A Sociology of Humankind: How We Are Formed by Culture, Cooperation, and Conflict (Routledge Advances in Sociology) [1 ed.] 1032608579, 9781032608570

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‘An elegant, condensed theoretical model, with a synthesis of far-flung research literatures and advanced simulation models.’ Randall Collins, Professor Emeritus, University of Pennsylvania, USA, and Author of Charisma: Micro-sociology of Power and Influence

‘Foragers are theoretically very important as so much of our evolutionary history is represented by people we call foragers. The author has done his homework very well on foragers. They are commonly treated by non-specialists in a simplistic way and that is emphatically not the case here.’ Peter J. Richerson, Distinguished Professor Emeritus, University of California Davis, USA

A SOCIOLOGY OF HUMANKIND

Based upon the interdependencies of human beings as we cooperate and conflict with each other, how we share information, and how culture evolves, this book proposes a sociology of humanity covering three hundred millennia. Grounded in empirical findings from archaeology, history, lab experiments, and field studies – supplemented for precision with computational network models of cultural evolution, cooperation, influence, cohesion, warfare, power, social balance, and inequality – this is the first attempt at encompassing sociology of humankind. Informed by the theory of cultural evolution, it extends the notion that cultural evolution connects humans of all times in a giant sociocultural network, thereby yielding coherence between a great many empirical findings. It will therefore appeal to scholars of sociology and anthropology with interests in historical sociology, cultural evolution, and social theory. Jeroen Bruggeman is Associate Professor of Sociology at the University of Amsterdam, the Netherlands, and the author of Social Networks: An Introduction.

Routledge Advances in Sociology

371 Cultural Values, Institutions, and Trust Seung Hyun Kim and Sangmook Kim 372 The Experience and Fear of Violence in the Public Realm Hegemonic Ideology and Individual Behaviour Charlotte Fabiansson 373 Cultural Sociology of Cultural Representation Visions of Italy and the Italians in England and Britain from the Renaissance to the Present Day Christopher Thorpe 374 Truth Claims in a Post-Truth World Faith, Fact and Fakery Erkan Ali 375 Doing Public Scholarship A Practical Guide to Media Engagement Christopher J. Schneider 376 How to Think Better About Social Justice Why Good Sociology Matters Bradley Campbell 377 A Sociology of Humankind How We Are Formed by Culture, Cooperation, and Conflict Jeroen Bruggeman

For more information about this series, please visit: https://www.routledge.com/RoutledgeAdvances-in-Sociology/book-series/SE0511

A SOCIOLOGY OF HUMANKIND How We Are Formed by Culture, Cooperation, and Conflict

Jeroen Bruggeman

Designed cover image: Author’s own photograph of textiles by an unknown artist, bought at a local market, West Africa. First published 2024 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2024 Jeroen Bruggeman The right of Jeroen Bruggeman to be identified as author of this work has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 9781032608570 (hbk) ISBN: 9781032608679 (pbk) ISBN: 9781003460831 (ebk) DOI: 10.4324/9781003460831 Typeset in Sabon by codeMantra

CONTENTS

List of Figures Acknowledgments

x xi

1

1

Introduction 1.1 Cultural dynamics 2 1.2 Goals and means 8 1.2.1 Patterns, principles, and models 9 1.3 Cultural evolution: a reprise 11 1.3.1 Combination 11 1.3.2 Selection 13 1.3.3 Environment 14

2

Forager Societies

15

2.1 Dispersion 17 2.2 Networks 18 2.3 Practices and culture 22 3

Cooperation 3.1 3.2 3.3 3.4

Shared intentionality 31 Collective action 34 The evolution of cooperation 38 Cooperation with strangers 39

30

viii Contents

3.5 Dyadic cooperation 41 3.6 Thick reputations 43 4

Agricultural Societies 4.1 4.2 4.3 4.4 4.5 4.6 4.7

5

45

Consequences of agriculture 46 Social inequality 50 Moralistic religions 58 America and Africa 61 Cooperation revisited 63 Cultural evolution and complexity 64 Environment, culture, and genes 67

Conflict

70

5.1 Patterns and principles 70 5.1.1 Constraint 71 5.1.2 Collective conflict 72 5.1.3 Conflicts between three or more factions 80 5.2 Wars and institutions 85 5.3 Protests and revolts 94 6

Imperialism and Industrialization 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13

7

98

Europe’s conquests 100 Warfare and bureaucracy 103 Nationalism 105 Industrial revolution 107 Organizations 110 Industrial society and its tensions 112 Imperialism and nationalism 116 The whole world at war 118 Consequences of the world wars 120 Innovation by teams 123 Globalization and rising inequality 125 Protests, revolts, and wars revisited 130 Environmental degradation and new pandemics 133

Digital Society 7.1 (Self)deception and radicalization 138 7.2 Polarization and democracy 143

137

Contents ix

8

Models

146

8.1 Social influence and cohesion 146 8.1.1 Influence 146 8.1.2 Polarization 147 8.1.3 Social cohesion 149 8.1.4 Power centrality 150 8.1.5 Power in inter-polity dynamics 151 8.2 Cooperation for public goods 151 8.2.1 Social balance 160 8.3 Cultural evolution 161 8.3.1 Combination 161 8.3.2 Selection 163 8.3.3 Environment 165 8.3.4 Generalizations 165 8.4 Appendix 166 9

Conclusions

Glossary Bibliography Index

170 175 181 225

FIGURES

1.1 2.1 2.2 3.1 4.1 4.2 5.1 5.2 5.3 6.1 6.2 6.3 8.1 8.2

Network structure of cultural evolution Network of Hadza camp groups in East Africa Levi walk foraging pattern, based on a simulation Cohesive network Sculptures of worship in West Africa Mosque in West Africa Consensus versus polarization Social balance theory Inter polity dynamics Astronomical observatory in India Tipping point in cultural evolution Increasing world population and inequality Ising model of cooperation Predictions by the Ising model

4 22 25 33 58 61 74 82 91 100 110 130 156 157

ACKNOWLEDGMENTS

A proposal for this book was rejected by three funding agencies with the argument that a project of this scope is unfeasible, but I proved them wrong. Fortunately, there were generous and broad-minded colleagues in various places who gave helpful comments in their areas of expertise, for which I am very grateful to Peter Richerson, Lucy Upton, Randall Collins, Nico Wilterdink, Steven Wilkinson, Virginia Pallante, Christ Klep, Fabian Baumann, Rudolf Sprik, Steije Hofhuis, Joost Beuving, Don Weenink, Raheel Dhattiwala, Michael König, Arnout van de Rijt, Thijs Bol, Wilbert van Vree, Agneta Fischer, and the Culture Club of the University of Amsterdam. I received weekly updates about the deteriorating situation in Eswatini (from 2020 onward; p. 145) from Pachanga Matsenjwah. Andra Simionescu brought me into the monastery in Corod, Romania (p. 60). Habibata Zerbo, my wife, kept me updated on the rebels and bandits who murdered thousands of citizens in our beloved Burkina Faso and kept me mentally sane when the piles of literature touched the ceiling.

1 INTRODUCTION

What should a sociology of humankind explain? Like scholars from other disciplines, most sociologists specialize in certain domains. They produce good results, but specialization makes answering domain-spanning questions very difficult, and the ideal of including all peoples across times is not fulfilled (Comte 1858). For example, sociologists observe culture (i.e., things that people learn from others) in many different social situations. This brings about many domain-specific theories but no general theory of cultural dynamics. Another example is cooperation, which is examined at protests, in lab experiments, and so on, without culminating in a general theory, even though domain-specific cooperation could be more easily understood if there was one. However, a grand theory of everyone and everything is not feasible, because social life is too complex and diverse to explain based on a finite set of principles. So, we return to the question about a sociology of humankind. We may start with the phenomena that most affect the largest number of people and see how far we get. Because we use knowledge that we learned from others for most of what we do, it will not surprise that culture in the broadest sense should be among the first phenomena of interest. Additionally, most of what we achieve is with the (in)direct help of others, which puts cooperation in the same league as culture. People not only help and love each other (including voluntary sex) but also quarrel, hinder, and fight—often with enduring damage. Many of the largest problems we experience are due to conflicts of interest with close or distant others. This makes conflict our third core phenomenon. These three phenomena reveal (dynamic) patterns, and we can try to find principles that explain (the processes that lead to) them. Moreover, since all three phenomena occur in interactions between individuals and groups of individuals, a network approach can tie them together. Part of the DOI: 10.4324/9781003460831-1

2 Introduction

information from others is about others, and it is used to decide whom to avoid and with whom to cooperate. Information that tells who hinders may trigger aggressive actions. In this book, I discuss culture, cooperation, and conflict over 300,000 years of human history, as well as their relations to other important phenomena such as group dynamics, power, and social inequality. This long time frame enables scholars to recognize patterns that are missed in studies of current society. I aim for a handful of general principles, more coherence than the social sciences currently have, and more cross-domain exchange of ideas, but without the iron cage of a single paradigm for all social phenomena. Consequently, I draw no boundary between sociology and other disciplines; every insight is welcome. The linking pins for this endeavor are concepts, which are italicized in the text and defined in the glossary (p. 175) or on the spot. The overall structure of this book is historical, with forager, farmer, and industrial societies largely in chronological order. The patterns therein are interspersed with elaborations on explanatory principles. This chapter discusses the principles of cultural dynamics, with separate chapters devoted to cooperation and conflict. Formal (usually computational) models1 of the core phenomena are discussed in Chapter 8. However, none of the chapters can stand alone, and all are interrelated. 1.1

Cultural dynamics

Whereas my approaches to cooperation and conflict will become clear on the way, my approach to culture requires an introduction. Culture is defined as “those aspects of thought, speech, action (meaning behavior), and artifacts which [are] learned and transmitted” (Cavalli-Sforza and Feldman 1981, p. 10). Cultural elements are thus shared by at least two people, but not necessarily among everyone (in a group); the number of people who share something depends on the process of transmission. For a general theory of cultural dynamics, we can start out with some principles that all social scientists likely agree upon. First, new cultural elements are created by people combining incumbent cultural elements (Linton 1936; Burt 2004) by using their imagination and knowledge, sometimes serendipitously (Merton 1968). Thus, new cultural elements have cultural predecessors, and when putting all cultural bricolage (Lévi-Strauss 1962) in one image, we obtain a treelike pattern (Linton 1949), from stone tools and fire at the tree trunk to trillions of leaves representing the cultural elements we have today. This pattern is illustrated in a drastically simplified form without combinations in Figure 1.1A, with time fanning out 1 Formal models are cast in a language that enables exact inferences. Traditionally, these models were mathematical, but nowadays, most of them are written in a computer language and run inside computers.

Introduction 3

from the center. When we zoom in on a small part of this tree, thus moving from coarse to fine grain, the pattern, which now includes combinations, no longer appears treelike; see, for example, the evolution of the cornet (a horn invented in 1830) in Figure 1.1B. Each model of cornet was based on precursors, but some were also grounded in concurrent or older models (Tëmkin and Eldredge 2007). One could go on to complete the network of combinations by embedding it in the social network of individual inventors and users, as Collins (1998) did to some extent for philosophers who get ideas from other philosophers, but I will leave this exercise for another day. Once invented, cultural elements can be imitated by, or taught to, others who consider them valuable or who are forced to accept them by powerful people, thereby diffusing throughout parts of society (Rogers 2003; Pastor-Satorras et al. 2015). Thus, people obtain cultural elements through combination and transmission. Second, people (learn to) use culture to navigate and prosper in their social and physical environments (Swidler 1986), even though some cultural elements harm them, for example, anti-vaxxer ideology that increases the chance of illness. After using cultural elements in (actual or imagined) interactions with others or with nature, people retain numerous elements and discard others. Most of the cornets in Figure 1.1B that were initially appreciated are no longer produced or used. Schumpeter (1934) spoke about creative destruction in relation to how transmission or use results in some cultural elements being outcompeted by others (i.e., “compete for people”; Mark 2003). However, once cultural elements are internalized and embodied, i.e., become part of persons’ habitus (Bourdieu 1990), they tend to stick and become difficult to discard, especially when people become emotionally attached to them, for example, their mother tongue. When people are disappointed about a cultural element, they sometimes modify it or invent a new one; then, the principle of combination applies. Following Arthur (2009), I make no distinction between innovation and invention; both are novel combinations, at least from the point of view of the inventor(s). However, many combinations, for example, standard phrases, are reproductions of what has been combined earlier (i.e., words) without novelty. Third, all changes, such as combining, modifying, transmitting, using, and discarding elements, in the previous two steps change the sociocultural environment and sometimes also the physical, including the natural, environment. The repertoire of elements available at any given moment constrains or facilitates further use (e.g., when cultural elements are interdependent), and impairs or encourages subsequent combinations, by which we come full circle. Therein, “we are all the agents and witnesses, as well as the beneficiaries and victims, of cultural change” (Cavalli-Sforza and Feldman 1981, p. 340). These principles imply an evolutionary take on culture (Thurner, Hanel, and Klimek 2018), which is novel in sociology. Surprisingly, the concepts one

4 Introduction

FIGURE 1.1 Coarse and fine-grained structure of cultural evolution. (A) Tree. (B) Tan-

gled tree with arrows in the direction of “inspired by”, not ordered along timeline as in Tëmkin and Eldredge (2007), where the data come from.

might expect in an evolutionary approach—variation, reproduction, selection, adaptation, and fitness—have not been mentioned explicitly, although they are implied. Variation can be explained in terms of combination, but not every combination increases variation. Certain cultural elements such as standard phrases are frequently expressed identically, and their reproduction is a special case of combination (here, of words into phrases), namely, without increasing variation. Instead of the invisible hand of selection, the three-step model, comprising the three principles, is much clearer because it addresses specific interactions with particular consequences for the survival or disappearance of cultural elements and their users. This clarity carries over to adaptation: solving problems in certain situations or with respect to specific (groups of) individuals. Across the literature, fitness can mean chances of survival, life span, health, number of offspring, offspring that make it to adulthood, or number of grandchildren. Again, specific interactions with particular consequences are much clearer. The three-step model thus offers a more precise conceptualization of cultural evolution, but the familiar concepts can still be used if they do not confuse, for example, if it is clear who selects what. The three main principles may then be summarized as combination and selection in a changing environment, like genetic evolution. In this process, culture evolves in a giant network wherein change is driven by people who cooperatively and competitively combine, transmit, use, and select information under various constraints (like a scarcity of attention and resources). Someone’s culture is someone else’s sociocultural environment, and everyone is surrounded by a cultural reservoir in the minds, behavior, and artifacts of many others that is continually updated. Evolution is not deterministic and incorporates random chance, in serendipity and especially in the consequences of innovations. Many songs are written, but songwriters cannot intentionally compose songs

Introduction 5

that will become hits. People act intentionally, but they often lack understanding of the consequences of their actions, which depend on others, in particular on others’ cultural preferences, which are also influenced by others. Furthermore, there are random errors in combination and transmission. Analogously, there is randomness in the evolution of genes. Although the causes of genetic mutations can sometimes be deterministic, e.g., a consequence of a chemical reaction, mutations are random with respect to the natural selection thereof. With this three-step model, cultural dynamics can be mapped out in principle in a non-reductionist way, with every historical detail displayed in full color. The historical record has too many loopholes to accomplish this for long stretches of time, but we can discover general patterns. For example, many subsequent innovations are improvements of or complements to earlier innovations in the environment where they are used (Tarde 1890). People first discovered how to domesticate horses, then invented the saddle, and subsequently the stirrup. Any other sequence would have been literally unthinkable. Moreover, without knowing every individual cornet constructor, or culture creator in general, we can glean from Figure 1.1 that cultural evolution is roughly path dependent, meaning that one thing leads to another, corresponding to Darwin’s (1859) descent with modification. More precisely, cultural evolution is history dependent, as some elements from a longer past are reused. The overall treelike structure of culture is analogous to the tree of life, and both have gangplanks where concurrent (and older cultural) information jumps branches, with many more gangplanks in cultural evolution than in genetic evolution (Zilhão 2019). A cultural field where the evolutionary approach is applied extensively is language, based on detailed studies of corpora that span multiple centuries. These studies show that new words are constructed by modifying incumbent words (i.e., combining part of a word with a phoneme) and are transmitted from inventors to others who, through interactions in their social lives, select some words over others, thereby progressively changing the sociolinguistic environment, including its rules (Dunn et al. 2011). In the investigated cases, evolutionary forces, manifested by changing frequencies of use, were stronger than random changes (Newberry et al. 2017), and some new words or spelling outcompeted incumbents over a period of time (Amato et al. 2018). The temporal pattern of new word entry and incumbent decline closely resembles the pattern of social norm change (Centola et al. 2018), when a critical mass of new norm users wins over the remainder of the population. Languages are more than the sum of words, of course, and people spend years learning a language as a larger cultural package of grammar and vocabulary. Language changes, but different words and parts of a language change at different speeds. Frequently used words change slower than rarely used words

6 Introduction

(Pagel, Atkinson, and Meade 2007). For example, an irregular verb that is used 100 times more frequently than another one becomes regular 10 times slower (Lieberman et al. 2007). Interdependencies of rules slow down change, but rules are clustered into modules of more strongly interdependent rules such that change in one module (e.g., conjugation) is largely independent of rules in other modules (e.g., articles). The weak interdependence of language modules prevents most local changes from becoming disruptive overall. The social network of language users is also modular, because all social networks happen to be clustered in groups. If some groups become socially isolated, accents and dialects develop. Due to the adaptability that modularity offers in changing environments, with weak interdependencies between modules and strong interdependencies within, modularity is one of the most general evolutionary patterns and has been found in all natural and artificial systems that thrive in changing environments (Simon 1962; Alon 2003). Other exemplary fields of evolutionary studies are musical genres (Youngblood, Baraghith, and Savage 2021) and social media (Acerbi 2019). Cultural evolution differs from genetic evolution because combinations of genetic elements are produced by a biochemical process, whereas combinations of cultural elements are made intentionally by people (although not always consciously). Furthermore, people have many cultural parents but only two biological parents, and unlike genes, people collect their culture during their lives. They may forget some of their culture but cannot “forget” their genes. In most cases, culture can adapt more rapidly than genes,2 and it offers us a fair amount of behavioral plasticity. This may suggest that we have a great amount of cultural freedom, but there is more in some directions than in others (Tinbergen 1968). A norm proscribing adultery is much more difficult to maintain than a norm proscribing incest. We have certain dispositions with a genetic basis such as empathy for others, caring for one’s children and friends, and the desire to belong to a group, which are molded to certain but limited degrees in sociocultural settings. For material culture, there are physical constraints (Thompson 1917), for example, the laws of fluid dynamics that constrain the shape of boats (Rogers and Ehrlich 2008) and horns. Instead of seeing ourselves as culturally unbounded, realizing that we are primates in the tree of life and similar to other apes (Whiten et al. 1999) enables us to better understand our psychology and behavior. Through the use of culture, especially agriculture, the natural environment changed considerably, as well as the natural selection of our genes (Richerson, Boyd, and Henrich 2010). An example is the control of fire and its use in cooking that are universal among humans (Goudsblom 1986), which not 2 Although culture usually changes faster than genes, the genes of our immune system can change in just a few generations, which happened, for example, after the Black Death (Klunk et al. 2022).

Introduction 7

only changed the landscape through burning but also reduced chewing and digestion time, shortened our intestines, and flattened our bellies (Wrangham 2017). Another example is drinking milk from cattle, which was an advantage during periods of famine and disease and eventually led to lactose tolerance in adults (Evershed et al. 2022).3 In turn, genes shape our cognitive abilities to use culture. Genes and culture coevolve as two complementary information systems (Richerson and Boyd 2005; Mesoudi 2017), usually at a timescale too long for us to notice but no less influential. This evolutionary process is further complicated by the ongoing selection of our social contacts and thereby the changing network, which changes the future chances of cultural combination, transmission, and use, as well as the survival chances of ourselves and our contacts. Evolution in general, encompassing both genetic and cultural evolution and their co-evolution, means transmitted and combined information4 that is selected in, and thereby changes, its environment. At this abstract level (and in the formal model), it does not matter if the information carriers use culture or are used by it (for genes, Dawkins 1976 believed the latter), although it is an interesting conundrum for philosophers, lawmakers, and social scientists. In the rest of this book, I will speak about culture users. At this point, it is important to clarify what (co)evolutionary theory is not. It is not a grand theory of everything because without additional approaches, it cannot explain the origins of life, consciousness, and many other emergent phenomena5 (Thurner, Hanel, and Klimek 2018, p. 226). Further, it is not a theory of social Darwinism and Herbert Spencer’s ethnocentrism, teleology, and determinism, even though there is a small minority of racist scholars and numerous lay racists who distort and abuse evolutionary theory for their political purposes (Carlson et al. 2022; Morris 2022). Therefore, it remains important to stress that genes for skin pigmentation do not constrain someone’s talents or behavior—rather, this happens through onlooking racists’ behavior. From birth to adulthood, most genetic influences on behavior are progressively modified, and in most cases, they are eclipsed by cultural influences, except when bouts of unanticipated fear result in fight, flight, or stifling reactions, as well as bodily functions such as sneezing, yawning, and, most importantly, giving birth (notwithstanding the medicalization thereof). None of these have anything to do with “race”, 3 Lactose intolerance first led to cultural innovations, namely, fermentation techniques to reduce lactose, yielding products such as cheese and yogurt, followed by genetic adaptation to milk, which happened in Africa and Europe independently. 4 In asexual reproduction, DNA is transmitted from parent to offspring, but errors during copying can entail new combinations of DNA. 5 Durkheim (2010) realized that many social phenomena at the collective level, such as group formation, group cohesion, and division of labor, cannot be explained by the properties of the individuals who produce them collectively. In short, “more is different” (Anderson 1972). These phenomena are currently called emergent (Mitchell 2009).

8 Introduction

which does not even exist biologically.6 Feminist scholars have rejected Darwin because of his misogyny, and understandably, I would say. After his famous book on evolution, he wrote another book, The Descent of Man and Selection in Relation to Sex, which avoided sex by all means but said that women are inferior to men and are basically incapable of making their own decisions (Darwin 1871). However, modern biologists have explicitly rejected Darwin’s ideas about women (Rosenthal and Ryan 2022), and evolutionary theory does not hinge on the condescending passages in this book but on a large community with different ideas about women than he had at his time. In other passages in Descent, Darwin was far ahead of his time and against common opinion; he understood that humans are similar to, and not fundamentally different from, other animals and that humans come from Africa, not Europe. Furthermore, he noticed parallels between the evolution of languages and that of animals and said that culture has a stronger influence on human evolution than natural selection has. These insights were revolutionary at the time and have been confirmed by modern research (Richerson, Gavrilets, and de Waal 2021), and hence, they are worth keeping. 1.2

Goals and means

The goals of my Sociology of Humankind are fourfold, namely, to outline asgeneral-as-possible theories of culture, cooperation, and conflict, and to use these to connect specialists’ findings from numerous fields into a more coherent and more general sociology than we currently have. Fulfilling these goals requires a historical treatment, which occupies much of the remainder of this book. The historical record reveals instances of culture abundantly but incoherently. Cultural evolution demonstrates how each instance of culture is part of a large process that encompasses all instances, without Procrustean stretching or squeezing of the data. The more data are available, the more accurate an evolutionary reconstruction can be. Therefore, the criticism that an evolutionary approach does no more than putting old wine in new bottles misses the point. Evolutionary theory does not mess with the wine, but it attempts to systematically reconstruct the tree of old bottles to provide a bigger picture of how wine evolved. I will limit my treatment to broad trends over the last 300,000 years and leave the finer-grained treatments of specific regions and times in the hands of historical experts. At the micro level (which in sociology means interpersonal interactions), I use network theory (Kadushin 2012), which renders a broad range of 6 There are small genetic differences between populations (Cavalli-Sforza 1997), but there are no genetic population boundaries (Lewontin 1972); the variances are much larger than the means (i.e., individuals differ much more than groups), and the cultural differences are much larger than the genetic differences.

Introduction 9

relational and interactional approaches systematic and precise (Emirbayer 1997). Many interactions are neither cooperative nor conflicting, but most are cultural, if only because people use language to communicate with, and transmit information to, others. There remain interactions that are none of the three (or only to a very limited extent), for example, when people sense and bodily react to the presence of others nearby. Some of these interactions can become important, for example, when a confrontation between opponents turns violent. Another example is the baby’s non-cultural efforts to draw the attention of caregivers, which prompts the latter to engage in (culturally modified) helpful behavior. I incorporate these non-cultural or pre-cultural interactions in my treatments of cooperation and conflict. My primary evolutionary inspiration is the work of Peter Richerson and Robert Boyd (2005). Around their work, anthropology has a vibrant subfield (Mesoudi 2017), whereas sociological advocates and practitioners of cultural evolution and coevolution are few and far between (e.g., Parsons 1964; Allison 1992; Lenski 2005; Blute 2006).7 The number of social scientists who use concepts consistent with or explicitly related to cultural evolution is much larger, such as cultural transmission (Hunzaker 2016; Kandel and Massey 2002), the dependence of network formation on its social environment (McFarland et al. 2014), the interplay of culture and networks (Vaisey and Lizardo 2010), and cultural influence on taste (Lieberson and Bell 1992). As a matter of fact, it turns out to be quite easy to interpret most empirical findings from a cultural evolutionary perspective and thereby relate them in a way that the authors of these findings were unaware of. 1.2.1

Patterns, principles, and models

When one examines the historical record, one finds patterns, for which one tries to identify principles to explain them (Bod 2013; Bourdieu and Wacquant 1992, p. 7). If one lives up to the challenge, historians and ethnographers readily complain that events are unique and that abstract explanations go at the expense of the detailed accounts they bring to life. They are right that abstract formal models are no more than castles in the air if they are not based on careful interpretations of social life, many of which they 7 In sociology, few scholars have written about evolution (Hopcroft 2018), and when they have, it has usually been with a strong emphasis on biology and genes. This history is reviewed by Schutt and Turner (2019). In current education, sociology students learn little about evolution (Takács 2019). Therefore, misunderstandings are widespread; for example, social Darwinists’ idea was that evolution would resemble unfettered capitalism where only the fittest survive, whereas biologists have shown that both species and individuals only thrive in ecological networks of manifold diversity (Thompson 1982; Loreau and De Mazancourt 2013). The survival of the fittest is inconsistent with the survival of grandparents and misses their importance for their children and grandchildren (Davison and Gurven 2022). Another persistent misunderstanding is the idea that evolution would be judgmental about stages of development (from primitive to civilized), whereas neither stages nor judgments about them are Darwinian.

10 Introduction

provide. In historical studies, formal models are rarely used, and they are critically received (Spinney 2012). The key point is that using computational models, one can answer important questions that cannot be answered by traditional historical and ethnographic methods. The reason is that in natural language, it is not possible to argue accurately about intricate (i.e., nonlinear) patterns, the overall effect of great many interactions, and the effect size of randomness. Randomness can be intrinsic or stand for gappy knowledge of the researcher, for example, incomplete data. To avoid these pitfalls, at least to some extent, historians and ethnographers tend to focus on one or few cases in great detail and to limit their questions and explanations to events within the chosen cases8 . As a result, they yield valid and important knowledge, but on the basis thereof it is impossible to explain the outcomes of complex processes that span many cases, like the question of whether moralistic religions precede or follow the growth of polities, whether one causes the other, or whether both have a common cause. Nonetheless, most people prefer discursive over computational explanations for all, instead of some of, their questions, as the former are intuitively understandable. Because our brain “is capable of rationalizing essentially any conceivable behavior, we are always able to find explanations that render the observed outcome understandable”. However, “explanations that are evaluated solely on the basis of understandability can be satisfying in ways that scientifically valid explanations cannot be” (Watts 2014).9 In other words, one risks being satisfied with a feeling of understanding without a proper understanding. Scientifically valid explanations, be they intuitive or not, must be based on empirical evidence. To this end, one must infer refutable predictions from one’s explanation and confront them with new empirical evidence. Depending on the kind of explanation, predictions do not have to be forecasts; many are about the past or present and can be quantitative or qualitative. Only by making predictions that are sufficiently specific to be proven wrong can one learn something. In this manner, historical computational models have rapidly advanced (Turchin et al. 2018; Turchin et al. 2022). From these experiences, it has also become clear that in a research team, at least some members must have extensive domain knowledge, or else, the models tend to become contrived and mismatch the substance they are supposed to model. In this book, models and their results are explained in natural language, and the math is put in one chapter at the end, so no one should feel hindered when reading the main story. Remember that Charles Darwin did not know math himself but was nonetheless a strong evolutionary thinker (except on gender). Because there is synergy between computation 8 Big history provides a synthesis of numerous smaller scale studies but can only deal with intricate patterns, great many interactions, and randomness if its practitioners borrow (insights derived from) formal models from other sciences, or else they tend to ignore the micro level and do not go beyond straightforward patterns at the macro level. 9 Ironically, Watts’ (2014) remark equally pertains to a large part of rational choice theory.

Introduction 11

and interpretation, and I have based part of the latter on the former, I hope that this book will encourage social scientists, historians, and computational and evolutionary scholars to learn across current divides. 1.3

Cultural evolution: a reprise

Before starting the historical tour, I elaborate some more on cultural evolution to preclude misunderstandings in subsequent chapters when I gloss over these issues. 1.3.1

Combination

Sometimes, cultural elements come about through rediscovery of lost elements, or spontaneous creation, such as (humans observing that) a lightning strike had created fire. In most cases, as said above, cultural elements are created by individuals or groups who combine complementary elements if they believe their effort will yield something valuable, possibly motivated by dissatisfaction about incumbent elements, for example, during a crisis. In order to be combined, the pertaining cultural elements have to be transmitted from the social environment to the knowledge broker (Burt 2004) first, which may happen long before the combination is attempted. Transmitted cultural elements are assigned meanings by the receivers, who are usually influenced by others, and adjust meanings upon use. Learning from others does not imply that people fully understand what they learn. For example, many technologies that people nowadays are familiar with cannot be repaired if they break. New technologies are more easily transmitted if they can be tried and the results can be observed (Rogers 2003). In general, chances of transmission increase if receivers expect that pertaining cultural elements will be valuable to them (thus stir positive emotions), senders and receivers share the same background knowledge and language (Centola 2010), senders actively explain and teach their receivers (i.e., provide feedback), multiple senders agree with one another (Morgan et al. 2012; Centola 2011), or if there are, respectively, good or powerful role models with high prestige or status using or obtruding certain elements (Richerson and Boyd 2005). Especially in the case of a new norm, transmission by multiple others is crucial, because adopting it only makes sense if most or all others (in one’s group) do. Multifold transmission is also important for the education of children. When a child misunderstands a word and uses it incorrectly, a social environment of adults and older siblings will correct the mistake. Repetition and feedback from multiple sources entail reliable transmission of languages and other cultures, yet many adults are smart enough to rationally reconstruct and even improve cultural elements that have been imperfectly transmitted to them (Osiurak et al. 2022). Ideas that are difficult to comprehend have lower transmission rates at the population level, but specialists with expertise in the relevant domain have more

12 Introduction

absorptive capacity (Cohen and Levinthal 1990) and can understand or rationally reconstruct these ideas more quickly than non-experts. Even then, strong social ties (in terms of time spent) are often necessary to transmit complex information (Aral and Van Alstyne 2011).10 In transmission, there are also effects of noise (misunderstandings and wrong explanations), bias (manipulation, systematic error), context, and, most of all, differences between individuals in terms of their interests, attention, prejudice, and prior knowledge. Consequently, different people may interpret given information differently, for example, some people find certain kinds of false information, such as conspiracy theories and irresistible “brain candy” (Pagel 2012), which renders them impervious to corrective feedback, whereas others see through it. Finally, institutions, such as religion, facilitate the transmission of some elements and impede others. In the context of combination, which always presumes transmission, transmission proper (i.e., before or without subsequent combination) can be regarded as a baseline case of combination. Receivers of cultural elements relate them to their incumbent knowledge, which may or may not lead to (new) combinations. Accumulated transmissions make it possible for everyone to innovate, some more spectacularly than others, by combining some of the things they learned. At the very least, people utter sentences never said before, but most of these sentences are quickly forgotten and have little impact on society at large. Producing sentences might be easy, for humans at least, but many other innovations require a great deal of trial and error before the results satisfy the users (Jacob 1977). For example, after making dishes with celery and others with banana, a creative cook may think one day: how would the combination of celery and banana taste? Before the new dish can be served in a restaurant, however, there is extensive tinkering with cream, broth, seasoning, and mushroom juice.11 Innovation is also emotional; inventors, such as our cook, experience play when they are busy innovating, excitement if they succeed, and dissatisfaction and sometimes anger when they fail. When the recipe for the new dish is finally settled, diners may be amazed by the cook’s creativity, but when investigating where the knowledge and skills come from, they will discover a network that connects the cook to many others over multiple generations. This network, a great many backstage failures in the kitchen, and the role of serendipity point out that the magnitude of creativity is always smaller than onlookers experience, although this does not belittle the quality of the result. Overall, many innovation attempts fail (Ormerod 2005), just like most genetic mutations are neutral or deleterious and only few increase fitness. 10 For knowledge brokers, a given amount of effort and time to access information implies a trade-off that was missed in earlier literature. They could use relatively few strong ties to access valuable complex information or a larger number of weak ties to access more diverse yet simple information (Aral and Van Alstyne 2011; Bruggeman 2016). 11 The combination of celery and banana I learned in Akrame Benallal’s restaurant in Paris. The remainder ingredients I guessed from tasting.

Introduction 13

Innovation attempts have higher chances of success if the input ideas are more valuable (Bruggeman 2016) and are more diverse (Page 2007). However, specializing in some domains goes at the expense of accessing more diverse information across multiple domains (Footnote 10), which implies that the chances of successful innovation decline beyond an optimal level of diversity (Vestal and Danneels 2022). For the record, modifications of incumbent cultural elements are also combinations; obviously, adding or substituting elements constitutes (re)combinations, and a simplification means that fewer elements than given are sufficient to obtain a desired result, which yields a new combination. Some combinations can be accomplished in different ways (e.g., seasoning a dish at the beginning or at the end), and some combinations are created multiple times (e.g., a cook making the same dish many times), but then there is no novelty. Along with creation, certain combinations lead to the destruction of cultural elements. For example, Tilly (1993) knew that “if one put [Harrison White’s] Identity and Control on the bookshelf next to James Coleman’s Foundations of Social Theory, the two books would destroy each other like matter and antimatter.” 1.3.2

Selection

Individuals use culture in cooperative, conflicting, or neutral interactions with others who also use culture and in interactions with the material environment. I use the phrase “individuals use culture” as a shorthand for a very complex biochemical process wherein information stored in the brain is turned into action. It is not always clear if people use culture or culture uses people. To sidestep intentionality issues and to keep focus on evolution as a process, we might therefore say more formally that people act according to (instead of use) certain cultural elements. Either way works for the tree-step model. Based on experiences or imagination, individuals keep certain cultural elements and reject others. For example, they may conform to a norm when fearing punishment and thereby keep the norm in their behavioral repertoire, or they may vote for a certain party when they believe that its policies are superior to another party’s policies (i.e., under cultural competition). People also use culture to establish, maintain, or reject social ties with others, and the network coevolves with culture. When people simultaneously copy and adopt others’ cultural elements, for example, a behavior perceived as leading to success, transmission and selection coincide. Individuals’ cultural and social decisions are based on expected value assessments, for which economists use utility functions. However, these functions do not imply that people are highly rational. In contrast, the majority of cultural elements are selected based on comparisons within very limited subsets of alternatives that are biased with respect to recency, visibility, availability, and social pressure. For this reason,

14 Introduction

most people do more or less the same things as most of their social contacts do most of the time.12 People also forget cultural elements or how to use them, and the chance of an entire group to lose cultural elements depends on its size (with a stronger effect of randomness in smaller groups), the group’s network cohesion, memory enhancers such as songs or dramatic stories (Candia et al. 2019), and the number of available role models who keep using the elements (Richerson and Boyd 2005). 1.3.3

Environment

From an individual’s perspective, most culture exists in the sociocultural environment (i.e., the minds of others and external sources), which changes as a result of demography and network dynamics, as well as when cultural elements are created, modified, transmitted, kept and used (as opposed to being ignored when received), rejected (after use), or forgotten. If an individual dies before their knowledge is transmitted to others or is recorded, it is lost. Along with deaths, new individuals are born who learn culture and will contribute once they grow up. In the three-step model, environmental change means the bookkeeping of all these changes, some of which are due to the effects of the physical environment. At every moment, the sociocultural environment enables and constrains subsequent combinations and interactions. These deliberations are made more precise in a formal model of cultural evolution in Chapter 8. By now the reader is prepared to perceive our history evolutionarily.

12 A modern preconception in Western Europe and North America is that cognition is (bounded) rational, opposite to emotions that tend to be irrational. Consequently, people in these parts of the world are educated to respond rationally to difficulties instead of responding emotionally. However, there is a problem with this point of view. The way we are built makes it impossible to think about anything important without emotions that often direct, and are strongly intertwined with, cognition (Damasio and Carvalho 2013). Moreover, if life is difficult, due to conflicts or food scarcity, which was often the case for most people before the 20th century, Darwinian selection leaves little room for doing spontaneously what one feels like without thinking; people who did “irrational” things instead of diligently caring for their children are not our ancestors (Pagel 2012).

