely” to “very likely”. These specific scenarios were chosen for the variation of crime that they capture. Assault and theft are fundamentally different crimes, which means that there had to be two scenarios asking for these crimes separately. Assault is split into two scenarios for the difference in the levels of

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provocation (NQS has more provocation than BSS) and in the previous chapter it was evident that these scenarios capture different levels of responses.

2.4

Questionnaires

2.4.1 The Environment Questionnaire The Perceptions of the Environment questionnaire was developed by collecting items from already existing relevant measures and reformulating them in an accessible way. These measures are the perceptions of reciprocity questionnaire, the Interpersonal Relationship (IPR) scale, and the Social Support Quality 3-item (SSQ3) as well as the 6-item (SSQ6) scale. All of those have been reworded to make them less specific to medicine and used for the final PoE measure. The only attempt to measure reciprocity I found was the 3-item perceptions of reciprocity questionnaire that is specific to medical general practitioners and showed Cronbach’s alpha of 0.74 (Bakker et al., 2000; Mechanic, 1970). However, there are other questionnaires that measure something relevant, and of those is the inventory relating to social support. Some suggest that reciprocity itself is a return of social support in response to the social support received by others (Uehara, 1995), making the two inseparable and hence creating the need to include the items of social support into the perceptions of the environment. Social support appears in two important scales: Interpersonal Relationship (IPR) and Social Support Quality (SSQ). The Interpersonal Relationship (IPR) scale has been developed through a grounded approach of interviewing people about what they perceive to be the most important aspects of the society that surrounds them. Factors that have been included in this scale are support, reciprocity, and conflict (Cronbach’s alpha 0.92, 0.83, and 0.91 respectively; Tilden et al., 1990). Five items of social support and reciprocity with highest factor loadings have been included in the final PoE questionnaire from the IPR scale. The full version of the Social Support Quality (SSQ) scale consists of 27 items. Shorter versions of SSQ also exist: the three-item questionnaire (SSQ3)

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and a six-item one (SSQ6). While SSQ3 has been found to be prone to kurtosis, SSQ6 was found to be as reliable as the original SSQ questionnaire (Cronbach’s alpha 0.90; Sarason et al., 1987). For the final PoE measure all six items from SSQ6 were used and reformulated in order to fit the research aims better. The PoE I used in this book was created by combining the original perceptions of reciprocity questionnaire (Bakker et al., 2000; Mechanic, 1970) with SSQ6 (Sarason et al., 1987) and social support items from the IPR scale (Tilden et al., 1990), the final questionnaire used for Perceptions of Environment (PoE) was further simplified in order to make the language easier to follow. The final 14-item PoE questionnaire is presented below. They are all measured on the 5-point Likert scale; three of the items are reverse-scored; the total score is calculated by reversing the scores of the three first items and then summing up all the numbers (Table 1). Table 1 All items of the perceptions of the environment questionnaire Item 1 2 3 4 5 6 7 8 9 10 11 12 13 14

I feel I put more into relationships with people then I get out of them; I feel I put too much effort in contrast to what I get back; I feel I give people a lot of time and attention, but get little thanks; I feel that I can count on people to distract me when I am worried about something; I feel that I can count on people to help me relax when I am stressed; I feel that people around me accept me totally including my best points and my worst points; I feel that I can count on people to care about me, regardless of what is happening in my life; I feel that I can count on people to help me feel better when I am unhappy; I feel that I can count on people to comfort me when I am very upset; I feel that I have someone that I can turn to; I feel that I have people who stand by me; I feel that I can talk openly about my problems; I feel that it is safe for me to show my weaknesses; I am satisfied with the balance of the things I give to the people around me and what I get in return

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This questionnaire was first used in the pilot study (Svingen, 2017) where its effectiveness was evaluated and further used again to show a similar level of internal consistency (Svingen, 2021). The scale ranges from a minimum of 14 and maximum of 70 by design. During the pilot study it showed a variation of 59, with 156 participants ranging from a score of 25–63 (M = 46.9; SD = 7.7), distributed normally. The scale demonstrated a Cronbach’s alpha of 0.89, which shows high internal consistency, with the alpha of every single item above 0.87, but each below the overall scale value of 0.89. That means that removing any item from the scale would not improve its explaining power. That means that the developed PoE measure is a reliable tool to be used in this research and that is why it was used in this book unchanged from its first version.

2.4.2 Self-Reported Criminality Questionnaire In the Self-Reported Criminality Questionnaire (SRCQ) participants were asked to answer questions about their involvement in crime in the last 12 months. The questions were taken from the Peterborough Adolescent and Young Adult Development Study (PADS+; Wikström et al., 2012). Questions included information on: theft, auto theft, assault, burglary (residential and non-residential), handling of stolen goods, rape, fraud, traffic offences, drug use, and drug dealing. A precise number of crimes was asked as opposed to expressing responses on a Likert scale. This method is far more precise at capturing variation since it allows for a wider range of options. Moreover, asking the participants to accurately estimate what “often” and “rare” means is likely to result in inaccurate data.

2.5

Summary

2.5.1 Scores and Measures All of the three tendencies (positive reciprocity, negative reciprocity, and retribution) are measured through the PGG, with different parts and

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behaviours in the game taken as measures. Since the values are close to zero and have a clear cut, the distribution of observations tends to have a strong skew. Therefore, these scores have been logarithmically transformed to reach a normal distribution. Positive Reciprocity Score (PR score) measures a participant’s positively reciprocal tendencies. It is a sum of all contributions to the PG as well as rewards, taking the data from all nine rounds of the game. Negative Reciprocity Score (NR score) measures a participant’s negatively reciprocal tendencies. It constitutes a sum of all punishments from part two of the game (rounds four to six). Retribution score (R score) measures participant’s retributive tendencies. It is a sum of all punishments of part three of the game (rounds six to nine). Perceptions of Environment Score (PoE score) is a sum of all items of the questionnaire after three reserve items were reversed back. Self-Reported Crime Score (SRCS) is a sum of all crimes from the SRCQ with the maximum capped at 20 to avoid extreme outliers. Individual scenarios are also used for analysis, referred to by their names: Bus Stop Answer (BSA), Nightclub Queue Answer (NQA), Dropped Wallet Answer (DWA). The first two refer to assault, the latter refers to theft. Violence score (V score) is a sum of BSA and NQA, ranging from zero to two. Combined Crime score (CC score) is a sum of NQA and DWA, ranging from zero to two. General Crime score (GC score) is a sum of responses from all three scenarios, ranging from zero to three.

2.5.2 Method Summary The summary of all the measures and scored used for this test are identified in the table below (see Table 2).

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Summary of all the scores and measures used for this experiment

Method

Construct

Score

Public Goods Game (PGG)

Retribution; Negative Reciprocity; Positive Reciprocity

Hypothetical Scenarios1

Tendency to respond with crime to a certain situation

Self-reported Criminality Questionnaire (SRCQ)

Self-reported criminality

Retribution Score (R score); Negative Reciprocity Score (NR Score); Positive Reciprocity Score (PR Score) Bus Stop Answer (BSA); Nightclub Queue Answer (NQA); Dropped Wallet Answer (DWA); Violence Score (V score); Combined Crime Score (CC Score); General Crime Score (GC Score) Self-Reported Criminality Score (SRC score)

3

Results

There are several hypotheses for this section, the main one being that the interaction between the independent variables of PoE, positive reciprocity, negative reciprocity, and retribution, would affect the dependent variable of willingness of the participants to report that they would commit a crime in a hypothetical scenario. The separate hypotheses are that all the independent variables would be able to affect the same outcome variables separately from one another as well as in an interaction. The null hypothesis is that there is no effect, and the expectation is that we would be able to reject the null.

1

Bus Stop Scenario (BSS); Nightclub Queue Scenario (NQS); Dropped Wallet Scenario (DWS).

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Descriptive Statistics

3.1.1 Overall Behaviour A grand total of 318 participants passed the first comprehension quiz and hence proceeded to the experiment, but from those six did not finish the first round and were excluded from the research. Six further participants failed to pass the second comprehension quiz. The full game was completed by 301 participants, however, a further three participants did not fill in the self-reported criminality questionnaire that came last on the experiment. All in all, the participants readily took part in the game and engaged in it well. They contributed to the social good, readily rewarded and punished other participants (Fig. 1). The amount of contributions increased every round despite the possibility of free-riding. That was true even in the second round where the amount of ECU participants had dropped slightly by the end of the round. This was especially interesting in the last round of each part since the players had no rational incentive to contribute in the last rounds,