2 FORAGER SOCIETIES

Homo sapiens originated at least 233,000 years ago in Africa (Vidal et al. 2022). The discovery of 315,000-year-old skulls in Morocco (Stringer and Galway-Witham 2017; Hublin et al. 2017) pushes this date back 80,000 years. These found skulls already had the same brain size although not yet the same brain shape as modern humans (Neubauer et al. 2018) and therefore might have been an intermediate species prior to “modern” sapiens (Bergström et al. 2021). However, the brain shape difference appears to be unimportant for classification (Weaver and Stringer 2015), so the earlier date could indeed be closer to sapiens’ origin. Because the Sahara was habitable at the time, humans may have walked to north Africa from east Africa over multiple generations, where the remains of older human ancestors have been found as well as similar (but older) stone tools. These tools were unearthed together with red minerals, possibly used for body decorations, and obtained over a distance that may have involved trade through a social network (Brooks et al. 2018). Because neither bones nor teeth have been found at east African sites, there is uncertainty about the place where the first sapiens lived, if such a unique place even exists. For a long time, sapiens shared the planet with Neanderthals, a close relatives to Denisovans, and other humans.1 The earliest Neanderthals appear in the archaeological record more than 400,000 years ago, well before sapiens entered the scene. They lived in a geographic range smaller than sapiens: a band across middle and southern Europe through the Levant into west Asia 1 Other humans living at the same time as sapiens for a while were Homo naledi (Berger et al. 2017), Homo floresiensis (Sutikna et al. 2016), Homo luzonensis (Détroit et al. 2019), Homo erectus (Rizal et al. 2020), and perhaps others. They merit a library instead of a footnote, but very little is known about them. DOI: 10.4324/9781003460831-2

16 Forager Societies

(Krause et al. 2007). Neanderthals lived in patrilocal groups, mastered the use of fire (Wrangham 2017), occasionally cooked their food (Sandgathe and Berna 2017; Henry 2017), had stone tools and glue, hunted animals with spears, and in all likelihood spoke language (Dediu and Levinson 2013).2 They performed burial rituals (Leroi-Gourhan 1975), and they created jewelry (Wade 2016) and cave art paintings (Hoffmann et al. 2018) well before sapiens did (Aubert et al. 2019), although there is ambiguity surrounding what art is (McDermott 2021). Yet, the desire to express oneself existed before sapiens. In short, Neanderthals were very similar to sapiens in many ways, including their attitudes toward violence. Their levels thereof were similar (Gómez et al. 2016), and violent deaths were more prevalent among men than women (Beier et al. 2018), as was the case for sapiens. There were also some differences. Their habitat was smaller, their groups were smaller (Skov et al. 2022), and their intergroup interactions were less frequent than ours (Ambrose 2001), although they might have sometimes teamed up in larger groups to hunt large game (Boyd and Richerson 2022). Neanderthals went extinct approximately 40,000 years ago (Higham et al. 2014). Their disappearance may have been caused by a competitive disadvantage with respect to sapiens, their small population size upon which the effect of random events (e.g., a harsh winter) is relatively large (Kolodny and Feldman 2017), the deleterious effect of inbreeding (Ríos et al. 2019), or a combination of these factors. Sapiens’ temporal overlap with Neanderthals was 14,000 years in Europe and much longer in the Levant. During this overlap, both cultural and sexual exchange took place on multiple occasions (Bergström et al. 2021). Consequently, modern Europeans and Asians carry 2% Neanderthal DNA (Hajdinjak et al. 2018). Melanesians and Aboriginal Australians have approximately 3.5–4% Denisovan DNA (Vernot et al. 2016), and modern Tibetans also have some Denisovan DNA. These admixtures have had social ramifications through health effects associated with these genes. The Tibetans inherited a gene that enables them to use oxygen more efficiently and to live in the high mountains. Neanderthal genes slightly increase the risk for depression, nicotine addiction, diabetes, skin lesions (Simonti et al. 2016), stroke (Gross 2022), and coronavirus infection (Zeberg and Pääbo 2020), as well as sunburn in Europeans. At the time, there was no negative selection (i.e., increased mortality) for these traits, whereas sapiens’ immune system became better adapted to the region traditionally inhabited by Neanderthals (Sankararaman et al. 2014), and blood clotting (with a risk of stroke) stopped bleeding when people got injured, which on average happened at younger ages when strokes were no issue (Gross 2022). Europeans migrating back to Africa over the past 2 Arguments in favor of Neanderthal language are anatomical (Dediu and Levinson 2013) as well as right-handedness of most tools (Ambrose 2001), which became typical once the brain’s left hemisphere specialized in language. Chimps have neither language nor right-hand preference.

Forager Societies 17

200,000 years brought a small amount of Neanderthal DNA into part of the African population (0.3%; Chen et al. 2020), but the sunburn susceptibility was rapidly selected out. As a consequence of sexual exchange, “the Neanderthals are still among us” (Gross 2022), and our family tree looks much more like a jumble (Figure 1.1B) than a proper tree (Figure 1.1A). Archaeologist Joshua Akey concluded, “We are amalgamations of the past, with little bits and pieces of DNA that originated all over the world and, in some cases, from different species” (in Gibbons 2015). I now abandon the other branches of the human tree and focus on sapiens. 2.1

Dispersion

Like Neanderthals, sapiens were hunter-gatherers or foragers. They wandered throughout all of Africa; some went even further to areas such as Greece where humans tried to gain a foothold 210,000 years ago (Harvati et al. 2019). Initially, these excursions did not last, and the small populations were quickly replaced by Neanderthals who had higher population growth rates at the time (Hawks 2017). Sapiens also moved into the Levant and the Arabian Peninsula around 180,000 (Hershkovitz et al. 2018), 100,000 (Groucutt et al. 2015), and 60,000 years ago, respectively. From then on, waves of migrants occupied the rest of the world, though some of their descendants moved back to Africa. From the Arabian Peninsula onward, sapiens arrived in Sumatra 68,000 years ago (Westaway et al. 2017) and in New Guinea and Australia 50,000 years ago (Tobler et al. 2017). Sea levels at the time were lower than they are now, so their sea faring route was much shorter than today. Other groups reached Europe 54,000 (Slimak et al. 2022) and 47,000 years ago (Hublin et al. 2020). The earliest traces of sapiens in China are perhaps 100,000 years old (Liu et al. 2015) but certainly 40,000 years old at least (Wang et al. 2022). Another group walked from Asia into America 15,000 years ago (earlier visitors were few in numbers and did not last) until walking was no longer possible (12,000 years ago); seafaring Paleo Inuit arrived there by 5,500 years ago. Throughout sapiens’ long past until the advent of agriculture, wandering and migrating were normal, and settling for a longer time was the exception. How natural environments are supportive for foragers can be measured by the local densities of populations and predicted by natural productivity (indicated by mean temperature and precipitation), biodiversity, and pathogen stress (Tallavaara, Eronen, and Luoto 2018). These three factors are stronger toward the equator, but given that the effect of pathogen stress on population density is negative, this does not imply higher population density overall. Close to the equator, natural productivity is high, but biodiversity’s effect levels off while pathogen stress has the strongest effect on population density (ibid). Far away from the equator, natural productivity is low but small

18 Forager Societies

increases in it enhance biodiversity, and both have a positive effect on population density, while pathogen stress remains low. In other words, in cold climates, population density is limited by low natural productivity and biodiversity, and in tropical climates, it is limited by pathogen stress. Hence, the sweet spot is in temperate climates. This is still largely true today, with a modern twist: whereas modern science has had a small effect on reducing pathogen stress in the tropics, modern transport has had a large positive effect on population densities in dry (e.g., North Africa) and northern regions. Natural environments impose constraints that also have behavioral effects on human foragers and animals alike, but how this works is not yet known except for obvious cases; along a river, for example, one finds more fish eating animals and humans than in dry habitats. It turns out that in a given environment, not only foraging behavior but also polygyny, divorce frequency, age at first child, fathers’ contributions to their children, and inequality of foragers, other mammals, and birds converged and became more similar than across different environments (Barsbai, Lukas, and Pondorfer 2021). Several of the similarities appear to be associated with food storage, but much is to be discovered in future studies. Many of these associations with the environment were weakened or disrupted by norms and practices of agricultural and industrial societies, like in the case of religions that imposed monogamy.3 Despite abundant food in Africa, some people left. Their numbers must have been small, because among all people outside Africa today, there is much less genetic variation than among the smaller number of people inside Africa (Tishkoff et al. 2009). Sapiens occupied a habitat much larger than that of any other animal, so we may ask: how did foragers adapt to the large variety of landscapes and climates around the globe? When leopards moved from the tropics into the Himalayas, they obtained thicker fur with snow camouflage, and their genetic adaptations took a considerable amount of time. Humans adapted to their new environments almost entirely culturally, at a much faster pace than genetic adaptations, which resulted in far greater cultural than genetic variation. When moving to the north, people did not develop thick fur, but they adopted the cultural element of wearing other animals’ fur. To go further into foragers’ culture and practices, it is necessary to first elaborate on the network structure through which their culture evolved. 2.2

Networks

Foragers lived in mixed male-female groups with strong, stable bonds (Shultz, Opie, and Atkinson 2011). I define groups relationally, as relatively denser 3 Because monogamy is associated with larger, often urbanized, groups, an evolutionary explanation is that it prevented sexually transmitted diseases and made monitoring and costly punishment of adultery worthwhile for norm maintainers (Bauch and McElreath 2016). Monogamy also reduced crime rates and increased fathers’ involvement in raising children (ibid).

Forager Societies 19

areas in a network, which correspond to individuals who identify and bond with one another. People have always known that their survival depends on belonging to a group; longing to belong, solidarity, and the pain of being separated from one’s group are among the strongest feelings that people experience, and being embedded in a group has strong positive health effects (SnyderMackler et al. 2020). An explanation of group formation starts with the observation that people meet at social foci, which are “social […] or physical entities around which joined activities” can be organized (Feld 1981). Foragers’ foci were the families into which they were born, the camp group(s) their family was part of, places of shelter, resource concentrations, and places where mountains or rivers could be traversed. There, people could meet, discuss, or undertake activities together and get to know each other. Consequently, these people had or developed more in common than random strangers, and the next time they met, conversing became easier, even more so if they had additional commonalities such as age, ethnicity, goals, social contacts, and, most importantly, norms. Norms, also called institutions, are “the rules of the game” that form “constraints that shape human interaction” through incentives (North 1990) in given situations where a lack of rules would lead to (sometimes imaginary) negative consequences for others.4 Because shared experiences, norms, and background facilitate conversation and interaction, people use the heuristic of assortative selection of social contacts (Lazarsfeld and Merton 1954; Apicella et al. 2012).5 Once they are connected for a while, they also attach emotionally (Bowlby 1977). Assortment could be said to be a variant of the law of least resistance, as more effort is required to communicate with people who show greater differences and follow different norms. Shared foci and cultural similarity facilitate, and correlate with, but do not explain cooperation (Handley and Mathew 2020). The same law of least social resistance explains another pattern, transitivity (Simmel 1908; Apicella et al. 2012): If A and B have a common friend C, who speaks well about both of them and introduces them to each other, there is nothing easier 4 Many norms are tacit and only noticed when children or others trespass them. Norms also comprise morals, which are emotionally charged (meta) norms about (absolute) good and bad. Institutions have scope conditions and may include rules for breaking rules (Edgerton 1985) to determine when exceptions are allowed or even obliged. In egalitarian societies, new norms are invented, and incumbent norms are changed in a communicative process similar to preludes to collective action (a modern example: Kellogg 2009), whereas in centralized agricultural or industrial societies, a portion of norms is changed and implemented top-down by leaders and their networks. To maintain norms, gossip and reputations are equally important as for the maintenance of cooperation (Gluckman 1963; Blau 1964), and both are more effective in locally denser (i.e., more cohesive) clusters in a network (Piskorski and Gorbatâi 2017; Coleman 1988). In the economic literature, institutions are distinguished from culture, but they are part of culture from an evolutionary angle. A formal treatment of institutional evolution can be found in Section 8.3. 5 Assortment is nonmonotonic, as people do not look for exact copies of themselves but rather for similarity combined with certain kinds of complementarity (Rosenthal and Ryan 2022). Assortment varies across goals, situations, and for given individuals over time.

20 Forager Societies

for A and B than to start a social relationship, if only briefly. The presence of foci, together with the mechanisms of assortment and transitivity, explains (bottom up) human group formation throughout history. Once a group has formed, social influence among group members makes their attitudes and opinions converge (Friedkin and Johnsen 2011), and the group culture more homogeneous (Section 8.1), leaving most variation across groups (for an overview of influence models, see Flache et al. 2017). This also holds true for linguistic variants (Bloomfield 1914), which makes sense, because it is beneficial to be able to communicate with all group members. However, homogeneity is not always beneficial, because groupthink blocks diversity and innovation that could solve complex problems in changing environments (Page 2007). It is therefore not entirely clear from an evolutionary perspective why most people tend to over-conform and become collectively inert when quick adaptation would increase their survival chances, no matter how eloquently these individuals can rationalize their cultural traditions in retrospect. In theory, information exchange between groups would in the (very) long run result in cultural homogeneity among all groups. This was predicted by one of the simplest simulation models based on assortment and influence. Its principle, repeated many times in a simulation run, can be stated in one sentence: “with a probability equal to their cultural similarity, a randomly chosen [individual] will adopt one of the cultural features of a randomly chosen neighbor” (Axelrod 1997). Of course, cultural homogeneity did not happen among foragers; the environment varied and changed locally, triggering local innovation efforts to adapt and making maladapted technologies and norms obsolete, resulting in divergent, not convergent, turnover of cultural elements. Only on a static planet with the same climate and landscape everywhere, culture might have become homogeneous. Nonetheless, within most groups we see cultural homogeneity. Due to foci, assortment, and transitivity, groups can grow but their size is constrained. A larger network of people can achieve more collectively, such as producing or collecting more resources and accessing more information, but at the same time there is stronger competition and more rapid resource depletion, and it becomes more difficult to coordinate (Hamilton et al. 2007). Foragers and their families joined campsite groups (through assortment and transitivity) in particular because these groups could buffer fluctuations of individuals’ food supply, but when groups reached about 20–25 people, some of them hived off to form new groups or to join other groups. Group sizes varied because they depended on local carrying capacities (resources that can sustain a population). From these fusion and fission dynamics, an embedded structure emerged (Figure 1.1A) with at least four levels. The smallest groups were families of about 4–5 people. Approximately four families would compose a residential group at a campsite with maximally 25 people, with a

Forager Societies 21

longitudinally changing composition. There were occasional gatherings of larger groups of 150–180 people, which were excellent opportunities to gossip and look for a partner. These occasions could be annual or seasonal. For example, group sizes of Inuit (in a cold climate) alternated seasonally between small volatile groups and large semi-permanent settlements (Mauss 1905). An entire ethnic group, covering about 4–6 occasional groups, dozens of residential groups, and many more families, would consist of 750–1000 people (Kelly 2013). The groups in this embedded structure were in a subset relation and, with few exceptions, were without overarching authority. The groups were not static, not even the permanently settled ones, and the embedded structure could be seen as an attractor to which people gravitated after fission, warfare, environmental or demographic changes, or migration. Compared to a homogeneous network, which is more efficient for information transmission in theory, a network with embedded clusters, or groups, is more efficient in practice (Centola 2010). The latter can adapt better and faster to varying circumstances (or goals; Kashtan and Alon 2005) and is more resilient to disturbances, because trouble and change in one group do not disrupt other groups and the overall structure remains intact (Simon 1962). In temporally and locally varying environments, only modular networks survive (Alon 2003; Kashtan et al. 2009). Figure 2.1 shows a cross-section of an embedded group structure based on a survey of sympathy ties (“With whom would you like to stay in the next camp?”) among 205 21st-century Hadza foragers at 18 campsites around Lake Eyasi in Tanzania (Apicella et al. 2012); numbers within nodes denote numbers of ties within camp ties. Without assuming that they live the same way as in the past, this intercamp network, which is broadly consistent with archaeological findings, illustrates how dynamics of and exchanges between ancestral settlements may have looked. Many of the strongest ties are between family members. Marriages are exogamous, i.e., with someone in another group, and establish “exchanges that construct alliances between groups of people” (Lévi-Strauss, paraphrased by Kelly 2013). Humans are the only primate with enduring ties between affines and between siblings in different groups after leaving their parents for marriage (Chapais 2011; Hill et al. 2011). Kinship has a strong bonding effect, but socially constructed kinship can be as strong as biological kinship. The ways people (culturally) related to one another and the embedded group structure affected their chances of survival, thereby influencing the course of evolution. The sparse connections between more densely connected groups allowed for “increased observation of rare innovations that are unlikely to be discovered by individual learning” (Hill et al. 2011). These bridging ties made it possible for bricoleurs, or knowledge brokers

22 Forager Societies

36

39 18 4 5

12 13 11 25

44

25

27

50 3

30

22 23

6 FIGURE 2.1 Sympathy network between Hadza camp groups represented as nodes.

Numbers in the nodes indicate the number of ties within each camp group. Data from Apicella et al. (2012).

(Burt 2004), to learn about and combine cultural elements beyond anything possible in more isolated groups (Linton 1936). Moreover, the treelike group structure has short average social distances (the number of hops to move through the network), which enabled finding others even if they were geographically remote (Watts, Dodds, and Newman 2002). These connections facilitated the exchange of information and cultural artifacts over long geographic distances, shown abundantly in archaeological findings. 2.3

Practices and culture

As we now understand forager networks, we can zoom in on forager practices and “cultural tool kits” (Swidler 1986). The easiest we could do would be to look at forager groups in modern times and from them infer how they lived in the past. However, “long before anthropologists arrived on the scene, huntergatherers had already been given diseases, shot at, traded with, employed, and

Forager Societies 23

exploited by colonial powers or agricultural neighbors” (Kelly 2013, p. 13), which changed their livelihoods. Therefore, much of what we see does not give “a glimpse of the past in the present” (ibid., p. 269). We can look at their practices and culture at a more abstract level, however, and compare with archaeological findings and outcomes of lab experiments. For example, the DNA of people who lived in the past indicates that inbreeding was avoided through exogamy (Sikora et al. 2017; Chapais 2011), similar to modern foragers, but unlike late Neanderthals who suffered from inbreeding. Hence, exogamy is similar across sapiens’ history, even though its cultural representations may have changed. Forewarned and careful, we will now explore foragers’ culture and practices. The number of tools foragers maintained was constrained by their need to carry all their possessions when moving to the next camp. This constraint implies strong competition between multiple variants of the same cultural item. Fishhooks, for example, were relatively costly to make and were quickly left aside if less effective than other variants (Bettinger, Winterhalder, and McElreath 2006). Nevertheless, foragers also made art, although it might be that they left it where they made it to save weight. An example is found in engravings on ostrich eggshells from South Africa (100,000–70,000 BCE). Therein, archaeologists discovered incremental changes: an increasing complexity that makes them more aesthetically pleasing and easier to memorize (Tylén et al. 2020). Because each new cultural element depends on earlier elements remembered or materially kept, the cultural niche of foragers shifted and expanded incrementally, like the use of fire for cooking lengthening the availability of light into the night, which resulted in the practice of storytelling around the fire (Wiessner 2014). Arguably, the most important part of culture was food collection and food processing technology. In regions with higher uncertainty in catching something to eat, more effort was spent on developing hunting gear, and more innovations occurred (Fitzhugh 2001). This does not suggest that people had a causal understanding of the technologies they invented, which oftentimes developed over generations through tinkering, improved practical understanding, and further improving the technologies (Osiurak et al. 2022). Although knapping techniques to make stone tools were refined over hundreds of thousands of years, and certainly involved cultural transmission, an experiment demonstrated that they can also be discovered by naive individuals without teaching (Snyder, Reeves, and Tennie 2022). Foragers used a wide variety of food sources across different habitats, with most variation across the north-south axis (Diamond 2002), and a smaller share of plants in colder regions. Their choice of food sources can be analyzed through a foraging model that is validated by field observations. Three factors explain most food source variation (Mithen 1988): the nutritional value, which foragers aimed to maximize, versus the (expected) time it takes to find

24 Forager Societies

the resource and the time it takes to process it once found, both of which foragers aimed to minimize (Bettinger 2009). To survive, the foragers thus faced a trade-off6 : spending much energy searching for and/or processing highly nutritious food versus going after easier found and/or processed food with lower nutritional value. When dealing with this trade-off, foragers were competent optimizers. Although they did not perform calculations like the scientists who study them, they used simple heuristics (Kahneman 2003) to nearly the same effect,7 for example, skipping the pursuit of a resource if there is a good chance of finding a higher ranked resource with higher nutritional value, a shorter processing time, or both. Foragers’ encyclopedic knowledge of plants (including their medical effects), animals, and landscapes gained through personal experience and lessons from others enabled them to make accurate estimates of these chances. The foraging model also predicts correctly that once foragers got hold of rifles (in modern history), the diversity of their diet diminished because the most nutritionally desirable animals became easier to catch (Kelly 2013). The geographic patterns of foraging round trips were often highly efficient for searching for inhomogeneously distributed resources with little cognitive effort. In these pursuits, called Lévi walks (Figure 2.2), the step lengths (the walking distance in roughly the same direction) are power law distributed (Raichlen et al. 2014). A power law means a highly skewed distribution with many small things (here, step lengths) but few that are much larger than one would expect in a normal (i.e., Gaussian or Poisson) distribution.8 In our case, the power law combined with random changes of direction after each step account for the efficiency (Viswanathan et al. 1999). 6 A trade-off means that multiple goals are pursued simultaneously, for example, being strong and fast, but pursuing one goal, e.g., becoming more muscular, inevitably goes at the expense of the other, in the example, running fast. Evolution yielded leopards and cheetahs, but no animal has leopard strength and cheetah speed. Humans can culturally aim at different optima for trade-offs depending on their environment, e.g., training arm and shoulder muscles for short distance runs or becoming slim for marathons. 7 In simple or often repeated situations where people get to know one another or their environment, they can (develop heuristics that) approximate utility maximizing calculations as in rational choice theory. In situations that are novel, out of equilibrium, or have multiple interacting individuals, most people cannot make accurate predictions (Camerer and Ho 2015) and certainly not for ill-structured problems (Simon 1973). Also in crucial decisions with long-term consequences such as mate choice, people do not choose rationally (i.e., transitivity of options is violated; Rosenthal and Ryan 2022). Furthermore, people have difficulty handling randomness and tend to perceive patterns and intentionality in random coincidences (Henrich 2016). 8 The step lengths of foraging trips, l, are power law distributed, P(l) = cl−γ , with c a constant and for efficient foraging trips, γ ≈ 2 (Viswanathan et al. 1999). There has been a longstanding debate about whether, in general, skewed distributions are power laws or not. By using a domain-specific slowly varying function f(x) instead of constant c, the equation can be generalized, P(x) = f(x)x−γ , to accommodate many real-world deviations from a perfect power law, showing that distributions can be power laws in different ways (Voitalov et al. 2019). Slowly varying means that, for any a > 0, taking the lim x → ∞ entails f(ax)/f(x) = 1.

Forager Societies 25

30

25

20

15

10

5

0 5

10

15

20

FIGURE 2.2 Lévi walk foraging pattern, based on a simulation.

Food entails another trade-off: Settlement versus movement, when resources near a settlement become scarce (Sahlins 1972). The pertaining heuristic is to leave before the time to search for resources around the settlement becomes longer than the average search time in the environment overall (Charnov 1976).9 Approaching the agrarian transition, foragers discovered how to store food through smoking, salting, and drying (Bailey 1981), which helped them to survive periods of food shortage without moving elsewhere. Given the diversity of human habitats, some groups moved over large distances, whereas other groups in resource-rich environments hardly moved at all. Moreover, food was not the only reason for moving, as social ties and the information acquired through them were also important (Kelly 2013). Deserts, possibilities to store food, the proximity to water with fish, seasonal variation, and friendly versus hostile relationships with other groups complicate the picture. Although foragers were often on the move, they were (to various degrees) territorial, depending on the costs of defending “their” resources with respect to the benefits they could obtain from them (DysonHudson and Smith 1978). Giving permission to use a territory was then considered to be a gift that put the receivers in debt, which could entail reciprocal, asynchronous exchanges between groups over a long period (Kelly 2013). Exchange was asynchronous in general (Mauss 1925) and sometimes spanned multiple generations (Wiessner 2014). Synchronous exchange was 9 Charnov’s (1976) paper is about moving around between different resource patches, but as I see it, his abstract idea also applies specifically to moving around between settlements in different resource patches.

26 Forager Societies

rare, in contrast to theoretical prisoners’ dilemma games. In many societies throughout history, a hasty reciprocation of a gift or service is often perceived as ingratitude rather than an effort at ongoing cooperation (Blau 1964). Along with pairwise exchange, foragers also cooperated on larger scales; they acted collectively in coparenting, sharing stories at communal gatherings around the campfire (Wiessner 2014), defending the group and its land, hunting large game, sometimes attacking outgroups or some of their members, and conflict resolution, among other things. Overall, men and women were equally cooperative; differences such as collective violence (men) versus coparenting (mostly women) can be partly explained by sex differences and partly by specific norms (e.g., Bjorvatn, Getahun, and Halvorsen 2020). There is ethnographic and archaeological evidence that foragers cooperated with hundreds, sometimes more than a thousand people at communal hunting, which could result in a catch of hundreds of animals driven into a corral or over a cliff (Boyd and Richerson 2022). This must have involved meticulous planning and coordination. Cooperation is so important and complicated that it has its own chapter in this book (the next one). For now it suffices to say that foragers mastered it, which is no small feat. Collective actions were often preceded by collective rituals temporarily increasing solidarity (Durkheim 1912) that facilitated (but does not explain) collective actions. Foragers also competed, sometimes violently, and violent competition was often followed by equally violent revenge. Groups of foragers competed with, and sometimes engaged in violent collective actions against, other groups to acquire food, territory, or other valuable resources, or to harm competitors (Kelly 2005; Kelly 2013). Participation in attacks was almost entirely men’s business, and usually voluntary, but defection was punished in some cases. Motivation was provided by the prospect of conquered resources and a prestige benefit that paid off in more opportunities to father children than freeriders (Chagnon 1988). Emotional resistance to using violence (due to empathy or fear) was lowered by rituals that made individuals feel strong and united (Atran and Ginges 2012) and by dehumanizing opponents. In violent intergroup conflicts, an attacking group would prefer to conduct a surprise attack or ambush members of another group (Glowacki, Wilson, and Wrangham 2017), which the opponent group wanted to avoid. The smaller the chance to get injured oneself, for example, by launching a projectile while keeping distance, the more lethal the violence became (Enquist and Leimar 1990). Projectile weapons have existed for at least 400,000 years or more (Kelly 2005). Defenders had the advantage of better knowing their terrain, and because their stakes were higher, they had fewer freeriders (De Dreu et al. 2016). Intergroup violence was less frequent among (non-settled) groups who could get out of each other’s way compared with sedentary groups that could not. Humans, not chimpanzees, often realized that peaceful interactions with competitors and strangers from different groups had more long-term benefits than the

Forager Societies 27

short-run benefits of stolen resources (Kelly 2005). When comparing animals (Tinbergen 1968), conspecific violence is highest among species that are both social and territorial; human foragers, with 2% of violent deaths on average, were just as violent as other primates who are social and territorial (Gómez et al. 2016). Apparently, the appeasing effect of friendly, usually ingroup, relationships was nullified by violence in other, often outgroup, relationships. One might think that a leader’s authority would serve as a mechanism in cooperation and conflict, but most forager groups were egalitarian, or at least much more so than other primate groups and the majority of agricultural societies (Boehm 2012; Boehm 1993). Foragers could not be commanded against their will; hence, leaders do not add much explanatory power to mechanisms for cooperation, except that if they took the initiative, they could win over others to join. Group leaders did not differ much from others in wealth and power, and their role was to motivate group members to cooperate and to resolve ingroup conflicts (Gintis, van Schaik, and Boehm 2015). Some people had more goods or prestige than others, but everyone had access to food and the cultural tools to acquire it (Woodburn 1982). Foragers’ faith—animism (Peoples, Duda, and Marlowe 2016)—was in line with egalitarianism because it was devoid of tight moral rules and one centralized god, which were to come in agricultural societies. Two main causes of egalitarianism were the availability of stone weapons to everyone, by which upstarts could be kept in check (Gintis, van Schaik, and Boehm 2015), and the high uncertainty of finding food, making group members strongly interdependent on help during bad days. However, egalitarianism did not last by itself and required both norms and continual attention and efforts to impede upstarts by ridicule or gradually more severe punishment (ibid). Similar behavior has been observed in modern lab experiments (Ridgeway and Diekema 1989). Moreover, people in centralized groups around a selfish leader who did not share the spoils had lower survival chances in uncertain environments, making these groups more vulnerable to exogenous factors such as resource scarcity and therefore less likely to endure. Because of its well-understood benefit for survival, sharing was highly valued among foragers, and generosity was a widespread norm (Kelly 2013). Although close kin were favored when sharing, a great deal was shared among remote kin and nonkin. Sharing the meat of a successful hunt yielded prestige (i.e., enhanced reputation) for hunters, by which they gained “indirectly in the form of return gifts on later occasions” (Dwyer 1974). Recipients scrounging for food were obliged to return a favor on another day (Winterhalder 1996), and debts were well remembered. “One thing is clear: generous people do better in the long term than stingy people” (Kelly 2013, p. 151). Despite widespread equality, there was some division of labor along gendered lines. Both men and women hunted, but high-risk hunting such as that for large game was more often done by men, and among foragers studied by anthropologists (19th century onward), it was done only by men (Haas et al.

28 Forager Societies

2020). Hunting is difficult and requires years of practice (Kelly 2013), and many young women spent much of their time on (co)parenting instead (Hrdy 2011), notwithstanding grandmothers and fathers (or men who believed they were) who lent helping hands.10 Moreover, young children would want to drink milk or frequently eat on journeys, and scared away animals with their noises, so children and their female caregivers were not well suited to join hunting trips. With children around, women instead foraged relatively easily obtainable foods close to the campsite. Surely, some women must have become excellent hunters if they had no children or others to care for. Men spent less time with children and relied on women for a portion of their food, and therefore had more time to learn hunting and to hunt large game, with the risk of catching nothing, which was a risk that mothers could not afford to take. If men did catch a large animal once in a while, they could not eat all the meat by themselves (Winterhalder 1996). Inevitably, there was then food to share with others, and sharing was a chance for hunters to improve their reputations. Nonhunting women did not have this opportunity. At places with predictably plentiful resources amid a scarcer environment, such as rivers with fish, foragers settled for longer times in much larger groups with many huts, sometimes with several hundred people. These large groups tended to develop a power hierarchy overlaying the embedded group structure, resulting in social inequality (Singh and Glowacki 2022), accompanied by ideology to legitimize it, and costly wealth displays to flaunt it (Kelly 2013). The principles and conditions of increasing inequality are discussed in Chapter 4. In these large groups with inequality, there was also gender inequality between men and women, with women more tied to households and powerful men using marriages strategically (as wife givers and wife takers) to establish bonds with important families (Kelly 2013). In summary, foragers lived in groups—their cultural survival units (Elias 1978)—where they invented, modified, and transmitted tools, huts, practices, and other cultural elements. They used them in their natural and social environments for food, shelter, rituals, conflict, cooperation, and expression, all of which affected their health and survival. Cultural elements that were created, received, and kept or discarded changed the sociocultural environment and thereby the repository of items available for subsequent use and innovation. For over 95% of the time sapiens that existed, they lived in small-scale societies. In the next chapter, we will see how they managed to cooperate dyadically (i.e., in pairs) and collectively in groups of up to several hundred people and how they ventured into exchanges with other groups. Cooperation also made it possible to transmit culture that was difficult to learn, such 10 Without help, there was a considerable chance that a mother would abandon her child (Hrdy 2011). To reproduce and maximize children’s survival given available help and resources, a birth spacing of about four years was optimal (Blurton Jones 1986). Long-term breastfeeding diminished the chance of becoming pregnant too soon.

Forager Societies 29

as language and tool making, by explaining and teaching through strong social ties (i.e., with frequent and enduring interactions). Culture and cooperation became also intertwined in another way: Part of the information from others that people used was information about others through gossip and observations, which entailed reputations. In the next chapter, we interrupt the historical tour with the principles of cooperation and discuss in subsequent chapters how culture evolved from mobile homes to mobile phones.

3 COOPERATION

Cooperation is one of the strongest forces in human evolution (Szathmáry 2015). Long before humans wandered the Earth, cooperation started among single-cell organisms, billions of years ago, that became responsive to certain chemical signals from other single-cell organisms in their local environment. At some point, some of these single cells transformed into multicellular organisms and gave up individual reproduction. Later, some of these multicellular creatures started to cooperate with each other, for example, ants communicating with chemical signals, cetaceans with languages we do not yet understand, and us—gossiping apes. Cooperation is defined as making an effort at a cost in terms of calories, time, or resources to provide a benefit to someone else; in case of public goods, it is a contribution to the entire group including oneself (Rand and Nowak 2013; Kollock 1998). In a dyad, actor’s1 cooperation with alter is asymmetric unless alter resists the temptation to defect and reciprocates.2 Given the temptation to freeride, neither actor’s initiative nor alter’s reciprocal cooperation is obvious, so how is ongoing cooperation achieved? I address this question in a way that facilitates generalizing the answer across time. I start right away with the more difficult-to-explain problem of collective action where multiple people are to contribute to a public good, shared among everyone. The larger the group, the less conspicuous a single individual’s contribution to the public good; hence, the larger the temptation to freeride and the more difficult collective action becomes. As said in the previous chapter, most foragers 1 An actor is an individual or a group with (shared) intentionality and (collective) goals. 2 The challenge of cooperation is often called a dilemma, which is less the case in inegalitarian interactions; an autocrat (who first solves their dilemma of cooperation with their associates) can freeride on commoners without facing a dilemma by forcing them to work for them. DOI: 10.4324/9781003460831-3

Cooperation 31

acted collectively on small scales in childcare, sharing stories, hunting large animals, defending resources and group members, attacks of (members of) other groups, and conflict resolution. Depending on where they lived, they occasionally assembled larger groups to build and maintain fish weirs (Kelly 2013), defensive fortifications, kilometers long drive lines to corner and capture animals (Boyd and Richerson 2022), dams (Boyd 2018), and to increase and protect habitat productivity by controlled fires (Bliege Bird et al. 2008). I use the principles of collective action to also explain dyadic cooperation later on, but I first discuss the prelude to collective action. 3.1

Shared intentionality

Before a collective action starts, people become aware that something bothers them or is missing and that others share their fate (or might be made to believe they do). Awareness can come from internal stimuli (e.g., hunger) or external stimuli (e.g., influence from ingroup members or an attacking outgroup). If the problem occurs frequently and members of a group have developed practices to deal with it routinely, the public good, as a collective solution to the shared problem, will be clear, and a working consensus (not necessarily a perfect agreement) on what to do can often be achieved through situational cues even without verbal communication, for example, when few hunters who know each other well perceive an antelope. Oftentimes, however, public goods are not clear-cut in advance, and people first have to agree on their common goal, i.e., the public good to be achieved, and reach a collectively shared framing of it (DeMarrais 2016; Ostrom 2000). This is also called a focal point (Schelling 1960). Depending on the proposed public good, not everyone may agree (initially), and if they do, they will still disagree on other things. Some people will only agree now if they are promised that they will have things their way on another occasion. If the public good is novel or rare, people do not know their benefits and costs (Footnote 7, p. 24), but they will distinguish, possibly wrongly, between valuable and nonvaluable public goods (see value, p. 180) and, net of punishment, will not participate in producing the latter. Attributing values, thereby distinguishing between and anticipating good and bad things, enables intentional (i.e., goal directed) behavior, which is indispensable for both humans and animals.3 3 From a biological point of view, human life means maintaining homeostatic balance, as well as social balance among relationships with kith and kin (see also social balance theory, p. 80). To these ends, bad and good things must be distinguished from neutral things and reacted upon, for which emotions are key because they stimulate learning and enable rapid (re)action (Damasio and Carvalho 2013). Part of emotions are dealt with subconsciously; feelings are emotions we become aware of. Emotions direct cognition to important matters and are to certain degrees influenced by cognition, for instance, when internalizing a social norm that one initially did not like. There is no meaning without emotion; hence, one’s emotional sensibilities and adequate responses determine the social and natural niches to which one can

32 Cooperation

To attain a working consensus in a given group, which may sometimes include the means to be used, the group’s network comes into play. A social influence model (Friedkin and Johnsen 2011), which is often replicated experimentally (Takács, Flache, and Mäs 2016), shows that if people converse, their different opinions will in most cases converge, even though it takes more time if initial differences are larger (Section 8.1). Consensus happens faster in more cohesive networks with redundant information channels with cross-connections, illustrated in Figure 3.1, and with shorter distances (Section 8.1.3). A larger number of paths, more cross-connections, and shorter distances increase a quantity called algebraic connectivity, which we might use as an indicator of social cohesion; if it equals zero, the network is disconnected in at least two parts, and it has a (size specific) maximum when the network is maximally connected (i.e., a clique). The effect size of increasing algebraic connectivity is largest at low levels, demonstrated in a lab experiment (Judd, Kearns, and Vorobeychik 2010). Participants were paid to reach consensus (i.e., choose the same color of their network node as their network neighbors) as fast as possible. Six networks (each with n = 36) were compared, and networks with higher algebraic connectivity were associated with less time to reach consensus, with the largest gain of time in the comparison of a nearly disconnected graph with a slightly better connected one; there was almost no improvement when algebraic connectivity went from a moderate to a high level (Bruggeman 2018). Beyond the experiment, consensus is slowed down when initial opinions are more diverse. In terms of the embedded group structure, consensus and cooperation of (individuals from) different ethnic groups without overall cohesion becomes more difficult with cultural distance, due to different norms and languages that stand in the way of mutual understanding, and is easier within cohesive mono-ethnic groups. The convergence of opinions also has an emotional side: a feeling of solidarity or bonding with the group, its members, and/or its goal(s). The cognition that corresponds to this feeling is the identification with one or several of these, which speeds up and enhances the internalization of the group’s norms. Solidarity can become a strong feeling of oneness (Swann et al. 2012; Whitehouse and Lanman 2014) during collective rituals (Durkheim 1912; Atran and Ginges 2012), especially intense arousal rituals (Atkinson and Whitehouse 2011; Konvalinka et al. 2011), or through shared negative experiences, such as combat (Whitehouse et al. 2014).4 Then, people feel the emotional warmth of one another’s solidarity, which can mount into a feeling of collective effervescence (Durkheim 1912). The more intense the shared experience, the longer the solidarity lasts, the longest for soldiers who have been —————————————-

adapt, within which one can thrive. Along with emotions, we have drives that urge us to satisfy basic needs such as breathing, thirst, libido, and attachment to relevant others (ibid). 4 Solidarity has aspects that people do not like and therefore prefer not to associate with it, e.g., a silence culture where nobody speaks out against trespassers.

Cooperation 33

FIGURE 3.1 Two node-independent network paths from sender S to receiver R.

Nexuses that increase redundancy (in B, not in A) render transmission of information more reliable.

in battle together. The relationship between solidarity and shared negative experiences, even if not at the same time and place, appears to be quite general and is currently also found among evicted people (Desmond 2012), war victims (Bauer et al. 2016), and participants of painful initiation rituals, provided that they maintained a feeling of self-control, which hostages lack; their solidarity is low. Solidarity feeds back into and reinforces the collective framing (Kelly 1993). If there is also a sense of urgency, due to agitating stimuli such as an external threat, collective action can be precipitated. I call a goal-oriented consensus that is accompanied by solidarity shared intentionality (Tomasello et al. 2005).5 Also chimpanzees seem to produce shared intentionality, through a particular kind of barking before they hunt cooperatively (Mine et al. 2022). When people do not share their experiences or otherwise have no collective framing or goal (Mitkidis et al. 2013), for example, due to a lack of interactions (Marx 1926), and find themselves “in [a] crisis situation, when the meaning of the world slips away” (Bourdieu 1985, p. 203), they do not increase cooperation (Silva and Mace 2014), which is apparently because they are uncertain about the situation and of what others intend to do, and lack shared intentionality. If a certain collective action has become routine, as a collective analog of individuals’ habitus (Bourdieu 1990), a shared intentionality can be tacitly understood among copresent individuals without discussion. The more shared intentionality has been achieved, the easier the dilemma of collective action can be solved, among others, by providing reasons and emotions to collaborate (O’Madagain and Tomasello 2021). Shared intentionality is generally insufficient of its own, though. 5 Although Ibn Khaldoun’s (1958) asabiya is translated as solidarity, I infer from his use of this concept that he meant more than solidarity, namely, shared intentionality.

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3.2

Collective action

When elaborating the prelude to collective action, we encountered the first principle of collective action—a cohesive network—that is already important before anyone cooperates. We now proceed to analyze the remainder principles. To cooperate collectively or dyadically, family members who are strongly genetically related need no more than awareness thereof to contribute, thereby fostering their own genes in one another (Levy and Lo 2022; Hamilton 1964; Birch 2017).6 Relatedness might be the second principle, but there is a catch that renders the scope of this principle to explain human cooperation rather small: if families grow large over multiple generations and all stay together, intragroup competition will at some point nullify the benefits of kin-based cooperation (Taylor 1992). Consequently, at least part of the family has to move out and cooperate with nonkin elsewhere. Forager family groups are minorities of camp groups (Dyble et al. 2015), and even smaller minorities of larger (e.g., seasonal) groups (Boyd 2018); hence, this problem does not occur among foragers, but then the question is: how do nonkin cooperate? A vital part of the solution is gossip, which diffuses in the same network used for shared intentionality. If the network is cohesive, individuals’ multiple connections facilitate monitoring by their network contacts (Hechter 1988). From direct observations onward, gossip diffuses into the network, and the social influence model (Section 8.1) implies that noise (accidentally incorrect observation or gossip) and bias, if not too much, are partly corrected by accurate or differently biased gossip from others, which has been confirmed experimentally. In the experiment, subjects took averages of gossips to determine alters’ reputations (Sommerfeld, Krambeck, and Milinski 2008); when implementing this heuristic in a simulation model of collective action in a cohesive network (Section 8.2), levels of cooperation remain high under modest noise. This result implies that the requirement of perfectly accurate reputations in earlier game theoretic models (Panchanathan and Boyd 2004) of which cohesion was no part can be loosened. Too much noise and bias in gossip result in cooperators being perceived as defectors, and if cooperators are punished, the level of cooperation drops (Kosfeld and Rustagi 2015), not to mention the personal suffering of the punished. Based on gossip, feedback on gossipers’ behavior is provided by group members who may reward someone for contributing, such as by reciprocating at a later time or punishing for defecting. Rewards and punishments can commence at low costs as (and sometimes not go beyond) subtle gestures (Goffman 1959). Game theorists argue that gossip poses a second-order dilemma (Santos, Pacheco, and Santos 2021) because individuals would be tempted to freeride on others’ gossip efforts, but 6 With c the cost of the focal individual, b the benefit for alter, and 0 ≤ r ≤ 1 their genetic relatedness, Hamilton’s rule (1964) says that it is advantageous to help alter if rb > c, or equivalently, if r > c/b.

Cooperation 35

I believe this is a mistake. In groups that are not too volatile, gossiping is a rewarding practice that requires little effort; juicy gossip (if not too negative about ingroup members) is received with great interest and gratitude (Besnier 2009; Elias and Scotson 1965) and reciprocated with other gossip or information, thereby mutually informing the gossipers and enhancing their (meta) reputations. According to evolutionary game theory, public goods dilemma’s can be solved by reputations, provided that there is feedback, such as when people’s chances for dyadic reciprocity are affected by their contributions to the public good (Panchanathan and Boyd 2004). This finding has been confirmed experimentally in both public goods and prisoners’ dilemmas (Gallo and Yan 2015; Cuesta et al. 2015; Simpson et al. 2014): cooperators broke ties with defectors, thereby withholding future benefits and, perhaps unwittingly, creating assortative clusters of cooperators. Being permanently monitored may cause anxiety; hence, gossipers prefer to take a time out now and then, temporarily withdrawing from social life, and keeping some things permanently secret. The desire for privacy is universal, although the ways in which it is achieved are culture specific (Altman 1977). In some cultures, people move far away from their settlement, and in others, they stay in a corner where they are temporarily ignored by their group members. In modern society, people go on holiday or withdraw into a bathroom. Group members’ reactions to gossip and their personal observations are informed and stabilized by prosocial (ingroup) norms (Fehr and Fischbacher 2004; Santos et al. 2018) that evolve over a longer period of time, vary across groups, are taught to children (House et al. 2020), and reveal who may be eligible for a particular kind of help under certain conditions. Prosocial norms are about reciprocity (e.g., do not harm someone who helped you; Gouldner 1960), spite (e.g., it is okay to defect against defectors against helpers; Swakman et al. 2015), generosity (share your food with hungry people), and other prosocial behaviors (e.g., forgive first offenders). Specific public goods and exchanges often require specific norms, which provide a context for gossip to build or damage reputations (Nowak and Sigmund 2005; Blau 1964, p. 16, 47), which in turn affect future chances of receiving help. Norm maintenance imposes a second-order dilemma of cooperation because if maintenance is costly, people would rather freeride on others’ maintenance efforts. It can be solved if the group consensus comprises punishment against norm violators, which is then seen as legitimate, and if punishing (scolding, shaming, or excluding from the public good) is carried out by multiple individuals (Mathew 2017), which makes the per capita cost of sanctioning very low (Oliver 1980; Boyd, Gintis, and Bowles 2010). The benefits of maintenance are larger from a longer-term perspective, just as time is the crucial factor in solving the prisoners’ dilemma (Axelrod and Hamilton 1981; Dal Bó

36 Cooperation

and Fréchette 2018). Furthermore, punishing yields reputational credit points attributed by group members (Molho et al. 2020). Finally, humans become sensitive at a young age to others observing them (Cooley 1902; Milinski and Rockenbach 2007; Hrdy and Burkart 2020) when they have become aware of potential consequences for their reputations. As they typically do not receive gossip about themselves directly, they try to infer from others’ emotional expressions how they are evaluated (Van Kleef and Côté 2022). If they internalize the group norms, a threat of punishment is often enough, and actual punishment is rarely needed (Rockenbach and Milinski 2006). At that point, norm maintenance requires little effort (Wu, Balliet, and Van Lange 2016). Note that punishment has limited explanatory value because although it may deter people from defecting, it does not positively motivate (Homans 1974; Oliver 1980), and it easily makes people angry instead of cooperative. The latter also happens when the distribution of payoffs is perceived to be unfair (Boehm 1993). Furthermore, direct punishment is inefficient because it decreases the payoffs of both the punishers (in terms of effort and risk of revenge) and the punished, whereas indirect punishment through gossiping and withholding benefits (or in the worst case, cutting ties) is cheap and fairly effective. Direct confrontation is more effective but can provoke retaliation and can therefore be risky if done alone (Molho et al. 2020). The effect size of prosocial norms can be illustrated for the Hadza (p. 21). There, people regularly change camp groups, and one might expect that their propensity to contribute to public goals depends on personality, age, or other individual traits with little variation when moving from one camp group to the next. This turns out not to be the case. People adapt their level of contributions to the pertaining group norm, with the result that groups’ level of public good provision stays stable over multiple years despite high turnover, thereby stabilizing the variation of cooperation levels between groups (Smith et al. 2018). Prosocial norms for the ingroup often include exclusionary norms against exploitation by outgroups, and determine who is entitled to access ingroup’s public goods (then called club goods in the literature). By contrast, there are rarely well-defined boundaries for participation in collective action; in many cases, some people enter or leave without disturbing ongoing collective action, e.g., looking after children whose fathers are hunting, or joining street protests in modern society.7 Norm internalization and responses to gossip, as well as anticipated reactions to one’s actions, trigger emotions (Trivers 1971; Trivers 2006; Jacquet et al. 2012) that are part of a broader prosocial psychology. Prosocial 7 I differ from Collins (2004) by restricting interaction rituals to proper rituals and do not believe that the concept captures all interactions. Furthermore, symbols do not necessary result, for example, when people dance. If people dance together, and experience collective effervescence, is this for them a symbol of something else? I do not think so.

Cooperation 37

emotions lean toward defection avoidance (fear of punishment and reputation loss), foster cooperation (pride, sympathy, and solidarity), encourage continuing cooperation (gratefulness and commitment), make people confront and sanction defectors (anger) or gossip about them (disgust), and help to rekindle cooperation after defection or conflict (shame, guilt, and forgiveness) through apologies and compensation (Wiessner and Pupu 2012; Martinez-Vaquero et al. 2015). Neither norm maintenance nor gossip would happen without emotional stirs. When a public good, or access to it, is distributed unevenly, people may become angry if the distribution mismatches their contributions (Doğan, Glowacki, and Rusch 2018), although there is a great deal of cultural variation in perceived unfairness (Henrich et al. 2010; see ideology in the next chapter). Combined with emotional sensitivity, a crucial psychological skill is empathy (Rumble et al. 2010), through which others’ emotions, thoughts, and needs are understood. Empathy entails compassion, sorrow, and other prosocial emotions (Smith 1790), and it is also crucial for reaching shared intentionality and transmission of culture. Even in conflicts, empathy is important to understand one’s opponents, to avoid damage, and to negotiate a solution. Empathy has to be stimulated and developed in childhood, but humans can also learn to suppress it when meeting outgroup members (De Waal 2012), as a first step in dehumanizing and excluding them from ingroup benefits. Empathy is combined with (sometimes detailed) memories of group members’ past behavior. At this point, it may seem that cooperators are cognitively overloaded, but in daily life this is not the case. People interact most of the time with relatively few individuals whom they know quite well (Stevens et al. 2018) and rarely spend time thinking through their reputations when meeting them again. Furthermore, once a group is established, cooperation becomes the norm, and the copresence of other group members socialized to the same norms raises awareness of mutual monitoring, which is usually sufficient for cooperation to commence and continue. Then, people ignore most reputations most of the time (Okada et al. 2018) and only pay attention when a reputation changes. The core of my argument is that not one or another principle explains human cooperation, but rather it requires a package of principles centered around information that people have about one another, cast in reputations. The remaining principles support the formation and working of reputations: A cohesive network structure facilitates monitoring and reduces noise, while prosocial norms channel gossip and responses to defection, and the emotions of people socialized into these norms nudge them toward cooperation, which often results in actors’ benefits exceeding their costs. Once “emotion work” (Hochschild 1979) has succeeded and prosocial norms are internalized, norm maintenance becomes easy most of the time (Wu, Balliet, and Van Lange 2016). Removing one of these principles and cooperation is short-lived, even

38 Cooperation

though it can start with few principles. Also in these cases, people will need at least some information about the goal and the other(s) with whom they might cooperate, which implies a (fledgling) network, where elementary reputations are perhaps only based on direct observations of others. People will also try to assess if cooperation makes sense, thus if benefits exceed costs (i.e., a cost-to-benefit ratio, c/b < 1). There may not be rules (yet), and the start of cooperation will have to do with three or four weak principles out of five strong principles and a common goal. For cooperation to endure after it starts, however, all five principles are necessary. If cooperation fails, this means that at least one of the five principles is not in place. An easy way to remember the key principles is as the 5r-package: Individuals in social relations (re)act on the basis of rules, reputations, and righteousness (as a shorthand for a much richer psychology), for which they expect that the rewards will exceed the costs, at least in the long run.8 3.3

The evolution of cooperation

When we rewind the clock millions of years to examine the evolution of the 5r-package, we see that other apes, and probably also our common ancestors, communicated with gestures and oral signals and cooperated mainly with close kin. Once humans were able collect more information from others by means of simple language, they could also collect more information about others—reputations—while the advantages of culture and cooperation beyond close kin will have had a positive feedback on cognition, offsetting the cost of a large brain (p. 67) and facilitating the development of more complex culture and somewhat larger scale cooperation. When culture evolved, and more things were made and exchanged, there was also more to cooperate for. In other words, the number of private and public goods whereof the benefits exceeded the costs increased, and thereby the scope and frequency of cooperation. Human cooperation thus coevolved with cognition and culture (Richerson and Boyd 2005), in particular language. The clustered network structure with cohesive groups (p. 20), which is more ancient than hominini, was a favorable condition for culture and cooperation as well as brains to coevolve. In these groups, members developed norms, and individuals in groups with more effective prosocial norms will have had better survival chances and more offspring than in groups with ineffective or antisocial norms. Better cooperating groups will have outcompeted worse cooperating groups, everything else equal, contributing to the proliferation of prosocial norms. This is called cultural group selection (Simon 2014; Handley and Mathew 2020), or Darwinian selection at the group level, which can happen when group members have a 8 The idea that multiple principles are involved in ongoing cooperation is of course not new (e.g., de Swaan 1984; Simpson and Willer 2015), but the synergy between these five principles and their evolutionary framing are new.

Cooperation 39

shared fate, in particular with respect to the public (i.e., club) goods from which all ingroup members benefit9 ; a formal argument is presented later on (p. 166). This seems to have been the evolution of the 5r-package in a nutshell. In its evolution, we can distinguish three different paces from slow to fast, analogous to, but not identical with, Braudel’s (1982) conception of history. The most stable principles are the embedded, clustered structure of networks as well as psychological dispositions; the latter are genetic and change very slowly (la longue durée). Both empathy and social emotions are present in primates and other animals that exist much longer than humans10 (De Waal 2012; Hrdy and Burkart 2020) and despite cross-cultural variation, people often correctly recognize expressed emotions in others with different cultural backgrounds, and only slightly better in their own cultural group (Van Kleef and Côté 2022). However, the triggers of emotions vary culturally much more than the emotions themselves, and so do norms, and both change much faster than genes, at an intermediate evolutionary pace. The disposition to use gossip for reputations came along with language at a slow pace, but gossips themselves change at the fastest evolutionary pace. The cultural microevolution of gossip is as follows: Gossips are made as combinations of words that are transmitted to others (step 1) and are used to update reputations of gossipers or are rejected (step 2), which changes gossipers’ local environment (step 3). Gossip can thus be seen as brief, swirling eddies around gossipers in a large reputational pond. 3.4

Cooperation with strangers

The 5r-package made it possible for groups of up to hundreds of people who did not know everybody else to collaborate. This must have occurred, among others, when foragers budged enormous stones from large distances to build the temple at Göbekli Tepe (Turkey; Dietrich et al. 2012), as well as in communal hunts with sometimes more than 1000 participants (Boyd and Richerson 2022). Such actions also comprised extensive planning and coordination. If large numbers of people were wanted for a collective action, leaders with prestige, but not necessarily dominance, could motivate and coordinate (Henrich, Chudek, and Boyd 2015). For as long as cooperation was organized among coethnics, coordination was facilitated by shared language and institutions. Moreover, because more prestigious leaders were supported by more friends, 9 For cultural group selection to happen, there must be many competing groups, each with a relatively inert culture compared with the speed of intergroup migration, such as an industry with many competing business companies that are selected by customers and other relevant actors in their social environment (Hannan 2005). The group selection argument is often misapplied to situations where the first condition does not hold true and situation-specific or random effects drown out selection. 10 For example, “shame and proto-shame display in both humans and primates involves slumped shoulders, downcast gaze, crouching, and a diminutive body posture” (Henrich 2016, p. 198).

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it was easier for them together to convince others to cooperate, to get collective action started, and to divide the yield (Glowacki and von Rueden 2015). In this manner, hundreds of people could cooperate for public goods without centralized authority. Although networks, norms, and reputations vary across time and places, I posit that the 5r-package in one variant or another is behind every case of ongoing cooperation.11 In groups with inequality, leaders embedded at central and delegated positions had means of power to force people to cooperate, and they could forgo the second-order dilemma of norm maintenance by giving themselves side payments or taking larger shares of public goods (Sigmund et al. 2010; Baldassarri and Grossman 2011; Gürerk et al. 2006; Hilbe et al. 2014). The cooperation of many hundreds of people or more is often regarded as very challenging, as it is without an embedded group structure and leadership; discussions among everyone to reach a working consensus and shared intentionality would take ages. The embedded group structure makes cooperation easier than expected because the large challenge can be decomposed into many small challenges of cooperation in subgroups, each of which can be solved by means of the 5r-package plus local leadership, while overall coordination can be done by central leaders, whose top-down orders are substituted for bottom-up shared intentionality. To stay in line with the five r’s, we may add rulers’ guidance as the sixth principle, yielding the 6r-package. Leadership did not replace any of the earlier principles but leaders’ influence became an additional principle, especially during the past 10,000 years. Despite the package’s merits, flawless cooperation is not guaranteed. Obviously, if incurred costs exceed benefits received in the longer run, cooperation is unsustainable (Van Veelen 2009). Part of the costs are spent on establishing and maintaining the network with reputations and norms, which is only feasible if the turnover is not excessive or if the group is embedded in a larger group where prosocial norms are maintained. Yet, there are always asymmetries and differences in interests, and some individuals go to considerable lengths to deceive and manipulate. Furthermore, information about others tends to be incompletely and unequally distributed, more to the benefit of some than of others. During a fieldwork among horticulturalists in Papua New Guinea, Wiessner (2002, p. 248) noticed that whereas “some men knew the history of vast exchange networks, others could hardly see beyond their garden fences”. This results in unequal chances to cooperate, which may entail grievances that obstruct cooperation. Finally, people not only develop prosocial norms but also develop (unintentionally) antisocial norms that harm cooperation (Herrmann, Thöni, and Gächter 2008; Boyd and Richerson 1992), for example, against nonconformist behavior to the benefit of the group (Irwin and Horne 2013). 11 To turn the 5r-package into design principles, it seems best to follow Elinor Ostrom’s (2010) eight rules for commons management.

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3.5

Dyadic cooperation

For dyadic cooperation, the most successful strategies (in terms of payoffs) are conditionally cooperative: actor cooperates to the degree that alter cooperates (e.g., tit-for-tat; Axelrod and Hamilton 1981, or win-stay, lose-shift; Nowak and Sigmund 1993). As a matter of fact, one of the r’s (receiving benefits that exceed the costs) implies conditional cooperativeness in many cases, as for public goods. It has often been assumed that if some people have found a successful strategy through random luck, rational deliberation, or a bit of both, they are imitated by others, and cooperation evolves through mimicry. In experiments with humans, however, the effect size of imitation is small (Traulsen et al. 2010), and people copy others only if the payoff difference is large and clearly visible (Weizsäcker 2010). Furthermore, reciprocal cooperation is unstable (García and van Veelen 2018; Imhof et al. 2005; Boyd and Lorberbaum 1987); it is highly vulnerable to noise (Dion and Axelrod 1988), deliberate manipulation (e.g., zero determinant strategies; Hilbe et al. 2014), and grievances about strongly asymmetric payoffs (Sahlins 1972). For dyadic cooperation to endure, the dyads should be embedded in a larger network (Granovetter 1985), which Simmel (1908) intuited for dyads embedded in closed triads (where three individuals are pairwise connected) with a minimum level of network redundancy and third-party norm maintenance (Bendor and Swistak 2001). If a dyad is embedded in a larger cohesive network, cooperation can be supported by prosocial norms, third parties’ prosocial psychology, and reputations that people can use to choose among a larger number of others (Wang et al. 2017), which, taken together, will often yield a lower long-run cost-to-benefit ratio. In short, dyadic cooperation does much better with the 5r-package than without it. In experiments that focus on one mechanism, for example, reciprocity, the five principles are tacitly in the backdrop: the experimental setting defines the rules of the game, as well as a network (e.g., dyad or random encounters), benefits and costs, and reputations (information about alter’s behavior), while emotions are present because they are impossible to switch off. Shared intentionality is not always necessary for dyadic cooperation, but some kind of (tacit) agreement, social focus, or focal point is important, and communication usually precedes cooperation. If the context is sufficiently structured and clear, cooperation can be coordinated nonverbally through mutual attention and empathy, which is also possible in small-scale collective actions. Reciprocity, which is seen as one of the fundamental mechanisms of cooperation (Nowak 2006; Gouldner 1960), is usually based on experience in (and possibly gossip about) dyadic interactions. Because it is often based on prior information and is supported by prosocial norms, reciprocity is rarely (if ever) fundamental, although it is practically indispensable. The mechanism that accounts for people engaging in reciprocal sexual interactions is biological; people enjoy immediate

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benefits of pleasure (and the prospect of children), which more than compensate for the efforts, and are sometimes reinforced by intense feelings of love. People sometimes (want to) cooperate one-to-one with strangers, even though most people cooperate most of the time with others they know well. Exchanges with strangers in different groups means overcoming distrust, not necessarily to build trust. Cooperation between strangers can be started through empathy, small talk, and by initially making small contributions. Alternatively, strangers may offer gifts (Mauss 1925; Blau 1964). As “gifts make friends” and “friends make gifts” (Sahlins 1972, p. 186), exchange partners can get to know each other, may listen in or ask about alters’ reputations, and get a fledgling reputation themselves. People can also do this indirectly through acquiring a good reputation in their own group that spreads around to other groups (Semmann, Krambeck, and Milinski 2005). Cooperation with strangers is more difficult and uncertain than with ingroup members, but it is feasible and happens often. If someone wants a share in an outgroup’s public good, they will first have to build a reputation by contributing, which biologists call costly signaling (Zahavi 1975). Some may want to add trust to the package (i.e., belief in or expectation of other(s)’ cooperativeness), but if not based on accurate information, trust cannot explain cooperation. As a matter of fact, people are unable to assess strangers’ trustworthiness on the basis of facial appearances (Jaeger et al. 2022), even though many people believe that they can. Trust is a conmen’s trap, into which their victims would not fall if they knew about conmen’s reputations. Trust smoothens ongoing cooperation, and is found in small groups where everyone knows one another (Cook, Hardin, and Levi 2005), but it cannot explain cooperation evolutionarily due to its vulnerability to deceit, which sooner or later will be exploited in larger and open groups. The causality is rather the other way around: cooperation increases trust. Reputations are also vulnerable to deceit, but in contrast to trust, they are specific and therefore easier and quicker corrected by true information, and more clearly related to prosocial norms. It is therefore no surprise that an experiment demonstrated that it was not trust but alters’ contributions that explained cooperation (Fischbacher and Gachter 2010). Because knowledge about others is both necessary and sufficient for people to decide with whom to cooperate and whom to avoid, it is more parsimonious to leave trust out of explanations of cooperation. Trust matters in other realms of social life where specificity does not matter, for example, later in history when money had been introduced and kept its value for as long as people trusted it, or more precisely, when people trusted that others kept trusting it (Harari 2011). More important is the effect of distrust, which prevents people from bridging social cleavages and to cooperate.

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3.6

Thick reputations

I end this chapter with a discussion about reputations in the literature versus social life. This section may be skipped by the general reader, although they may not want to miss the chapter summary in the end. In (evolutionary) game theory, reputations concern information about cooperative behavior only, whereas in actual life, people make reputational distinctions with respect to the goal of cooperation; an excellent hunter is not necessarily a good babysitter. To find suitable cooperative partners, people also pay attention to other traits, which, taken together, might be called thick reputations.12 Different people know different parts and have different interpretations of actor’s reputations, and the notion can put together a confusing host of mostly unrelated traits in the literature: honor (Bourdieu 1990), prestige (Henrich and Gil-White 2001) through costly signaling of valuable skills (Macfarlan and Lyle 2015) such as hunting (Smith 2004), as well as social approval (Homans 1974), legitimacy (Weber 1922), standing (Sugden 1986), face (Goffman 1955), stigma (Goffman 1963), fame (ibid), respect (Lamont et al. 2016), roles (Merton 1968), the veracity of one’s gossip, which establishes meta reputations, facial attractiveness, likes on social media, scent (Simmel 1908; Glausiusz 2008), trustworthiness (Kollock 1994), behavioral consistency across situations, toughness against competitors (Chagnon 1988; Papachristos 2009), talent, generosity, interests, beliefs, obedience to norms, and treatment of norm violators. Also one’s connections, including family, group membership(s) (Gould 1999), and status13 are taken into consideration when assessing reputations, and in modern society with inequality, elites signal their differences from commoners and competing elites (Gluckman 1963; Fine 2019), while firms with high status manage to receive higher prices for the same products as low-status firms (Podolny 1993). Toughness, honor, and status relate reputation to conflict and are important to deter competitors or freeriders in the absence of policing leaders or their representatives (Nowak et al. 2016). Of note, also groups can have reputations, for example, of vengefulness (Szekely et al. 2020) or prestige (Selznick 1948). People try to influence their own reputations positively, and onlookers therefore try to distinguish between impressions that people accidentally give off (accurate signals) and signals they deliberately give (biased signals; Goffman 1959) such as identities they project (possibly 12 The notion of thick reputation I encountered in Ferris cum suis (2014), where it is used with a different meaning than mine. 13 By network scholars, status is used interchangeably with power (Bonacich 1987; Section 8.1.4), but others use it as alternative for reputation, or for both, for example: ”In Dobu, women take an important part in gardening, and have a share in performing garden magic, and this in itself gives them a high status” (Malinowski 1922). Network power and socioeconomic status are very strongly correlated (Luo et al. 2017), but there will be exceptions.

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varying across audiences).14 People make great efforts on their impression management, but when overdoing it by bragging, it can ricochet and damage one’s reputation. Daily interactions often involve reputation work, which emits an intricate mixture of true and false signals about someone, to be disambiguated by the receivers. The listed items have been studied separately for the most part; they have rarely been explored together, whereas in daily life, people receive, collect, combine, and act upon various pieces of information about others. For example, a fierce competitor can still be regarded as a valuable information source in a specific domain. For a given goal, people order along specificity; if specific information on someone’s previous behavior is not available, they go for less specific information, for example, status or skills. Since only few out of all reputational information is used (or arrives) at the same moment, people do not become overburdened. By integrating these different findings into one broad concept of thick reputation, we can better comprehend cooperation. To wrap this chapter up, ongoing cooperation is based on information called reputations, which are thicker for people who are stronger connected or embedded. For reputations to be effective, individuals should be in a cohesive network of social relations wherein they act on the basis of rules and feelings of righteousness (as a shorthand for a broader prosocial psychology), and expect that in the long run, their rewards will exceed their costs. This is the 5r-package, sometimes extended by rulers to the 6r-package. The theory of cooperation contained in these packages will be applied and complemented in following chapters. Questions to be answered include how, in a relatively short time, people enlarged the scale of cooperation and expanded their camp groups to large empires.

14 An identity is a metaphysical body double onto which identity bearers and others project traits they deem essential and perennial, varying from social security number and (nick) name to nationality, religion, and sexuality. Identities can become real in their consequences if they entail commitments through internalized norms (Akerlof and Kranton 2005), for example, when helping elderly through internalization of prosocial norms. Also groups are ascribed identities.

4 AGRICULTURAL SOCIETIES

During the Pleistocene (a period that lasted from 2.6 million years ago until 11,700 y.a.), foragers roamed the earth, and climates fluctuated frequently and strongly. Agriculture was attempted (Snir et al. 2015) but its cultural evolution could not keep up with the climate fluctuations, and hence, early attempts failed (Richerson, Boyd, and Bettinger 2001). Temperatures became more stable and at higher averages during the Holocene (starting 11,700 y.a.), and the climate got warmer and wetter in a broad zone around the equator. When environmental conditions stabilized, it became possible to permanently domesticate plants and animals.1 Agriculture took several thousand years to develop, at a pace far too slow to speak about an agrarian revolution. It is a form of genetic engineering that was discovered through a great deal of trial and error (Christian 2011) and many complementary inventions, among other things, to figure out how to compose a nutritious diet, agriculturally. During this time of discovery, people both farmed and foraged in parallel (Bellwood 2009). Agriculture was actually invented by insects, first by ants that domesticated fungi (Mueller, Rehner, and Schultz 1998) and subsequently by others. Humans followed 50 million years later. Farming insects are more efficient than humans because they clone their crops, which reduces within-crop competition (as all specimens become genetically identical) and increases yield (Schultz, Gawne, and Peregrine 2022). Farming had widespread ramifications, such as the progressive overturn of most forager societies, discussed in the next section. Increasing inequality 1 Local climates continued to fluctuate to some degree, with dire consequences for agriculture, causing famines and temporarily decreasing population sizes (Bevan et al. 2017; Xu et al. 2019). DOI: 10.4324/9781003460831-4

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is another consequence, explained in the second section, followed by a section on moralistic religions and another section on agricultural societies in the Global South. Subsequently, I discuss how inequality and religion influenced cooperation and talk more generally about evolution in agricultural societies. The final section is about agriculture’s effects on the natural environment and our genes. 4.1

Consequences of agriculture

After the invention of agriculture in Mesopotamia, it was invented independently in China (rice, 7000 BCE) and America (maize, 4000 BCE),2 and from these places, it diffused to other areas where the local climate, soil, and domesticable plants and animals were similar. Thus, transmission was much easier along the east-west axis than along the north-south axis (Diamond 1997; Hibbs and Olsson 2004), and the latter required many local adaptations. Higher north-south language diversity is broadly congruent with this pattern, even today (Laitin, Moortgat, and Robinson 2012). All places where agriculture was practiced have an optimal environmental trade-off between biodiversity and pathogen stress, which both increase toward the equator but in different ways (p. 17). Agriculture did not arise where resources were scarce and new food sources were sought, or in places where parasite load was excessive (e.g., sleeping sickness and malaria in Africa), but in favorable environments already populated by relatively dense populations of foragers (Kavanagh et al. 2018). In multiple places, such as in China and Mesoamerica, foragers had already started living in villages before the advent of agriculture (Cohen 2011), whereas many pastoralists remained nomadic until modern times and thrived in vast habitats that were too dry for agriculture. To blur the foragerfarmer distinction more, several important innovations attributed to farmers were actually accomplished by foragers. They baked bread made from wild cereals in the Levant (Arranz-Otaegui et al. 2018), built temples (Dietrich et al. 2012), baked pottery in East Asia (Lucquin et al. 2018), and transmitted baking technology all the way to Eastern Europe (Dolbunova et al. 2022). The consequences of the agricultural niche were by no means fortunate. Work became harder and health deteriorated. In the villages, clean drinking water was lacking, and people lived densely together with domesticated animals—the main source of diseases during the Holocene (Diamond 1997). Due to this proximity to domestic animals, more infectious diseases were transmitted to humans, and in denser and larger populations, 2 In Papua New Guinea, agriculture also seems to have been invented independently, 6000 BCE at the earliest, but for a long time it did not entail noticeable social changes (Shaw et al. 2020).

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diseases easily passed the percolation threshold3 and spread far and wide (McNeill 1976). For several of these diseases, micro parasites and human hosts adapted to each other over multiple generations, so most adults who did not die became immune, while micro parasites became a children’s disease (ibid). Following the agrarian transition, birth rates increased first because agriculture yielded more food and birth intervals reduced. This was soon matched by increasing mortality rates. Consequently, the overall population growth slowed down after a brief increase (Bocquet-Appel 2011), to a low long-term average growth (e.g., 0.04% per year in Wyoming and Colorado) for quite some time, which was almost the same as it had been for the last 10,000 years of the Pleistocene (Zahid, Robinson, and Kelly 2016).4 Hence, the agrarian transition was not accompanied by a demographic transition. After thousands of years and many agricultural innovations, the population growth rate increased, and eventually, agriculture supported a human population two orders of magnitude larger than that of foragers. When some groups of farmers became relatively more successful in agriculture than others, they outcompeted less successful groups as well as forager groups. The diffusion of agriculture occurred in some places through foragers learning from farmers (Northwest Africa, Simões et al. 2023), in other places via their replacement by farmers (Europe), and to various degrees through farmers mixing with them (Bellwood 2009; McColl et al. 2018). Many foragers were enslaved, killed, or died by infectious diseases that originated in cattle, to which the pastoralists and farmers had already become resistant. Any remaining foragers were chased to the peripheries that were unsuited to cultivate crops or to herd cattle, such as semideserts and tropical rain forests. In particular, the best places, where large, unequal forager groups had lived, were (probably violently) taken by farmers (Singh and Glowacki 2022). Whereas farming was impossible during the Pleistocene, it became necessary during the Holocene to survive the competitive pressure of other farmer groups (Richerson, Boyd, and Bettinger 2001). Hence, most people could not go back to the more relaxed and healthier forager way of life. Although foragers affected their natural environments, e.g., through fires (Bliege Bird et al. 2008), farmers and their societies had a much larger impact. Karl Marx (1844) distinguished humanly adapted nature from nature proper, and the notion of the former used today is niche construction (Laland, OdlingSmee, and Feldman 2000). A famous example of a cultural niche in the natural environment is the village of Çatalhöyük (7100–5950 BCE) in Turkey, with 3 At the percolation threshold, a disease (or information) circulating in a small part of the network can all of a sudden percolate through almost the entire network. 4 Variation of mitochondrial DNA makes it possible to estimate prehistoric population sizes in different locales around the world (Atkinson, Gray, and Drummond 2008).

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around 8000 inhabitants in 2000 houses at its peak, all decorated with paintings and sculptures. There were very few streets, and people had to climb onto the roofs and descend into their homes through a hole in their roof. Surprisingly, most people within a household were not genetically related (Larsen et al. 2019). At the beginning, there was still much equality, also between men and women, and there were no large monumental buildings that would indicate inequality (Hodder 2004; Hodder and Cessford 2004). Eventually, the population increased, as well as workload, inequality (including gender inequality), violence, and diseases (Larsen et al. 2019), until at some point, the remainder of the inhabitants relocated to other settlements, and Çatalhöyük was abandoned. This boom and bust pattern is characteristic for agricultural societies.5 In farmers’ villages, the division of labor (i.e., specializations in different practices; Smith 1776) increased. Some people became specialists in astronomy to assess the seasons for sowing and reaping, while others became artisans, priests, traders, or soldiers. The specialization of both individuals and groups follows a characteristic learning curve (Argote and Epple 1990) that implies economies of scale that reduce costs per unit produced and make exchanges with different specialists more profitable (Smith 1776; Simmel 1908). Some individuals in the same village specialized in the same activities, which made them stronger competitors, whereas others, probably with fewer economies of scale and weaker competitive power, avoided niche overlap by specializing in different activities, thereby reducing competitive pressure (Roughgarden 1976).6 Either way, in the growing villages, new social foci emerged where people could meet, establish ties, and exchange information and goods. Social networks connecting villages expanded through the exchange of seeds, animals, and information on farming, and these “interregional contacts [drove] shared parallel development” (Cohen 2011). These networks also enlarged the range of gossip and therefore the possibilities of finding people with whom exchange or cooperation was possible. Some villages became quite large and included monumental buildings, even before the transition to agriculture was completed and social inequality increased. In and around Mesopotamia, large villages grew into urban areas, with many non-farming specialists speaking multiple languages living together (e.g., Jericho, 9000 BCE; Çatalhöyük, 7100 BCE; Bahra, now in Kuwait, 5000 BCE; 5 The demographic boom and bust pattern of agricultural societies has often been described as cyclic, as if there were a steady periodicity to it, but when better data became available, it turned out that this is only true in some areas, such as northeast Asia (Xu et al. 2019) that has a 500-year monsoon cycle (affecting agricultural yield), whereas in Europe there was no periodicity; its pattern is not only more capricious but also different for every (sub)region within the same climate zone (Shennan et al. 2013). 6 The trade-off between small and large niche overlap (the former known as resource partitioning; Roughgarden 1976) has been examined in modern times (Carroll and Swaminathan 2000), not yet on archaeological data.