Fig. 1 Summary of overall behaviours in the Public Goods Game

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since there were no more rounds to follow and they could freeride without consequences; however, participants contributed, rewarded, and punished in the last round too, reflecting cooperative tendencies that were not directed towards maximising gain. In parts one and two, as participants contributed more, the amount of ECU increased, and they tended to have more money to spend on contributions, so contributions increased as ECU increased. The relationship between punishments and rewards, on the other hand, was nonlinear and thus far more interesting. One tended to decrease as the other increased, reflecting an opportunity cost-players had limited amounts of funds that they could spend on punishments and rewards, and they seemed to be making a choice of whether to punish or reward instead. Nevertheless, the participants still chose to spend money on rewarding and punishing, even in the last round where they knew they would not be making any money through the game and hence had no financial incentive to do so. In the beginning of every new part, average rewards were higher than average punishments, perhaps reflecting the general reciprocal tendency of the population. However, punishments, especially in the third part, tended to increase, reflecting people’s retributive tendencies. That increase is interesting, as in the last three rounds players did not earn ECU, they started with 20 and that is all they had for rewards and punishments every round. On average, participants spent more than half of their ECU on punishments and rewards, despite not gaining anything out of it. Punishment showed an unusual pattern. While contributions and rewards increased as the amount of ECU increased, punishments showed an almost negative relationship with the number of ECU. That means that they were not just a reflection on how much money people had to spend on the game. Variation among the numerous participants is evident; they adopted different strategies to play the game, with some people (sometimes more than half, especially in part three of the game) not spending any money at all and some spending everything that they had. That meant that the game was capable of capturing a wide variety of individual differences in relation to reciprocity and retribution, as well as reflecting that people showed very different tendencies and could not be summarised

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as following solely one trajectory. Since this chapter introduced retribution and reciprocity as individual differences, it becomes increasingly important that this variation is captured. Moreover, it proves the idea that people do have very different levels of tendencies towards retribution and reciprocity with some people being much more sensitive than others and some choosing a free-riding approach. Despite the fact that most people engaged fully with the game and showed reciprocal and retributive tendencies, some people demonstrated gain-driven strategies that used the opportunities to free-ride. Every single round had a number of people who decided to keep the money for themselves (the table excludes the participants that physically had no more CU to spend because they spent everything in the previous rounds), which is also quite important for our understanding of retribution and reciprocity as it creates the group to compare against. Nevertheless, free-riders still remain a minority in most cases except for a couple of rounds (Table 3). Given that there was no rational reason to either reward or punish anyone in the third part of the game entirely, it was expected that there would be many free-riders. It is more interesting that many of the participants still did. Interestingly, fewer people chose to free-ride in the last part where their earnings were not tied to the performance of the group rather than in the previous part where they could actually get some benefit out of incentivising and deterring other players. However, the influx of free-riders in the last round of all three parts demonstrates that a lot of people still chose the rational option of maximising earning, albeit not everyone. In general, this once again evidences that people tend to be retributive and reciprocal, both engaging with contributions, rewards, and punishments even in the third part of the game where all participants played the role of the Third Party Punisher (TPP). There was an obvious drop in the ECU that the participants left in the third round that not only supports the merit of looking at RRM as a theory but also shows that this is a good method for studying and capturing the variation in these tendencies.

1:1 26 (8%) – –

Round Participants who did not contribute

Participants who did not punish

Participants who did not reward

Part 1

–

–

1:2 19 (6%)

Table 3 Summary of free-riders per round of the PGG

–

1:3 35 (11%) – 165 (55%) 151 (50%)

2:1 11 (3%)

Part 2

107 (36%) 192 (64%)

2:2 21 (6%)

2:3 53 (16%) 154 (51%) 216 (72%)

103 (34%) 112 (37%)

3:1 –

Part 3

104 (35%) 151 (50%)

3:2 –

121 (40%) 187 (62%)

3:3 –

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However, this chapter is not about overall trends and behaviours, it is about capturing the individual differences. Therefore, it is time to start looking at just those.

3.1.2 Demographics As shown in chapter four of this book, neither sex, age, nor nationality played a very important role in retribution and reciprocity and hence they are of little significance in the model. As previously established, there are still important differences when it comes to gender and crime, and it is common knowledge that males commit more crime than females. However, despite the fact that gender might be a good predictor of crime, it is not a cause of crime, otherwise we would see all members of one gender commit crime while others do not. Gender might be a relatively good proxy for things such as differences in social rules, upbringing, or even biological mechanisms; however, my reluctance in including it into the model as an explanatory factor lies in the interest of finding the causal mechanisms that lead to crime, in which neither gender nor nationality have proven to be relevant. Demographic measures were still collected in order to make sure there is enough variation to make it easy to generalise. Of the total number of participants 203 (68%) identified as female, and 97 (32%) as male, representing 50 different nationalities. Age varied from 16 to 58 (M : 24.2; SD: 4.9). Apart from some much older outliers, the observations are normally distributed albeit slightly skewed to the younger 20s. The numbers do not change much if the participants older than 40 are excluded from the analysis (M : 24.0; SD: 4.2). That gives enough variation for the generalisation of the model to all groups as well as helps us capture people with different social norms, environments, and upbringings as well as experiences to create the variation in the other scores (Fig. 2).

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Fig. 2 Histogram of Age distribution of the participants excluding outliers (Tukey’s).

3.2

Scores

3.2.1 Perceptions of the Environment The Perceptions of the Environment (PoE) score is calculated by adding up the answers for all items on the questionnaire (three are reverse scored and have hence been recoded). There are 14 items on the questionnaire, hence the minimum score a person can receive is 14 and the maximum is 70. From 300 recorded observations the minimum score was 16, maximum 65 (M : 45.8; SD: 8.3), which shows good variation and depth. The histogram appears to be normally distributed (Fig. 3).

3.2.2 Self-Reported Crime The prevalence and volume of crime, as expected, were not particularly high in the sample. There are many reasons for that, starting from the lack of motivation to report despite the anonymity of the questionnaire to the features of this particular sample. However, some variation was still captured that could be analysed even though the main dependent variable we are looking for is the crime measures from the hypothetical scenarios (Table 4).

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Fig. 3 Histogram of the distribution of scores of Perceptions of the Environment questionnaire

Table 4 Summary of the self-reported crime Variable

Prevalence

Min

Max

Mean

Shoplifting Residential burglary Non-residential burglary Car burglary Theft Auto theft Vandalism Arson Robbery Assault Traffic offence Handling stolen property Fraud Drug use Drug dealing Rape

7 1 0 0 7 1 15 5 3 13 44 3 29 78 4 0

SD

1 1

10 1

2.9 1

3.2

1 1 1 1 1 1 1 1 1 1 2

20 1 20 5 10 9 100 8 10 320 10

4.9 1 2.4 2.4 5 3.4 5.4 3.7 1.6 11.8 6.5

7.2 4.9 1.7 4.6 2.6 15 3.8 1.8 39.4 4.1

All the numbers are summed up into one comprehensive SelfReported Crime Score (SRCS) which is created by summing up all the numbers of self-reported crime for all crimes. Some participants reported abnormally high amounts of a certain crime, for example, one

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participant reported 320 counts of illegal drug use or another participant reported 100 traffic offences. Those participants made for extreme outliers and skewed the analysis, however, excluding them from the analysis would not be very beneficial since those are the most interesting participants from a criminological viewpoint. Therefore, all crimes were capped at 20 to allow the outliers to be included in the analysis while still keeping them as significantly different from those participants that reported much less crime. The change affected one participant and traffic offence crime and five participants in illegal drug use. The final variable 210 observations that are not equal to zero (M : 6.2; SD: 8.7), meaning that those participants reported committing at least one crime in the last year.

3.2.3 Hypothetical Scenarios The Self-Reported Criminality Questionnaire is a useful tool but has several drawbacks: first of all, despite the fact that the crimes would be quite recent (the questionnaire only asks for crimes committed in the last year), it doesn’t measure crime propensity at the time of the participants taking part in the experiment. Moreover, it does not capture a good enough variation to analyse in sufficient detail and also does not present an accurate enough measure of crime propensity. It is likely that the participants just have not been in a criminogenic environment to let crime happen. For those reasons, hypothetical scenarios were used asking the participants how likely they would be to commit a crime in a given environment. Three hypothetical scenarios, adapted from PADS+study (Wikström et al., 2012) were used: the Bus Stop Scenario (BSS), the Nightclub Queue Scenario (NQS), and a Dropped Wallet Scenario (DWS). BSS and NQS both measure the participant’s propensity to commit a violent crime (i.e., to punch a person who pushed them), and the DWS is asking the participants how likely they would be to steal a wallet that did not belong to them. All scenarios adapted the gender of all characters to be the same gender as the participant. Response options were a 4-point Likert scale ranging from “very unlikely” to “very likely” (Table 5).

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Table 5 Summary of all participants’ answers to the hypothetical scenarios Bus Stop Nightclub Queue Dropped Wallet

Very unlikely

Unlikely

Likely

Very Likely

162 (49%) 96 (28%)

107 (32%) 130 (39%)

16 (5%) 54 (16%)

15 (5%) 20 (6%)

157 (47%)

92 (28%)

27 (8%)

24 (7%)

For the sake of simplicity of the analysis the variables have been treated as binary with “Yes” or “No” options where “No” is coded as a “0” and “Yes” as a “1”. Presented as a binary, 31 people (10.3%) would punch a person on the bus stop, 74 (24.6%) would punch a person that confronted them in the nightclub queue and 51 (17%) would steal a wallet lying on the ground. The summary of scores shows a good level of participation with most people engaging with the game by contributing, rewarding, and punishing, demonstrating tendencies towards reciprocity and retribution. Only three people (1%) did not contribute anything throughout the game, 59 (17.7%) did not punish, and 38 (11.4%) did not reward (Fig. 4). The game captured a good variation of behaviours, demonstrating that when it comes to all three tendencies there are big differences in people’s behaviours. That further supports the merit of studying those tendencies separately and in terms of individual tendencies rather than overall trends. None of these variables, however, are normally distributed even after the removal of some extreme outliers. That may mean that some further transformations will need to be made in order to run the analysis using these scores.