Agricultural Societies 49

Uruk, 4000 BCE). Some of their goods were mass-produced, such as beveled rim bowls in Uruk (3700 BCE). In these urban areas, and later in the rest of the world, trade was facilitated through the invention of money. The first money—hack silver—was issued, and its purity and weight were controlled by the government around 3000 BCE, more than 2000 years before the first coins appeared (Hudson 2004). The novelty was a system of comparison through price; its main use was account keeping, and it assessed obligations between military, religious, and mercantile elites (ibid). Commoners hardly used money because the unit (the shekel, worth a monthly ration of barley) was too large for most of their purchases. Instead, they kept debt balances of their payments, which were paid at harvest time. Trade was mainly a debt relationship, and barter was rare (Dalton 1982). Because in larger cities specialists had better chances to meet complementary specialists, they could foster more profitable relationships and make more money than in smaller cities (Arvidsson, Lovsjö, and Keuschnigg 2023). Accumulating wealth over time resulted in much more wealth in larger cities, as well as more inequality. Wealth concentrations made life in larger cities more expensive, and large numbers of poor people ended up in worse conditions than they would have been in smaller cities and villages, which is still the case in modern society (ibid). In summary, just as we see today (Batty 2008; Glaeser 2011), cities offered many opportunities to privileged few, as well as to criminals, pathogens, and vermin, while many poor people suffered and waste accumulated. Through the Eurasian network, all kinds of information diffused, and after the onset in Mesopotamia, other areas at the same latitude, including China, Egypt, and the Indus valley (Petrie et al. 2017), developed extensive urbanization and irrigation shortly before 3000 BCE. In each of these regions, cities and villages were embedded in extensive trade networks. In Harappa (largely in Pakistan) and a Persian polity (Shahr-e Sukthé), sewers were constructed in cities, and clean water was delivered by pipes if it could not be obtained from wells. In these cities, most houses had toilets and clean drinking water; 5000 years before, such facilities were built in large numbers in modern cities. They also had other sophisticated cultural elements, such as bas-reliefs and children’s toys. Their culture vanished, though, possibly due to a multitude of causes (draught, among others), and it took a long period of political division and conflict before the first (i.e., Maurya) empire was established over India (322 BCE). The influx of Indo-European speakers (1500 BCE), and then Persian, Greek, and other invaders, who all admixed with incumbent Indians, left long-lasting cultural influences (Chakravarti 2009).7 Genetic studies are especially interesting where historical data have loopholes. For example, when did the caste system, which was the prime example of institutionalized inequality, 7 At a coarse-grained level, genes and languages correlate, but fine-grained data show many counter tendencies, such as cultural without genetic transmission, and predominantly male or predominantly female migration, followed by genetic admixture (Racimo et al. 2020).

50 Agricultural Societies

begin? At the time, strict endogamy rules were imposed (i.e., having to marry within one’s own group), but historical data are lacking. Genetic data help us to see that endogamy started 70 generations ago (approximately 1575 years; Basu, Sarkar-Roy, and Majumder 2016), during the Gupta period. 8 4.2

Social inequality

With farming and trading, the number of things people made and collected increased beyond anything foragers could have imagined. Although all foragers and other territorial animals understand the concept of ownership, farmers and city dwellers had to step up their efforts to protect their fields, tools, seeds, stored items, and transport, for which they thoroughly institutionalized property (Bowles and Choi 2013; Gallagher, Shennan, and Thomas 2015). Inheritance was institutionalized soon after, often from father to eldest son, to avoid competition between offspring and fragmentation of possessions.9 Due to increased material interests, kinship became much more elaborate for farmers than it had been for most foragers. Consequently, once property was acquired, it would (in principle) not readily dissipate and could be accumulated over generations. Institutions of appropriation also covered cases where interaction partners were beyond gossip range, for example, when meeting strangers at markets, but long distance trade remained risky. Later, special purpose groups were organized in some places to reduce these risks, for example, associations of traders in the Mediterranean, where ongoing cooperation was fostered through (thick) reputations (Greif 1989). Following the establishment of these new institutions, wealth could be acquired in three ways (and which remains the case today): exchange, brokerage, and patronage. Of course, it could also be acquired by violent means, described in the next chapter. In agricultural societies, there was not just surplus production, but variation in products across different settlements and specialists. If someone specialized in, say, growing crops, and had a surplus of potatoes, and someone else was specialized in raising animals and had a surplus of pigs, they could exploit their difference by exchanging potatoes for pigs. If one of the two was more needy, the other could impose a favorable exchange rate and thereby gain in the asymmetric exchange. There was even more opportunity to gain goods if the potato and pig farmers were unaware of one another, like if they lived at opposite sides of a mountain. In this case, a third person aware of the two could broker in between and gain twice, keeping some pigs as well 8 Long-standing institutions such as caste are not applied equally strictly at all times. During the Mughal Empire, for example, the caste system was loosened. 9 In the Islamic world, the principle of heritage to the eldest son was not institutionalized, and hence, multiple sons fought each other over the inheritance.

Agricultural Societies 51

as some potatoes for themselves (Wiessner 2002). A broker, such as a merchant, positions themselves in between others separated by geographic barriers, distance, secrecy, deceit, or cleavages in the network, and broker’s contacts are only connected indirectly through the broker themselves (Wiessner 2002; Geertz 1978; Burt 2004). By bridging between others who produce or desire different things, plus some luck or competitors’ bad luck (for which some brokers gave an extra push), a broker could establish transactions, make a profit, and accumulate wealth. By doing so, merchants unwittingly spun a web of weak ties (i.e., with infrequent contact and low emotional investment; Granovetter 1973) wherein farmers’ settlements became embedded, not to mention the cities that offered even better opportunities. Trade and brokerage were also practiced by foragers, but the scales and diversity of surplus production, storage, transport, and accumulation were new. For the brokers, not all went well all the time; when brokering between competing (e.g., ethnic) groups, members of each might distrust the broker, with adverse consequences for the broker’s reputation and future brokering chances (Barnes et al. 2016). For any given product from land or water, there was variation in productivity across different places related to soil, water supply, and other geographic factors. Hence, some people had more food than others, and people who had insufficient resources searched for resource-rich areas. The variation in productivity was exploited by patrons who, through luck, heritage, help from kith or kin, or competitors’ misfortune, found themselves in a favorable position with respect to others who were needier. Those without access to fishing water or fertile land (because access was guarded by patrons and their associates) and who were out of other options might accept submission to a patron who offered them access or food, but then they had to reciprocate, often with their labor. An additional motive could be to belong to the patron’s powerful group, which could sometimes improve survival chances but required costly displays of loyalty. If this resulted in an enduring reciprocal but unequal exchange relationship of resources or services, then patronage, or clientelism, was established (Eisenstadt and Roniger 1980).10 Consequently, a patron could accumulate more resources than their clients could and thereby increase both their absolute and relative wealth. In modern parlance, one could say that a client obtains social capital by establishing a tie with a superior placed individual (Chetty et al. 2022) at the cost of certain obligations. People in our time (as well as in the past) are more inclined to ask patrons’ help when they are more needy and when they face more uncertainty of subsistence (Bobonis et al. 2022). Patrons also obtain social capital, but at a lower cost than their clients. 10 Smith and Choi (2007) showed how unequal patronage-client relationships can emerge among equals in an evolutionary agent-based model. Patronage is a form of hierarchy formation, which has often been studied in modern society, for example, in school classes (Chu 2023).

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We have seen that exchange is part of both brokerage and patronage. To sharpen the contrast between the latter two, let us return to the example of pigs exchanged for potatoes. Whereas a broker makes profits by establishing exchange relationships with suppliers of pigs on the one hand and of potatoes on the other, a patron and their clients will choose a narrow passage on the route across the hills—a choke point of the trade route—where they will act “as stationary raiders or pirates requiring payments for safe passage” (Earle 2011). Despite the differences between the two strategies, some individuals were both patrons and brokers at the same time, so-called power brokers (Padgett and Ansell 1993). An example is the workshop system (Verlagssystem) in 14th- to 19th-century Germany (Weber 1922), where an entrepreneur bought raw materials, sent them to households for various jobs to be done, and assured that intermediate products were passed on until a final product was completed and sold to customers. Often, patronage can be almost like a friendship—material friendship, that is (Martin 2009)—and can also be (like) kinship. Patronage is a continuum, with father-son (like) apprenticeship at one end, of which apprentices may cherish fond memories later in life, and slavery at the extreme opposite, where enslaved people work involuntarily for their patron, receiving in return just enough food to survive, or even less. In the literature, slavery is typically excluded from patronage, but this exclusion blinds us from perceiving extreme cases in the general and persistent pattern of enduring unequal exchange with coercion. “Slavery is not a simple matter of one person holding another by force; it is an insidious mutual dependence that is remarkably difficult for the slaveholder as well as the slave to break out of” (Bales 2002), just like patronage relationships in general. The higher the inequality, the less empathy patrons have for their clients, unlike clients’ empathy for their patrons (Van Kleef et al. 2008). This does not imply that more powerful people are less helpful than powerless people, in a neutral comparison when interacting with anonymous strangers in a laboratory experiment (Andreoni, Nikiforakis, and Stoop 2021), but the more powerful happen to be much better positioned to serve their interests at the expense of the less powerful. For example, in battle, soldiers might know they are going to lose, but leaders at their safe outposts tend to underestimate the suffering of their soldiers and allow battles to continue longer than makes sense, thereby increasing the carnage (Trivers 2011). The forager-farmer distinction is also blurred with respect to inequality. Agriculture in and of itself did not cause inequality, and some agricultural societies remained equal at least for some time (e.g., Çatalhöyük in its early days). Additionally, there were settled forager groups that did become unequal, with private property, patronage, heritage, and, in North America, shell money (Bettinger 2015). In these cases, their prosocial institutions of

Agricultural Societies 53

generosity and humility diminished and gave room to “hoarding and boastfulness” (Kelly 2013, p. 266). A popular explanation of inequality is increasing population pressure (density divided by available resources), but this alone will only make people hungry, not unequal. Necessary conditions are the same as for agricultural inequality: valuable resources can be stored and guarded by those who have taken possession of them (Smith and Codding 2021), and the hungry have no feasible options to go elsewhere (Carneiro 1970); in other words, resources are scarce. For a long time, foragers did not need to submit to patrons because they could migrate to and find resources in unoccupied places, which explains why inequality among foragers emerged relatively late, once most of the good spots were taken. When it emerged, there was often a scale advantage of larger groups, too, for example, in conflicts with other groups or for the maintenance of fishing weirs (Kelly 2013). Wealth inequality entailed a new kind of assortment in agricultural societies, namely, assortment of wealth, in particular when it came to marriage. For assorted inequality, I resist the temptation to use the notion of social classes, because for explanatory purposes, individuals and their networks are necessary, whereas classes, no matter how useful as a descriptive, will not do. Moreover, another inequality emerged in agricultural societies that crosscuts all classes: gender inequality. One of its causes was that many women were more occupied with a larger number of children with shorter birth intervals, which rendered men relatively more important in food production and strengthened their bargaining position. Another cause was men clustering in patronage networks, whereby they could collectively exert much more power over women than one man could individually in his household. Collectively, men developed patriarchal norms. The concentration, or clumping, of valuable resources, which is the necessary and often sufficient condition for patronage, can be generalized to factors of production (Bogaard, Fochesato, and Bowles 2019) to explain how inequality, and in particular gender inequality, further increased. When people used hoes to work the land, with hoes being dispersed across many farmers, labor was relatively valuable compared to land, and the society remained comparatively egalitarian. In contrast, if the land was best worked by expensive oxen that towed plows (for which fewer people were necessary for a given surface area), land was more valuable than labor and yielded larger benefits. In this case, initial wealth differences between land, plow and oxen owning patrons and non-owning farmers continued to grow, resulting in large inequality over multiple generations (Mulder et al. 2009). The larger the population of a polity was, the greater the wealth inequality became (Kohler et al. 2017), which follows from social mechanisms such as patronage under surplus production and from a very simple computational model (p. 129). These societies

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(starting perhaps 3000 BCE, but the first, wooden, plows have not been preserved) were typically patrilineal.11 Hoe and plow cultures are endpoints on a continuum, and some societies went through a transition from hoe to plow (Bogaard, Fochesato, and Bowles 2019), which co-depended on soil texture (Carranza 2014). When there was work for fewer people in the fields, women were no longer crucial in production, which further weakened their bargaining position.12 This was another step in increasing gender inequality, which manifested as lower survival rates of girls due to infanticide (ibid), more domestic violence by men against women (Giuliano 2017), and fewer rights (a.o., no divorce). In plow cultures in particular, gender inequality became institutionalized, thereby persisting until today, even in places where agriculture was largely abandoned during the 20th century (ibid). Not all societies became patrilineal, however, and matrilineal societies were more beneficial for girls’ and women’s health, education, and bargaining position. Once inequality of any kind increased, people who benefited from it the most legitimized it through an ideology that framed it as natural and inevitable (Harari 2011), by stating that it was god’s will, traditional, meritocratic, or otherwise justified by some general principle. Ideology proved to be successful to attenuate commoners’ grievances for a while, until it was no longer credible and commoners revolted, which in most cases was not successful either, unless part of the elite sided with the commoners. Most difficult for patrons was to cow their clients into submission, because the latter had front-row seats to see their patron become rich, and were therefore unlikely to believe the ideology as strongly as the patrons wanted for their own safety. Patrons thus tended to encourage competition and distrust between their clients, trying to prevent strong ties from forming between them (Gambetta 1988). The best-known examples are from modern times. To divide and conquer, the 20th-century emperor of Ethiopia shuffled his employees between offices every three years to prevent them from becoming too close and too competent (Woldense 2018). Sometimes, clients become subpatrons by establishing relationships with clients one level below them, resulting in a hierarchy with multiple levels, often geographically spread out, with farmers or soldiers at the lowest level. An example is the Muscovite elite (17th–19th century), with non-working landed gentry below, and enserfed peasants (as slaves) at the bottom (Domar 1970), which was a system full of tensions and desires wonderfully depicted and sometimes ridiculed in 19th-century Russian novels (e.g., Tolstoy’s Anna Karenina and Gogol’s Dead Souls, respectively). Another example is feudalism in medieval Europe. 11 Heritage could be along the father’s family (patrilineal, most frequently) or the mother’s (matrilineal). Patrilocal means that a married wife moves in with her husband at his family’s settlement, the most frequently occurring arrangement. 12 Communities under resource-scarce conditions had stricter rules of access to resources, and men took more superior positions with respect to women, controlling them more tightly, especially their access to men (Wolf 1982).

Agricultural Societies 55

Martin (2009) explained that patronage, established through dyads, can grow into large structures that nobody (fully) plans or controls, also called chieftaincy (Earle 2011) or chiefdom. Modern examples include rebel armies and the international drugs trade. Ascending through the ranks of a patronage network occurs mostly through nepotism, such as by being the child of the patron, and rarely through meritocracy alone. Once patron-client interactions result in an established rank order, subsequent conflicts are reduced for a while, until, at some point, some clients feel deprived and influence others to join attempts to overthrow their patron in a coup d’état (Martin 2009). The patronage network connected farmer-soldiers (part-time military) at the bottom through intermediates to the ruling elite at the top levels of their network, forming the main structure of power in agricultural (as well as industrial) societies. For this network to become collectively powerful and resilient, shared intentionality was needed (p. 31). This could be enhanced through collective rituals and shared negative experiences on the battlefield (Whitehouse et al. 2014). Much more on collective power will be said in the next chapter; here I elaborate on individuals’ power in a (patronage or other) network. Power is the chance to influence others, possibly against their interests (Weber 1922), and I add to Weber that this influence should be intentional (rather than accidental). Power, as an imposition of one’s will upon others, is not to be conflated with influence as a, possibly unintended, effect on others’ opinions or attitudes. Power asymmetries can lead to all sorts of conflict, elaborated in the next chapter; conflicting, or negative, network ties are not discussed here. In order to obtain a network concept of power, Weber’s definition can be unpacked as follows. First, an actor is more powerful if they influence more people directly. Second, if these people (are made to) believe that actor has great promise (e.g., charisma13 or spiritual powers) or poses terrifying threats, they more readily accept actor’s requests, or if they have received something from actor and (have been made to) feel obliged to meet their end of the deal. The strength of alter’s belief, deference, or endorsement is expressed as tie strength (time and resources spent) from alter to actor in the network. The stronger these ties, and the less reciprocal (Elias 1978), the more powerful actor is. This asymmetry is visible in behavior, for example, when cooperation goes awry; less powerful people are more likely to gossip and less likely to directly confront more powerful people, which is the other way around for the latter (Molho et al. 2020). A case in point is the relationships between 13 Charisma is best seen as a network effect where an upstart projects an image of themselves that resonates with followers who re-enforce one another’s belief in actor’s extraordinary qualities, which feeds back to the upstart who adjusts their behavior and fine-tunes their habitus. The network concept of power can therefore also be used for charisma and its consequences.

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(PhD) students and their supervisor. When alters have alternative social contacts to choose from (substitution), actor’s power weakens, and the relationship becomes more symmetric. Third, actor is more powerful if alters in turn are more powerful and spread actor’s influence to more people at two steps removed. This network interpretation of Weber enables a mathematical concept of power (Section 8.1.4; Bonacich 1987) that predicts quite well. Bonacich’s conception has two caveats, though. Neither someone’s resources, such as social skill, physical force, or weapons, nor someone’s formal power to set the rules that others have to obey is incorporated, although both will be partly reflected in actor’s network power. At the receiving end of influence, people are simultaneously influenced by multiple others, while powerful individuals among the alters have a proportionally stronger influence. For example, people worry most about their reputation in the eyes of their highest status contacts (Yeung and Martin 2003). Power inequality inevitably entails contention among upstarts, incumbents, and disgruntled commoners (i.e., people at the lowest ranks of the patronage tree). Patrons with their clients vied for territory, often competing or at other times cooperating with other patrons and their networks. Because rulers can only rule with support from others, I often interchange “ruler” and “patron”; outside the domain of politics though, for example, in guilds, I will not. Their struggles for territory and other resources (some of which were concentrated in large quantities in guarded places), and the desire for revenge, status, or to weaken opponents resulted in much higher death rates among farmers than among foragers (Gómez et al. 2016). A main reason for the high frequency of wars was that leaders in agricultural societies could reap larger benefits at lower cost and lower risk than their commoners and soldiers (Jackson and Morelli 2011), and their protected positions entailed more overconfidence than among soldiers on the battlefield (Trivers 2011). Some farmer-soldiers worked their way up through the patronage tree into the military elite, thereby enlarging the group that wanted commoners to work for them in the form of tax or rent (as part of the patron’s land yield), which increased contention. To overcome resistance of commoners, soldiers threatened them to the marrow, coerced them through violence, and offered them protection in return (McNeill and McNeill 2003).14 If there was no immediate threat, patrons would invent stories about foreign aggressors and conspiracies to make their tax-protection exchange look reciprocal and fair— patrons as racketeers (Tilly 1985). However, if that did not work they would 14 Protection taxes were also demanded from merchants (brokers), but because they had ties with and could easily move to different communities, they could be less easily coerced, and since elites wanted precious goods, protecting merchants was generally preferred over squeezing them (McNeill and McNeill 2003).

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raid the commoners’ possessions, often in coalitions with pastoralists. During these field trips, soldiers raped many women, thereby erasing the difference between tax collection and warfare. In agricultural societies, some people became predators of many others, and this violent inequality was there to stay, including rape as a weapon (Lamb 2020). Commoners, despite having a majority in numbers, had low cohesion and a lack of shared intentionality, which put them at a disadvantage with respect to the cohesive, violent minority with shared intentionality—and a lower threshold to use violence. Even today, well-organized (non-violent) business companies often get their way against the will and interest of the larger population, unless citizens manage to mount massive protests. This inequality was no stable configuration, as it was interrupted by wars and revolts that were in turn countered by violent repression and sometimes by concessions, depending on the balance of power. Protests and revolts were often commoners’ only communication with their rulers, hoping that they might be listened to. Because clients tend to be remarkably loyal to the patron with whom they identify, patronage networks were the units of solidarity and collective action against groups of revolting commoners; hence, clashes were not between neatly partitioned classes, and rarely are in general. The population structure of agricultural societies was tree like, similar to that of forager society but with different units: households, often with more people than parents and children, were embedded in villages or city neighborhoods that in turn were part of cities, that were (possibly with an intermediate layer of provinces or districts) in polities (i.e., city-states up to empires). One main difference from most forager societies was that at most levels in the tree, from households to empires, there were rulers. Units in different branches were often connected by social ties and groups sometimes overlapped. Despite patrons’ intentional interventions in groups and their sizes, the embedded group structure remained at least to some extent the result of an emergent process, because at each level, there are inevitably trade-offs between positive effects of more people together (e.g., complementary specialization, or military power) and negative effects (e.g., conflicts, garbage, and costly governance, infrastructure, and resource consumption). After a very long period of egalitarian groups of foragers, we saw that in some groups settling in places where valuable resources were concentrated, inequality increased. This started relatively late, and it started among foragers, and eventually emerged in all agricultural societies when patrons seized their chances and established their patronage networks, by means of which they protected the confiscated resources against competitors. While many women in agricultural societies became occupied with larger numbers of children, men embedded themselves in patronage networks where they established patriarchal norms. Gender inequality further increased in oxen cultures, and without merit, half of the population put themselves above the other half. A different

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FIGURE 4.1 Sculptures of Lobi ancestors to honor their spirits with sacrifices (southern

Burkina Faso and northern Ghana). In early agricultural societies, animism was prominent, and it never disappeared despite the influx of moralistic religions.

path to inequality was taken by brokers who bridged between (groups of) others with different products and wishes, who were not in direct contact with each other, to make a profit. Once asymmetric exchange relationships were established, patrons and brokers could accumulate wealth over many exchanges, for which growing cities offered plenty of opportunities, as well as through fierce competition. With more luck than merit (Denrell and Liu 2012), initially small differences in wealth evolved into large differences (see also the random exchange model, p. 129). When cooperative exchange relationships became more asymmetrical, they constrained more than they helped the people at the short ends of the dyads. This sometimes lead to conflicts that were rarely won by the poor (see protests, p. 94) because the rich were nearly always better connected, and their ideologies both legitimated and stabilized their wealth. 4.3

Moralistic religions

In agricultural societies, people believed in spirits and/or many local gods, not much different from foragers, and animism persists today (Figure 4.1). Starting in Egypt (1500 BCE) and continuing until the present, a hugely influential

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innovation spread throughout Eurasia and later through large parts of the rest of the world. Incumbent religions were replaced by moralistic religions with, respectively, supernatural rewards and punishments for social behavior in line with or against universal moral rules (Bellah 2011; Turchin et al. 2023). These rules could be non-agentic, as in karmic religions such as Buddhism, Confucianism, and Hinduism, or be maintained by a universal god, as in Zoroastrianism, Judaism, and later Christianity and Islam. In all these religions, traces of cultural evolution can be seen in elements they took from predecessor religions (Lapidus 2014). Rituals became standardized, which established common ground among large, diverse populations and made it easier for people to identify with strangers sharing the same rituals (Whitehouse et al. 2021). Collective rituals could be practiced by large groups, usually with synchronous motion and/or singing (McNeill 1995), and entailed increased conformism and solidarity (Durkheim 1912).15 These had a positive effect on cooperation among co-believers but not with most others (Purzycki et al. 2016), except for people perceived as victims of misfortune (e.g., poverty or disaster). These rituals were a double-edged sword; they facilitated collective actions on behalf of the ingroup, such as irrigation (Lansing and Fox 2011) and defensive warfare, but they also facilitated offensive warfare and impeded creative thinking necessary to solve difficult problems (Gelfand et al. 2020). Rulers cooperated closely with religious specialists16 and demanded that collective rituals were performed on their behalf, a practice that continues into our time with inaugurations and military parades. They also used religion in their ideologies, transmitted through propaganda, to influence commoners and legitimize their goals and means. Their stories could be convincing if they integrated “reason, morality and emotion” (Mann 2006) and were based on shared sacred values (Atran and Ginges 2012). Powerful stories made sordid wars look like holy wars, wherein many soldiers felt honored to participate. Because rulers had, and still have, the habit to frame attacks against other groups and polities as defensive (De Dreu and Gross 2019), soldiers did not feel guilty. However, the effectiveness of rulers’ stories is largely limited to preludes of wars. Once soldiers experience combat and reality sinks in, ideology goes to the background while solidarity with comrades increases, with feelings of mutual obligation that motivate more strongly than leaders and their ideology (King 2013). Moreover, rulers had to obey the same religious rules as the soldiers and commoners, which constrained their capricious behavior 15 Conformism is expected after collective rituals (McNeill 1995); when someone wants to appeal to someone else, especially a superior (Blau 1964, p. 53); under uncertainty (Morgan et al. 2012); under positive externalities (e.g., speaking the same language as the majority); when norms are enforced without the possibility of migrating to other groups; and when people identify with a certain group. Conformism with peers is age dependent and higher during adolescence. 16 In China and the Incan Empire, military and spiritual leadership were embodied in the same person, thereby avoiding conflict between competing elites.

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to some degree and decreased the number of human sacrifices. The effect of religion on non-fighting commoners was persistent; once they believed in an afterlife or future life, they became more obedient and protested less, “for the source of discomfort” had become god’s will, which preserved “the myth of the leader’s infallible wisdom” (Bailey 1969, p. 66). For example, after crop failures in China, counties with stronger Confucian norms had fewer peasant rebellions (Kung and Ma 2014). Also, tax and rent collection became easier (McNeill and McNeill 2003). Moralistic religions also played different roles, and part of their appeal was the promotion of care for the “poor, sick, orphans, and other sufferers” (McNeill and McNeill 2003, p. 103).17 Furthermore, religion had positive health effects via hygiene rules (not understood at the time) and can lower stress and anxiety, which I observed during a three-day stint in a Romanian monastery (in 2012). By doing daily work in a ritualistic and often rhythmic manner, anxiety and sorrow become easier to bear (Bellah 2011; Berkessel et al. 2021). Moreover, people get a good feeling (trough dopamine) when acting in morally right ways. Through its moral norms, religion offers plenty of opportunities for small dopamine boosts, for example, by helping people in need (Prickett 2021). The diffusion of religions was helped by the invention of writing, which occurred earlier (3400 BCE in Mesopotamia). This invention rendered religions with diligently copied written texts more inert than orally transmitted religions with more modifications and errors in transmission. Whereas standardized rituals preceded the rise of large polities and increasing social complexity, moralistic religions came afterward and therefore played no role in explaining the emergence of large, complex societies (Whitehouse et al. 2021). The evolutionary explanation of moralistic religions is still incomplete. At the macro level, they can be statistically explained by polities’ intensity of warfare (Turchin et al. 2023), but what would this mean at the micro level? Religion has certain advantages for believers, such as cooperation and health, but also has disadvantages. Many more people died in wars and were exploited by elites; despite the universal moralistic principles, men relegated women to subordinate positions, particularly in plow cultures. Religion was not necessary for the positive effects, however, and people had invented alternative ways to cooperate and care before moralistic religions were created. In our time, thousands of people cooperate in research projects and in business companies entirely without moralistic religion. Thus far, scholars of religion have not systematically compared alternative options. 17 People who were not yet converted to a moralistic religion were more easily converted than those who already had a moralistic religion, and people who thought they could improve their condition were more easily converted to a (more recent) competitor religion, e.g., poor Hindus to Islam (McNeill and McNeill 2003).

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FIGURE 4.2 Islam in West Africa: mosque in Bobo Dioulasso, Burkina Faso. The

wooden sticks are used to climb on for repair work when part of the plaster is washed away during the rainy season; carved markings indicate the level up to where the plaster is supposed to be.

4.4

America and Africa

The Eurasian continent where these developments took place was disconnected from the American continent with its own history and innovations. Agriculture was invented independently; maize was domesticated in Mexico (4000 BCE) and spread from there to Peru (3000 BCE), where small egalitarian polities emerged (Haas and Creamer 2006). The Olmec in Mexico (1300 BCE–400 BCE) developed the first relatively complex society with water reservoirs, irrigation, a long-distance trade network, huge (and small) sculptures, and possibly also writing (and if not the Olmec, then the Maya get the credit). Their inventions and style inspired subsequent societies, such as the Maya, who in turn invented mathematics with the number zero and a precise calendar and built a temple complex in Teotihuacan (100 BCE–700 CE; Cowgill 1997). This largest temple was at least as stunning as the largest ones in Egypt and Eurasia. The wheel (4000 BCE, Mesopotamia) was not invented in Mesoamerica; transport was performed by lamas, boats, and people. Inequality was lower than in Eurasia (compared with the same number

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of years after domesticated plants were introduced, and controlling for population size), possibly because of the differences in domesticable draft animal species, wheeled carts for transport, and higher levels of economic specialization in Eurasia (Kohler et al. 2017). Not all polities in the Americas were autocratic; for example, Tlaxcallan had a senate of approximately 100 men who made the most important decisions collectively (Fargher, Blanton, and Espinoza 2010). The last pre-Spanish empire in Peru was the Incan one, which was based on forced labor instead of tax. Of the three empires in Mesoamerica, the Aztec is probably the best known. Network connections lead up north to the Mississippi, where chiefdoms oscillated irregularly between booms and busts, as chiefdoms always did (Khaldoun 1958). Eurasia was also disconnected from most of Africa. Between 5000 BCE and 2000 BCE, the Sahara became drier (Kuper and Kröpelin 2006) and (except along the Nile) was almost impossibly difficult to cross, until Arabs discovered how to traverse the desert by camel (300 CE). The practice of cultivating grains entered North Africa shortly after it was invented in Mesopotamia; first in Egypt, which also became the first African empire, and from there it spread into Aksum (Ethiopia). Considerable modifications had to be made in order to farm in sub-Saharan Africa, due to its different plants, climates, and parasites compared with Egypt (Diamond 1997; Hibbs and Olsson 2004). Furthermore, many local modifications and innovations were performed in iron melting and processing (Ogundiran 2005). From about 500 CE onward, polities with cities were established, which were interconnected by trade networks (Kusimba, Kusimba, and Dussubieux 2013; Monroe 2013). In east Africa (800 CE onward), Swahili culture blossomed through extensive trade and some cultural exchange with Persia, India, and China. Although all polities thrived on farming, foraging was never abandoned. Due to environmental and economic uncertainties, households did not specialize, but rather undertook a variety of activities. A portion of polities were politically centralized and had divine rulers, whereas others were more (but not entirely) egalitarian and governed by frequently changing constellations of corporate entities, such as age grades, secret societies, and lineages (McIntosh 1999). These polities had no monumental architecture, contrary to Eurasian polities of comparable size, because status differentiation was often regarded as much more important than economic differentiation and its material display (ibid). With the expansion of Islam, smaller polities in the Sahel were incorporated in empires (e.g., the Songhay Empire; Monroe 2013; Figure 4.2). Islam appealed because it offered access to trade and came with accounting, a legal code, and money, and thus, it diffused relatively easily on the expanding trade network (Michalopoulos, Naghavi, and Prarolo 2018). Mesoamerican and African histories refute three influential ideas. First, if smaller groups evolve to become larger and more complex societies, they pass through the same intermediate stages (i.e., from tribe to chiefdom to

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state). This was refuted when different routes to larger polities were discovered in Africa (McIntosh 1999; Vansina 1999). Second, the idea that evolution is unidirectional, moving toward greater complexity, was refuted by large but unstable polities falling apart into smaller and more enduring ones. Third, the notion that larger polities tend to become autocratic. This was true for many polities, but with exceptions that are too important to disregard as unexplained variance. 4.5

Cooperation revisited

In agricultural societies, people cooperated to irrigate; to work the fields; to defend their settlement and resources; to attack others; to build houses, cities, temples, and palaces; and for many other goals, often on larger scales than did foragers. Along with realizing public goods, they maintained and protected these goods against wear and overuse (Hardin 1968; Gordon 1954). To achieve public goods, patrons implemented institutions top-down and ordered collective and individual actions to be executed, monitored, and sanctioned. The 5r-package was still the powerhouse of cooperation, but in many places, it was expanded to the 6r-package; a portion of relationships became hierarchical, authoritarian norms were substituted for part of the egalitarian norms, and egalitarian righteousness was replaced by obedience to a patron. Patrons’ power often made it possible to forgo the shared intentionality as a prelude to cooperation and to make people work (collectively) for them even if they would rather not. Some patrons were more successful at fostering cooperation than others. Experimental research shows that leaders who strive for the equality of everyone’s contributions and who punish defectors achieve substantially higher levels of cooperation than leaders who fail to do one or both of these and that the lowest levels of cooperation are due to antisocial leaders who punish cooperators (Kosfeld and Rustagi 2015). Nevertheless, underperforming patrons persisted, in particular when competition on the basis of merit was undone by nepotism. Only when patrons frequently competed with one another, were ineffective patrons selected out. The introduction of moralistic religions also affected cooperation. With respect to the 5r-package, religion influences public goods and their framing (e.g., victory over a contiguous polity); the norms regulating reputations, such as obeying taboos and performing certain practices (Power 2017); people’s psychology (e.g., reactions to disobedience); the network, due to people meeting and establishing contacts at large gatherings; and expected benefits and costs through promises of an afterlife.18 Consequently, believers tend to be more cooperative with anonymous cobelievers, but not with others (Purzycki et al. 2016). Note that across the world today, nationalism has the 18 In modern China it turned out that not the fear of divine punishment, but rather reputational concerns explain cooperation (Ge et al. 2019), possibly due to temporal discounting.