3.3

Retribution, Reciprocity, and Crime

3.3.1 Tendencies Interaction The way we tend to study human cooperation often involves putting a lot of different behaviours under big umbrellas such as “cooperation” or “altruism”. While in this book I recognise the importance of looking at

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Fig. 4 Histograms of scores for the three tendencies: Positive reciprocity, negative reciprocity, and Retribution

these things, I also posit that it might be more useful to break it down and look at the relationships separately. Reciprocity, for instance, tends to be treated as a singular entity, without being broken down into its positive and negative side. This section explores whether there is a merit in looking at the three tendencies separately by looking how closely these behaviours are correlated (Fig. 5). What can be seen from the graph is that there does not seem to be a real trend and that showing one tendency does not necessarily mean that the participants will show any other tendencies. Sperman’s2 correlations (Table 6) show very weak numbers that also support the notion of there not being much correlation. The strongest relationship can be observed between Retribution and Negative Reciprocity, which is unsurprising 2

Since the data are not normally distributed the normal correlation could not be used.

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Fig. 5 Scatterplots of all three tendencies

Table 6 Correlation of the tendencies NR R PR

NR

R

PR

1 0.47* 0.03

1 −0.12*

1

*.05

considering that it could reflect an individual’s inclination to punishment in general. However, the correlation is still not strong enough to claim that these behaviours could be studied as the same behaviour. Therefore, these results support what I claimed in the third chapter of this book: all three tendencies use different mechanisms and hence should not be grouped together under one umbrella of cooperation or altruism. They might interact to some extent, which should be taken into account for the future regression models, but it is important to see how these tendencies seen separately might help us explain crime.

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Table 7 score

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Chi2 analysis of perceptions of the environment and the general crime

Retribution score (3 quantiles)

General crime score 0

1

2

3

Total

1

90 (45%)

12 (20%)

7 (25%)

1 (8%)

2

61 (31%)

25 (41%)

8 (29%)

4 (31%)

3

47 (24%)

24 (39%)

13 (46%)

8 (62%)

198

61

28

13

110 (37%) 98 (33%) 92 (31%) 300

Total Pearson

chi2

(6)

= 25.5565; p < 0.001

3.3.2 Role of Retribution and Reciprocity in Crime Causation The RMM model states that it is the interaction between the tendencies and the perceptions of the environment that explain crime; however, it is useful to examine whether the tendencies can explain any crime on its own.

Retribution Retribution is a behaviour that captures the tendency to punish a violator of a social norm. In this chapter, I define it as strictly being a third party in the situation rather than being provoked themselves. As already observed by the overall behaviour in the game, people were in fact more likely to punish and reward other participants when they were themselves not involved in the game. Therefore, it is unsurprising to see that retribution predicts crime (Table 7). The General Crime Score (GCS) combines the crimes from all three scenarios and shows the general direction of the relationship between the two variables. The Retribution score was split into three quantiles to group people into high, low, and medium intensity to see which groups are more likely to commit crime in general. All the numbers align in the predicted fashion. People with a higher retribution score were more

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Fig. 6 Box plot of retribution score against general crime score

likely to have a higher general crime score, meaning that they were more able to commit any type of crime. These findings are supported while looking at retribution as a continuous variable too. When split over the general crime score, we can observe that the mean is going up as the crime score is increasing. A Wilcoxon rank-sum test3 run for every scenario separately supports these findings (for BSS z = −3.929; p > |z| = 0.0001; for NQS z = −3.816; p > |z| = 0.0001; for DWS z = −3.945; p > |z| = 0.0001) (Fig. 6).

Negative Reciprocity Retribution and negative reciprocity are both measuring the tendency to punish, but in contrast to retribution, negative reciprocity is focused on people responding to something that was directed at them. Both, however, seem to predict crime (Table 8). As a general rule, we can observe that the higher the negative reciprocity score, the more likely the participants are to say they would 3

For not normally distributed data.

Pearson chi2 (6) = 19.6114; p = 0.003

1 2 3 Total

Negative reciprocity score (3 quantiles) 89 (45%) 61 (31%) 48 (24%) 198

0 18 (30%) 18 (30%) 25 (41%) 61

1

General crime score 5 (19%) 13 (48%) 9 (33%) 28

2

Table 8 Chi2 analysis of negative reciprocity score against general crime score 3 3 (23%) 2 (15%) 8 (62%) 13

Total 115 (38%) 94 (31%) 90 (30%) 300

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Table 9 Chi2 analysis of negative reciprocity scores against scenario answers NR group 1 2 3

NR group 1 2 3

NR group 1 2 3

DWS No

Yes

103 (41%) 77 (31%) 69 (28%) Pearson chi2 (2) = 6.2145 p = 0.045

12 (24%) 17 (34%) 21 (42%)

BSS No

Yes

110 (41%) 85 (32%) 73 (27%) Pearson chi2 (2) = 11.5804 p = 0.003

5 (16%) 9 (23%) 17 (55%)

NQS No

Yes

95 (42%) 70 (31%) 61 (27%) Pearson chi2 (2) = 6.1105 p = 0.047

20 (27%) 24 (33%) 29 (40%)

commit a crime. The findings get slightly less linear around the middle of the table where the participants responded with a “Yes” for two crimes between the medium and high negatively reciprocal groups or where they responded with “Yes” for all three crimes between the low and medium groups. However, that may be a reflection of the scenarios representing different crime types. When all scenarios are analysed separately, the relationship between negative reciprocity and crime remains (Table 9). These findings are also supported by the Mann–Whitney U test where negative reciprocity is treated as a continuous variable (for DWS: z = − 2.483 p > |z| = 0.0130; for BPS: z = −3.333; p > |z| = 0.0009; for NQS: z = −2.467; p > |z| = 0.0136). That means that the findings show us that people who score higher on negative reciprocity are more likely to say that they will commit a crime, whether it is violence or theft.

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Table 10

Chi2 analysis of positive reciprocity against overall crime score

PR group 1 2 3 Total

Overall crime score 0

1

2

3

Total

58 (29%) 63 (32%) 77 (39%) 198

23 (38%) 24 (40%) 13 (22%) 60

14 (50%) 10 (36%) 4 (14%) 28

6 (46%) 4 (31%) 3 (23%) 13

101 (34%) 101 (34%) 97 (32%) 300

Pearson chi2 (6) = 13.1779; p = 0.040

Positive Reciprocity In contrast to negative reciprocity and retribution, the expectation for positive reciprocity is that the higher the person scores the less likely they are to commit a crime. The findings support that assumption (Table 10). As the positive reciprocity scores increase, the participants are less likely to say that they would commit a crime. All of these results are also statistically significant on the Wilcoxon rank-sum test (for DWS: z = 2.128 Prob > |z| = 0.0334; for BSS: z = 2.465 Prob > |z| = 0.0137; for NQS: z = 2.481; Prob > |z| = 0.0131). As a result, it is possible to conclude that all the tendencies are related to answers to hypothetical scenarios. Higher rates of negative reciprocity and retribution tend to increase the criminogenic likelihood, while higher rates of positive reciprocity tend to decrease it. Therefore, all the tendencies are important to the model and cannot be excluded. The next step is to integrate Perceptions of the Environment to the model.

3.3.3 The Role of the Environment If crime, retribution, and reciprocity are a response to the environment, it is impossible to study their influence without looking at the environment, or the perceptions of it. In fact, results show that perceptions of the environment can predict some crime on their own (Table 11). As with the tendencies before, the PoE score was split into three quartiles to group people into high, medium, and low intensity groups. The results are not completely straightforward, however, it is relatively clear

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Table 11

Chi2 analysis of perceptions of the environment against crime score

PoE group 1 2 3 Total

221

Overall crime score 0

1

2

3

Total

70 (35%) 61 (31%) 67 (34%) 198

24 (39%) 18 (30%) 19 (31%) 60

13 (46%) 11 (39%) 4 (14%) 28

6 (46%) 4 (31%) 3 (23%) 13

101 (37%) 101 (32%) 97 (31%) 300

Pearson chi2 (6) = 5.8310; p = 0.044

that as people view the environment in a more positive way they are less likely to say they would commit a crime in all hypothetical scenarios. These findings are supported by one way ANOVAs on all scenarios (for DWS: mean“No” = 47.0, mean“Yes” = 43.5, F = 1.33, Prob > F = 0.504; for BSS: mean“No” = 47, mean“Yes” = 42, F = 3.87, Prob > F = 0.0431; for NQS: mean“No” = 47, mean“Yes” = 43.7, F = 1.39, Prob > F = 0.440). The mean PoE score of the participants that responded with “No” was in average lower than that of those who responded with a “Yes”; however, the difference was not particularly large, measuring around four points of the scale. Nevertheless, the results show that even the small differences on the scale can affect people’s crime propensity.

3.3.4 Retribution and Reciprocity Model The findings in the previous section are important: they show us that even taken separately, the tendencies of Retribution, Negative Reciprocity, and Positive Reciprocity as well as the Perceptions of the Environment can already help us predict and understand crime. However, the focus of the RRM model is on the interaction between all of these factors and the environment. Therefore, this is the section with the most important findings of this chapter that actually tests the explanatory power of RRM. In order to do that, several regressions were run against various scores. The final model features seven interactions: the variables of positive reciprocity, negative reciprocity, retribution, and perceptions of the environment were all interacted with one another as well as included

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separately. This way the model had more explanatory power than any other options (such as no interactions or only interacting the PoE score with all the tendencies separately). The results are presented in the Table 12. The findings show that the relationship between factors of RRM and crime exists, and it is relatively strong. For the hypothetical scenarios, 20% and 21% of variation in the answers to general crime score and violence scores can be explained. There is slightly less explanatory power when it comes to the self-reported measures of crime; however, the model still explains 16% of the variation, which is a good amount for real-world data. However, even more becomes clear once we look at all the scenarios separately (Table 13). Only 21% of variation in the General Crime Score are explained by the RRM. However, that is already more variation than can be explained by the violence score, suggesting that removing DWS from the variable makes it less useful. In fact, when looking at all three scenarios separately, a clearer picture arises. RRM explains 37% of variation of the BSS, which is a significant improvement on all the other numbers, especially in contrast to the 20% of NQS. This is not the first problem of this kind that we encounter with NQS. There is a possibility that that scenario in the way it is set up Table 12 Summary of regression models for perceptions of the environment against crime scores SRCS

GCS

VS

Linear F (15, 276) = 1.28 p > F = 0.0216 R2 = 0.1649

Ologit LR chi2 (15) = 63.79 p > chi2 < 0.0001 Pseudo R2 = 0.2128

Ologit LR chi2 (15) = 45.34 p > chi2 < 0.0001 Pseudo R2 = 0.2037

Table 13 Logit regression models of perceptions of the environment against scenario responses BSS

NQS

DWS

LR (15) = 54.54 p > chi2 < 0.001 Pseudo R2 = 0.3741

LR (15) = 31.62 p > chi2 = 0.0073 Pseudo R2 = 0.1959

LR chi2 (15) = 39.16 p > chi2 = 0.0006 Pseudo R2 = 0.2453

chi2

chi2

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attracts too many positive answers and hence dilutes the scores. Therefore, by removing it from the analysis and focusing on other types of violence crime (BSS) we can achieve better explanatory models. BSS had more variation explained than DWS, which suggests that this model might be better suited for the explanation of violent crime rather than economic crime. Nevertheless, the numbers suggest that RRM still explains both and that warrants further investigation.

4

Discussion and Limitations

This chapter has established two things: whether RRM is a good theory to predict crime and if it is useful to look at individual differences in order to do so. All in all, what can be seen from observing the results of the decision-making game is that people do behave differently and show quite different levels of retributive and reciprocal tendencies. Despite the fact that most people will show some levels of reciprocal and retributive tendencies since it is evolutionarily advantageous to do so, there is a lot of variation between how far people will go in showing them. The level depends on many things, such as social learning, the culture around the person, and past experience, all the reasons that go beyond the scope of this chapter and could be studied in further research. What this chapter demonstrated, however, is that people differ in how reciprocal or retributive people are, but also which tendencies they show at all. The findings showed that negative reciprocity, positive reciprocity, and retribution are not correlated with one another, which makes them mostly independent from one another, and the fact that the participant shows one does not mean that they would show the other. That is an important finding since in the past most of these tendencies have been grouped together under umbrellas of “cooperation” or “altruism”. Therefore, if we want to understand why people commit crime, we need to break those behaviours down and study them separately. The next important finding is that all of these tendencies separately are able to predict crime of their own. People who show higher levels of retribution and negative reciprocity are more likely to commit crime than people with lower levels of those tendencies. That finding is true for both

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violent crime and theft. People who score higher on positive reciprocity, however, are less likely to commit a crime, be it violent or economic as well. Therefore, tendencies of retribution and reciprocity are important to look at when analysing crime. However, they are not enough on their own. Crime is a response to the environment, and as so are retribution and reciprocity. RRM assumes at the most fundamental level that there is something that provokes a response, and then there are two things that determine what the response will be: how the person interprets the situation and how reciprocal or retributive they are. Therefore, this chapter introduced another very important measure: the perceptions of the environment that help us establish how people view the world around them. Perceptions of the environment themselves depend on many factors, including biological differences, personality traits, as well as social learning and past history. The results of the chapter demonstrated a wide variety of people’s perceptions as well as explained that on their own perceptions of the environment can come a long way to explain crime of their own. The more negatively the participants perceived the world around them, the more likely they were to say they would commit a crime in the hypothetical scenarios. Taken together, the tendencies and the perceptions of the environment explain a significant variation of crime, including the 37% of variation of the bus stop scenario. The RRM model seems to explain violent crime slightly better than it does theft, however, more research is needed to establish the exact nature of the different crime types. The nightclub queue scenario captured too much crime, which meddled with the results slightly, and the dropped wallet scenario by itself is not a perfect approximation of how people commit theft. When it comes to explaining crime, a lot more variation is explained in the hypothetical scenarios than in the self-reported crime questionnaire scores, which is perhaps also unsurprising. The self-reported crime questionnaire had questions ranging from traffic offences to aggravated assault, and despite the fact that RRM has a potential to explain most if not all crime, it is of yet unclear how much exactly can be explained. However, even in the SRCS the RRM explained 17% of variation, which is not insignificant.

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Therefore, we can conclude that RRM is indeed a promising tool for explaining crime and warrants future attention. We can also conclude that the new method used to measure retribution and reciprocity that was introduced in this chapter is a good method to measure and capture variation in people’s reciprocal and retributive tendencies. The same can be said for the perceptions of the environment inventory. Nevertheless, there are a lot of things that RRM does not do. No model can explain 100% of variation. Perhaps if that model did exist in the field of criminology, there would be no reason to conduct any future research. Ultimately, RRM is not enough to predict why all people choose to commit or not to commit crime, but instead it serves as an additional building block that can and should be used by other, general theories of crime, to make our understanding more nuanced and complete. It explains enough to be relevant and to enhance our understanding of why crime happens, however, there is still a lot of variation that is not explained. Moreover, there are a lot of people who behave contrary to what the model would suggest. That is normal taking into account how complex human behaviour is, but that also indicates that there is something in the model that is missing. There are many factors that play a role in crime causation, including the particular circumstances of the event, morality, biological factors, and many others. However, this chapter brings us closer to understanding what is going on within the “black box” of human personality and judgement, as well as brings forward the tools that will help us study these tendencies in the future to see exactly how much they can and cannot explain.

5

Conclusion

It is being part of the society we live in that allowed humans to survive as a species despite not being especially strong or well-prepared to take on the world. Humans are super-cooperators, and despite the fact that a lot of other animals show signs of empathy and compassion, we take it to a level to which no other animal even compares. We can queue

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in an orderly fashion, we go through complex transactions like mortgages and creating international organisations, and we help one another. Everything we do is deeply rooted in cooperation, and, as such, guides our behaviour. One of the most basic forms of cooperation is a simple tit-for-tat, or an eye for an eye. One of the most basic arguments between children often begins with a simple “s/he started first” which then leads into a long discussion of who threw the first punch, implying that there is an inherent and universally recognised desire to respond to a negative act by punishing the violator of a social norm. That relationship also works the other way around and is so universal that many societies for instance build their whole salaries in the service industry by relying on customers tipping the servers they found good. We respond with kindness to kindness and with aggression to aggression in everything we do. If that is such a deep-rooted behaviour, then crime will also be affected by it. In criminology we often talk about provocation. In terms of violence, one of the most common situations we encounter is someone being insulted, then insulting somebody back, being punched, and then punching somebody back. People are known to commit crime out of spite, out of concern for the honour of their families, or simply to respond to somebody’s hostile act. Therefore, it is worth diving deeper into the concept of tit-for-tat. In behavioural economics these behaviours are often studied under the name of reciprocity, retaliation, and fairness. There are many concepts and definitions being used in the field, bringing some confusion to an already complex phenomenon, therefore these concepts had to be remodelled and clarified. The behaviours that I singled out for the model, or, as I refer to them, tendencies, are positive reciprocity, negative reciprocity, and retribution. In the most simple of terms, positive reciprocity is responding with kindness to kindness, negative reciprocity is responding with hostility to hostility, and retribution is responding to a general violation of the social norm as the third party. Even though the majority of people exhibit these tendencies, people differ in how much of these tendencies they are showing and under what circumstances. Therefore, a lot of time in behavioural economics and evolutionary psychology is devoted to studying these behaviours, but not

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much has been done in order to determine whether people have different trajectories. Moreover, so far no attempts have been made to marry these tendencies to the field of criminology in order to see whether they can help us explain crime. In chapter four I demonstrated that the Retribution and Reciprocity Model (RRM) has merit in explaining crime by manipulating the retributive and reciprocal characteristics of the hypothetical scenarios. However, those manipulations only showed us the general trend. In this chapter, I used the well-used tool of a decision-making experiment, more specifically the Public Goods Game (PPG) in order to measure people’s individual differences and see how they relate to crime. The participants’ behaviours in the game were varied. Most people choose to participate in the game by contributing to the Common Good but also by punishing and rewarding other participants, however some people demonstrated free-riding tendencies, which further supports the claim that people differ in their reciprocal and retributive tendencies. Moreover, all participants followed different trajectories and showed different levels of all behaviours, which means that even though positive reciprocity, negative reciprocity, and retribution are linked, they are not the same behaviour and should not be studied together. On top of creating and introducing a new tool for measuring retributive and reciprocal tendencies, this chapter also introduced the perceptions of the environment and the inventory to measure them. Since by definition retribution and reciprocity are a response to something, it is very important to study how people see the world around them. The same situation can be interpreted differently depending on people’s mindsets and individual situations, therefore the perceptions of the environment are a very important framework to study and introduce into the model. Results demonstrated that all of these components can predict crime by themselves. When run against results from hypothetical scenarios, people scoring higher on negative reciprocity and retribution were also more likely to say that they would commit a crime. On the other hand, people scoring higher on positive reciprocity and perceptions of the environment were less likely to say they would commit a crime. That means that all of these factors are important in explaining crime and should

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not be overlooked by the field in the future. However, when all of these factors are taken together, they can explain even more. RRM relies on the interaction between the tendencies and the environments, and the findings support that view. When interactions are added in the model it can explain a significant variation of crime in both hypothetical scenarios and the self-reported crime measures, suggesting that RRM has merit and is a useful model to employ when trying to explain crime that should be taken forward. There are a lot of things that RRM still does not do and a lot of variation that it still does not explain. However, it introduces new concepts into the field of criminology that are evidenced to enhance our understanding of crime. While it is not a standalone theory of crime, it has the potential to be included in my other models and introduced into other theories.