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same effect on the in/outgroup difference in cooperativeness (Romano et al. 2021). The cooperation of very large groups was achieved by means of the 6rpackage and the embedded group structure, sometimes with ideology and religion. Leaders at central and delegated positions ensured that norms were maintained through selective incentives (i.e., side payments and punishments; Olson 1965). For example, when a village was to be defended, there was no need for everyone to converse with many others, as the external threat will have helped to quickly attain shared intentionality. With centralized coordination, small groups could be dispersed around the village, where in each of these groups the 5r (or 6r) package will have fostered collective defense at the group level. While the overall network was sparse, and most individuals interacted with few out of all other villagers, shared intentionality and cooperation could be high in each of the small groups. One main challenge for large groups trying to cooperate effectively is resolving conflicts at the subgroup level. Along with conflict resolution, centralized leadership has a decisive advantage in large-scale warfare, which (together with weapons and group size) explains the demise of foragers and other decentralized communities in conflict with farmers’ armies. If, by contrast, there is no overarching leadership, it may take a long time for smaller groups to cooperate on larger scale and scope. For example, roaming unemployed young men in Medieval Europe were useful to farmer communities in summer to help in the fields, but costly to feed in winter. The nourishment problem was first addressed at the community level by means of Christian institutions, but then the stingy communities freerode the generous communities where the needy went; the problem of collective action had simply been shifted up one level of aggregation instead of solved (De Swaan 1988). Without overarching leadership, it took hundreds of years of institutional trial and error, as well as conflicts between polities, to solve the problem within Europe, only for it to reemerge at the global level after the Industrial Revolution. This does not mean that all intergroup problems remain unsolved. Sometimes leaders negotiate an agreement and establish intergroup norms. 4.6

Cultural evolution and complexity

Let us pause the evolution of culture and cooperation to take stock just before the Afro-Eurasian and American networks were connected in 1492. During the previous millennia, which is a relatively brief period on an evolutionary timescale, there were large shifts in religion, inequality, urbanization, government, warfare, institutions, trade, and the scale of cooperation, with a deep impact on people’s lives and their natural environment. Many of the social changes were driven by innovations. Plants were domesticated, tools and irrigation were invented, and domesticated animals

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were used for plowing (oxen), transport (horses, camels, Asian elephants, and lamas), and as food. To store and transmit information, writing (3400 BCE) was the most influential invention, initially used for stocks, taxes, debts, and contracts and later for almost everything else, complemented by the inventions of paper (9th century, China) and printing (movable types, 13th century, China). Couriers on horses could reliably transmit information on clay tablets, parchment, or paper over large distances, which made the ruling of large polities easier (McNeill and McNeill 2003). Trade was expanded by the invention of coins (about 700–500 BCE, Lydia, Turkey), preceded by cowrie shells and silver weights. Because a coin’s value was unrelated to its weight (that could be kept low), coins were easy to carry and store, simplified exchanges, and encouraged further specialization. Soon after coins were invented, trade around the Mediterranean became monetized. This happened likewise later in the rest of the world. More trade required more money, and coins in large numbers became too heavy. The solution was paper money (China, 1024 CE; Elvin 1973), preceded by certificates introduced by houses of commerce. As with many innovations at their introduction, fine-tuning is necessary to make them work, and the introduction of paper money was no exception. It was accompanied by a great deal of fraud and riots before the government managed to iron these wrinkles out, by making counterfeiting extremely difficult (Elvin 1973). Whereas paper money spread far and wide, diffusion of means of transport (boats, horses, lamas) depended on geography and the natural availability of animals and their food. Religious and institutional innovations, such as large-scale rituals, bureaucracy (China; Kiser and Cai 2003), and Roman law, parts of which later ended up in Africa and Latin America through European colonization, spread much wider than their language and polity of origin but were often locally adapted. Institutional change was no longer discussed in small groups but, if not imposed top-down by a leader, became an emergent phenomenon in large groups where most people did not know one another, which has been replicated in lab experiments (Centola et al. 2018). These groups could sometimes get stuck in a disliked norm if the conversation about, and coordination of, the transition to a better norm failed (Willer, Kuwabara, and Macy 2009), for example, if everyone thought—wrongly—that almost everyone else was in favor of the incumbent norm. Sometimes a crisis could shake the group loose from its inconvenient norm (Eq. 8.13, p. 164) or a leader could bring about the transition (Schelling 1960). Leaders became important for coordination when groups’ sizes made bottom-up change too time-consuming, but many leaders primarily served their own interests, which reinforced social inequality and increased conflict. It is well beyond the scope of this book to trace all inventions, transmissions, and uses of culture, but the overall dynamic is clear and also holds true in fields such as music, architecture, painting, poetry, and cooking, which I

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skip for brevity. I also skip daily life in farmer societies (see Le Roy Ladury (1975) for a case study). Taking all innovations together, we can imagine a massive tree of culture containing all innovations of the past. They were used to farm, construct, seduce, cooperate, remember, transport, trade, pray, fight, and protect. Innovations were kept if considered valuable and further developed or discarded when they disappointed. The ones kept changed the cultural and sometimes the natural environment and were often used in subsequent refinements and innovations. Cultural impact on the natural environment was widely visible three thousand years ago, when many large animal species had gone extinct, portions of once fertile Mesopotamia had salinized and were turned into a desert (Diamond 2002), and considerable deforestation had taken place (Archaeoglobe 2019). Decade-to-century long periods of colder or drier climate decreased harvests and caused famines, migration, and cultural adaptation, which played a role in polity overturn (Büntgen et al. 2011; DeMenocal 2001), next to warfare. The natural environment in turn affected norms and behavior. In places suitable for wetland rice farming, for example, more cooperation and coordination are required than for wheat. Consequently, tighter (i.e., more extensive and stronger sanctioned) prosocial norms were developed in rice culture than in wheat culture and maintained for thousands of years. Even if people moved to towns and stopped cultivating rice, they kept their tight norms for at least several generations, with less innovativeness as an unintended consequence (Talhelm and English 2020). Overall, social complexity increased after the agrarian transition, but often irregularly, and interspersed with periods of complexity decline (Currie et al. 2010). Not all cultural phenomena have become more complex, though; for example, languages did not.19 Social complexity has been measured at 30 sites across Eurasia, the Americas, and Oceania (so far only two sites in Africa, where archaeological data are hard to come by), using the Seshat databank (Turchin et al. 2015; Turchin et al. 2018). To memorize the numerous complexity measures, it helps that they can be grouped into two sets (Shin et al. 2020). The first set contains indicators of information and coordination, including money, infrastructure (bridges, markets, ports, water supply, irrigation, etc.), information systems (writing, record keeping, etc.), and other texts (calendar, sacred, philosophical, practical, etc.). The second set contains measures of scale: scale proper (population size of the polity, size of the 19 Grammars become simpler with population growth (Raviv, Meyer, and Lev-Ari 2019). Larger populations create more categories, but once a tipping point (i.e., critical threshold) in the proportion of users is passed (≈ 20-25%), frequently used categories converge—even across cultures—resulting in fewer categories (Guilbeault, Baronchelli, and Centola 2021). The reason we can speak language is that our laryngeal anatomy is simpler, not more complex, than that of other apes and monkeys (Nishimura et al. 2022). In nature, complexity increased only for some organisms, whereas the majority today are single cells.

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largest settlement as a proxy for urbanization, and territory size), hierarchical levels (in administration, military, and settlements), and specialization of government officials (comprising education, merit promotion, exam system, court, legal code, etc.). For the sample of polities in Seshat, all measures from both sets correlate strongly, to the point that a single vector in a principal component analysis explains over 77% of their variance, which was called “social complexity” (Turchin et al. 2018). Centralized states with specialized bureaucrats emerged when population size surpassed a few hundred thousand people, whereas the emergence of neighboring states was nearly irrelevant (ibid). Moralistic religions also came into being above this population size (Turchin et al. 2023). Among small polities, there is much variation on the complexity dimensions, but if they grow larger, there appear to be constraints that make them more similar; most places in the space of the polity dimensions were never occupied (Shin et al. 2020). When social complexity, in particular scale, increases, and it becomes more difficult to understand society, people increasingly attribute social phenomena, especially social problems, to supernatural causes (Jackson et al. 2023). Perhaps commoners’ lack of understanding made them more receptive to ideologies and other forms of misinformation. 4.7

Environment, culture, and genes

Culture coevolves with genes, which becomes clear when we take the perspective of a very long time frame. During the past four million years, human ancestors became taller (and then smaller in warmer areas; Will et al. 2021). Approximately two million years ago, our ancestors lost their hair in warmer climates at lower altitudes, but then had to warm themselves at night by lighting fires in their caves (Dávid-Barrett and Dunbar 2016). Once there was a fire every night, it was also used for cooking, and more digestible food resulted in smaller jaw muscles and intestines (Wrangham 2017). A smaller gut with more efficient digestion made calories available to nourish larger brains (Aiello and Wheeler 1995), which is of course no explanation thereof. Natural selection favors small brains because large ones are very costly; at 2% of our body weight, our brain consumes up to 25% of our energy intake. This is even more for children, which is why they and their brains grow slowly compared with other animals and take much time for their cultural nourishment (Heldstab et al. 2022). Moreover, a large brain requires continual hydration to survive. Given these hefty requirements, only if large brains yield advantages that offset the costs and constraints can their owners survive. For steady food provision, humans live in cooperative groups, where large brains give access to accumulated culture from many people over multiple generations that helps to provide (more) food and to innovate or migrate if food

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becomes locally scarce. Due to these interdependent advantages, we can infer that cooperation, culture, and brains must have coevolved (Dediu and Levinson 2013). There is no simple relationship between brain size and intelligence (Will et al. 2021), however, which in turn seems to depend on the number of cortical neurons rather than total brain size (Herculano-Houzel 2016). Costly brains can make sense if they help to solve pressing problems that cannot be solved by genetically coded behavior or learning from others (Richerson and Boyd 2005). Because most of our problems we do not solve by ourselves, however, we do not need super large brains. Through social learning, the dangers of solitary exploration of the social and natural environment can be avoided, and the more difficult or dangerous solitary learning is, the more beneficial social learning becomes, provided that the relevant environment is sufficiently predictable for at least some role models to understand (Boyd and Richerson 1985). In very slowly changing environments, however, culture is redundant and individuals could get by on genetically coded behavior plus a bit of solitary learning, just like many animals do. An extreme example is natural light, which behaves the same for the duration of life on Earth, to which eyes could slowly and almost perfectly adapt over many generations and species. Culture became beneficial when environmental change sped up (i.e., glacial cycles were shorter than a millennium; ibid., pp. 125–131) and individual adaptation became too difficult. If the environment were to change too fast, however, culture could not keep up, and people would have to learn mostly by themselves, if they could. Even in a cultural niche between the extremes, people have to discover some things on their own because if everyone were to learn only from one another, culture would gradually mismatch the changing environment (Rogers 1988). Brains and guts were not the only body parts that changed during human evolution. When sapiens left Africa and spread across the world, their new environments entailed various genetic changes in their offspring. Less UV radiation at higher latitudes acted as selective pressure toward decreasing skin pigmentation,20 and extreme conditions in the arctic, high altitudes, tropics, 20 Skin pigmentation is the result of a trade-off: dark skin protects against the destruction of folate by ultraviolet radiation, whereas light skin facilitates the synthesis of vitamin D. The optimum skin pigmentation depends on the average amount of sunlight one is exposed to, indicated by latitude (Jablonski and Chaplin 2010). If one can get enough vitamin D from food, like the Inuit in Greenland, it is safer to have a darker skin to prevent skin cancer. When European hunter-gatherers started with agriculture, their vitamin D intake declined, and they became whiter through natural selection and admixture by lighter skinned farmers from the southeast; European paleness is relatively recent (Wilde et al. 2014). Much earlier, Neanderthals had also developed lighter skin tones when moving north (Gross 2022). Across the African continent, humans have never been uniformly exposed to natural selection, which shows up in their much larger genetic variety than that of all other people on Earth, who are descendants from a few Africans (Henn et al. 2012). The concept of race misses the point that the largest genetic differences are found between individual people, not groups; the average genes of putative racial groups do not significantly differ from each another and there are no genetic group boundaries (Barbujani and Colonna 2010; Lewontin 1972).

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and toxic areas (Fan et al. 2016) all had noticeable effects. In the long run, the effect of culture on genes became progressively stronger versus the other way around (Laland, Odling-Smee, and Myles 2010), in particular after the advent of agriculture. In the very short run, genes affect behavior through hormones and neurotransmitters, but most genetically predisposed behaviors are culturally modified or overruled during childhood. In the 21st century, we can even intentionally engineer our genes and bodies to some degree. Humans select their partners based on a mixture of genetic (e.g., odor; Grammer 2015), cultural (Douglas and Shepard 1998), social, situational, and biological factors (Rosenthal and Ryan 2022). There are cultural influences on perceived facial attractiveness, but not so much that its relationship with a good immune system is lost (Mengelkoch et al. 2022). Racism, however, excludes racialized outgroup members from being seen as attractive. People who are seen as more attractive can more easily get (better paid) jobs, support, friends, and higher status partners; in short, it facilitates social mobility. Early 21st century, 99% of respondents in 83 countries spend at least ten minutes per day on enhancing their physical attractiveness, women slightly, but not much, more than men (Kowal et al. 2022). Neanderthals also paid attention to their looks; they used pigmentation, shell beads, feathers, and eagle talons to look more attractive (Kowal et al. 2022). Despite medical (e.g., plastic surgery and condoms) and algorithmic (e.g., dating websites) innovations, most adults have long-lasting relationships with one partner at a time (for some after a period of recreational sex during early adulthood), even in polities where multiple partners are allowed (Wilson, Miller, and Crouse 2017). As far as we know, (serial) monogamy was also the most frequent in the past. Exceptions were rulers of pristine polities, some of whom had hundreds of children with hundreds of women. In general, men have more variation in numbers of children than women (ibid), which can be seen in more frequent rivalry about partners among (coalitions of) men than among women, as well as men’s greater physical strength and shorter life span (Puts 2010). These differences are found in all cultures, although they vary in magnitude. Biologically, males and females are not separate categories. Along with sex chromosomes, sex is determined by hormones, gonads (ovaries or testes), and genital anatomy, which are not always aligned and can vary in numerous ways, forming a spectrum in between male and female (Ainsworth 2015). Even sets of cells inside a body can differ genetically from the majority of cells. Abrahamic religions and the social groups under their influence deny this spectrum and pretend that males and females are separate categories without overlap, with dire consequences for people who do not fit their tight expectations. After this biological interlude, we will go deeper into the third main theme of this book—conflict.

5 CONFLICT

Agricultural societies became much larger than forager societies, and so did their wars, and there were many smaller scale conflicts too. To understand the dynamics of conflicts, we have to complement the principles of cooperation, culture, and inequality with principles dedicated to the subject matter. I will first review general theories of dyadic and multi-faction conflicts on individual and group levels, with a strong emphasis on violent intergroup conflicts because they have the largest and most lasting impact. In their dynamics, collective action is crucial in defense and attack, for which I use a new model when the 5r- or 6r-package is insufficient. After combining the model with incumbent theories into a new theory of conflict, we can explain many violent collective conflicts in past and present, although several important questions remain unanswered. Subsequently, I discuss how agricultural societies evolved through warfare, using culture (weapons and institutions) under constraints imposed by opponents. Whereas wars were often initiated and coordinated top-down by elites, commoners’ protests and revolts against their elites emerged bottom-up, which I discuss in the final section. 5.1

Patterns and principles

Conflicts of interests are inevitable, but most of them do not lead to violence. Across different species, scarcity of resources increases competition that might result in confrontations, but often it does not. Cooperation may continue during competition, for example, researchers who vie for the same scholarship often maintain friendly, cooperative relationships. Competition can be indirect, as often in commerce, and in most cases, it does not result in direct confrontations. Moreover, every society has institutions to resolve conflicts, DOI: 10.4324/9781003460831-5

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which most people prefer to apply before violence breaks out, for instance, mediation, spatial separation of brawlers, apology rituals, compensation for damage done, sanctioning of aggressors, and, most importantly, socialization of children. Societies can only exist if internal violence is largely avoided, at which they succeed to various degrees, but even the worst crises do not come close to a Hobbesian war of all against all. Conflict resolution tends to be biased in favor of the more powerful, though, and outgroup members are given the least consideration. With biased resolution, open conflicts may be suppressed, at least for a while, but the underlying conflicts of interest and the suffering of the powerless linger on. Yet, the vast majority of humans are reluctant to use violence and have to pass through a trajectory of multiple steps before they do: feeling constrained by others, radicalization, and agitation. Therefore, our main question is how, and under which conditions, does violence break out? 5.1.1

Constraint

The likelihood of violence is increased when individuals (or groups) constrain each other and cannot get out of each other’s way (Martin 2009; Sarazin 2021). This might be because they are tied to the land they claim to possess and/or one party attempts to conquer or monopolize a scarce and valuable resource upon which others depend (Allen et al. 2016). This could also be an immaterial resource such as status. The stronger the constraint, the larger the chance that the constrained will at some point resort to violence. Then, initially positive or neutral ties turn unambiguously negative. This most often happens within households, which are among the most dangerous places in society if there is no war going on (Daly and Wilson 1988). Households are extra dangerous in patriarchal societies, where women accused of “wrong” sexual desires are molested or killed by male family members for the sake of family honor. These men perceive kindred women to be constraining their honor-based reputations, which ruins cooperative relationships among close family. In all kinds of conflicts, if others do not interfere, stronger or better connected individuals (or groups) will get their way, who are often (groups of) men at the expense of women (see network power, p. 55). The weaker and the weaker connected are the most likely victims and losers. Moreover, men have a genetic disposition to be more violent than women, just like among other primates: across cultures and times, men are more violent among each other as well as far more violent against women than the other way around, and most homicides are executed by men (Puts 2010). Nonetheless, the distribution of violence is strongly skewed, with a small portion of men committing most of the murders, whereas most men rarely or never fight (Collins 2008; Glowacki et al. 2016). Differences between individuals are large and not only genetic; the effects of culture and social environment on violence can be stronger than

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that of genes. Women can learn to become violent too, for example, female camp guards in Nazi Germany, and men can learn to become less (Elias 1939) or much more violent than the baseline forager level. Evolutionary game theory (Maynard Smith 1974; Maynard Smith and Price 1973) can help to make the notion of constraint more precise. In the context of conflict, constraint can be defined as the reduction of one’s life expectancy (or expected number of offspring) when under threat and/or having no access to certain critical resources. In empirical studies, life expectancy can be proxied by indicators of health, well-being, or survival, while humans can also perceive immaterial resources as critical and their loss as life threatening, such as their reputations.1 Under (anticipated) constraint, violence can occasionally yield benefits, but unless one is much stronger than one’s opponent, the costs tend to be forbidding, which is the reason animals and most humans avoid violent conflicts most of the time. Most people avoid threatening others, if they can, and first try to get what they want by means of discussion and/or threats before getting involved in violence. The credibility of threats will co-depend on contestants’ reputations (Schelling 1960). However, if one is about to lose one’s (or one’s children’s) life, or believes this will happen, the stake becomes infinitely large, and thus, the cost-to-benefit ratio of fighting becomes very small, despite the huge cost of fighting (Grafen 1987). I repeat (Footnote 7, p. 24) that people cannot calculate these ratios but that they quickly guess, just as other animals do, sometimes guessing wrong in a way that proves fatal. A low cost-to-benefit ratio in the eyes of the beholders increases the temptation to fight with the constraining actors, provided that the constrained individuals are able to fight. If too much weakened, frightened, surprised, or ignorant about the (magnitude of) constraint, fighting is unlikely. Superior strength and preparation, in contrast, make it possible to chase away or preemptively strike against competitors and opponents even before they constrain (too much), for example, a robber who surprises a victim by suddenly pulling a knife and confiscates resources before the victim can muster support. Constraint thus increases the chance of, but does not imply, violence, and for an individual, the chance to fight as well as its success also depends on the presence of bystanders (Chu 2023), associates who help (p. 55), and individual characteristics such as experience and socialization. 5.1.2

Collective conflict

The dynamics of inter-individual and intergroup conflict differ in one important aspect: within-group interactions. These can aggravate collective violence beyond a same number of mutually disconnected individuals on their 1 For a group, constraint can be assessed in terms of mortality rate, lifetime uncertainty (p. 80), or as perceived constraint in terms of collective framing.

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own (rare serial killers and suicide bombers set aside), because group members can mutually support and encourage one another. At the onset of intergroup conflicts, ingroup members may want to pursue a controversial goal that goes against the interest of, and therefore constrains, outgroup members. In contrast, they may themselves feel constrained by the outgroup and want to do something about it. Either way, the controversialness2 of issues related to the outgroup tends to intensify the discussion (Elias and Scotson 1965), and interactions thus become more frequent and emotionally involved. Usually, intergroup interactions result in a working consensus on the issues discussed (Figure 5.1A), as a compromise between different points of view, such that controversies are attenuated or largely resolved (see also the influence model, p. 20). If a critical threshold of controversialness is passed, however, which co-depends on interaction strength, a solution is no longer feasible, whether intergroup discussions continue or not. Then, ingroup opinions become more extreme (Baumann et al. 2020). If also outgroup opinions become more extreme, polarization emerges (Figure 5.1B). Radicalized opinions can then mount to a shared intentionality against the outgroup. A formal model and a comparison with the influence model are in Section 8.1. How intergroup opinion or attitude convergence can turn into its opposite, of radicalization and polarization, is illustrated in a 21st century example of politics in the USA. If members of one group, here Republicans, feel more constrained and therefore interact stronger among each other than members of another group, here Democrats, I implement stronger interactions among Republicans in the model instead of one value for everyone (as in Baumann et al. 2020). Then, the model yields more radicalization among Republicans than Democrats, or in other words, asymmetric polarization with a mood swing of the average voter to the right (Santos, Lelkes, and Levin 2021), just like that which occurred in the USA over the first decades of the 21st century (Figure 5.1B).3 For this illustration, a small network was used (a karate club with two factions; data from Zachary 1977). A realistic network would be larger but yield qualitatively the same pattern. Interestingly, to get this realistic outcome, it is not necessary to assume that controversialness differs between groups, even though this will probably be the case.4 During the influence process, the network may change; when people’s opinions change, they may toggle their ties to others with opinions similar to their revised opinions, which 2 Controversialness is the name of a variable in a mathematical model (Section 8.1.2) that describes the extent to which an issue has become controversial. 3 The asymmetric Republican-Democrat polarization was also found in an experiment, where Republicans but not Democrats who received online messages with opposing views became more extreme in their own opinions (Bail et al. 2018). 4 Other patterns can also be explained by this model by adjusting the ingroup versus outgroup interaction strengths, for example, a radicalizing ingroup interacting with an opposing outgroup with moderate opinions, which, after a period of interaction, is won over to somewhat agree with the more extreme ingroup.

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B 3

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FIGURE 5.1 Modern political conflict in the USA. Social influence among Republicans

(continuous lines) and Democrats (dotted lines), with initial opinions randomly drawn from, respectively, the intervals [−1, 0] and [0, 1]. (A) Convergence toward a working consensus, characteristic for the 1940s and 1950s. Inset: example network. (B) Asymmetric radicalization in the 21st century.

accelerates polarization but does not change the qualitative outcome (Baumann et al. 2020). When the critical level of controversialness has been passed, people look for contacts with opinions similar to but also more extreme than their own (Goldenberg et al. 2023), which ratchets up radicalization further. For simplicity, I left network change out of the model. Radicalization progresses rapidly when the constraint is clear and immanent, but oftentimes, radicalization develops slowly. It may also take decades, as in the aforementioned political example. When discussions are frequent and radicalization is slow, ingroup discourse easily derails into outgroup dehumanization, which means a framing of all outgroup members in a strongly negative and stereotypical way (Tajfel and Turner 2004). If outgroup members have different looks than ingroup members, these will often become part of the stereotype, or if not, distinguishing features are likely to be fantasized to make discrimination easier, for example, that outgroup members are stupid. Humans are genetically predisposed to make in/outgroup distinctions, a tendency that can be culturally activated through propaganda (and the underlying ideologies) that define distinguishing, yet arbitrary, outgroup features. Moreover, humans are more talented than every other animal in socially constructing conflicts of interest with outgroups where there are none, for instance, by framing innocent people as witches who are a danger to society.5 Arbitrarily defined subgroups of the ingroup are then turned into outgroups 5 For a cultural-evolutionary analysis of witch hunts, see Hofhuis and Boudry (2019).

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imposing fictitious constraints. Negative information about them is more easily accepted by the ingroup majority when there is social uncertainty and turmoil, both of which make people more credulous (Light et al. 2022) and can engender hatred against imaginary opponents with real consequences. To explain how verbal conflict can lead to violent conflict, we need to go deeper into the cognition and emotions of the contestants, as well as the effect of randomness (below). We have already encountered framing and assessing costs and benefits under the influence of others, and to this we can add indignation, which fuels anger, as well as the fear of getting hurt or losing loved ones. Arguably, the most important cluster of emotions and thoughts is morale (Ulio 1941). Morale incorporates solidarity with the group and its members (Zeleny 1939), as well as pride and (sometimes euphoric) joy when collective goals are achieved (Motowidlo and Borman 1977). The goals are internalized as moral norms that specify the emotional value of reaching these goals (Akerlof and Kranton 2005). People feel strongly attached to their moral norms, reflected in self-discipline to live up to these norms, even if this causes suffering. This makes morale more influential than shared intentionality on its own, as it makes people more resilient in difficult situations (Motowidlo and Borman 1977). High morale can plummet into discouragement; however, if many group members are lost, individuals or subgroups become isolated from one another, and/or the goals become unachievable. A lack of resilience is a reliable indicator of low morale. Morale can be reinforced by inspiring leaders, ideology, training,6 or victories, but most strongly by intense arousal rituals (e.g., bootcamp) and shared experience on the battlefield (Konvalinka et al. 2011; Whitehouse et al. 2014), where people are physically copresent and sense one another (Collins 2004). Morale requires regular reinforcement to preserve, or else it withers. It is also strengthened when the larger population (in which the ingroup is embedded) supports the ingroup’s goals and legitimizes its means. A population can even motivate solitary operating individuals to commit heroic or terrorist acts (depending on whose side the onlooker is). Moralistic religion (p. 58) is often used to legitimize violence and to foster solidarity in large armies where most people do not know one another. Without strong arousal rituals in physical copresence, the effect of religion is comparatively small but can still be significant. Because oftentimes opponents on both sides have a moralistic religion, there is no competitive advantage; everyone believes that god is on their side. When ingroup morale and morality are elevated, radicalization is emotionally reinforced. The intricacies of the larger intergroup network offer opportunities to power brokers (i.e., political entrepreneurs) to fabricate false information 6 Military training has existed at least since the Greek and the warring states in China, approximately 500 BCE.

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and to manipulate the reputations of in- and outgroup members. They combine this information with general moral principles, situational contingencies, and existing fears and prejudices into a story intended to scare and convince a following, thereby heavily leaning on people’s predisposition to make in/outgroup distinctions. They can become (temporarily) successful when they manage to turn their ideology into their followers’ morality. Accordingly, they often frame outgroup members as nonbelievers, vermin, or other derogatory terms and exclude them from ingroup moral principles. When further radicalized, these power brokers present mass murder as (ethnic) cleansing, as if opponents are simply washed off, and make it appear that fighting against “infidels” is a deserving end. Morale thus binds as well as blinds—to opponents’ humanity and to criticism on ingroup’s goals and means. The influence of ideology on morale and constraint perception is striking. Whereas all animal mothers (and fathers in some species) are willing to take great risks to fight for their offspring, human parents can be made to feel proud (although begrudgingly) that their sons die for their god, king, or fatherland. They have become blinded to the aggression of their own leaders and are made to believe that imaginary, or at least avoidable, opponents impose constraints. Nonetheless, despite social pressure and personal risk, some individuals defy ideology and outgroup stereotypes and keep valuing in/outgroup members equally, but their numbers are often too small to avert impending disaster. When morale is low, there will be many defectors who, all together, render collective violent action unfeasible; a few zealous individuals may still want to fight, but the group as a whole is ineffective (a critical threshold of the proportion of defectors is made precise in Chapter 8). This impedes largescale violence, but in some groups, a fanatic minority or a leader with their associates threaten to punish defectors with the death penalty, thereby enforcing higher numbers of participants. However, non-motivated participants are clearly less effective in combat than highly motivated ones. Taken together, I conjecture that intergroup conflicts result from constraints that, if unresolved through communication, lead to radicalization of at least one group or both. Alternatively, an elite of one of the groups that has more to gain in a conflict than the commoners radicalizes first. In the latter case, elite members discuss controversial topics and goals that, if realized, constrain and harm the outgroup, resulting in more extreme elite opinions through intensified social interactions. Part and parcel of radicalization is the dehumanization of opponents, which may lead to progressive suspension of an ingroup’s (or its elite’s) moral rules for outgroup members. If an elite radicalizes first, they will try to prepare their followers and remainder commoners for violence by means of ideology-based propaganda that scandalizes (possibly fictitious) opponents, promises benefits for participation, and makes it clear to reluctant commoners that disloyalty and desertion will be punished.

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A modern example is the inner circle of the Russian elite preceding the colonial invasions of Ukraine (2014 and 2022). More effective than (threats of) punishment is strong morale, though. If in a violent confrontation, opponents’ strength in weapons and size (elaborated below) is not too different, the group with stronger morale is likely to win, but if their strengths differ considerably, arms and group size are decisive, as well as support from other groups. If the group under attack is on its own territory, it has more to lose, better knowledge of the terrain, and often higher morale than the attackers, who have to compensate with superior weapons and much larger group size. So far, we have assumed that radicalization, ideology, and morale result in collective violent action, but we need to explain when it will and when it will not. Moreover, we cannot assume that all violent groups have a fully developed 5r- or 6r-package. For example, commoners who intend to revolt against their elite may have very little knowledge about one another, may not yet have developed norms for their protest group, and have no leader. They will certainly not have a morale like an experienced army. To begin with, all collective actions, including violent ones, are preceded by shared intentionality among at least a subgroup of the ingroup (p. 31). Here, we have shared intentionality toward a controversial goal. Morale comprises shared intentionality plus internalized valuation of its goal(s) by its adherents, yet even strong morale does not explain collective violence, not to mention occasional violence by groups with weak morale such as recreational hooligans. The reason is that most people feel (empathy based) emotional resistance to as well as fear of committing face-to-face violence against other human beings that are stronger than their anger, solidarity, and morale (Collins 2008). To overcome resistance and fear in face-to-face confrontations (when threats of ingroup punishment are low), additional agitation is necessary. This can be opponents’ insults, provocations, violence, or revealed weakness, for example, if someone falls. Alternatively, agitating stimuli can be generated by ingroup members themselves, typically through (false) accusations that increase radicalization and hatred, or by creating tempting visions of the loot that can be conquered. When provocations against the outgroup accidentally result in a violent act on their part, the blame for using violence can be othered, which legitimizes ingroup’s use of it. After passing a critical threshold of controversialness that marks the start of radicalization, there turns out to be a second critical threshold—that of agitation. When it is passed, ingroup members lash out. In a (sub)group with shared intentionality, the threshold tends to be collectively shared, also in groups with an incomplete 5r-package and without leader. I formalize the dynamics in a so-called Ising model (Section 8.2) as a special case of collective action with attack of or defense against opponents as public good, with an empirical example of urban violence recorded on video. The effect of agitation involves a portion of randomness, and the

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Ising model is crucial to assess its effect. When the intensity of agitating stimuli increases, there is an increasing chance that someone accidentally contributes to the collective goal (in this case starts fighting), but this does not entail collective action yet. At a critical threshold of agitation, however, few accidental cooperators win over many others to join (in less than two seconds after the first in the case of street fights) and collective violence starts. This only happens if the group radicalizes first; in a calm situation with neutral or moderate opinions, people do not act upon agitation, although ongoing agitation will increase controversialness that may have consequences later on. Social networks are always clustered, and the Ising model demonstrates that violence breaks out in small clusters first (unless leaders coordinate the action), in line with empirical observations (Collins 2008). The critical threshold is lower in (sub)groups where people have stronger morale, positive attitudes toward the use of violence (McDoom 2013), and when leaders take the initiative (Glowacki et al. 2016; Oberschall 2000). The latter can also happen among foragers who have neither leaders with authority nor sanctions to command collective action. In agricultural and industrial societies, leaders try to lower the threshold through ideology and religion, along with coordinating collective violence. Additional factors that lower the threshold are shared experiences of violence and strong feelings of honor supported by honor norms (i.e., honor culture). Individuals on their own can also be agitated to a point where they become violent, for example, when they are first radicalized in the idea that their status is at stake, and then respond with force upon a remark that sounds like innocently teasing to bystanders. Presumably, individual radicalization happens through imagined interactions with others. Not every individual or every group has to overcome a high agitation threshold, because violent behavior can be learned either in street fights or through professional education (King 2013) and practiced to a level where the use of fighting skills becomes part of one’s habitus (Collins 2008). In that case, a leader’s command suffices to ignite collective violence. In addition to training, experienced fighters tend to operate in small cohesive groups, e.g., platoons, where individuals encourage one another. For small minorities in the population, violence is exciting, and these individuals are often on the lookout for opportunities to fight. Context also matters; in situations where social norms are suspended (e.g., dark alleys, prisons, and wars) and there is strong asymmetry of power, more powerful individuals (with or without a leader) may take what they want by means of force without much of a threshold in their way. In other situations, attack is meticulously planned and rehearsed, which increases self-confidence and lowers or removes the threshold. Hence, most face-to-face violence is committed by small minorities of experienced individuals, whereas the majority is ineffective at it, in part due to the associated spikes of adrenaline and cortisol. Notably, the

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ineffective majority includes most conscripted soldiers (Collins 2008; King 2013).7 In most cases, collective violence is also highly inefficient. This was demonstrated in a lab experiment, without actual violence of course (Abbink et al. 2010). Compared with two individuals pitted against each other, ingroup members (connected anonymously through computer terminals) spent twice as much on conflict with an outgroup and spent a great deal on punishing passive group members, which reflects practices of punishment for deserting or failure that we see throughout history (Lovell 2006). Even without leaders commanding subjects what to do in the experiment,“more than three-quarters of the prize that parties[were] fighting over [was] dissipated by direct conflict expenditures” (Abbink et al. 2010). Whereas attackers can choose to abstain from violence and engage in peaceful interactions, defenders do not have that choice. For defenders, selfdeception can boost their morale and self-confidence and be their survival strategy. A modern example is the guerrilla war in Congo (Nunn and de la Sierra 2017). When villagers under attack had to overcome their dilemma of cooperation, they created a gri gri ritual, which they believed would protect them from bullets, if performed exactly right. If someone was shot, they argued that during the ritual, the victim had made a mistake. This nonfalsifiable belief decreased the perceived cost of defending the village, won over more people to participate (ibid), and made the false belief in defenders’ strength self-fulfilling. This example shows that when competing with an opponent (group) not much stronger than one-self (or one’s ingroup), a strong belief in one’s (ingroup) superiority can tip the balance to one’s (collective) advantage (Trivers 2011). Once violence starts, there is a specific pattern in the interevent times of violent acts within each war, protest, one-on-one fight, and so on, namely, a power law (Johnson et al. 2013; Bohorquez et al. 2009). This means that if, say, for a war, one chooses a day length as the unit of time, there are few average days; most days feature either less or more violence than one would expect (if the distribution were Poisson). It also means that if violence starts at a slow pace, it will escalate if it endures, and if it starts in a burst, it will slow down.8 Knowing this power law pattern, it is possible for an ongoing collective conflict to predict the severity of it with some accuracy, for example, the number of victims in the war in Syria (starting in 2012) several months beyond a given dataset (Scharpf et al. 2014). This pattern is remarkable because the causes 7 Using long-range weapons without seeing the facial expressions of opponents, e.g., artillery, is psychologically much easier than face-to-face combat (Collins 2008). For the latter, people have a higher threshold. 8 A war starting at a slow pace means that it starts in small bursts of violence by, for example, small border patrols before the main army gets into battle. The opposite way to start a violent conflict is a large-scale attack wherein the majority of an army participates. In a one-on-one fight, a slow start means pulling and pushing before punching and kicking (and weapon use).

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of interevent times in one-on-one fights (with fatigue), warfare (with logistics and the wear and tear of materials), and other conflicts are quite different. Violence ends if one person or party is clearly defeated (runs away, surrenders, or is wounded or dead), opponents endure in a stalemate to the point of exhaustion (and may negotiate peace), or third parties intervene. It does not end if both parties are too weak (or reluctant) to defeat the other, for example, when modern day street gangs occasionally kill one or few of their opponents but cannot wipe out all of them. Without intervention by the state and when gangs’ status remains at stake, each assassination provokes the other group to counterattack, making intergroup violence self-perpetuating despite group membership turnover (Papachristos 2009). If conflict ends in places where prosocial institutions apply, and the losers have not been strongly humiliated, reconciliation may be feasible, which can sometimes even re-establish cooperation (e.g., Wiessner and Pupu 2012; Casey and Glennerster 2016). However, this co-depends on participants’ socialization into reconciliation skills (De Waal 2000). For humans, chimpanzees, and other social animals, conflict resolution is much more likely among ingroup members than with outgroup members (ibid). Victims’ chances to heal their emotional wounds increase if they are embedded in a cohesive network where they can talk about their hardship (Snyder-Mackler et al. 2020). Others avoid becoming victims by migrating or lying low, accidentally increasing the pressure on those who stay behind and do not hide themselves (Gambetta 1988). Experiencing or witnessing violence entails feelings of vulnerability, and its effects can be measured as lifetime uncertainty, which means the variation of time of death in the relevant population. In modern society, this uncertainty entails lower life expectancy; lower savings and investments in education and healthy lifestyles; and increasing stress, anxiety, and chances to participate in violence and other high-risk behavior (Aburto et al. 2023). 5.1.3

Conflicts between three or more factions

Many violent as well as non-violent conflicts expand beyond dyads (of two individuals or groups) and throw the network (of individuals or groups) off balance by provoking bystanders (or groups) to become participants joining one side or the other when their reputations or material interests are at stake or to intervene and deescalate. Although conflicts between multiple actors may look chaotic, a network pattern tends to settle down that can be predicted by social balance theory (Heider 1946; Cartwright and Harary 1956; Davis 1963). Note that in this theory, balance does not have any moral connotation, and the tendency toward it is not to be confused with reconciliation. In this theory, network dynamics are driven by three main principles: “a friend of my friend is my friend” (transitivity), “an enemy of my friend is my enemy” (triadic solidarity), and “a friend of my enemy is my enemy” (escalation). These

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principles result in groups of individuals, which could, for example, consist of a patron with their clients, connected by cooperative (positive) ties, with animosity (as negative ties) in between the groups—the definition of a balanced network (see Figure 5.2 on the right hand side). Neutral ties (e.g., acquaintances) are best left out of this model because they often confound the dynamics. Transitivity is by far the most frequent principle across empirical studies, followed by triadic solidarity (Sadilek, Klimek, and Thurner 2018), and their effects are stronger in groups with stronger overall solidarity (Rawlings and Friedkin 2017). Interestingly, the balance principles are scale independent and also apply to networks of polities (Maoz et al. 2007; Antal et al. 2005), where sometimes also a fourth principle applies: “the enemy of my enemy is my friend” (cynical triad). It rarely holds for individuals, though (Isakov et al. 2019). Balance theory thus explains to some degree how a dyadic conflict can escalate or deescalate by others being drawn in, broadly consistent with empirical findings, and can be used to analyze the changing configuration of multi-faction conflicts between individuals or groups. It cannot explain when animosity turns into violence, which we have just explained by other means. There are also exceptions, such as power brokers thriving on conflicts between their contacts. To keep it simple, I assume here (but not in general) that social ties are either positive or negative, as well as reciprocal. Thus, if B starts a conflict with A, then A will quickly react negatively against B. For ambiguous ties, like that of a client who is both helped and constrained by their patron, the researcher building the model has to decide if, at a given moment, the positive or negative aspect of the relationship dominates (or expand balance theory beyond its current state). Lies and treason can be at the root of flipping ties; for example, British colonizer in America pretended to be friends with Native American group X to fight against group Y, but once Y was defeated, they stole the land of X (Zinn 2003). To model the dynamics toward balance, we can summarize and generalize the principles by saying that triads with an even number, or absence, of negative ties are balanced (Harary 1953). Influence over longer paths than three (i.e., beyond triads) is weak (Christakis and Fowler 2009; Pinheiro et al. 2014; Luo et al. 2017) and is left out. To the model, we can add the effect of aligned versus opposed opinions through the principle of assortment: people prefer to have friends with the same opinions as they themselves. The causality can go both ways; people choose similar friends or try (sometimes collectively) to influence their friends to become similar to them. Increasing balance and/or assortment decrease dissatisfaction (frustration or stress), which implies that the dynamics can be modeled by minimizing overall dissatisfaction, based on local decisions by individuals about their ties and opinions (Minh Pham et al. 2020; Section 8.2.1). Human brains are sensitive to social (im)balance (Chiang et al. 2020), which substantiates decision-making toward increasing

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FIGURE 5.2 Left: a balanced social network with positive ties (drawn thick) within and

between two groups (squares and circles) that tolerate opinion diversity. Center: B aligns their opinion with their group and gets involved in a local conflict with C (thin line) that sets the network off balance. Right: consequently, B′ s friend A becomes involved in the conflict, adding a negative tie that restores social balance but destroys the friendship between B and A.

balance in the model. When social networks are compared with their randomized counterparts, most social networks turn out to be strongly balanced (Kirkley, Cantwell, and Newman 2019). The process is illustrated in Figure 5.2. In the figure, there are two ethnic groups with friendly intragroup and intergroup ties (left-hand image), and little social pressure to conform to one’s group, reflected in opinion diversity. Due to mounting social pressure, B conforms to the common opinion of their group. Then, B gets involved in a conflict with C, a member of the other group (thin lines in center image). Consequently, social balance is disturbed. According to the balance model, the most likely subsequent change is the positive tie between A and B turning negative (right-hand image), where the negative tie can represent both betrayal and face-to-face violence.9 This re-establishes balance, but how will A and B feel about it? Because B changed their opinion recently, B probably started the conflict with A and will rationalize their aggression by means of their new opinion, whereas betrayed friend A will be in shock. The balance model also explains a puzzling outcome, namely, that during intergroup conflict, when long-term interethnic friendships are ruined by betrayal or violence, some interethnic friendships continue. This has been observed in India10 (Dhattiwala 2022) among many other places. Looking back at Figure 5.2, we see that B is in a closed triad with A and C. If, in contrast, B had not had the tie with C, it is less likely (although still possible) that B would start a conflict with A and ruin their friendship. In other words, conflicts tend to escalate much more through closed triads than through cycles 9 The same outcome results if A and B in Figure 5.2 would have had gray color instead of white at the outset. 10 There is wide intercultural variation of the meaning of friendship, but in this particular case it means that someone provides shelter to a neighbor from a different, persecuted ethnicity.