References Bakker, A. B., Schaufeli, W. B., Sixma, H. J., Bosveld, W., & Van Dierendonck, D. (2000). Patient demands, lack of reciprocity, and burnout: A five-year longitudinal study among general practitioners. Journal of Organizational Behavior, 425–441. Bartlett, M. Y., Condon, P., Cruz, J., Baumann, J., & Desteno, D. (2012). Gratitude: Prompting behaviours that build relationships. Cognition & Emotion, 26 (1), 2–13. Gächter, S., & Falk, A. (2002). Reputation and reciprocity: Consequences for the labour relation. Scandinavian Journal of Economics, 104 (1), 1–26. Henrich, J., Chudek, M., & Boyd, R. (2015). The Big Man Mechanism: How prestige fosters cooperation and creates prosocial leaders. Philosophical Transactions of the Royal Society b: Biological Sciences, 370 (1683), 20150013. Hoffman, E., Smith, V., & McCabe, K. (1995). Ultimatum and dictator games. Journal of Economic Perspectives, 9 (4), 236–239. Fehr, E., & Gächter, S. (2000). Cooperation and punishment in public goods experiments. American Economic Review, 90 (4), 980–994.

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Li, D., Chen, Z., & Liu, J. (2019). Analysis for behavioral economics in social networks: An altruism-based dynamic cooperation model. International Journal of Parallel Programming, 47 (4), 686–708. Mechanic, D. (1970). Correlates of frustration among British general practitioners. Journal of Health and Social Behavior, 87–104. Sarason, I. G., Sarason, B. R., Shearin, E. N., & Pierce, G. R. (1987). A brief measure of social support: Practical and theoretical implications. Journal of Social and Personal Relationships, 4 (4), 497–510. Slonim, R., & Roth, A. E. (1998). Learning in high stakes ultimatum games: An experiment in the Slovak Republic. Econometrica, 569–596. Svingen, E. (2017). Retaliation, reciprocity, and crime: A pilot study. Institute of Criminology, University of Cambridge. Svingen, E. (2021). Retribution, reciprocity, and crime. Institute of Criminology, University of Cambridge. Tilden, V. P., Nelson, C. A., & May, B. A. (1990). The IPR Inventory: Development and psychometric characteristics. Nursing Research, 39 (6), 337–343. Uehara, E. S. (1995). Reciprocity reconsidered: Gouldner’s moral norm of reciprocity and social support. Journal of Social and Personal Relationships, 12(4), 483–502. Wikström, P. O. H., Oberwittler, D., Treiber, K., & Hardie, B. (2012). Breaking rules: The social and situational dynamics of young people’s urban crime. OUP Oxford.

6 Lessons Learned: What We Know About Retribution, Reciprocity, and Crime

1

Introduction

Despite the fact that criminologists tend to concern themselves with relatively rare criminal events, it is important to look at the way humans organise their day-to-day lives, especially at how people cooperate with one another. Cooperation may be understood as a defining feature of human society (Henrich et al., 2010; Spitzer et al., 2007), and hence understanding it might shed more light on the crime. Although cooperation does not seem like an obvious conclusion of the “survival of the fittest” understanding of evolution, numerous mathematical models (Axelrod & Hamilton, 1981) have shown that quite often, cooperation is the course of action that conveys an evolutionary advantage. To a degree, cooperative tendencies are believed to be hardwired into humans, as cooperators were more likely to pass on their genes to future generations (Rilling et al., 2002). Looking at cooperation is of great importance in criminology because cooperation plays such an important role in human (and animal) lives. If it plays a role in almost every interaction with the surrounding world, it plays a role in crime as well. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. Svingen, Evolutionary Criminology and Cooperation, Palgrave’s Frontiers in Criminology Theory, https://doi.org/10.1007/978-3-031-36275-0_6

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Most acts of crime can be understood as a classic case of defection against society: acting in a selfish manner that damages the group as a whole. By studying crime within the framework of human cooperative tendencies, more understanding of the causes of crime can be brought about. I posit in this book that the most important aspects of cooperation are the ones of reciprocity and retribution. These tendencies seem to be the most central ones in understanding the mechanics of cooperation and have the biggest potential to yield results relevant to explaining crime (Bowles & Gintis, 2011). In order to test the role of these tendencies in crime, I organise them into a Retribution and Reciprocity Model (RRM). RRM is based on the understanding that people possess different levels of negatively reciprocal, positively reciprocal, and retributive tendencies. These tendencies, in turn, interact with the individual’s perceptions of the environment and elicit a response that may or may not result in a crime. Essentially, retribution and negative reciprocity make crime more likely to occur, and positive reciprocity makes crime less likely to occur. This book is devoted to presenting, explaining, and testing this theory. The first chapter sets the scene: it presents the merits of studying biosocial criminology, as well as evolutionary criminology in particular. If we remain blind to the additional depth and complexity biosocial criminology brings, we are doomed to make mistakes in our attempts to explain criminality. In addition, evolutionary criminology can serve as an irreplaceable tool in attempts to sift the chaff from the grain of criminological theory. The second chapter outlines what we know about cooperation from the fields of evolutionary science and game theory and summarises RRM by bringing all this knowledge together. It defines the concepts used, how they fit together, and introduces RRM as a full model that is tested in the later parts of the book. The third chapter delves into the neuroscientific basis of retributive and reciprocal mechanisms. It explains the neurophysiology behind RRM’s claims and demonstrates how biology shapes human behaviour, especially in settings that may lead to crime. This chapter synthesises existing knowledge in neurobiology, evolutionary science, and criminal psychology, and bridges it together. Not only does this chapter bring out

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more evidence for the validity of the claims for RRM, but also explains how these tendencies are largely innate although with a social learning element to them. The fourth chapter tests the basic assumptions of RRM that retribution and reciprocity influence crime instances. It does so by manipulating the factors of the particular situations that relate to reciprocity and retribution in hypothetical scenarios and test whether that leads to an increase or decrease in the number of participants that report they would commit a crime. The participants were given slightly different versions of each scenario in which the environment is manipulated in order to elicit different levels of reciprocal or retributive responses. There are four situations in total. For every situation, the three tendencies were manipulated and there are four levels to each manipulation including a baseline. The results showed a steady increase in crime with intensity in retributive and negatively reciprocal scenarios and a decrease in the number of crimes with the rising intensity of positively reciprocal scenarios. Despite the fact that the scenarios do not show whether each respondent would actually commit a crime, they are a useful predictor of whether retribution and reciprocity do play a role in crime-related decision-making. As a result, the data aligns very well with the basic hypotheses of RRM. The fifth chapter expands on our understanding of RRM by measuring the individual differences in retributive and reciprocal tendencies. I do so by making use of a Public Goods Game (PGG), in which participants play a simple cooperation game online. In the game, the participants can show their tendencies towards retribution and reciprocity by either cooperating and contributing to the Public Good, or by defecting and keeping the money for themselves. They can also show both negative and positive reciprocity by either rewarding or punishing other participants and retribution by rewarding or punishing other participants as a Third Party Punisher. The experiment also measures the participants’ perceptions of the environment by using the scale specifically developed for the purpose and asks about crime both in the self-reported criminality questionnaire and additional hypothetical scenarios. The results show that people’s relative strengths of the tendencies can predict crime on their own, and so can perceptions of the environment. However, taken as an interaction, these factors can explain

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a bit proportion of people’s crime propensity, further supporting the use of RRM in order to enhance our understanding of crime. Altogether these chapters should offer a detailed examination of RRM as well as present enough evidence to conclude whether it is a worthy avenue for future research. This concluding chapter summarises the most important aspects that were introduced in this book as well as explains what we learned from it and what is yet to learn.

2

Reintroducing RRM

Humans always respond to their environments, and over aeons of evolution, they learned to adapt to their surroundings. It is highly unusual for anyone to wake up one morning and decide to commit a crime out of the blue. A person walking down the street and thinking about work is way less likely to assault someone at that very moment than someone who has just been gravely insulted. RRM recognises that a crime is always an interaction of an individual and the environment they find themselves in, and the aim of this model is to characterise that relationship. I believe that the best way to understand this interaction is through the lens of retribution and reciprocity. Of course, behaviour is complex and multifaceted, and yet on the most fundamental level, we respond with kindness to kindness and with hostility to hostility. That is to say, we are reciprocal. However, we are also norm-driven and punish defectors that violate social norms. In other words, we are retributive. That is the basic model of the theory. In this model, reciprocity (both positive and negative) is influenced by the perceptions of the environment (PoE), because “positivity” and “negativity” of the environment largely depend on people’s interpretation. For example, if a friend insults you, you are likely to perceive it as a joke or friendly banter. Whereas, if a stranger insults you, you are more likely to see it as an act of hostility and retaliate accordingly. Perceptions of the environment can refer to many aspects, such as people, places, organisations, or even whole nations or the world in its entirety. For the sake of this theory, “positive” refers to when a person feels they have been treated fairly, helped and supported by their environment, and feel they

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are getting something out of it. In contrast, “negative” means the opposite, that people feel that they were treated unfairly and being exploited. Perceptions of the environment are not binary, but exist on a spectrum. As a result, the score can also be neutral. Encountering the environments that are perceived as negative interacts with people’s tendencies towards negative reciprocity, whereas encountering environments that are perceived as positive interacts with people’s feelings of positive reciprocity. The more positively reciprocal a person is, the more affected they are by the positivity of the environment, and the more negatively reciprocal the person is, the more they are affected by the negative aspects of the environment. Therefore, if a person already has an initial motivation to commit crime, they will then be influenced by the interaction of reciprocal tendencies with the PoE that will then make them either more or less likely to commit that crime. Retribution, on the other hand, can serve as a motivation to commit a crime. Retribution refers to the urge to punish a violator of a social norm in a third-party situation, i.e. when the hostile act is not directed against the actor themself. Therefore, the retributive tendency is in itself the initial motivation to commit a crime. Retribution plays a vital role in many societies, as demonstrated by the proliferation of the retributive justice model of the criminal courts in many of the states, especially in the West, and serves as a powerful motivator. This book discussed a lot of evidence that demonstrates that these tendencies are strongly hardwired into us. There is, after all, a strong evolutionary advantage to retribution and reciprocity. Mostly that is due to the fact that humans as a species were unlikely to survive without working as a group, hence making cooperation a vital component of our behaviour. People who did not choose to cooperate were likely ostracised from the group and died, and their genes were not selected for in order to be passed to future generations. There is a dual model of culture-gene coevolution that is responsible for our retributive and cooperative tendencies that encompasses the aspects of social learning and neurophysiological mechanisms. There are a lot of behaviours that we learn—to be polite to others, to share, and to drive on the right side of the road. All of these are important for our society to function, and hence not only do we repeat these rules

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to one another, but also allow others to learn by imitation. However, to some extent cooperation is innate: there exist many neurological mechanisms that hardwire us into acting in reciprocal and retributive manner. Often we do not choose to be angry over an issue; we simply are, and sometimes, we cannot even explain to ourselves why we are so affected. Moreover, at times even when we want to act differently, we cannot. There is extensive evidence from numerous economic games that shows that even when we lose money over punishing a violator of a social norm or rewarding the other players for being cooperative, we still do so. The reason why this happens is that oftentimes some pathways get activated in our brain and make us experience emotions and urges to act in a certain way. Positive reciprocity, negative reciprocity, and retribution activate different pathways. Nevertheless, there is some overlap that exists between all three dependencies, making it into a comprehensive model of neurobiological mechanisms of RRM. For positive reciprocity we observe activation in the ventromediate prefrontal cortex (VMPFC) and the anterior cingulate cortex (ACC). The ACC is involved in many high-level functions such as reward anticipation and morality and is believed to be the area responsible for overriding selfish motives. The VMPFC is activated in all situations that involve emotional regulation and is known to be connected with social behaviour. More specifically, the VMPFC seems to be reacting to a reward in general, whereas the ACC regulates that by mainly being influenced by intentionality, i.e. deciding whether they were treated with kindness by accident or by design. The ACC, as a result, is also active in negative reciprocity, because not only does it react to the intentionality of hostility, but also to the intentionality of hostility. Apart from the ACC, the area that responds to hostility is the anterior insula (AI), and both together urge us to retaliate against hostile acts. However, there is a mechanism that prevents us from retaliating indiscriminately, and that is the right dorsolateral prefrontal cortex (rDLPFC). The rDLPFC is consistently activated when there is a need to suppress emotional responses or selfish motives. Therefore, there is a dual-process model at play, in which the AI and ACC get activated

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to punish the hostile act, but the rDLPFC regulates that urge to make a more rational decision on whether to punish or not to punish. Retribution is also a behaviour that involves punishment, however, the studies do not show many activations in the rDLPFC or the ACC. Nevertheless, the AI consistently activates as a reaction to a violation of a social norm, be it a direct act of hostility or a third-party scenario. The other area that plays an important role in the dorsal striatum (DS), which is an area strongly associated with reward processing following a decision. What does it all mean together? A lot of things. First, consistent activation of various brain areas tells us that there is indeed evidence to suggest that behaviours of retribution and reciprocity are hardwired into us. Second, this knowledge serves as additional evidence for RRM and explains the way it operates. Third, differences in activation prove once again that all these three tendencies are separate from one another and should be treated differently accordingly. Fourth, it gives us tools to study RRM in the future to understand more deeply how our biological underpinnings influence our decisions to reciprocate or act in a retributive manner. Nevertheless, all the neuroscientific evidence that was discussed for the purposes of this book was based on other studies. The data that was, on the other hand, used and analysed in this book, came from other fields.

3

Methods to Study RRM

Studying retribution and reciprocity presents an exciting challenge. How do you study something that is so ingrained in our behaviour and at the same time so intertwined with a lot of other behaviours and emotions? It becomes even harder when the object of the study is something as harmful as crime, as you cannot put people in harm’s way and watch the situation unfold. Therefore, the experiment has to be designed in such a way as to measure people’s actual behaviour, but not to make it happen in real life. Luckily, a lot of good approximations already exist. One of the ways of studying crime is through hypothetical scenarios. Hypothetical scenarios are a simple tool that asks the participants to put

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themselves in a certain situation and report how they would respond to it. The participants are presented with a written situation and are then asked how likely they are to react in a certain way. We can then manipulate certain aspects of the situation that is being presented to see if people become more or less likely to report that they would, for example, commit a crime. Hypothetical scenarios turned out to be useful for the study of RRM on two counts. In the first experiment they were used to test the general hypothesis of RRM that people are more likely to commit a crime when their negatively reciprocal and retributive motives are being activated, and less likely to commit a crime when people are acting out of positive reciprocity. In order to study that, the participants were presented with four situations: two that result in crime, and two that result in theft. Every situation had a multitude of versions of scenarios in it. Every scenario had a baseline, and then the baseline was manipulated for every tendency separately with intensity gradually rising from low to medium to high. Therefore, every violence situation had 11 scenarios, and every theft situation had 10.1 The results showed a consistent trend with the number of participants that reported they would commit a crime rising as the intensity of both negative reciprocity and retribution scenarios rose, and with that number dropping as the intensity of positively reciprocal scenarios rose. Therefore, hypothetical scenarios are a good and easy method to study RRM in various situations and contexts, making it easy and flexible in order to test RRM for many types of crime as well as different backgrounds and types of crime. Nevertheless, that is not the only use for this methodology for the study of these tendencies. Chapter 5 made use of hypothetical scenarios to directly measure how likely the participants were to commit a crime. The participants were presented with three simple scenarios, and then their responses were studied as an outcome variable. That made a very useful addition to the self-reported crime (SRCS) measure, as that score did not capture

1

The discrepancy has to do with the difference in the baseline scenarios, as the violence one has a separate baseline for retribution. To find out why, please revisit Chapter 4.

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enough numbers of variation for all the analyses that were interesting and revealing to run. In order to measure the individual differences, I adapted a method from behavioural economics, a so-called Public Goods Game (PGG). In the basic PGG, subjects choose how many of their Experimental Currency Units (ECU) to put into a public good (PG). The ECUs in the PG are multiplied by a factor (double in my version for the simplicity) and the PG payoff is evenly divided among all players, regardless of how much they contributed. Many versions of this game exist, involving different numbers of players, the ability to reward and punish, etc. In this experiment, I created my own game and programmed it from scratch, and there are several reasons for doing that. The first difference lies in the questions that this game tries to answer. In standard economic experiments, the game is designed to study people’s interactions and group behaviour, whereas I attempt to measure individual differences. Therefore, in my version of the game, all participants play with bots, not with real participants. That is done in order to make sure that all participants are responding to the same stimulus. Secondly, the game was specifically calibrated to measure people’s different tendencies. Participant’s levels of contributions in response to the bot’s contributions measured positive reciprocity. In part two of the game the participants got an opportunity to reward and punish other participants, and the punishments were a proxy for participant’s negatively reciprocal tendencies. In part three the participants become the third party who only observe the game and make no ECU out of it but can still reward and punish, and their levels of punishment as the third party was a proxy for retribution. Lastly, I wanted to make sure the players could play from home and not in the laboratory. The new game, based on the tested old method, was a success in providing enough variability and sensitivity to capture people’s differences and accurately grasping the different strategies that the participants employed when playing. Therefore, the new PGG is a good method to study tendencies towards retribution and reciprocity because of the sensitivity and the close approximation it has, because it measures the actual behaviour of participants in an experimental setting without hurting anyone.