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of four or larger. The model thus predicts almost certainly ruined versus possibly continued friendships, which can be empirically tested. One refinement beyond the current model is that bystanders can gossip about rabble rousers, with feedback on their reputations. Depending on incumbent norms and bystanders’ interests, aggressors can be seen as tough, and be respected (Chagnon 1988), or as villains, and be depreciated and sanctioned (Ridgeway and Diekema 1989). If the perpetrators are overwhelmingly more powerful than the bystanders, neither the latter nor a bad reputation will stop the violence. These cases involve directed ties (e.g., A attacks B but B remains passive toward A), from which I abstract away, but balance theory can handle them (Cartwright and Harary 1956) beyond this book. Consequences of status differentiation are that lower status individuals have a higher chance to receive negative ties from higher status individuals, who in turn have a higher chance to receive positive ties. Status, or power, is indicated by power centrality (calculated on positiveties only; p. 55; p. 150). The most likely victims of violence, then, are individuals who are weakly or negatively connected to more powerful others, and violence can happen to them even in an instant when they are briefly spatially isolated. Incapacitated individuals then drop out of the network. Further, positive ties are reciprocated much more often than negative ties (but see Livan, Caccioli, and Aste 2017), and negative ties tend to dissolve more quickly than positive ties (Harrigan, Labianca, and Agneessens 2020) because conflict is typically very costly in the long run. If, on top of tie sign and tie direction, one also distinguishes tie strength (reflecting social closeness) and when other variables (e.g., cultural similarity) come into play, there is a panoply of possibilities that I will not go into here, except for a few remarks on deescalation. Simmel (1908) noted that in a closed triad with a conflict in one of its dyads, the third person often tries to deescalate. Third person depreciation of antisocial behavior in a dyad is indeed the most frequent response and can already be observed in eight-month-old children (Kanakogi et al. 2022). A deescalator temporarily increases imbalance by establishing two positive ties with the two conflicting others (connected by a negative tie), hoping to break the negative tie or to (re)establish a positive tie. One may think that when deescalators intervene physically (not only verbally) in a violent conflict by separating assailants, they risk life and limb, but in a cross-cultural study it turned out that their risk of getting hurt is fairly small (Liebst et al. 2021). The chance of successful deescalation increases when bystanders act collectively (Weenink et al. 2022) and/or when they offer a face-saving exit to the conflicting parties (Phillips and Cooney 2005).11 11 Once video data were used to study violence instead of surveys and lab experiments, the socalled bystander effect (the more bystanders there are, the less they intervene) turned out to be virtually nonexistent in every culture examined (Philpot et al. 2020; Weenink et al. 2022).

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We thus know quite a lot about dyadic conflicts, and numerous things about conflicts in configurations of three or more factions, but we have unanswered questions as well. I will pose four of these questions. First, we do not know why many commoners admire their leaders who, through their wars, decrease these very commoners’ survival chances. (Self)deception is a proximate cause, no doubt, but on an evolutionary timescale, one would expect that self-damaging self-deceivers would have lied themselves to death or would have abandoned their belief. For example, when witch hunts in Europe (1450–1750) ran out of control, people abandoned this practice, just like viruses that kill many of their hosts eventually leave individuals who are immune or catch the disease in a milder form (e.g., as a childhood illness; Hofhuis and Boudry 2019). So, the question is of why there are so many self-deceivers to this day remains. Second, conflict redistributes groups’ power, resources, legitimacy, and support and may lead to institutional change, but we do not know in general how these redistributions and changed institutions feed back into the conflict dynamics, wherein some people change sides, some groups dissolve, new groups emerge, and relationships change. Third, why was the trend toward decreasing interpersonal violence in Europe since 1850 (which of itself has not been fully explained) unperturbed by the world wars, urbanization, and all other societal changes (Eisner 2008)? Are there similar trends elsewhere? Fourth, although a larger power difference tends to worsen the weaker party’s fate, we can rarely predict the intensity of conflict when it starts and cannot answer questions such as when and why vindicators who are fully in control of captured opponents continue their violence by using torture to inflict unspeakable horrors, long after hormone levels (adrenaline) of combat have returned to normal. Another case of power difference is men versus women in patriarchal societies. There we also know that the chances of violence increase, but we have no evolutionary explanation for why. After all, men who care after their wives instead of beating them yield psychologically healthier children with higher survival chances and lower inclination to beat their partners when they are grown up. To conclude, much work remains to be done before we have a general theory of conflict. I will now present an evolutionary model of dyadic conflicts between agrarian polities wherein institutions play a role. The effects of firearms will be added in later chapters about more recent history. Frequent warfare in agricultural societies was a continual incentive to invent new weapons, defenses, and logistics, which intertwined collective violence more strongly with cultural evolution than in forager societies. —————————————Bystanders do intervene, often collectively, and in all likelihood they have been doing so in the past as well. The obvious exceptions are unarmed bystanders facing well-armed bandits or soldiers.

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5.2

Wars and institutions

At the onset of agriculture, when farmers increased their possessions, institutions of appropriation (for property, heritage, and tax) were created or expanded if their forager ancestors already had such institutions. These institutions did not remain fixed for a long period. When inequality increased, more powerful patrons (i.e., rulers), if not kept in check by others, tweaked these institutions to their own interests, extracted more wealth from commoners, obstructed or killed competitors, and legitimized such actions by inventing ideologies. Rulers presented themselves as divine, enslaved people, and consolidated their power through intimidation and human sacrifice. The victims of sacrifice were typically individuals at the bottom of the patronage network (mostly enslaved), thereby preempting retaliation from the victims’ families (Watts et al. 2016). Clearly, those with more power had a stronger influence on institutions, including how the yield of agriculture was to be divided (Acemoglu, Johnson, and Robinson 2005). Would a ruler with extractive institutions give up some of their power to benefit others, thereby decreasing inequality? Rulers rarely did so voluntarily, as they would have to act against their interests, and if they made promises to do so, there was no authority above them to hold them accountable if they did not keep their promises. They also knew that if they made a deal with their opponents to give away part of their power in exchange for peaceful retirement, there would be no incentive for the opponents to keep their end of the bargain once they rose to power (North and Weingast 1989). Consequently, autocrats and their entourages tended to make their institutions more extractive rather than less extractive. Nonetheless, more inclusive institutions emerged and thrived, at least for a while, and the question is: how? Extractive versus inclusive institutions span a continuum on multiple dimensions, one of which is highly unequal versus more equal opportunities for the commoners (Acemoglu and Robinson 2012). According to Amartya Sen (1999), who did not use the term explicitly, inclusive institutions enable commoners to develop capabilities to live a life valuable to them. Further, inclusive institutions (cast in formal laws or not) protect commoners’ property, contracts, and trade, which makes these institutions public goods. Ingroup(s) inclusion implies that outgroups are excluded from access to ingroup property and public goods, and hence, there is protection against invaders and freeriders, including tax-evading ingroup members, which convinces taxpayers that most others comply or will be punished (Levi 1988). There is also education (at least for bureaucrats), meritocracy instead of nepotism (at least in the army and for bureaucrats), and authority for conflict resolution. Inclusive institutions have some checks on rulers to prevent the abuse of power and to hold them (to

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some degree) accountable for their decisions.12 Furthermore, other public goods such as roads, bridges, marketplaces, and water supplies are provided (Blanton and Fargher 2008). One may add moralistic religion to the list of inclusive institutions because it diminished some rulers’ excesses (e.g., human sacrifice) and promoted cooperation, at least among fellow believers. If the rulers themselves are not bound by the rules, extractive institutions are quickly (re)established, typically under the threat and application of violence—the hallmark of extractive institutions. Once commoners perceive inequality as unfair, and top-down rules of resource sharing are not considered legitimate, they decrease their cooperation for public goods (Anderson, Mellor, and Milyo 2008).13 Then, elite’s short-term advantage of extraction diminishes or evaporates. Moreover, because extractive rulers do not want to be criticized by educated staff, whom they perceive as jeopardous, these rulers “choose incompetent officials over competent ones because they are more loyal even if such a choice leads to a downward trend in the capacity of the government to perform” (Woldense 2018). Also inclusive rulers dislike criticism, but there is (some) more freedom of speech under inclusive than extractive institutions. Inclusive institutions differ in kind, degree, and over time and are more likely to appear when there is less inequality (Acemoglu, Johnson, and Robinson 2005), which is exactly why they rarely appear once inequality is established. At the beginning, during the last millennium BCE, inclusive institutions did not include women, minorities, or enslaved people, and they simply included more men than extractive institutions did at the time, which was already quite something to behold. For rulers, inclusive institutions could yield long-term advantages by increasing their legitimacy among multiple ethnic groups and thereby facilitate and augment tax collection. Inclusive institutions also facilitate economic growth in the past and present (Acemoglu and Robinson 2012). The less inclusive institutions are, the more ideology 12 Democracy implies some (degree of) inclusive institutions, but not the other way around. Democracies often originate in some form of political participation or representation of the commoners. Rather than projecting modern democracy (with elections) onto the past, democracy is perhaps best seen as an institution where rulers represent the majority of the commoners and can be held accountable with the possibility of removal, which makes the rulers care about their reputations. Democracy was typical among egalitarian foragers, and various kinds and degrees of democracy were fairly common among African farmers long before Europeans arrived, up to the level of the ethnic group (Kizza 2011) and the village group (Uchendu 1965). A form of democracy (i.e., corporate governance) may have also been established in a few city states in Mesoamerica (Tlaxcallan and Teotihuacan; Fargher, Blanton, and Espinoza 2010), although the absence of autocracy (e.g., a palace) is more certain than the presence of democracy. The main challenge was to maintain or develop democracy on larger scales. 13 Whether poor or rich individuals are more cooperative in dyadic interactions among themselves is culture specific (Brooks et al. 2018), but in general, competition and conflict between rich and poor make cooperation more difficult, whereas equality makes it easier (Bardhan 2000). Between-group competition enhances ingroup cooperation (Puurtinen and Mappes 2009) among men, not among women, though (Van Vugt, Cremer, and Janssen 2007).

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is necessary to legitimize inequality, and thereby to keep the disgruntled at bay. To collect taxes only to the benefit of the elite requires increased monitoring and coercion of the commoners resisting to the force of noncommitted and thieving soldiers. Especially in areas far from the center of power, tax collection was highly inefficient, and elites were too busy fighting their numerous enemies to worry about the fate of commoners. The maintenance of extractive institutions is relatively inexpensive (i.e., soldiers steal their own income), whereas the establishment and maintenance of inclusive institutions are expensive. The question is how the latter diffused against the odds. Inter-polity dynamics model. Turchin cum suis (2013) addressed this question through an actor-based simulation model, tested on 3000 years of historical data (1500 BCE–1500 CE) on Eurasia and some on Africa. Although modeling historical processes is rare, there is a small tradition. The earliest models were differential equations with roots in ecology and cybernetics (e.g., Von Foerster, Mora, and Amiot 1960). Turchin (2003, 2009) is one of few scholars, along with Goldstone (2017), Korotayev (2016), and a handful of others, who advanced this field in the 21st century. The actors in the model at hand are polities, not individuals, that start wars with certain probabilities against neighboring polities, which they may win or lose with certain probabilities, depending on geography, army size, military technology, and institutions. The geography of Eurasia and Africa is modeled by a grid with 100 × 100 km cells, coded for agriculture, mountains (i.e., too mountainous for agriculture but good for hiding), steppe, desert, and water. Agriculture slowly expanded over this time period, whereas the other factors remain constant in the model. For the wars between polities, there are three variables. The first variable is military technology, most of which originated on the Asian steppe and spread from there on.14 Social technologies of strategy, logistics, and training (Footnote 6) are not explicitly modeled, but we could imagine them to be part and parcel of military technology without altering the simulation code. The second variable is the sizes of polities on the grid; the simulation starts with all polities having the size of one cell, but they can grow through conquest and occupation of neighboring cells. The number of agricultural cells in a polity is used as a proxy for army size in the polity’s power calculation, which seems to be a reasonable approximation since empirically, the two measures strongly correlate (Turchin et al. 2018). 14 Examples of influential military technologies in agricultural societies are the compound bow (2350 BCE), horse riding 3000 BCE–2500 BCE; Trautmann et al. 2023), the horse-drawn chariot (1800 BCE), iron armor (1200 BCE), cavalry archers (600 BCE), and the stirrup (200 BCE). Bow and arrow technology is more than 60,000 years older. Because horses could only be kept in large numbers with access to broad grass lands, central Asian pastoralists (Mongols, Turks, and others) were able to maximally benefit from them.

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The third variable is inclusive institutions.15 Because an extractive polity with substantial size advantage can win wars against smaller polities regardless of what institutions they have, such as Russia that progressively occupied northern Asia from the 15th century onward (they even took Alaska, which they later sold), the military advantage of inclusive institutions is not obvious. Turchin’s explanation of inclusive institution’s effect is brief, but we have strong and detailed evidence from recent history. Young males in the USA who benefited more from public spending in the 1930s contributed more during World War II in terms of volunteering, financial sacrifice, and performing heroic acts to reciprocate their nation (Caprettini and Voth 2023). Furthermore, inclusive institutions increase tax compliance and provide better infrastructure, while moralistic religions strengthen morale. Evolutionary theory adds that, in order to be effective at the macro level, micro-level conflicts between different factions have to be (largely) resolved, which is a recurring theme in the evolution from single cells to multicellular organisms and multi-organism communities (Szathmáry and Smith 1995). The important point here is that humans use inclusive institutions to reduce ingroup conflict. The causality can go in both directions at the same time: an external threat, or constraint, can help to foster ingroup cooperation through establishing more inclusive institutions that in turn make the ingroup stronger in combat. Additionally, a more meritocratic and better educated army contributes to the army’s effectiveness. The invention of inclusive institutions plays no role in Turchin’s model, so they appear randomly with low probability. Because they are costly to maintain, their chance of disappearing is larger than the chance of appearing, and the chance of disappearing increases with polity size (i.e., its overextension; Collins 1992), but decreases with the degree of inclusiveness. Consequently, inclusive institutions largely disappear from the simulation without a military advantage. Resources are not explicitly modeled, but we can read them into it by realizing that they correlate with army size, military technology, and with strength of inclusive institutions. An example may help to understand what transmission of institutions through warfare can look like. The example is interesting on its own because it involves the consolidation of China and shows that transmission of institutions may be accompanied by institutional innovation. From the period of warring states, Qin came out as winner (221 BCE) and established an empire. Throughout the empire, the Qin government imposed uniform writing, a uniform system of coins, synchronized almanacs, harmonized rituals, and had roads built, water transport and postal services improved, all commoners named and registered, and farming intensified (Fang, Feinman, and Nicholas 2015). They also established a centralized bureaucracy with promotion based 15 Turchin (2013) discussed “ultrasocial” institutions and asserted that inclusive institutions are a subset thereof (SI of their paper), but because nobody else adopted this notion, I stick to inclusive institutions (Acemoglu and Robinson 2012).

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on merit, written rules, fixed salaries, and monitoring, the latter aided by the improved means of communication and transportation (Kiser and Cai 2003). (The obligation to pass exams came later, after Qin.) The Qin bureaucracy was an innovation based on a combination of elements from the warring states such as merit promotion, when nepotism led to failure in war, and keeping records of the great many soldiers, which entailed training of officials (Kiser and Cai 2003).16 Many of these institutions and public goods served not only the government in their attempts at political consolidation but also the interests of the commoners. At the same time, taxes were raised, and the army was expanded, thereby increasing commoners’ burden, but also offering them opportunities to climb the social ladder through a military career. The ongoing wars (during 75% of the 500 years before Qin, there was war) had weakened local warriors (aristocrats) who could have posed a threat to the Qin Empire and its institutions. Once Qin bureaucracy had been developed, as well as a large army with officers loyal to the centralized government, aristocratic upstarts could no longer (re)conquer power (Kiser and Cai 2003). Qin’s tax regime was too harsh, however, and a rebellion against it ended the short-lived dynasty (206 BCE). This was not the fate of the bureaucracy, which prevailed beyond the fall of the dynasty. On Turchin’s paper website, the succession of historical polities and the output of the simulation model are shown next to each other. During a simulation run, a polity is randomly chosen every other year starting a war against a randomly chosen neighbor cell with a certain probability. A polity’s power, hence its chance of winning wars, increases with relative size advantage and by having inclusive institutions. The effect of military technology on winning wars is curiously absent in the simulation, but has been examined in more recent publications. After a war, the winner takes the loser’s territory and can use a subsequent period of peace and stability to build administration (Grzymala-Busse 2020), implement its own institutions, and co-opt influential locals (Tilly 1990).17 The simulated pattern of polities and their sizes resembles the historical pattern well (overall R2 = 0.65), also in a replication study (Madge et al. 2019). Tilly (1963) said that although statistics do not speak for themselves, they do make “muffled noises”. But what do these noises tell us here? Turchin cum suis and others (Harari 2011) assumed that winners obtrude their institutions on losers. This surely has happened, but there are important counter examples: long-stay Mongol invaders of China adopted numerous incumbent institutions, and in Persia, Mongols adopted Islam, which they later took to Mughal (i.e., Mongol) India (Lapidus 2014). 16 Compare Chinese Qin bureaucracy (p. 88) with Weber’s model of modern bureaucracy in Footnote 10, p. 104. The earliest onsets of bureaucracy can be found in Mesopotamia more than 5000 years ago. 17 Tilly is famous for the phrase “states make war and war makes states” (Tilly 1990), but it does not occur in this book (nor in Tilly 1985); hence, it may have been attributed by others.

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It therefore seems more accurate to say that institutions sometimes diffused through imposition by winners and at other times through imitation of role models. Because many conquests were followed by indirect rule, there were also cases where very little diffusion took place. When an invading polity was inclusive and gave everyone access to public goods, many of the dominated people identified with their new polity after a few generations, leaving their former ethnicities behind (McNeill and McNeill 2003; Blanton 2015). Assimilation in the dominant group could be a strategy to gain social status (Wallerstein 1960). Because polities that win wars become larger and larger polities become more similar (Shin et al. 2020), it might seem that also the difference between inclusive and extractive institutions decreases with increasing scale, and the explanatory problem largely disappears over the course of warfare. We have seen, however, that growth of scale was only sustainable when accompanied by growth of infrastructure, information systems, specialization of officials (Turchin et al. 2018), and other inclusive institutions from which many commoners benefited. These institutions came with growth of scale, which made large polities more similar indeed. But this is not the whole story. Other inclusive institutions (p. 85) were implemented by some polities, not by all, and may not have had a military advantage18 but were important for commoners’ well-being, such that they could live a life that they found valuable (Sen 1999). Since Turchin’s (2013) computer program is long and many parameter values have to be chosen, I provide a different explanation of inclusive institutions’ advantage in war by using a differential equation (inspired by Abrams and Strogatz 2003; Section 8.1.5). For my own polity dynamics model, I compare an extractive and an inclusive polity with the same amount of institutionbased power, specifically their strengthening effect on military power, as the area under the curves in Figure 5.3A. The equal overall power constraint is much tighter than in Turchin’s inter-polity dynamics model, wherein the sum total of a polity’s inclusive institutions (counted as the sum of cell values of a vector) is larger than that of a contender polity with extractive institutions. In the figure, the territory size is indicated on the horizontal dimension. Extractive institutions (continuous line) are inexpensive, and the polities are powerful at small sizes, similar to modern guerrilla groups, whereas inclusive institutions require more resources and therefore a larger polity size (dotted line) to reach their maximal power. Both extractive and inclusive institutions lose power upon overextension, when the polity occupies a territory that is too large to control (Collins 1992). When the two polities go into battle against each other, territory taken by one goes at the expense of the other’s territory. Figure 5.3B shows that if an area is occupied by an extractive polity, initially 18 Evidence for this proposition can be found in data on the past two centuries, which demonstrates that when controlling for coalitions, democracies and autocracies have equal chances at winning wars (Graham, Gartzke, and Fariss 2017).

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A

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FIGURE 5.3 Inter-polity dynamics. (A) Institution-based power of two equally sized

polities. (B) When at war with an inclusive polity, an extractive polity shrinks (i.e., the change is negative), until the magnitude of change has become zero, which is at approximately 0.4 of its initial size.

with size x = 1 on the horizontal axis, an inclusive polity, with size 1 − x, can conquer territory for as long as its power exceeds its opponent’s power and take more than half of the extractive polity (negative change means shrinking), until the magnitude of change becomes zero. If the attacker is an extractive polity, it can only take half of its opponent’s territory; hence, the advantage of inclusive over extractive institutions is approximately 10% of contested territory. My model thereby supports Turchin’s proposition on the military advantage of inclusive institutions, although its effect size is more modest. To fully overtake an extractive polity, an inclusive invader would also need a larger army or superior weapons (that, when added to the model, would yield a larger surface area under the curve). In both Turchin’s polity dynamics model and in mine, the effect of strategic alliances (often through marriages; Padgett and Ansell 1993) is not yet implemented, whereas forming coalitions with other polities does make a difference and could be indicated by means of Bonacich power centrality (p. 55; Section 8.1.4). As said about networks, a tendency toward social balance is expected (p. 80). Another issue is geographic adjacency: many African polities had no boundaries touching neighboring polities or very fuzzy boundaries, and migration out of extractive polities into unoccupied land was relatively easy. A main challenge for leaders was to retain commoners (Stahl 2004), which might have been an alternative route to inclusive institutions. The problem of free moving people and scarcity of labor was also solved by doing the opposite: keep commoners by enserfing them, which was also practiced in various forms in numerous places outside Africa, among others in Europe during the Middle

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Ages, starting later and lasting till the 19th century in Central and Eastern Europe. Turchin’s model is explicitly evolutionary. At the time, nobody knew how to make a state, or which institutions would contribute to its military power. There was no design that rulers could copy, and in the process of making war, states were emergent. Over the course of thousands of years of trial and error in combining institutional elements under resource constraints, it happened to be that certain institutions contributed to military supremacy. These institutions diffused, altered the social environment, and became building blocks for subsequent institutional innovations. In this cultural evolutionary process, military gains and losses were shared by groups, which are cases of group selection, or multilevel selection of groups and individuals at the same time (Turchin et al. 2013).19 To recapitulate, through radicalization and agitation, which were often stimulated by rulers and their associates, soldiers (or ingroup members in general) got to a point where they started fighting collectively against another polity (or outgroup in general). Turchin’s model predicts higher chances of success for those better armed, larger, or in possession of superior institutions, resulting in the diffusion of said institutions. Ongoing struggles continually overturned the political landscape for thousands of years, analogous to the convection in a pot of boiling water on a long-lasting fire. Because rulers had an incessant desire for conquest, and commoners kept producing children, the pot never boiled dry. Although many commoners suffered and died, recurrent violence between polities turned out to be very robust against attempts to establish permanent peace. This is perhaps the best example of emergent social dynamics going against the intentions and interests of nearly all people involved. Polities’ growth in territory is usually accompanied by the increase of other indicators of scale (see the social complexity indicators, p. 66), which have been reexamined through the expanded Seshat databank (by 2022, it covered the period 9600 BCE–1900 CE). After comparing more than 100,000 dynamic regression models, it turned out that the growth of scale (scale proper as well as hierarchical levels and specialization of government) is best explained by (the use of) military technology, which is strongly correlated with the intensity of warfare (r > 0.9; Turchin et al. 2022). The latter is also the best 19 On the polity dynamics model, there are five more points of discussion. (1) The diffusion of moralistic religions is not exactly the same as the diffusion of other institutions and happens only partly through conquest, and once in place, religions tend to be stickier than polities. (2) Although inclusive institutions are associated with more tax income and less internal conflict, privileged soldiers in an extractive regime can be highly motivated to engage in combat. (3) By applying concentrated force (Mann 1986), small numbers of soldiers can beat much larger numbers of dispersed opponents, so army size is not all there is to it. (4) Wars in the model end because there is a winner, but in actuality they also end through arduous negotiations, foreign intervention, or mutual exhaustion. (5) Small polities won’t attack much bigger states unless they are weakened, for example, by food shortage (Jun and Sethi 2021).

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statistical explanation of moralistic religions, even though we do not know yet how this works at the micro level. Looking at Turchin’s papers from 2013 to 2022, inclusive institutions got a more modest role in explaining conquests, in line with my inter-polity dynamics model (Figure 5.3, p. 91). Turchin’s studies demonstrate that war does make states indeed, as Tilly (1985) said, or more specifically, that war makes winning states grow and losers disappear. Moreover, a current level of military technology sets a limit on how large a polity can grow. Once a superior technology is invented, in particular horse riding and iron weapons in the first millennium BCE, warfare intensified, and some empires grew much larger than before. This leap in scale was repeated once gunpowder was used in canons and guns (Turchin et al. 2022). The relationship between military technology and scale indicators also confirms Weber’s (1922) proposition that warfare is associated with bureaucracy, which makes taxation more efficient and the war efforts less dependent on financial support by few wealthy individuals (Mann 2012). The relationship between war and bureaucracy is important to understand the cause of weak bureaucracies in Africa before the transatlantic slave trade. Because practically useful firearms were rare before the late 19th century, most boundaries between polities were fuzzy, and labor—not land—was scarce, wars had lower intensity and frequency than in Eurasia. Hence, most African polities were hardly challenged to develop efficient bureaucratic armies and states (Herbst 2014). This persists in modern times, because the transatlantic slave trade and colonial extractive governance, inherited by African polities, also stood in the way of African bureaucracies. Whereas the geographic expansion of polities is well explained by few variables (mainly military technology), the decline shows considerable variation, both in terms of speed and causes. If not encroached upon by foreign invaders or torn apart by internal conflicts (e.g., about succession of leadership), societies could often adapt to climate change (to various degrees), such as the Indus valley (Petrie et al. 2017) and the Maya lowlands (Douglas et al. 2015). A period of drought did not need to be fatal, but if it lasted too long, it became fatal, for example, for the Akkadian Empire (2200 BCE; Weiss et al. 1993) and the Himyarite kingdom that gave way to Islam (530 CE; Fleitmann et al. 2022). The East Roman empire (Byzantine Empire) was burdened by a colder climate (1–3 degrees cooler in Eurasia, 536–630 CE) but contributing to its demise were multiple simultaneous causes, including outbreaks of the bubonic plague (Black Death; Bar-Oz et al. 2019), along with migration and intra-elite competition. Angkor (Cambodia) in its heyday had the most expansive system of irrigation and flood regulation channels in the world (13th century CE), but it was also vulnerable. During intense summer monsoons, the system was severely damaged by water overload, which was one (but presumably not the only) cause of Angkor’s decline (Penny et al. 2018). Çatalhöyük (p. 47) suffered from a combination of diseases, population pressure, and violence

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(Larsen et al. 2019), and the Ming dynasty collapsed after expensive wars, corruption (which indicates extractive institutions), and a volcano eruption that covered the sky and cooled the air (Liu et al. 2020). Some elites in decline raised taxes to survive, which competing elites used as a pretext to mobilize disgruntled commoners against them. If half a century, or a lifetime, is used to distinguish gradual polity decline (more than a lifetime) from rapid collapse (less), then there are many examples of both (Currie et al. 2010), usually followed by part of the surviving population migrating out of the area. 5.3

Protests and revolts

Since political and economic elites first existed, they have exploited and constrained commoners. Many resigned to their fate, but sometimes groups of commoners have resisted against their leaders. There must have been many more tax rebellions and farmer protests than the historical record accounts for; protest sizes are power law distributed (Biggs 2018) and only the largest had a chance to leave lasting traces. Extractive institutions, for example, in the form of excessive taxes, cause relative deprivation (Merton 1968) and distress and decrease the legitimacy of rulers, particularly among discriminated minorities. In terms of conflict theory, the elite and its practices became controversial, which then became the controversial issues in discussions. If the hungry, relatively deprived, institutionally abandoned, or distressed discussed their problems with others who shared the same fate, they would converge toward a shared radicalized framing of their problems and goals. If there was no network before, it would have been established over the course of conversations and the recruitment of additional protesters. The networks (being formed) may not have been connected into one large network, though. At times before social movements as we know them in national states existed,20 it was exceptional when informal leaders brokered multiple large clusters and projected a common framing of the problem at hand, the goal(s) to be achieved, and offered moral justification (Fischer et al. 2013; Atran and Ginges 2012). Hence, an overall working consensus may often not have been reached, but if the same problems were experienced across the clusters, collective action in some clusters could have encouraged collective action in others once the news and turmoil spread. Moreover, a negatively defined goal (e.g., “get rid of the king”), which does not define what is to come afterward, can unify a motley configuration of protesters in a short-term working consensus, while each subgroup may have different and sometimes mutually inconsistent 20 In modern nation-states, we are accustomed to seeing organized protest movements with resources, centralized coordination, norms, and leaders who inspire participants. These factors and actors can all help in the preparation and (long run) execution of protests (Tarrow and Tilly 2009; Goldstone 2001), but they are not necessary to get protests started. As said, smaller protests without charismatic leaders, etcetera, are less likely to have been recorded, which might bias our understanding of protests in general.

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goals for the future. The extent of radicalization depends on the controversialness of the issues discussed and the willingness and capabilities of the elites to resolve the problems, which may decrease the controversialness. Solidarity among protesters could be locally increased through interaction rituals, which typically involve synchronous marching, singing, and noise making (McNeill 1995; McPhail and Wohlstein 1986), and could easily be scaled up to large groups once small groups got together. To this very day, this holds true. Online interactions without physical copresence, in contrast, do not yield strong solidarity (Collins 2004). However, most protesters do not become violent, but small subgroups can do so. As we have seen, shared intentionality does not explain collective action, and at the beginning of a protest, there may not be norms, reputations, or leaders to provide selective incentives (Olson 1965). In that case, the 5r-package is weak and incomplete. The outbreak of protests is usually explained by critical mass theory (Marwell and Oliver 1993), which is advantageous in that it takes recourse neither to the 5r-package nor to selective incentives, and applies to fledgling groups of people who hardly know each other. It uses strong rationality assumptions that are unlikely to hold under the uncertainties of protest, although its main results can be inferred without strong rationality. When confrontations with opponents (ruler’s forces) are risky and payoffs are unknown, a majority of people tend to huddle and conform to (not yet protesting) others. From an evolutionary perspective, conformism, here as conditional cooperation in protest, is a heuristic that can foster survival in an uncertain environment (Van den Berg and Wenseleers 2018). It was conjectured for a long time that agitating turmoil, such as opponents’ provocations (e.g., a tax raise; Dunn 2002) or a triggering event (e.g., the arrest of a protesters’ representative; ibid), causes anger and arousal that result in a burst of collective protest by commoners (Lieberson and Silverman 1965). A revelation of opponents’ weakness also enhances agitation because in times of threat and tension, it poses a low-risk opportunity to hurt opponents. In the words of Schelling (1960, p. 74), “people require some signal for their coordination, a signal so unmistakably comprehensible and so potent in its suggestion for action that everyone can be sure that everyone else reads the same signal with enough confidence to act on it”. As said, a simple mathematical model (Section 8.2) demonstrates that a (sub)group of conditional cooperators can be agitated by such signals to a critical level where few accidental cooperators entail a burst of cooperation. If the participants wish to continue their protest over a longer time span, provocations will not suffice, and they will have to flesh out the 5r-package with norms and reputations, keeping in mind that the greater the group size, the greater the importance of leaders to motivate and coordinate. Under stronger repression, there were fewer protests, but they did break out if protesters overcame their fear (Collins 2008). This was more likely to

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happen if many people showed up in physical copresence and to outnumber their opponents, and if they performed interaction rituals (Collins 2004). Some zealous protesters provoke their opponents (ruler forces) to reciprocally induce fighting. Protesters engaging in violence have to solve two successive collective action dilemmas: one of protesting and one of fighting, clearly with much larger costs and therefore appealing to fewer individuals. Although grievances (i.e., felt constraint) usually do not lead to violence, small subgroups of protesters may feel less deterred by the high risks or become agitated more than the rest (see constraint, p. 72). Most protesters do not get that far because of fear or disagreements among each other, for example, when a regime’s violence against protesters raises doubts about the need for social change (Elias 1977), thereby decreasing solidarity and crumbling the common framing. Moreover, protesters’ opponents are usually better coordinated, equipped, and trained and have learned to suppress their emotional resistance to use violence. Consequently, most protests against extractive regimes have failed and still fail today. Rarely, protests result in a revolution (a rapid overturn of incumbent rulers by many commoners), not because of superior strength but because a weakened regime is taken by surprise. When none of the (in particular military) elites change sides, a revolution is unlikely to succeed; protesters’ success is oftentimes due to government’s weakness. A ruthless regime may accept modest demands to preclude a revolution, but will do everything to prevent challenges to the balance of power. However, some of the regime’s reactions are perceived as additional provocations, resulting in more protest instead of less. If commoners succeed, they constrain and agitate the elites, which often provokes a counter revolution. In summary of this chapter, we have used the game theoretic concept of constraint to understand the onset of conflicts in general. The polarization model tells us that if due to (sometimes imaginary) constraint, an outgroup and its issues become controversial for the ingroup, its members are likely to interact more strongly, at least among each other. If discussions with the outgroup do not diminish the constraint or do not even take place, a critical threshold of controversialness can be reached where the ingroup radicalizes, resulting in an extremely negative framing of the outgroup that becomes part of the ingroup’s shared intentionality. If both groups radicalize, their network polarizes in two opposed camps. The Ising model points out that when radicalized individuals become agitated and a critical threshold of agitation is reached, collective violence breaks out. It also predicts when collective violence will not break out, namely, when shared intentionality has not been achieved, when people are not yet radicalized enough (and their sensitivity to agitation is low), when agitation is below its critical threshold, or when the proportion of defectors is too large (e.g., due to low morale). From agrarian societies onward, leaders and their networks have played a role in framing constraints and creating

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ideologies, thereby increasing (and rarely decreasing) opponents’ controversialness and the ingroup’s radicalization, as well as coordinating collective violence. When leaders constrained their commoners, the latter sometimes protested against them, but because most protesters are reluctant to use violence, even when they radicalize, only small subgroups with low agitation thresholds become violent. These small subgroups are usually no match for the much better armed and more experienced ruler forces. If intergroup violence breaks out, better armed and/or larger groups tend to win or group with stronger morale when arms and size differences are relatively small. Help from other groups can be crucial. For groups the size of polities, it also matters if they have more inclusive institutions, despite the maintenance costs. Once collective conflicts start, the interevent times of violent acts of all sorts of conflicts are power law distributed, and the configuration of participants and others (e.g., bystanders) evolves toward increasing social balance. If neither group is completely crushed and prosocial institutions are present or established through negotiations, the conflict may at some point be resolved, although the survivors will have bitter memories over multiple generations. Taken together, we now have a coherent theory of intergroup conflict from beginning to end, connecting with cooperation (shared intentionality, reputations, and the Ising model) and cultural evolution (framing, institutions, ideology, and weapons), which enables to explain a broad variety of intergroup conflicts and helps to point out which questions remain unanswered. In the next chapter, in particular Section 6.12, the theory of conflict will be expanded to cover wars and protests from 1500 CE till the turn of the millennium. The effects of the internet will be added in Chapter 7.