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Nevertheless, tendencies towards retribution and reciprocity is one side of the model, there are also perceptions of the environment (PoE). Since PoE is also something that is quite specific for the RRM model, a special measure was devised from some of the existing inventories, and the PoE Inventory turned out to be a measure with great variability and internal validity, as proven by a Cronbach’s alpha of 0.89. Therefore, we can conclude that this book created and tested many useful methods to study RRM, however, some pieces are still missing. For example, there are still some unanswered questions about the role of social learning that we know play an important role in learning of retribution and reciprocity. Additionally, there are no measures described in this book that allow us to test the neurophysiological method of RRM, outlined in Chapter 3. Therefore, even those the PGG, the PoE questionnaire, and the hypothetical scenarios proved to be a success, more methods should be developed in order to test the whole model as outlined in this book. Additionally, from the point of view of Game Theory, the PGG was selected for being the most accurate game that captures all three tendencies at the same time, however, other games exist that could be both exciting and simple to adapt to the study of retribution and reciprocity, such as the Centipede Game2 and the Ultimatum Game.3 Therefore, these are the methods that could also be adapted for future research. Nevertheless, the existing methods were already rather useful for studying RRM, and they allowed me to answer many of the questions that I posed before embarking on this research.

2 A game in which two players take turns choosing either to take a slightly larger share of an increasing pot, or to pass the pot to the other player. In this game the incentive is to always take the pot as passing it on requires a lot of trust, hence why it will be an interesting study of reciprocity. 3 A game I referenced many times before. One player, the proposer, is endowed with a sum of money. The proposer is tasked with splitting it with another player, the responder. The responder can then accept the offer or reject it, that would lead to both parties receiving nothing. That is an interesting study of negative reciprocity or retribution in third-party scenarios.

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241

What RRM Explains and Does not Explain

There are many questions that this book poses and attempts to answer, but the most fundamental one is this: can RRM explain at least some crime variation, for that is a minimum requirement for being a model that is useful for criminology. Judging by the results of both of the experiments, we can conclude that the data indeed supports the basic assumptions of RRM and that retribution and reciprocity can, indeed, explain some crime variation. Exactly how much and in what conditions becomes somewhat more complicated. In Chapter 4 we learned that by manipulating the specific circumstances of the hypothetical scenarios we can cause the participants to become more or less likely to report that they would commit a crime. As the scenarios elicited more positively reciprocal feelings, the participants were less likely to report that they would commit a crime. On the contrary, when the intensity was rising in the negatively reciprocal and retributive scenarios, the number of participants that reported that they would commit a crime increased. Importantly, not only was there an increase from the baseline, but there was also a steady change as the intensity was rising for each scenario. What does it mean? That means that all three tendencies that were identified for this research play a role, and that these tendencies are important enough for crime causation that we can see a clear effect in participants’ responses following the manipulation to the circumstances of the scenarios. Nevertheless, there are some problems with that methodology: for instance, there are many other explanations that could play a role. After all, even though it is possible to manipulate the environment quite carefully, it is still virtually impossible to make sure that the aspect being manipulated is solely reciprocity and retribution, and that the results are not affected by something else. The use of the Public Goods Game in Chapter 5 of this book, however, solves this problem. The way that the game was designed was to measure the actual behaviours of the participants, which makes the independent variable more reliable simply by being collected through a more specific method. The results from Chapter 5 are consistent with the ones

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from Chapter 4, supporting those findings once again. The data in both scenarios is consistently showing that higher levels of negative reciprocity and retribution lead to the participants being more likely to report they would commit a crime, and higher levels of positive reciprocity lead to crime reduction. “Crime” encompasses many things. Perhaps it is not fair to compare instances of petty theft to murder or terrorism. RRM has the potential to explain all crime, mainly due to the neurobiological mechanism explained in Chapter 3 showing that these tendencies are deeply ingrained in us. Reciprocity and retribution are inherent behaviours we all share not only between many peoples and cultures, but also with other species, and hence they will likely influence all aspects of our lives, including all crime. However, that thinking is only theorised, as there were only two types of crime that I studied in this book—theft and violence. Nevertheless, theft and violence are two rather different behaviours that could already demonstrate whether RRM has the potential to explain two very different behaviours. It turns out that indeed both theft and violence variations can to some extent be explained by retribution and reciprocity, which presents a potential that more crimes can be explained. Therefore, it is worth considering designing more scenarios and situations to see how universally applicable RRM is and where it might be at its strongest. The full model, which included the perceptions of the environment and the interaction with the tendencies of negative reciprocity, positive reciprocity, and retribution, explained over 20% of the variation in logistic regressions for crime scores. In the bus stop scenario, it explained as much as 37%, which is a high number for real-world data. That means that at least one-fifth of variation in people’s reports of whether they are likely to commit a crime can be explained by RRM. That means incorporating RRM into any other type of model will aid it at explaining crime. However, it is important to remember that the other 80% is explained by something else. Moreover, the results of Chapter 4 demonstrated that even in the highest intensities of all scenarios, more than half of the

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participants said that they would still not resort to crime. That is unsurprising, since most people do not commit crime very often, and even in the life of the most prolific offender, crime does not occupy much time. Moreover, as discussed earlier, crime is a very complex behaviour that could not possibly be reduced to being explained by four simple constructs. There are a lot of other factors that also play a role and were not included in this model, and it is worth exploring other motivations or interactions with other theories in the future, especially since the initial motivations are already included in the model.

5

The Future of RRM: A Theory of Everything?

There are many issues that criminologists aim to solve: such as police corruption, explaining why some behaviours are criminalised, and others are not, or mapping individual criminality. Nevertheless, not many theories attempt to bridge the three areas of criminology: rule making, rule breaking, and rule enforcing. If we were to take the approach of integrating all the aspects in one model, we would end up with a very complex mechanism that might end up being both untestable and unparsimonious. Perhaps, that is why those areas of criminology are often treated separately, and there is not much communication arising between the scientists studying different things. In this final chapter, I present a thought that I introduced in the very first chapter of this book: that RRM could help us understand all three aspects of criminology within one framework. Instead of combining everything we know about crime, law-making, and the criminal justice systems into one mechanism, I take the opposite approach. I take a step back to devise and describe a simple evolutionary mechanism that encompasses the question of why we make the rules, why we break them, and why we punish the people that do. In this book, I argue that crime is embedded in reciprocity and retribution. Sometimes crime itself may be an attempt by a person to punish a perceived injustice imposed on them by society or a specific person and serve as a retributive act. An evident example is the activity of vigilante

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groups that take justice into their own hands by punishing offenders without legal authority. In other cases, someone might be deterred from committing a crime by their positively reciprocal feelings, such as a reluctance to steal from a person who has been nice to them. Conversely, someone might be encouraged to commit a crime in response to a hostile act towards them, eliciting negatively reciprocal feelings, such as punching someone who has previously hit them. These tendencies (retribution and reciprocity) can be both motivating and constraining factors, and as such, they cannot be overlooked if we wish to understand crime. It can also help us understand the topics of functioning of the criminal justice institutions. Legitimacy theory (Bottoms & Tankebe, 2012) brings forward the idea that people cooperate with the police and the courts not because they fear punishment but because they believe in the legitimacy of those institutions, i.e. that they are perceived as fair. Feelings of reciprocity are likely to underpin the public’s perceptions of the legitimacy of the institutions and contribute to their feelings of fairness. In the same way, if they base their understanding of police and the courts on negative terms, for example, if they had experiences of being mistreated by the police or the courts, they are likely to experience negatively reciprocal feelings and hence tend to not cooperate. Therefore, understanding retribution and reciprocity can be instrumental in helping us understand what social forces shape our criminal justice systems, as well as examine why people choose to break the law or not cooperate with it. The purpose of this book is to present RRM as a theory of offending. Even though I introduce the potential of the model, the question of whether RRM can be used to explain rule enforcement and rule making remains in the domain for future research. Nevertheless, the possibilities are diverse and numerous. There are many questions about RRM that we should be willing to ask.

6 Lessons Learned …

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What We Have Learned

This book developed, created the methods to study, and presented the results in support of the retribution and reciprocity model. In Chapter 1 I presented a philosophical argument for why evolutionary frameworks are indispensable for criminology. From the second chapter, we learned what RRM is, how it operates, and what evolutionary mechanisms it is based on. From Chapter 3 we learned about how RRM works, what neurophysiological mechanism underpins the working of retribution and reciprocity, and what evidence exists in support of the innate nature of these tendencies. In Chapter 4 we learned that people are indeed retributive and reciprocal, and these tendencies indeed can explain some criminal behaviour. In Chapter 5 we learned that individual differences matter, that different levels of retributive and reciprocal tendencies within a person lead to different crime propensities, and that the perceptions of the environment can explain some crime variation as well. What does it all mean? That means that this book created a new mechanism that could help us understand crime and already can explain a fifth of the variation of the answers to hypothetical scenarios. RRM is a useful model to be taken forward by criminologists as it can enhance our understanding of crime propensity by using it. Nevertheless, RRM does not stand on its own: there is still a lot of variation that retribution and reciprocity do not explain, and most of the participants still chose not to engage in crime. Therefore, for the development of this theory, we should focus on integrating it with the existing general theories of crime. By the nature of it, RRM is compatible with many of those and could easily be adapted and incorporated. We found that the methods that were designed and tested by this theory are useful and fit for purpose, which means that they can be taken further to explore other crime types and cultural backgrounds. More methods should be developed to not only test other factors that might play a role, but also to study the neurobiological mechanisms that underpin RRM.