6 IMPERIALISM AND INDUSTRIALIZATION

By now we have reviewed sapiens’ history until 1500 CE, emphasizing and delving deeper into cultural evolution, cooperation, and conflict. In this chapter, we review the period 1500–2000, again along these three main themes, in particular their relations to urbanization, ideology, and imperialism, and we will explain the rapid increase of cultural elements during the Industrial Revolution that saw disastrous consequences for the natural environment. The general patterns of forager and agrarian societies in Asia, Africa, and America, described in the previous chapters, also characterize Europe. There, agriculture arrived relatively late, and until the Roman Empire (Mann 1986), Europe’s influence on other parts of the world was much smaller than the other way around. The Roman Empire covered central and southern Europe and the Mediterranean approximately around the time of the Qin Empire in China (p. 88), until its decline in the fifth century that left Europe in shambles. Eventually, around 1000 CE, the European population started to grow a little, only to be thwarted by the Black Death (i.e., bubonic plague, 1346– 1352)1 which decimated half of the European population (Alfani 2022). Until the Renaissance, Europe did not compare with China, India, and the Islamic world, which were more advanced in many ways, and it lacked centralization and tight control. It was a patchwork of warring polities competing with one another, with rulers (patrons) dependent on resources and manpower from local elites (clients) to fight wars, who in turn had bargaining power over their rulers. The relatively weak rulers meant more freedom for people who 1 The Black Death killed perhaps 100 million people worldwide (Alfani 2022). The notion of quarantine was invented then, with 40 days of isolation for incoming ships in the Mediterranean, although this did not prevent rats from these ships from transmitting the disease (McNeill 1976). DOI: 10.4324/9781003460831-6

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wanted to study and practice with foreign achievements, such as the mariner’s compass, gunpowder, and manufacturing methods from China, mathematics, astronomy (Figure 6.1), experimental science, and medicine from India and the Islamic world; and old Greek books preserved in Arab and Persian translations, which would become crucial for future developments. By the 1500s, Europe already had 50 relatively autonomous universities (Cantoni and Yuchtman 2014).2 These were largely beyond the control of the small polities at the time; if someone were obstructed by a ruler fearing loss of power, they could simply move to another polity. The Christian church was against intellectual freedom, but its resistance (and that of rulers) was less strong than that of centralized empires elsewhere. Consequently, Europeans had plenty of opportunities to accumulate knowledge, including ideas and objects from different continents. Knowledge entrepreneurs of various backgrounds learned, exchanged, and combined ideas, many of which would apply in engineering, warfare, finance, science, and other fields, in ways that would ultimately change the entire world. In China (Moore 1966, Ch.4) and the Islamic world (Kuran 2018), centralized governance left less freedom for commoners to experiment with foreign ideas.3 The Chinese government, which from the 10th century had stimulated science, technology, and education by publishing books, discouraged or forbade commoners to publish. During the 14th century, “Chinese society became inward looking” (Elvin 1973, p. 205); contacts with the rest of the world diminished, and innovation was halted for reasons currently not well understood. Also, foreign products and trade became forbidden, which may have started with the aims to starve competing polities from Chinese goods and to prevent theft from Chinese traders abroad, but these reasons do not explain why isolation lasted till the 2 The first university was founded in India (Nalanda, 5th century). Also in the Islamic world, universities were established earlier than in Europe. This was done under waqfs, which made them less independent and flexible than European universities. A waqf is a charitable trust, or more precisely “an endowment established under Islamic law by a single person through income-producing private real estate to provide a designated social service in perpetuity” (Kuran 2018), for example, schools, parks, hospitals, and mosques (ibid). Because waqfs were seen as sacred, they could protect against confiscation by the state when private property rights were weak. A waqf is therefore an inclusive institution (in the sense that the central elite cannot extract all resources), but it constrained economic competition with Europe in later centuries, which could not have been foreseen. It was forbidden to merge cash waqfs, which prevented the pooling of capital and the development of banks (Kuran 2018). Further, firms had to be broken up and divided over the family members of the owner when the latter died, which made it nearly impossible for firms to grow large and to reap economies of scale of industrial production, as European firms did (ibid). Waqfs still play a role in the modern world. In Indonesia from the 1960s onward, waqfs protected land and schools of Islamists with a conservative ideology who used them in cooperation with Islamist politicians to foster their interests (Bazzi et al. 2020). 3 Note that a lack of centralization is no guarantee for innovation, and strong centralization does not necessarily obstruct innovation if it is combined with freedom for individuals to experiment in certain domains in line with autocratic regimes’ (e.g., military) interests.

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FIGURE 6.1 An astronomical observatory, Jantar Mantar, in Jaipur, India (1724–

1735). Although it is more recent than the European Renaissance, it underlines the importance of knowledge from outside Europe, such as Indian astronomy, for Europe’s expansion.

19th century (Elvin 1973). The consequences are clear, though: diminished combinatorial opportunities and a lack of background knowledge to understand the opportunities offered by foreign inventions, which, on rare occasions, were presented by Christian missionaries to the Chinese court, but were regarded as entertainment without practical application (Lovell 2006). These missionaries and the imperial court withheld many crucial pieces of European knowledge, thereby making it unfeasible for the Chinese to keep up. 6.1

Europe’s conquests

Slowly but surely, Europe recovered after the Black Death. Renaissance Italy had a head start a century earlier through the invention of banking, corporations, and other organizational novelties (Padgett and Powell 2012, Ch. 5, 6), which enabled European finance and commerce to grow. When money could

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be borrowed and invested, capitalism took off.4 Bankers and merchants invested in gun making, shipbuilding, and mining upon which the ruling elites depended. By 1480, across Europe, there were easily transportable cannons that could destroy any fortification, well-trained soldiers and officers who could coordinate artillery with cavalry and infantry, well-built ships that could navigate at sea, universities, finance, and double-entry bookkeeping (McNeill and McNeill 2003). Backed by these capabilities, Columbus accidentally connected Europe to America (1492) by trying to reach India from the other side, instead of the usual route around Africa.5 Actually, Europeans (i.e., Vikings) discovered America in 1021 (Kuitems et al. 2022) but they did not establish a permanent connection, news about the discovery did not spread wide, and in contrast to Columbus, they did not expect to find gold. The Spanish conquistadores who followed in Columbus’ footsteps into Central and South America and subsequently the British who went into North America killed not only many people by force but also many more through the diseases they brought with them, as Americans had not yet become resistant. Consequently, over a period of a century and a half, the indigenous population shrunk by 95% (Dobyns 1966). Combined with the Spanish and British obtruding their own culture, the effects on American cultures were devastating (McNeill 1976). For example, the Maya had an elaborate astronomy with a 260-day calendar, but when Spanish priests found their richly illustrated books with hieroglyphs on bark paper, they said that the contents were proof of superstition and burned them all (Sokol 2022). Spain and Britain’s violent explorations were followed by colonization, while Portugal took (an initially smaller) Brazil. The colonizers imposed forced labor, slavery, and a socioracial rank order with themselves at the top.6 A short while later, Spanish, Portuguese, British, French, and Dutch entrepreneurs accompanied by warriors sailed across the world to raid and trade (Wallerstein 2011; Wolf 1982). Part of an explanation why these adventures started in Europe was competition between medium-sized polities that 4 Capitalism became the dominant mode of production during the Industrial Revolution (Collins 1980) but if financial markets are seen as a key trait, Renaissance Italy is the starting point. Another key trait—profits invested to make new profits (Wolf 1982)—was invented much earlier. 5 To be precise, Columbus discovered the Bahamas, Haiti (Hispaniola), and Cuba, not mainland America, but he got close enough for others to discover it subsequently. 6 Diamond (2014) and Acemoglu and Robinson (2012) posit that colonizers implemented inclusive institutions where local labor was scarce, parasite load was low, and many colonizers settled (e.g., in the USA and Australia), but it is important to realize that only a minority of wealthy Europeans were included, versus the native populations, women, enslaved, and poor Europeans. In the American Declaration of Independence (1776), for example, “the phrase ‘all men are created equal’ was probably not a deliberate attempt to make a statement about women. It was just that women were beyond consideration as worthy of inclusion” (Zinn 2003, p. 73). Yet, North American institutions with British pedigree (e.g., patents) were more inclusive for European workers than Spanish institutions in South America.

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were large enough to mount expeditions but too small to monopolize all others (Scheidel 2019); this inter-polity competition was absent in China, India, and the Muslim world. Because European entrepreneurs were ideally positioned as brokers in between otherwise unconnected parts of the world and were quickly able to convince everyone to accept gold and silver as currency, they made enormous profits for their kings and themselves. Note that the network structure of brokerage (p. 50) is scale independent, and a brokering actor can also be a corporation, for example, a trade association where profits are shared among its members or the elite of a country. When Europeans arrived at the African coast, they readily noticed an opportunity to connect to the existing slave trade. Enslaving and trading people had been practiced for thousands of years, among others from Scandinavia to Egypt, and in the 12th century, wealthy Chinese already enslaved Africans (Wolf 1982). With bureaucracy and modern ships in their arsenal, Europeans could scale up this trade significantly; until abolition, African traders violently removed 15–20 million people from their communities and families and sold them to European traders who shipped them to America and other destinations, where patrons bought them and enforced themselves as their new masters (Michalopoulos and Papaioannou 2020).7 Many abducted Africans died as a result of the appalling conditions in which they were transported overseas, and when the survivors arrived, their ordeal was by no means over. They had to work long days and were severely tortured for the smallest disobedience by slaveholders who considered themselves highly civilized (De Kom 2019) and good Christians, quoting from the bible to justify their deeds (Douglass 1845). This violent trade had a disruptive effect on African institutions, which became more extractive notwithstanding revolts, and damaged the economy for centuries (Nunn 2008). African rulers tried, sometimes successfully, to centralize their states (e.g., Asante, Dahomey), enabling elites to become rich, whereas many large states (e.g., Kongo, Ndongo) disintegrated into smaller ones. The Eurasian relations between population size and centralization and of war-making and (centralized) state formation did not hold in Africa (OsafoKwaako and Robinson 2013) due to the lower intensities and frequencies of wars (Herbst 2014), the disruptions caused by the Atlantic slave trade, and the scarcity of people rather than land. In African polities that were already centralized before the Atlantic slave trade, relatively fewer people were enslaved than in decentralized and weaker polities, and higher bureaucratic and tax capabilities were inherited by successor states (with exceptions) after 20thcentury decolonization8 (Michalopoulos and Papaioannou 2020). European 7 Attempts to enslave Native Americans failed because they were at home on their land and were embedded in their networks and therefore able to fiercely resist. 8 Decolonization means that colonies became legally independent. However, former colonizers continued to serve their own interests by exerting political, economic, and sometimes military influence.

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traders introduced new crops into Africa from the Americas (e.g., maize and manioc), which entailed a shift in the division of labor; in many African regions, farming became mostly the work of women, while men (if not enslaved) tried to make money in the lucrative commerce of goods and enslaved (Ogot 1992). In order not to alleviate any guilt resulting from doing so much harm to so many, slaveholders rationalized away their deeds through a fiction-based theory about skin color and races, just like Arabs had been doing before Europeans entered the slave trade (Hall 2011).9 Although Christian faith was unsuited to distinguish enslaved from slaveholders, the bible was reinterpreted to make this distinction and to legitimize both racism and slavery. This toxic ideological mixture added to the victims’ suffering and has continued to inspire racists until today (Du Bois 1903; Smedley and Smedley 2005; Dovidio 2009). North American elites also promoted racism to prevent poor European immigrants from joining forces with enslaved Africans (or their descendants) to overthrow them (Zinn 2003). Some enslaved people escaped to live in freedom until they were caught or formed groups to support one another and revolt (De Kom 2019; Zinn 2003), whereas others were repressed to the point that they resigned. In the Islamic world, in contrast, which reached from North Africa to India and Central Asia, slavery was a meritocratic route to success for some poor families that started before the transatlantic trade; with talent and extensive training, sons from poor non-Muslim families could ascend through the military ranks of their master’s army and even gain political influence. In the 19th century, slavery was officially abolished in Europe and the Americas, but it was the slaveholders, not the formerly enslaved, who were compensated. From a longer-term perspective, slavery did not end, but rather changed places and practices. International abolition was followed by more local slavery in Africa, and work shifted from agricultural production and mining in the 19th century to 20th century construction and manufacturing in the Soviet Union and Nazi Germany to 21st-century sweat shops, sex work, and domestic labor. 6.2

Warfare and bureaucracy

From the 16th to the 18th century in Europe, innovations in military technology, such as firearms and the social technologies of drill, discipline, and logistics, made armies more powerful. New weapons were used by foot soldiers and mercenaries, which made warriors on horse-back (aristocracy) largely redundant in warfare, and made it possible for kings to centralize their states (Elias 1939). Part and parcel of centralization was a progressive—but never 9 Associations between skin color and properties of mind, character, and culture were rampant in many cultures before European racism and became influenced by European racism during and after colonization (Dikötter 1992).

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complete—replacement of the army’s patronage and plunder, with bureaucracy staffed by specialists assigned on a meritocratic basis, the latter resulting in far more efficient armies (Weber 1922).10 This development accelerated after the French Revolution. Bureaucratization coevolved with the strengthening of military power, which can be seen throughout agricultural societies in Eurasia (Turchin et al. 2022). Governments of centralized states claimed— but never completely realized—the monopoly of violence (Weber 1922), and gradually disarmed their populations (Tilly 1990). Since the new institutions made states more peaceful internally (indicated by decreasing homicides; Eisner 2003), they were able to become better organized in wars against one another (Elias 1939), which, together with firearms, resulted in a “geopolitical shakeout” of weaker states (McNeill and McNeill 2003, p. 179), as well as the survival of fewer but stronger and more centralized states. A similar process had taken place two millennia earlier in China, but with a different outcome: a single state prevailed. Wherever states’ rulers implemented bureaucracy, it was followed by economic progress (Evans and Rauch 1999). This is in part due to predictable promotion steps based on merit that make bureaucrats (somewhat) less inclined toward corruption (ibid), not to mention the advantages of rules and monitoring for cooperation. In bureaucratic states on both sides of the North Atlantic, a commercial elite emerged (part of the bourgeoisie, which also comprised lawyers, notaries, and bankers) that brokered global trade and made a fortune. During the same period (16–19th centuries), African long-distance trade with Europe increased, and African traders became wealthier and more powerful. Brokerage and patronage as well as asymmetric exchange on its own were the mechanisms of increasing inequality, similar to Eurasia. African elites enslaved more people because, as noted earlier, labor was scarce. Army size, standing army (versus farmer-soldiers), and European firearms were important factors to win African wars, as everywhere else, but African warfare did not result in the diffusion of bureaucracy or inclusive institutions. Meanwhile, wealthy European traders increasingly financed their rulers’ ruinously expensive wars and used their leverage to demand more inclusive institutions in return. They adopted an ideology to back up their demand: a ruler should have the consent of the governed (an idea originating in the Roman Empire, forgotten during the Middle Ages, and retrieved by Locke). This ideology also motivated elites and militants in the British colonies in North America in their eventually successful, but decades long, revolution against Britain. They rendered the consent of the governed more specific: no 10 Weber’s model (1922) of bureaucracy boils down to one key feature: formal, impersonal rules. These rules define positions (job qualifications, rights, and duties) independent of the people who fill them; task execution and its output; the separation of work from private matters; record keeping; and administration. The positions stand in a hierarchy of authority, also defined by rules. Weber forgot to mention explicitly that in this hierarchy there is (supposed to be) unity of command (Fayol 1949), i.e., every employee follows orders from one superior.

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taxation without participation. Notably, their success inspired the French to overthrow their own aristocracy in the most famous revolution of all time (1789). 6.3

Nationalism

Around the time of the French Revolution, another new ideology became popular among political upstarts, as well as rulers who perceived a chance to legitimize and consolidate their power: nationalism. Although cultural unity was already felt in some places in the 16th and 17th centuries (e.g., Germans felt German), these feelings had not yet been developed into an ideology. One source of inspiration for such an ideology was the American Declaration of Independence, the dissemination of which encouraged nationalists to stipulate that polities become self-governed nation-states with a unity of language, religion, ethnicity, history, and territory. To turn fictitious fraternity into actual solidarity, the historical record was revised: an army’s rapes, theft, and murders of the past, as well as rulers’ exploitation, were carefully deleted and replaced by a narrative about heroes leading a population in distress to a fair and glorious future, including when under threat by foreign conspirators (Anderson 1991). Nationalism spread during the 19th and 20th centuries among countries’ populations through education, military service, and the expanding networks of roads and railroads (Weber 1976), turning commoners into citizens. The creation of a collective memory was further aided by the press, propaganda, maps, museums, monuments, and other national symbols. Since nationality is not ingrained in DNA, emotion work had to be accomplished for people to love their nations, which was more successful in some places than in others. In his famous study of France, Eugen Weber depicted peasants as passive recipients of nationalism, but subsequent studies of a series of countries showed that peasants actively negotiated nationalism and social reforms and that they were not so different from urban citizens after all (Cabo and Molina 2009). Once people internalized nationalism, they became more cooperative with anonymous others from their own nation than with people from different nations, which has been investigated in the 21st century in 42 nations across the world (Romano et al. 2021). Furthermore, once all adult males (and later everyone in a nation-state) were surveyed from cradle to grave by its national bureaucracy, it became easier for governments to recruit soldiers and other contributors to war efforts and to collect more taxes, resulting in more effective warfare (Tilly 1990). National cultures became tighter when collective action was required more frequently or at larger scale, in particular to address natural disasters of various sorts (e.g., pandemics) and under real or imaginary threats from other polities (Gelfand et al. 2011). Tightness comes with preferences for authoritarian leaders, moralistic religion, and avoidance of strangers with different

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norms, but varies at the subnational level, which has been assessed in the USA (Harrington and Gelfand 2014) and in China (p. 66). Tightness tends to become long-lasting because “it is easier to strengthen norms than to loosen them” (Jackson, Gelfand, and Ember 2020). The inter-polity diffusion of nationalism grew rapidly from successful role models to the rest of the world. However, the implementation of the concept was less swift than its diffusion, and it was resisted by rulers whose power depended on brokering between large ethnic groups (Habsburg and Ottoman empires) until they could no longer hold. When an incumbent elite weakened, nationalists tried to damage their legitimacy by scandalizing them, all the while recruiting followers. Nationalist efforts had a higher chance of success if nearby polities recently became nation-states (Wimmer and Feinstein 2010).11 Nationalism could engender solidarity of populations that had no overall cohesion due to gaps in the sparse, national network. As a mental exercise, we can recapitulate nationalism in terms of the threestep evolutionary model, not to gain new insights, but to get a coherent understanding of this complex process. A coarsely grained evolutionary reading is as follows. (1) Once the concept of state was combined with the ideas of self-governance and multifold unity (of language, etcetera), it was enriched with flags, parades, and other symbols, and diffused within polities by education, military service, and (rail)roads, and between polities by word-of-mouth and pamphlets. (2) It was used successfully to increase solidarity among citizens, in intra-elite competition (meso level), and by national states competing with other polities (macro level), which fed back into (1) more widespread diffusion. Note that transmission and use differed between different groups (upstarts, incumbent elites, and commoners). (3) This diffusion and group selection lead to the demise of non-national polities (group selection) and changed the polity landscape to the extent that by the 20th century, other (i.e., non-national) polities were non-viable. Consequently, (2) leaders’ legitimacy claims shifted from religious to nationalistic ideology. An unintended effect was that (2) local elites and other groups in Europe’s colonies sooner or later proclaimed independence on the basis of nationalist ideologies. Nationalism often evolved at the expense of ethnic minorities. In contrast to the empires of the past, where multiethnicity was largely unproblematic, the presence of multiple ethnicities in nation-states was framed as problematic by the dominant elites (Mann 1999). One could say that they turned ingroup minorities into outgroups. People belonging to the majority developed the idea that they had the proper national culture in their blood, and looked down on minority groups with fervor and disdain, especially on the largest minority, whom they saw as a potential threat. As is often the case, minority’s putative threat and constraint were majority’s fantasy and construction, 11 Mass literacy, direct rule (instead of indirect rule through local elites) and industrialization had no effect on nationalism (Wimmer and Feinstein 2010).

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but majority’s radicalization and its consequences were real. Hate crimes by the majority are committed most often against members of the largest minority, decreasing according to minorities’ size rank order position within the national state (Cikara, Fouka, and Tabellini 2022). To make matters worse, leaders occasionally ordered violence to be carried out against ethnic minorities, or they diminished minorities’ access to collective goods—practices that continued until today. In turn, these actions encouraged ethnic and religious group building (Blanton 2015) and provoked organized protests or counterattacks. The chances of violent clashes increase if conflicts of interest are ethnically framed, but less so if ethnicity is unrelated to wealth inequality (Acemoglu and Robinson 2006). 6.4

Industrial revolution

We have seen that knowledge entrepreneurship in politically divided Europe entailed capitalism, imperialism, shifting elites, and nationalism, and we are about to see more changes. The fact that these developments were initially European was contingent on the absence of centralized control and the presence of a wide-ranging network of knowledge exchange, discussed in this section. Had these two conditions held elsewhere, we may have seen similar developments there (although I’m less sure about nationalism). The approximately 50 European universities in 1500 were connected to a continent-wide network of a relatively small number of intellectuals, called the Republic of Letters (Mokyr 2017). In this network, new knowledge was created through knowledge brokerage of complementary ideas, observations, and practices (Schumpeter 1934; Fleming 2001), which we can nowadays trace through patent data (Jaffe and Trajtenberg 2002) and scientific citations (Uzzi et al. 2013; Price 1965). Many innovations were incremental improvements such as simplifications (Mokyr 2002) and makeshift solutions with whatever resources were available, as “seeing the potential in new ideas, or devising new uses for old ideas, is just as important as the initial generation of novel ideas” (Brown 2013). The paper trail documents the ideas successful enough to make it to publication. However, most attempts at novelty failed before publication (Ormerod 2005). Joel Mokyr (2017) interpreted this history of ideas from an evolutionary perspective, and argued that the intellectuals who exchanged and created ideas competed mainly for reputations and to a lesser extent for power or money. For the latter, they depended on wealthy patrons for support, who in turn hoped to benefit from high-quality advice and sought the halo of intellectual prestige. The Republic of Letters developed its own institutions bottom-up, of openness, criticism, egalitarianism, intellectual property rights, and most important of all, empirical testing of propositions. Some of these ideas must have been (tacitly) developed at other places in earlier times, as the great many inventions in Africa, Asia, and

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America would have been impossible without empirical testing and critical feedback. In science, these norms have continued to this day, and fly in the face of (self)deceivers with dubious claims of authority. Although most discoveries, including those outside of Europe, have been ascribed to men, women made many more discoveries than those for which they received credit, because men maintained baseless gender stereotypes (Napp and Breda 2022) and prevented women from receiving academic affiliations that would have made them more visible (Bod 2022). Furthermore, many ideas developed in Europe were based on findings and innovations from other parts of the world (p. 99). For example, it was at one moment crucial for the British industry to transform scrap metal into iron bars. To get it done, a British entrepreneur patented a technology that he copied from enslaved Africans working in Jamaica without giving them credit (Bulstrode 2023). In 18th-century Britain, the local arm of the Republic of Letters consisted of artisans with technical skills and knowledge, some of whom also mastered science. It was the exchange between and combination of scientific and technical knowledge that resulted in the most influential innovations (Mokyr 2002), which were further refined to become practically useful (Usher 1954; Mokyr 2015).12 Tinkering (Jacob 1977) with trial and error was far more important in discovery than formal education, which very few had. Apprenticeship rather than education transmitted most of the useful but largely tacit knowledge (Polanyi 1958; Ó Gráda 2016), which impeded quick copying by competitors (Nelson and Winter 1982). Along with a network of scholars, technicians, and businesspeople, Britain had favorable economic and material conditions, namely banks (since 1696), the London Stock Exchange (1773), business-friendly laws, commodity and labor markets, cheap printing, newspapers, infrastructure (e.g., postal services), and last but not least, two natural resources in large supply: iron and coal. Under these favorable conditions, the evolving network brought about the Industrial Revolution. Coincidentally, these circumstances happened to be in Britain, versus elsewhere. Moreover, had no large supplies of coal and iron been available, the Industrial Revolution would have quickly fizzled out. The question of why technological growth was both rapid and sustained (Christian 2011, p. 352), in contrast to earlier waves of innovations that extinguished even if resources were present (e.g., China), can be answered by the evolutionary model, which shows that at a critical level of principles (knowledge on how incumbent elements can be combined into new elements), the process of innovation will rapidly accelerate and then level off. In other 12 Some say that there was little contact between technicians and scientists; “probably more useful knowledge was transmitted through espionage than through learned societies” (Ó Gráda 2016). However, it was not the amount of contact that mattered; for specialists with absorptive capacity based on expertise (Cohen and Levinthal 1990), rare encounters suffice to acquire valuable ideas.

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words, the cultural evolutionary process reveals a phase transition (if enough resources are available to make it happen), which we know as the Industrial Revolution (Hanel, Kauffman, and Thurner 2005). This phase transition glosses over smaller and local bursts of innovation, for example, after technological paradigm shifts (Dosi 1982). The phase transition can also be simulated, which is shown in Figure 6.2; a formal proof is in Section 8.3. Note that the plateau after the rapid increase is a consequence of choices made for the simulation: the probabilities, respectively, that innovation attempts fail and that elements are rejected. Without randomness and loss of elements, the increase of the number of new elements levels off smoothly. By increasing chances of failure and rejection, for example, during a major crisis, the number of cultural elements could even decrease instead of increase, which is a matter of parameter estimation through data analysis. The phase transition entails three empirical predictions.13 First, at a critical level of knowledge, the creation of a little more knowledge will result in a cultural explosion of many new cultural elements, including principles. Many scientific discoveries and technological inventions were already made prior to the Industrial Revolution (e.g., Newton’s mechanics), and had brought the knowledge level close to the critical level. In fact, relatively few additional discoveries and inventions seem to have triggered the onset of the Industrial Revolution. Second, the model predicts sustained growth, which can be clearly seen in the numbers of patents and scientific papers (Youn et al. 2015).14 Third, after a period of rapid growth, the total number of cultural elements will level off. Exactly how this leveling off occurs depends on the number of cultural elements and the number of principles among these elements, which we do not know (yet). The evidence for the leveling off is more subtle, since the outputs of engineering and science continued to grow. However, when controlling for the numbers of engineers and researchers, productivity fell dramatically over the past half-century (Bloom et al. 2020). In other words, to maintain the same research output, increasingly larger teams were necessary, which underscores that novel ideas are progressively harder to find. This in turn suggests that the discovery of principles that are useful to create novel ideas has become progressively harder, for which there is evidence. Since the 1960s, all fields of engineering and science have been characterized by decreasing novelty at a decreasing rate (Park, Leahey, and Funk 2023), in line with the three-step model (Hanel, Kauffman, and Thurner 2005). (Whether artificial intelligence will change this trend remains to be seen.) Traversing from 13 Alternative models of the Industrial Revolution may yield the same predictions, but the ones I saw do not model evolution in general and are based on stronger assumptions, such as the functional form of the rapid increase. 14 Whereas the sound and fury of the steam engines and power looms drew most of the attention, other innovations were at least as influential, such as concrete, and fertilizers that increased food production for a much larger population.

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Time FIGURE 6.2 Industrial Revolution. Simulation of the three-step model with numbers of

cultural elements over time; the units do not correspond to actual numbers and years, respectively.

the model to the real world, one also encounters hard limits to materialize novel ideas; there are physical limits (e.g., sunshine for solar panels), finite material resources (e.g., to harvest energy), and it is not possible to build a society on immaterial goods only (Murphy 2022). If, and in what pattern, the total number of cultural elements will eventually level off (or even decline) remains to be examined in the future. Interestingly, the evolutionary model points out that the Industrial Revolution was an emergent phenomenon composed of a concatenation of combinations that did not depend on the genius of individuals. Although some inventors and technicians were surely smart and creative, “even the greatest human innovators are, in the great scheme of things, midgets standing on the shoulders of a vast pyramid of midgets. […] No single innovator contributes more than a small portion of the total” (Richerson and Boyd 2005, p. 50). 6.5

Organizations

When the innovations of the Industrial Revolution became available, their production was implemented in numerous organizations15 that by this time, 15 An organization is a group of (at least two) people in formal authority and informal coworking relationships, with collective, but not necessarily shared, intentionality (goals), at least among its leaders (managers). From the Italian Renaissance onward, organizations began to be regarded as actors independent of their members (Padgett and Powell 2012, Ch. 5).

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and into our time, had features of Weber’s description of a bureaucracy (Footnote 10, p. 104). The extent to which bureaucratic rules were fleshed out varied greatly, and nepotism and corruption were never completely ruled out, especially when monitoring could be evaded through secrecy or distance. Organizations “vacuumed up” (Perrow 1991) many activities performed in (and resources derived from) households and communities; first manufacturing, and later commerce, teaching, and health care (Coleman 1974), all accompanied by specialization (i.e., more refined division of labor) and economies of scale. Organizations also developed novel activities, such as motorized transport and after World War 2, mass tourism. One may say that organizations were deliberately established, special purpose communities (Stinchcombe 1965), in contrast to ethnic communities. Most of them were at least as unequal as the societies wherein they blossomed, with very few (if any) women in higher positions, even though some women seized new opportunities in lower ranks to have their own income and independence from men and family. During 5000 years of record keeping, enabled by bureaucracy (and nowadays by all kinds of bureaucratic organizations), the memory of past actions was greatly improved, thereby making reputations more accurate and reliable (Basu et al. 2009) and enlarging the scale and scope of the 6r-package. In repressive regimes, however, bureaucracy also created opportunities for surveillance, denunciation, and betrayal (Elder 2006). The formal hierarchy helped managers to transform collective action challenges into dyadic contractual relationships with employees (Simon 1997), which enhanced managers’ power and control. Employees’ reputational concerns and feelings of pride made most of them contribute more “than the minimum that could be extracted from them by supervisory enforcement of the (vague) terms of the employment contract” (Simon 1991, p. 33). During industrialization, the majority of labor became commodified (or conscripted, in the military’s case) and ended up in organizations under strict discipline (Merton 1947). This was accompanied by a loss of control by people working in such organizations (Coleman 1974), analogous to single cells becoming multicellular organisms early in evolution. Many jobs in industry and offices were tedious and repetitive, and some outright dangerous (Braverman 1974), such as the mining immortalized by photographer Sebastião Salgado (1993). After modern bureaucratic organizations proved themselves in wars, they were copied by European industrialists and other entrepreneurs, and then copied around the world, where they were developed in many forms adapted to local concerns, for example, when they replaced waqfs (Footnote 2, p. 99) in the Islamic world. This diffusion coincided to some degree with the spread of industrial —————————————Since industrialization, all organizations have formal bureaucratic features (Footnote 10, p. 104) while keeping the informal properties of social groups, such as gossip, jokes, friendship, solidarity, power play, conflicts, and all that (Roethlisberger and Dickson 1939), (Selznick 1948).

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production, and continued well into the 20th century. Because it resulted in economies of scale and the rising power of the industrial elite, it was often obstructed by incumbent elites who saw the rising industrialists as a threat (Michels 1915), until their resistance was overcome. Interdependent organizations, such as car manufacturers and their suppliers and financiers, clustered in organizational fields with both competitive and various sorts of cooperative ties, through which reputations spread. A well-researched example is the bio-industry (Powell et al. 2005). One can look at these fields from an evolutionary perspective, and observe dynamics over decades or centuries driven by competition and innovation that result in the disbanding of some organizations and the proliferation of others (Hannan and Freeman 1977). Whether they survive or perish also depends on their routines (skills and habitus of the organization; Pentland and Rueter 1994), which they use to produce and engage in relationships with their audiences (suppliers, customers, government, labor market, and others; Hannan 2005). Routines are partly cultural and partly unique to the organization (Nelson and Winter 1982). When developing routines, organizations face a trade-off between specialization (highly adapted to a small audience) and generalism (moderately adapted to a larger audience), where the optimum depends on fluctuations of the socioeconomic environment (Bruggeman and Ó Nualláin 2000). When figuring out what to do, organizations attempt to imitate successful competitors (DiMaggio and Powell 1983), but routines tend to be difficult to copy because they partly consist of tacit knowledge (Nelson and Winter 1982). Just like rivaling groups and polities at war, organizations tend to overspend on competition (Bruggeman, Visser, and van Rossum 2003), for example, through advertising that has little effect. 6.6

Industrial society and its tensions

Following the diffusion of innovations in communication (telegraph) and transport, the entire global population became a “small world” (Milgram 1967; Watts and Strogatz 1998) by the second half of the 19th century, wherein a financial crisis or an epidemic (e.g., the Spanish flu, 1918) could spread rapidly worldwide (Vespignani 2012). In this global network, “almost no one was left out—though many were left behind” (McNeill and McNeill 2003, p. 155). When it became possible for poor people across Eurasia to access newspapers and read where work was to be found, they migrated in masses within and across countries to places where they believed they could have a better life. Among the destinations where places where vacancies had opened up due to the legal ending of slavery, particularly in America. Thereby, these new migrants became disembedded from their local communities, and their labor was commodified on a global labor market (Polanyi 1944). Following closely in the footsteps of industrialization, the vast majority flocked

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to the growing cities, a trend that persists today. Newcomers had to adapt their habitus to deal with the large numbers of heterogeneous strangers they encountered there (Simmel 1908; Milgram 1970), co-depending on the social groups they entered. A small few made fortunes, while many had only meager salaries in industry, or did not posses the skills that were most in demand and thus faced high economic uncertainty and poverty. Newcomers brought with them diverse knowledge and skills, and thereby unintentionally enlarged the urban reservoirs of cultural elements. Larger cities have a larger diversity of knowledge, enabling the making of more, and more complex, innovations (Gomez-Lievano, Patterson-Lomba, and Hausmann 2016). In the rapidly growing cities (and later in the countryside), an evolutionary miracle happened (Newson and Richerson 2009). Instead of trying to raise as many children as possible (i.e., to maximize fitness), as all animals including people used to do, citizens had fewer children, opposing social pressure from government and church (and often family). This happened first in Europe and North America during the second half of the 19th century. Over a century and a half, all countries followed (and the few exceptions will soon follow). Initially, urban mortality rates declined by separating drinking water and sewage. Subsequently, steady progress in medicine contributed to the same effect. In particular, the death rate of young children decreased, and for the first time in history, survival rates in cities eclipsed those of the countrysides. Consequently, population growth rates increased, but at the same time, people became more confident that their children would make it to adulthood, and therefore, felt less urged to have many. Their confidence was further fostered by certain national states starting to provide healthcare and education (De Swaan 1988). Furthermore, raising children in cities (and subsequently in the countryside) became more expensive, which discouraged having many (Mace 2008), and the successful role models that citizens were exposed to— the rich—had few children themselves (Richerson and Boyd 2005). Birth rates then started to drop, where after growth rates fell, although the latter stayed overall positive (Lesthaeghe 2014); see Figure 6.3. Once birthrates declined, families became smaller, and a much larger proportion of people’s social contacts became nonkin, whose modern habits and norms reinforced the trend of having few children, compared with extended families that encouraged their members to have many. The difference is clear when comparing poor people’s sources of socioeconomic support in cities before and after the demographic transition. For example in Mexico City in the 1960s, birthrates were high and the lion’s share of poor citizens’ support came from reciprocal ties with extended family members (Lomnitz 1977), whereas in another place where birthrates were low (Milwaukee, 40 years later), most support came through “disposable ties” with nonkin (Desmond 2012). Because we are currently way above the carrying capacity of the planet, this demographic change is fortunate and does not depend on benevolent politicians and their policies. While

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our overshooting of the earth’s carrying capacity is due to overconsumption by a minority of super wealthy rather than the absolute number of people (Millward-Hopkins 2022), a halted growth is still certainly beneficial in the long run. At the beginning of industrialization, health conditions in factories were bad: people worked many hours (12 a day, six days a week, normally with no holidays), huge numbers of workers died or were wounded on the job, salaries were low, and the workforce included children. Socialism became the counter ideology (Marx and Engels 1848) that proclaimed inclusive institutions and spread as fast and nearly as wide as nationalism. Citizens in industrializing countries started to organize collective protests and created labor unions, a new form of organization that put political and industrial elites under pressure (De Swaan 1988). The invention of the newspaper, the telegraph, and the railroad made it possible to increase the scale and scope of protests (e.g., García-Jimeno, Iglesias, and Yildirim 2022) through wide transmission of framings of social issues (Tarrow and Tilly 2009; Goldstone 2001), thereby agitating large numbers of people. Social movements and other social interest groups were formed in national arenas (Weber 1922; Mann 2012), where they created their own ideologies (Hale 2013; Steinberg 1998). They used modern means of communication and transport to contest the government, or one another, and attempted to materialize their goals or to prevent others from changing what they had achieved (Tilly 1976). With labor progressively commodified and concentrated in cities, many of the grievances leading to group formation and protests were in the minds and conversations of young men without (proper) work.16 To act collectively, the modern organizational form was often used to muster resources, coordinate, and develop and maintain norms. Some of these groups also had charismatic leaders. Moreover, the new urban landscape provided unique possibilities (and constraints) with regard to attacks and escapes, something that protesters and police alike considered (Dhattiwala 2019). If violent repression or buying off protesters did not end protests, north European and American industrial elites would reluctantly, and in a stepwise manner, improve the conditions in the factories, among others by abolishing child labor and reducing working hours, while political elites would sometimes increase the scope of suffrage. These changes toward inclusiveness were not granted out of generosity, but because north European and American citizens became better organized; repressing them became more difficult, while 16 Unemployment or low-quality work can be caused by population growth beyond the carrying capacity (Goldstone 2017), an economic crisis, too much immigration in a short period of time, or a combination thereof. Governments have addressed the problem in various ways, by sending the unemployed to colonies abroad (British Empire), buying them off (late 20thcentury Gulf states), putting them in prison (dark-skinned males in the US; Pettit and Western 2004), forcing them into slave labor (North Korea), or killing the drug addicts among them (Philippines, under Duterte).