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This book is not the only attempt to explore what neuroscience or evolutionary psychology has to add to the conversation of crime causation. Nevertheless, it is an ambitious attempt to bring together evidence from evolutionary science and neuroscience, adapt the methods from behavioural economics to study that evidence, and then to adapt it all to the study of criminology. Crime is a multifaceted and complex behaviour, and as such it requires a multidisciplinary approach, and it is refreshing to see that in the end a model such as RRM led to expanding our understanding of what motivated people to commit a crime. Through spending more time examining the lessons we learned from this book, we will be able to explain even more variation. Through doing that, I hope we can use this knowledge for crime prevention.

References Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. Science, 211(4489), 1390–1396. Bottoms, A., & Tankebe, J. (2012). Beyond procedural justice: A dialogic approach to legitimacy in criminal justice. Journal of Criminal Law & Criminology, 102, 119. Bowles, S., & Gintis, H. (2011). A cooperative species. Princeton University Press. Henrich, J., Ensminger, J., McElreath, R., Barr, A., Barrett, C., Bolyanatz, A., … & Ziker, J. (2010). Markets, religion, community size, and the evolution of fairness and punishment. Science, 327 (5972), 1480–1484. Rilling, J. K., Gutman, D. A., Zeh, T. R., Pagnoni, G., Berns, G. S., & Kilts, C. D. (2002). A neural basis for social cooperation. Neuron, 35 (2), 395–405. Spitzer, M., Fischbacher, U., Herrnberger, B., Grön, G., & Fehr, E. (2007). The neural signature of social norm compliance. Neuron, 56 (1), 185–196.

Index

A

C

Altruism 7, 37, 38, 41, 52, 192, 213, 215, 223 Anterior cingulate cortex (ACC) 41, 42, 99, 103, 104, 106, 107, 109–111, 116, 121, 236, 237 Anterior insula (AI) 99, 105–109, 111, 112, 117, 121, 236, 237

Cooperation 7, 21, 28–32, 35–42, 44–50, 52, 54, 55, 63, 64, 80–82, 85, 86, 93, 94, 97, 98, 101, 103, 105, 113, 119, 120, 198, 213, 215, 223, 226, 231–233, 235, 236 Correlate 3, 17, 18, 108, 111, 199 Culture-gene coevolution 43, 48, 63, 235

B

Behavioural economics 28, 29, 52, 87, 93, 192, 195, 196, 226, 239, 246 Biocriminology 2, 4, 8, 13, 19 Biological variation 13 Biosocial criminology 2, 5, 6, 8, 9, 11–14, 232

D

Decision-making game 100, 108, 194–196, 223 Determinism 7, 8, 13 Direct reciprocity 50, 51, 53, 56, 57 Dorsal striatum (DS) 46, 55, 111, 112, 116, 117, 121, 237

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 E. Svingen, Evolutionary Criminology and Cooperation, Palgrave’s Frontiers in Criminology Theory, https://doi.org/10.1007/978-3-031-36275-0

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Index

Dorso-lateral prefrontal cortex (DLPFC) 103, 107, 108, 110, 111, 114, 118 Dual Inheritance Theory 43, 47 Dual-processes model 108 E

Empathy 30, 31, 83, 84, 86–89, 95, 96, 98–100, 119, 163, 225 Evolutionary criminology 1–6, 11–18, 20–23, 29, 191, 232 Evolutionary science 4, 5, 8, 15, 16, 22, 28, 60, 61, 232, 246 Evolutionary theory 2–5, 8, 13, 17, 20, 60 Experimental Currency Unit (ECU) 197, 198, 205–207, 239 Explanation 2, 5, 10, 15, 16, 21, 23, 36, 60, 61, 99, 107, 152, 174, 184, 223

185, 186, 194, 196, 199, 204, 210, 212, 220–222, 224, 227, 228, 233, 237, 238, 240, 241, 245

I

Imitation 31, 36, 42, 43, 54, 236 Individual difference 9, 13, 29, 34, 42, 50, 62, 64, 65, 95, 96, 109, 147, 188, 191–194, 198, 206, 207, 209, 223, 227, 233, 239, 245

J

Justice 1, 28, 30, 32, 45, 54, 83, 89–92, 94, 95, 120, 235, 243, 244

L F

Fatalism 13, 14 Free-riding 45, 93, 205, 207, 227 G

Game theory 29, 35, 38, 53, 87, 116, 193, 194, 232, 240 General reciprocity 15, 51, 56, 57, 61 H

Hardwired behaviours 13, 16, 31, 37, 81, 120, 121 Hypothetical scenarios 29, 64, 144–148, 162, 170, 179, 181,

Learning 3, 10, 11, 15, 16, 31, 36, 42–44, 53, 54, 57, 61, 63, 80, 81, 83, 96–98, 107, 112, 114, 115, 117–119, 121, 165, 166, 223, 224, 233, 235, 240 Levels of analysis 10, 15, 61

M

MAOA-L 8, 114, 115, 118, 119 Mechanistic criminology 12, 28 Morality 9, 101, 103, 115, 186, 225, 236 Multidisciplinary 22, 27, 246

Index

N

Negative reciprocity (NR) 34, 50, 52–58, 62, 82, 88, 92, 96, 100, 104–111, 114–117, 119, 121, 145, 148–157, 159, 160, 164, 174–177, 179, 180, 182–184, 186, 187, 199, 202, 204, 214, 217, 219–221, 223, 226, 227, 232, 235, 236, 238, 240, 242 Neurological mechanisms 9, 46, 236 Neurophysiology 7, 19, 82, 100, 119, 232

P

Paradigm shift 11 Perceptions of the environment (PoE) 35, 55–58, 64, 88, 144, 193, 194, 196, 200–202, 204, 210, 216, 220, 221, 224, 225, 227, 232–235, 240, 242, 245 Positive reciprocity (PR) 50–53, 55, 58, 82, 88, 96, 100–106, 115, 116, 121, 145, 148–157, 159, 160, 164, 177–180, 182, 183, 185–187, 198, 202, 204, 220, 221, 223, 224, 226, 227, 232, 233, 235, 236, 238, 239, 242 Predisposition 18, 19, 28, 31, 42, 46, 81, 84 Prisoner’s dilemma 38, 39, 107, 108, 111, 113 Prosocial behaviours 14, 81, 85–87, 95 Public Goods Game (PGG) 64, 93, 193, 194, 196, 197, 202, 204, 227, 233, 239–241

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Punishment 30, 31, 33, 35, 44–46, 54, 55, 82, 92–94, 108–112, 116, 118, 198, 199, 203, 206, 207, 215, 237, 239, 244

R

Reciprocity 7, 21, 28, 29, 31–33, 35, 37, 41, 48–53, 56–61, 63, 64, 80–83, 86–90, 97, 98, 100–103, 107, 112, 113, 115, 117, 119, 120, 144, 146–148, 161, 163–165, 167, 168, 170, 175, 178, 181, 184–186, 191–194, 196, 200, 201, 206, 207, 209, 213, 214, 220, 224–227, 232–235, 237, 240–245 Retribution 21, 28, 29, 31–35, 49, 53–56, 59, 62–64, 80–83, 92, 96, 97, 100, 102, 109–112, 115–117, 119–121, 144, 145, 147–149, 152, 155, 161, 164, 165, 167, 168, 170–175, 177, 178, 180–186, 191–194, 196, 199, 202, 204, 206, 207, 209, 213, 216, 217, 220, 221, 223–227, 232–245 Retribution and Reciprocity Model (RRM) 3, 15, 16, 20, 21, 23, 29, 33–35, 49, 50, 55, 58–65, 80–83, 86, 89, 92, 96, 98, 114, 115, 118–122, 144–148, 161–163, 165, 168, 173, 174, 177, 179–188, 191–194, 207, 221–225, 227, 228, 232–234, 236–238, 240–246 Revenge 92, 111, 145, 165, 192 Risk factors 14, 17, 33

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S

Situational Action Theory (SAT) 9, 10, 60, 143 Social norm 20, 30–33, 43, 45, 48, 53, 54, 57, 97, 98, 101, 102, 106, 108, 109, 111, 112, 117, 120, 168, 171, 177, 181, 183, 193, 209, 216, 226, 234–237

Third Party Punisher (TPP) 54, 198, 207, 233 Tit-for-tat 39–41, 49, 52, 53, 95, 226

U

Unified criminology 10

T

V

Theft 30, 32, 90, 146, 148, 155, 164, 166, 167, 171, 172, 174, 175, 177, 178, 180, 183–187, 196, 199, 202, 203, 211, 219, 224, 238, 242 Theoretical fragmentation 20 Theory of mind (ToM) 84, 87, 97, 100, 101, 105

Ventro-medial prefrontal cortex (VMPFC) 101–104, 116, 121, 236 Vignettes 145 Violence 21, 146, 148, 155, 164, 166, 167, 170–172, 174, 175, 178, 180, 183, 184, 186, 187, 219, 222, 226, 238, 242