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their human capital became too important, both to the economy (Acemoglu, Johnson, and Robinson 2005) and modern warfare (Gintis, van Schaik, and Boehm 2015). Owners of the land and natural resources (oil, diamonds, gold) obstructed worker rights and democracy because they had much to lose (Moore 1966),17 whereas capital owners were dependent on skilled workers and therefore gave in to worker demands more easily (Acemoglu and Robinson 2006). Democracies thus emerged and were developed further under continual pressure by large groups of citizens who were important for the elites (Blanton and Fargher 2008). In pertaining countries, the elite-worker relationships were more reciprocal (Elias 1978) than in countries where labor could easily be replaced by migrants or unemployed people, as was frequently done, for example, by land-owning elites in Latin America.18 If working conditions improved, it was partly thanks to technological innovations; a portion of the most dangerous and unhealthy work was, in some cases, taken over by machines, also in many autocracies. Some work remained unsafe or dull everywhere. Because of the invention of electric light, sweatshops maintained long working hours; they also had bad working conditions. Certain innovations became new sources of stress and danger or boredom, for example, driving a tank on the battlefield or doing repetitive simple work, respectively. Yet to keep the elites satisfied with (new) services and to design and maintain new machines, increasing numbers of citizens were given access to education and health care. Around the world, women had to struggle harder than men for their rights because they also faced male prejudice against their competences (Jacobs 1924), spent more time than men on childcare, and had to struggle even harder if they simultaneously faced racism (Crenshaw 1989). These prejudices were transmitted over generations and turned out to be extremely persistent. When educational opportunities for men improved, they largely excluded women, which widened the gender gap between opportunities and income. Numerous feminist movements over the past two centuries (e.g., Wollestonecraft 1792; Rosenthal et al. 1985) accomplished a gradual closing of these gaps and provided crucial support in the rise of democracy (Chenoweth and Marks 2022). Nevertheless, the gaps have not yet closed and there have been continual counter-efforts by men who want to preserve (or regain) their institutionalized privileges. In industrializing countries (first), abortion was rendered illegal (at least for some time) and in all countries, women’s rights, in particular self-determination and gender equality, have remained contested by men who want to stay in control. 17 Contrary to Moore (1966), most democracies were established despite middle classes, not thanks to them (Heller 2022). 18 In Latin America, democratization was also obstructed by the widespread use of violence to resolve both political and non-political conflicts (Loveman 1999) and after WW2, by the USA’s efforts to prevent the spread of communism.

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6.7

Imperialism and nationalism

When European countries and the United States reaped the fruits of the Industrial Revolution, their elites became wealthier and increased their military power. Competition between these states grew, as did their desire for resources and access to markets, and their abilities to obtain them. To this end, territories elsewhere in the world were colonized, with India the most populous and industrious. “The economic figures speak for themselves. In 1600, when the East India Company was founded, Britain was generating 1.8% of the world’s GDP, while India was producing 22.5%. By the peak of the Raj [i.e., the British occupation], those figures had more or less reversed: India was reduced from the world’s leading manufacturing nation to a symbol of famine and deprivation” (Dalrymple, cited by Sen 2021), with a life expectancy of 32 years. Other colonizers were roughly equally extractive in their colonies, but nowhere, the loot surpassed that of the British in India. The United States colonized Puerto Rico, Hawaii, and the Philippines, and have violently interfered with governments in (Spanish) Cuba, Nicaragua, and many other places. Russia industrialized later than Europe, but it had a large army that conquered parts of Eastern Europe (Ukraine, Poland) and Central Asia, none of which were industrialized either and could not effectively defend themselves. Until the 1800s, the occupation of Africa was limited to coastal areas due to geographic hindrances and parasite load. During the 19th century, however, these geographic limitations were overcome; Western European explorers, traders, soldiers, and missionaries went out in “the rush for Africa”. The various arbitrary boundaries in Africa, drawn by Europeans in Europe (from 1884 onward) and which accidentally cut across ethnic and language groups, often prevented that Europeans from clashing with one another during their land grabbing.19 The explorers did whatever they could to weaken the power of African elites and to serve European interests. Revolts happened but were crushed. European elites and colonizers “not only ignored but even positively discouraged or denied” the fact that Africa was the birthplace of music, dance, sculpture, painting, storytelling, architecture, cooking, and culture in general; “Africa’s heart was beating except that the Europeans were too deafened by their own prejudice, preconceptions, and arrogance […] to hear it” (Adu Boahen 1985, p. 804). Traditional culture could only survive in villages and forests, far away from Europeans in cities. Railroads were built to connect natural resource-rich regions to ports, not to connect cities with each other (except in North Africa). Land was commodified and the best parts were taken by Europeans, who forbade women to work for or participate in 19 Napoleon’s army colonized Egypt for a few years around 1800. It was a role model for subsequent French and other Europeans to believe that they could do the same in other parts of Africa.

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activities organized by the Europeans. Men were forced to work, but industrial production was not implemented (with very rare and relatively small exceptions, e.g., in Algiers). Colonial governance undermined the African institution of slavery, which eventually came to an end (Austin 2009) even though some of it continued illegally. Yet, working for Europeans could turn out worse than slavery for some, depending on how much one resisted and how cruelly one’s employer would punish. Taking into account the atrocities committed, especially in Congo, the effects of colonialism on survival chances and African culture were ruinous. During the colonial era, larger numbers of missionaries transmitted Christianity and Islam to more people than in centuries past. Islam spread in colonies where it had been already established, in the northern parts of Africa. These two moralistic religions were often practiced along with animist religions without replacing them (Figure 4.1, p. 58). Missionaries also provided primary education for minorities of the children (which in French colonies was mostly done by the state), and for the 70– 80 years that colonization lasted, many of these children learned the lingua franca of their colonizers but no science or industrial production (Adu Boahen 1985). Some Africans became part of colonial patronage networks and learned administrative skills on the fly. Very rarely, someone was given more than primary education, but more so after WW2. Accidentally, the missionaries also transmitted European ideas about nationalism and governance, which were spread further along and by the colonial railroads. Nationalism became a potent ideology among literate Africans against colonization, reinforced by Islam (versus Christian colonizers) in Muslim countries. The irony is that the ideology that eventually motivated the colonized to fight for their independence originated in the countries that first took their independence away. Spain and Portugal were lagging behind northern European countries, and their military power had weakened after the French invasion in 1808. When Latin Americans noticed the successful revolution against the British colonizers up north, they mounted revolts against their colonizers, successfully establishing 16 new independent nations on the map between 1810 and 1830. However, social reforms were not implemented, and the new states inherited extractive institutions with weak and venal bureaucracies (Centeno 1997). Inhabitants’ resistance to Spanish taxes was so strong that the new governments had no legitimacy whatsoever to impose direct taxes. For most of the remainder of the 19th century, the new states became engaged in interstate and internal wars, but in contrast to Europe, where peace after war made states, the interwar periods were too brief, debt was too high, the bureaucracies were too weak, and the elites were too indifferent and divided (except in Brazil) to strengthen the states and to abide by the rule of law (Centeno 1997). Within the divided playing field, and lacking a dominant elite, the Catholic Church became a more powerful actor than in contemporary

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Europe. Armies consumed most of the public resources, and even though they were too weak to outcompete the caudillos with their private armies, they had a larger say in politics than in the rest of the world. Aware of states’ continual problems, the armies always found a reason to intervene to protect la patria (Loveman 1999), which, together with the involvement of the land-owning elites, obstructed the development of inclusive institutions. On a positive note, the mixture of European, African, and American cultures yielded innovations in music, dance, literature, and food, just like in other places where different cultures cross-fertilized. In the second half of the 20th century, active social movements contributed to the democratization of Latin America’s countries (to various degrees; Heller 2022), but toward the end of the century, the lucrative export of drugs and the availability of firearms, under conditions of high inequality and poverty of many, resulted in a surge of violence and fear that destroyed many lives and large portions of public space (Briceño-León and Zubillaga 2002). Criminal organizations took control of areas where government control was weak, often in (at least tacit) cooperation with the government (Feldmann and Luna 2022; Earle 2011), which severely undermined democracy. Criminal patronage networks have no ideology that makes their practices legitimate, hence they have to operate in the dark unless they become so powerful that they can openly do their trade and suppress interventions by the state. 6.8

The whole world at war

Modern armies with industrially produced weapons in large quantities made it possible to kill on an industrial scale, resulting in 20 million people killed during World War 1. This war had a prelude of a turbulent, imbalanced network of interdependent nations (p. 80) and an onset that was provoked by an incident (the murder of a prince). The war was exceptional in the number of countries that participated, less so in the number of casualties; the earlier Taiping rebellion (China) had at least the same number. Perhaps even more surprising than the numbers was the enthusiasm and excitement of the soldiers at the beginning. When millions of soldiers were killed in the trenches, though, the initial enthusiasm was quickly extinguished. It was the first war in which the public at large could see in cinemas the true devastation. After three years of war (1917), the weakened Russian elite was overthrown by revolutionaries with a communist ideology. They reduced economic inequality, and gave women access to education and male-dominated professions, but for other aspects of social life they established an extractive, violent regime. Stalin had millions of citizens, most of them chosen at random, incarcerated or killed in the name of “the people,” which was in fact a rather peculiar interpretation that involved an arbitrary assignment of “the others” (Mann 1999). From the

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1920s onward, a growing number of Chinese became interested in their neighbors’ communism, but it took a long internal war against nationalists, who lost legitimacy by fighting against communists instead of the Japanese occupation, before communist China was established (1949). During the so-called Cultural Revolution (1966–1976), the number of violent and famine-related deaths eclipsed the number of victims in Russia. Inspired by the massive violence of WW1 and the Russian Revolution, political upstarts in Europe (first in Italy) transformed nationalism into fascism. This extremist ideology legitimized violence against political opponents and fictitious enemies—ethnic and other minorities—under the guidance of a strong leader who promised a grandiose future. Because Germany (which had lost in WW1) had to pay reparations, Germans felt humiliated and were longing for positive feelings, which made them more receptive to this ideology than other Europeans were to their respective fascist movements (e.g., more Italians than Germans remained against it). Hitler20 and his Nazis cleverly seized this opportunity to muster massive support, rise to power, get rid of the short-lived democracy, and to prepare for building an empire through war. At the opposite end of Eurasia, the Japanese military elite (befriended by the Germans) had similar ambitions. Hitler’s variant of fascist ideology was based on incumbent prejudices against Jews, the largest ethnic minority in Germany, whom he turned into the scapegoat of all problems. Framing parts of the population as outgroups and dehumanizing them on the basis of fictional ideas precedes all ethnocides, but the massacre of Jews became the largest ever. A particular prejudice in Germany started when the bubonic plague decimated the European population (1346–1352). At that time, many Germans (and also others) believed the falsehood that the disease was caused by Jews poisoning the wells and burned them alive. In German counties where many Jews had been killed, there was much more support for the Nazis due to the cultural transmission of the well-poisoning meme over more than 20 generations (Voigtländer and Voth 2012). During the Industrial Revolution, migration in some counties diminished hatred against Jews by disrupting the transmission network and the arrival of large numbers of newcomers without these beliefs (ibid). Hitler also believed, like many others at the time, that humanity was divided into races that compete with one another, with an Aryan race supposedly superior to the others, under threat of a global conspiracy of the Jews of whom most were in reality poor and powerless. The Nazis used a pseudo-scientific theory of eugenics to back up their racial prejudice and decided that killing people they saw as inferior was the best thing to do. 20 Although sociologists strongly prefer to comprehend society in terms of interactions between multiple individuals at the expense of unique traits of singular individuals, it is impossible to understand WW2 without taking Hitler’s character into account (Haffner 1978).

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To this end, many concentration camps were built in Eastern Europe. Concentration camps had already been used in a genocide committed by Germany in Namibia (1904) and were now combined with the (pre-WW2) Nazi practice of killing handicapped people with gas. The coordination and logistics were managed by the highly effective bureaucracy. With this lethal combination, as well as bullets and starvation, six million Jews, hundreds of thousands of Roma, and large numbers of people from other minority groups were killed. The entire war took 60 million lives. During the conflict, both cruelty and occasional self-sacrifice went beyond the explanatory power of our scientific theories. Life and death in the concentration camps defy imagination, and the only way to obtain some understanding is by reading the memoirs of those who endured the experience, such as the work of Primo Levi (1959). Eventually, the Nazi empire was defeated by Russia, the United States, Britain, and Canada because it overextended beyond its ability to control (Collins 1992). Japan’s war in Asia ended when the USA dropped two nuclear bombs on the country, an invention that from then on prevented war between countries that obtained this weapon and who could thus credibly threaten total elimination of their opponents. After the war, the worst perpetrators against unarmed citizens blended into the population. If they were asked (and their initial reaction of denying involvement was untenable due to the evidence), they pretended to have had no other choice than to obey their leaders—which was false (De Swaan 2015)—and they complained about their hardships without any compassion for their victims (ibid). The theory of eugenics, which had also been used to legitimize the sterilization of many people in the USA and other countries, became less popular after the war and was rejected by most scientists. 6.9

Consequences of the world wars

After beating Nazi Germany, Soviet Russia expanded its territory into Eastern Europe, acquired Cuba, North Korea, North Vietnam, and China as allies, and aided numerous rebel groups against US-supported groups and (mostly autocratic) regimes in Latin America, Africa, and former Indochina. The two blocks got involved in numerous proxy wars tangled with struggles toward decolonization from Europe. At the time, nobody knew for sure whether communism or capitalism would become the dominant ideology, until the Soviet empire collapsed (1989–1991). In other parts of the world where there was at least a well-functioning state bureaucracy and an independent judiciary, the fear of communist revolutions gave democratization an extra push, and the most powerful country (USA) became a role model (Hyde 2020). Once there were more democracies, a majority of democratic neighbors of a focal country increased its chances of becoming democratic (ibid), and regimes’ international reputations became dependent on democracy, all of which contributed

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to its self-reinforcement. Around the turn of the century, the majority of countries had become democratic, if only during ceremonial activities (Meyer and Rowan 1977) such as (rigged) elections that were “celebrations of the rulers rather than moments of choice” (Guriev and Treisman 2022). The more democratic a country became, the more other inclusive institutions it implemented, in particular welfare (Heller 2022), which is why people who flee from war and misfortune prefer to go to democracies, not autocracies. Women obtained suffrage later than men, and the poor hardly ever had a chance to make their opinions heard, which is still true today (Pande 2020). For democracies to succeed, political leaders and other citizens must first learn to solve conflicts and accept losing elections without using violence (Riedl et al. 2020). This was most easily established if preceded by democracy at the local level, with chosen (rather than hereditary) leaders (Giuliano and Nunn 2013) and without violent transitions (Wimmer 2014). Furthermore, scarcity of labor and soldiers, such as that which occurred during WW1, increased citizens’ negotiation power in Europe versus the Global South (Heller 2022). Everywhere, modern democratization started or increased when certain groups, such as social movements, the bourgeoisie in Europe and the USA (North and Weingast 1989), or a coalition of groups, became more powerful and demanded to be heard, or, by contrast, when rulers were in full control and could reverse democratization if they wished (e.g., Ghana, Taiwan, Britain, Belgium, Sweden; Riedl et al. 2020). Rulers may allow for democratization if they think, possibly wrongly, that by giving in they can acquiesce the opposition, win elections, or retire without looking over their shoulder, while keeping their wealth. In these cases, a strong conservative party is more conducive to democratization than a weak one (ibid). The largest exception to the 20th-century democratization trend was China. Its economic reforms started bottom-up after Mao’s death (1976) by small-scale entrepreneurs who developed their own norms and practices of finance and commerce, carefully avoiding head-on clashes with the authorities by (at least ceremonially) conforming to norms of state owned corporations, thereby maintaining their legitimacy (Nee and Opper 2012). When the communist party realized that they could reap more tax by legalizing the informal arrangements than by enforcing norms top-down (ibid), they developed a state-directed capitalism under communist ideology (Padgett and Powell 2012, Ch.9), driven by entrepreneurship and facilitated by the commodification of rural labor (in turn facilitated by the hukou institution of household regulation; Alexander and Chan 2004). This resulted in rapid economic growth despite ongoing corruption, as well as rising inequality and (often illegal) migration of half a billion people to the ever-growing cities, while the communist party stayed firmly in charge. Also, the infrastructure of China’s worldwide trade network (Belt and Road Initiative) was progressively expanded and reinforced. Consequently, the global economic center of gravity

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moved back toward China in the early 21st century, where it had been until 1800. Russia, in contrast, enforced capitalist institutions top-down after the Soviet collapse, with adverse consequences for life expectancy and society at large. Europe had been significantly weakened during WW2, a fact of which the peoples they had colonized became aware. Small numbers of Africans serving in colonial administrations received higher education, sometimes in Europe, and this strengthened their nationalist ideas against their colonizers. Once India and Indonesia became independent, they became role models for the colonized people in Africa.21 They initiated louder protests and were joined by Africans who had gained military experience in European armies during the war (Tarrow 2015), eventually resulting in European retreat, whether peacefully or violently. African nationalism offered no positive feelings of solidarity; due to intensified racism and exploitation during colonization it was “a negative [feeling] generated by a sense of anger, frustration and humiliation” (Adu Boahen 1985, p. 786). Consequently, solidarity decreased soon after legal independence. The fledgling nations inherited the colonial boundaries, feeble bureaucracies, infrastructure, money economy, standing armies, and judicial systems (which were modified in Islamic countries), with much variation across the nation-states. When boundaries partitioned ethnic groups, as they often did, these groups had a higher chance of becoming involved in internal wars (Michalopoulos and Papaioannou 2020). The establishment of nationwide inclusive institutions was also hindered by the template of violent extractive institutions left behind, the IMF’s imposition of a narrow-minded idea of development (Easterly 2007), and the large sums of developmental aid from north-western countries that diminished pressure on African states to care for their own citizens. Against the odds, and mostly after the fall of the Soviet Union, social movements succeeded in establishing democracy in a dozen African countries, some more stable than others (e.g., Senegal versus Mali). Urbanization expanded further, and the primary social division became that between cities and rural areas, although many of the urban poor continued farming. Around the turn of the millennium, economic growth increased, and so did environmental problems, just like everywhere else. 21

The formal model of collective action driven by agitating turmoil (Section 8.2) applies to the struggles for independence. For Indonesia, it started with the Japanese invasion in 1942. Indonesians considered the Dutch colonizers as invincible, but when they saw that the Japanese army occupied many parts of the country in just a few days, they realized that they could liberate themselves more easily from the Dutch than they had thought. They radicalized and started to make plans for their independence while sitting through the Japanese occupation (Van Reybrouck 2020). A final portion of agitation came after the Japanese lost the war in the Pacific and the Dutch elite (liberated from Japanese prison camps) retook their former positions; then, anti-colonial war broke out.

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6.10

Innovation by teams

Factories that produced the consumer goods of the industrial age, as well as other kinds of organizations, became centers of innovation and test beds for scientific research. The latter started with Taylor’s (1911) time and motion studies, followed upon by the Hawthorne experiments (Roethlisberger and Dickson 1939), and a surge of studies from the 1970s onward. Most of these studies served to increase productivity and economic growth, ignored the broader consequences of rising inequality and environmental degradation, but found interesting patterns that also seem to apply to groups and organizations earlier in history. Here, I elaborate on the productive subgroups within organizations: teams. One might say that humans have always worked in teams, from the first foragers onward, but during the Industrial Revolution, workers in factories were isolated as much as possible and were not allowed to speak with each other out of fear of revolts and productivity decline. Teams and teamwork seemed to have been almost abolished. With respect to productivity, the Hawthorne experiments accidentally demonstrated the value of teams (whereas the initial treatment was the amount of light in the factory hall), and managers rediscovered these special purpose groups with their informal interactions and culture as a means to make money. These groups, now called teams, worked inside organizations that ensured that the challenge of cooperation was solved, typically through employment contracts with salaries and sanctions. Once people are willing to work together, several patterns emerge in their collective efforts. One of the most stable patterns is the learning curve: when practices are repeated, e.g., ship building, the time it takes to complete the task decreases exponentially (Argote and Epple 1990). Through task repetition with incremental process innovation, teams, organizations, and other groups develop collective routines and become ever more efficient at exploiting them, which enables organizations to satisfy their audiences (p. 112) at lower costs and to survive competition.22 Audiences, their tastes, and their culture change in response to each other. For organizations to adapt, they (or teams therein) may explore new possibilities and innovate (March 1991), and subsequently either specialize, starting the learning curve anew, or failing to do so and disband. Given an organization’s experience, developing new products that match incumbent capabilities (including routines) is more likely to happen than 22 Organizations that have a downstream position in a supply chain have an extra advantage on top of their own learning: all organizations upstream also learn, which leads to lower expenditures on their inputs (McNerney et al. 2022). Assessing this extra advantage can be done with a simplified power centrality measure (not truncated to two steps, as I do in Section 8.1.4, but over the entire production chain). This measure applied to industries explains why production based on long production chains becomes cheaper, versus services (e.g., education and health care), hence why China with a production-based economy grows faster during the first decades of the 21st century than the USA with a more service-based economy.

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inventing completely different products that require entirely different capabilities. The relative inertia of capabilities with respect to changes in the socioeconomic environment also holds true for individuals and countries (Hidalgo et al. 2007). Ongoing economic viability under competitive pressure thus depends on cooperation, innovation (including updating capabilities), and the institutional environment. The combination of these three factors results in a path-dependent development of economies at all scales, from households to the global economy. One of the implications of path dependence is that economically peripheral countries face great challenges in moving towards the global economic core (Hidalgo et al. 2007; Wallerstein 2011). Just like individuals, teams and organizations exploring their socioeconomic environment benefit from brokering their network (i.e., bridging “structural holes”, p. 175; Burt 1992). Over and above individual brokers, however, collectives can change their composition and internal social network, which they need to integrate the knowledge that they muster and harbor (Vestal and Danneels 2022). Knowledge diversity in teams increases the chances for novelty, and diversity of backgrounds, ages (Guimerà et al. 2005), ethnicities (Freeman and Huang 2014), locations (Jones, Wuchty, and Uzzi 2008), and gender (Yang et al. 2022) all enhance innovativeness, even though they make communication more difficult and go against people’s tendency towards assortment, and are therefore rarely at optimal levels. Countering intuition, a diverse group solves complex problems better than a same-sized group of smarter but similar people (Hong and Page 2004). Small, egalitarian teams are better at breakthrough innovations, whereas large and more hierarchical teams are better at incremental innovations (Azoulay 2019; Xu, Wu, and Evans 2022). A clustered internal network fosters a coordinated search for new information, but to turn the discovered information into innovations or solutions to complex problems, team members do better if they free themselves from the constraints of consensus in their own cluster and toggle their ties to other, more remote colleagues with different points of view. For this, a sparse network that spans the organization (and beyond) is most conducive (Shore, Bernstein, and Lazer 2015; Derex and Boyd 2016), analogous to the Republic of Letters that spanned Europe in earlier times (Mokyr 2017). These networks on different scales have in common a low density such that many structural holes can be bridged, but not so low as to prevent valuable information and cooperators from being found (Uzzi and Spiro 2005). Because exploration is inefficient, an organization may allocate some teams to it, but would go bankrupt without compensation by other teams that efficiently exploit their routines. For an organization to survive in the long run, the exploration-exploitation trade-off must be resolved over time; the two strategies have to be alternated (net of some dedicated innovative teams) in

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response to or anticipating environmental changes (Carnabuci and Bruggeman 2009).23 To switch from one strategy to the other, a centralized, or hierarchical, authority can be seen as one extreme in a trade-off between speed and accuracy, with a decentralized, egalitarian organization at the other extreme (Michels 1915; Sueur, Deneubourg, and Petit 2012). A collective, egalitarian decision based on everybody’s expertise takes more time, but leads to better solutions if the problem is complex (Page 2007), especially when individuals’ expertise on the matter is acknowledged by the others (Bruggeman 2017), and lowers stress levels for the participants compared to being bossed around (Bosma et al. 1997; Sapolsky 2005). “The consensual decision requires no enforcement, does not put the leader’s authority at stake, and [...] should leave no-one disgruntled. But the swift unexplained command can provoke resistance, even hatred and does require sanctioning: unless, as in a well-drilled army or a group of fanatical supporters, they have been specifically trained to accept authoritarian sanctions” (Bailey 1969, p. 68). Large organizations were, and still are, run like autocracies, including the suppression and denial of criticism, the maltreatment of whistleblowers (Glazer and Glazer 1989), and the extractivism of their elites (of managers, but mostly owners), all at the expense of innovativeness and individuals’ wellbeing. As a matter of fact, the potential of fast centralized decision-making is often unused because of managers’ denial of the problems for which their centralized decisions are necessary. For high-risk endeavors such as warfare, autocratic decisions can sometimes be collectively (but not individually) advantageous. A study of mountain climbing showed that hierarchical teams more often reach the summit than egalitarian teams, but with a higher death rate (Anicich, Swaab, and Galinsky 2015). When the fates of many lay in the hands of one leader, the consequences of bad decision-making or hampered communications can be devastating. Therefore, large human groups (e.g., armies or corporations) that are the most successful in competitions with others combine speed with accuracy; centralized overall coordination and modularity of semi-independent smaller units that, within certain bounds, are allowed to decide by themselves how to adapt to local circumstances. Notably, though, the inequality in these organizations has overwhelmingly negative consequences for those among the lower ranks (Marmot et al. 1991) and for the natural environment. 6.11

Globalization and rising inequality

From the Industrial Revolution into the third millennium, many of the highimpact innovations—radio, TV, film, cars, airplanes, telephones, nuclear bombs, electronically amplified music, and contraceptives—took place in a few capitalist countries, with a leading role taken by the USA during the 23 Along with organizations, also individuals alternate exploration and exploitation (Liu et al. 2021).

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20th century. These innovations were accompanied by standardization, modularization, and miniaturization (Mokyr 2002). Vast amounts of industrially produced goods were transported across the growing global trade network, resulting in an economic hierarchy of nations (García-Pérez et al. 2016). The increasing connectivity also homogenized the world; all polities had become national states, and all used a few international (second) languages, similar rules of law, and the same measures, science, technologies, and styles of dress such as suits for men (Harari 2011). Moreover, if a certain technological standard (e.g., the width of train rails) had been set, vast investments around the globe made it forbiddingly expensive to discard it in favor of another standard (i.e., lock in with sunk costs; Arthur 1989; David 1985). This is one of the reasons why it remains so difficult to get rid of fossil fuel technologies. In some fields, though, globalization entailed increasing variation, such as modern art, which became more heterogeneous. Over the 20th century, the living conditions of many people in industrialized countries improved. For the first time, the recurring famines of agriculture could be avoided, mostly with the help of modern transport, whereas agrarian populations could not escape the boom and bust pattern. When the wealth of many increased and survival was no longer a pressing concern, many more people than the wealthy elite could afford to spend time and money on entertainment, recreational sex, and the purchase of nonessential goods. Despite these advances, many people experienced their consumerist, individualistic lifestyles as insufficiently valuable, for which they tried to compensate by way of drugs or new forms of religion (Bellah 1976). Moralistic religions from agricultural societies mismatched recent social changes, however, and in particular sexual liberties provoked conservatives to mount countermovements that mutually competed for purity. With economic progress, inequality increased as well, in part due to tax policies that were favorable to wealthy people but burdened workers and their salaries (Piketty and Saez 2014). Between countries, both income and wealth inequality increased from the early 19th century into the 1980s, to which extraction from colonies contributed. Over time, many poor countries caught up to some degree, decreasing inequality between countries to some extent (Johnson and Papageorgiou 2020), while inequality was increasing within countries (Ravallion 2018), and more so in the largest: India, China, Russia, Brazil, and the USA. Inequality of wealth surpasses that of income, and both are considerably higher in industrial than in agricultural societies (Kohler et al. 2017). The share of heritage in wealth is lower (and therefore intergenerational mobility is higher) in early 21st century China and Russia than in Europe, with the USA in between. Overall, social mobility decreased in many countries because the rich could afford extra schooling and other opportunities for

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their children that others could not (Grusky 2022). Data on Europe (unavailable elsewhere) demonstrate a monotonic increase of both income and wealth inequality from the Black Death until WW1. Taxation favorable to the rich played a major role (Alfani 2021), as well as the growth of cities that offered more opportunities to a few well-connected people resulting in accumulated advantages over time (Arvidsson, Lovsjö, and Keuschnigg 2023). The increase in within-country inequality from industrialization onward was interspersed by a period of inequality decline in Europe and North America (WW1 until 1980), famously discussed by Piketty (2014). Financialization of the global economy and deregulation of trade—a euphemism for increasing extractivism—in the 1980s (Ravallion 2018; Davis and Kim 2015) made possible larger scale exchange, brokerage, and patronage, which were often combined with tax evasion and fraud (e.g., the financial crisis in 2008; Griffin 2021). This does not imply that rich people are intrinsically more selfish than poor people, but in contrast to the poor, they are much better positioned to organize themselves and attract the services of financial specialists who facilitate tax evasion and make it look like the normal thing to do. Meanwhile, membership of labor unions has decreased, workers have become less organized, and they have received less political support from socialist parties, who have become more capitalist. Most people remain clueless about inequality to this day, though. They are wrong about the approximate wealth distribution in their own country (Gimpelson and Treisman 2018), and underestimate the income of the top earners by an order of magnitude (Kiatpongsan and Norton 2014). Wealth inequality is much more extreme than income inequality, and most people know little about it, or about the magnitude of political influence that money can buy. Clearly, Marx and Engels’ class consciousness never materialized. The more unequal countries become, the more strongly people believe that the happy few acquired their wealth through meritocratic ways (Mijs 2021), except in some autocratic countries with ostentatiously extractive institutions (Fearon and Laitin 2003). Even in countries with inclusive institutions,24 the rise of inequality, combined with decreasing incomes of the poor (which happened in many countries during and after the coronavirus pandemic) and a lack of social mobility, have made life for many citizens more difficult. Therefore they would benefit from better knowledge about the causes and distribution of wealth and income. In 24 An ideal set of inclusive institutions in a modern state would provide clean drinking water and air (which become more challenging with pollution and overconsumption); affordable electricity; public transport; accessible health care; affordable childcare; freedom of speech; equality before the law; pensions; and education for everyone, i.e., “equal opportunities to become unequal” (Pagel 2012, p. 125). These would come in addition to earlier established inclusive institutions, of supervised markets; roads and waterways; national defense; protection of citizens’ property, contracts, and trade; meritocracy instead of nepotism and corruption in the bureaucracy; an authority for conflict resolution; and democratic institutions (p. 143).

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evolution in general, there is no advantage in basing one’s decisions on a lack of, and certainly not false, information about one’s (social) environment. In the next chapter, we will see why improvement is unlikely. Inequality has also increased due to migration within and between countries. As people in former colonies grew wealthier, their aspirations regarding what makes a valuable life rose (a.o. due to media exposure). Consequently, as they obtained the means to travel, migration from the Global South to the Global North increased, as well as remittances in the opposite direction, even though the vast majority felt attached to their local communities and stayed (De Haas 2021). Both migrants and home stayers benefited financially, but many migrants faced restrictions, discrimination, and were exploited in their destination countries. Many became poorer than the natives while incumbent elites benefited from their labor (ibid). Consequently, inequality in the Global North increased. A small few migrants from poor countries were very rich, which also contributed to increasing inequality. They, and a small percentage of highly educated migrants with a background in scarce professions, such as high-tech, were welcomed without restrictions. Many citizens in the destination countries, some of whom had been migrants themselves, opposed immigration of poor people for various reasons, among others feared competition for jobs, houses, and spouses, all of which was cultivated in bad faith by fearmongering politicians for political gain. The latter exploited nationalistic ideologies to mask the advantage of cultural diversity (for knowledge brokerage) to argue in favor of cultural homogeneity, in line with citizens’ fears. A very stable pattern of inequality is that rich people live longer than poor people (Cutler, Deaton, and Lleras-Muney 2006), and the difference is higher if there is more inequality. It is not money that makes the difference, though, and the effect of health on income is much stronger than the other way around. For example, employees in lower ranks at workplaces have more stress-related diseases due to a lack of control (Bosma et al. 1997; Snyder-Mackler et al. 2020), which remains if they receive higher salaries. What matters most for health are two inclusive institutions: public health (clean water, sanitation, vaccination, hospitals, draining swamps) and education for everyone, in particular girls. Once these institutions were implemented in poor countries, the life expectancy of the poor increased rapidly, for example, in China before it became capitalist and in India. These two inclusive institutions cross-cut the distinction between overall inclusive (i.e., democratic) and overall extractive (e.g., autocratic) countries. Inclusive institutions do not explain all health differences, though. Habits matter, too (such as exercise versus smoking, and eating vegetables versus pork fat), as well as interactions with others (being supported versus discriminated against). The wealthiest have a sizeable, be it indirect, influence on institutions and workplaces through spending large amounts of resources on politicians and

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their parties, lobbying, and fraud (Zingales 2017). Consequently, wealthy industrialists have been allowed to pollute at unprecedented levels, sell toxic and addictive products, and camouflage dangers through false information campaigns (Farrell 2016). For example, during the 20th century, at least 100 million people died from smoking cigarettes (Proctor 2001), and more than half a century after the dangers were scientifically known, lobbies managed to prevent effective intervention policies. This was no exception; the production of other toxins such as PFAS continued despite the producers knowing the associated dangers since the 1970s, with governmental support and protection. Once the ideology of market fundamentalism (with individual decisionmaking) had become widely accepted, the producers of toxic products could put the blame on the users (“they keep buying our products”) and get away with it instead of being sued for mass murder. Random exchange model. Both believers in meritocracy and their critics who emphasize institutional failures tend to overlook the effect size of random chance, which happens to be sufficient on its own to produce empirical inequality distributions (Drăgulescu and Yakovenko 2001), even though other mechanisms are at play. The effect size of randomness can be demonstrated as follows. Suppose that everyone (or every household) at a hypothetical beginning has the same amount of money. Then over some time interval, each time a pair of individuals is randomly chosen (without assumptions about network structure); the chosen pair makes an exchange, and the result is modeled as a random division of their pooled money among them. When repeating this for some time, a lognormal equilibrium distribution with a large variance emerges (Chatterjee, Sinha, and Chakrabarti 2007); see Figure 6.3B, with a Gini coefficient of 50. The only cause is a great number of random exchanges and nothing else. People who accumulate a lot of money—the richest 5%–10% in the model—can afford to save up a portion of it at each exchange, which is no longer exposed to potential losses during subsequent random exchanges. Almost any distribution of savings will do; then, a Pareto distribution of wealth among the richest people emerges (Chatterjee, Chakrabarti, and Manna 2004).25 This is missed in surveys since rich people do not tend to fill out questionnaires, and wealth is much easier to hide than income (Alstadsæter et al. 2019). The lesson is that if random exchanges and arbitrary savings already yield strong inequality, very strong institutions are necessary to keep inequality at bay. If “we want a less unequal society, then we have to act to create [the relevant institutions], as it seems unlikely that inequality will begin to decline on its own” (Alfani 2021). 25 There are n players each with m money at the beginning, with a fixed sum total of Q money in the economy. After many exchanges, described above, a steady state is reached, P(m) ∼ exp(−m/T), with T = Q/n. With an arbitrary distribution of savings, P(λ), the distribution of inequality becomes Pareto-like, P(m) ∼ m−(1+v) when m → ∞; v = 1.

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A

B 1.0

0.8 6

Relative frequency

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FIGURE 6.3 Industrial society. (A) Increasing world population. Data: Worldometers.

(B) Social inequality, with very few individuals with very high incomes. Simulation model.

6.12

Protests, revolts, and wars revisited

Strong inequality in modern society leaves many disgruntled, some of whom protest and/or revolt. Portions of protesters against their misfortunes have wanted more than an improvement of their conditions, namely an overthrow of their government. Such a will has posed a threat to the elites, who have defended themselves violently, where possible, with unpredictable consequences. Both protests and revolts are more likely if they have recently taken place, respectively, in neighboring cities or countries (Kuran 1991; Biggs 2005), thereby agitating disgruntled citizens who identify with the protesters or their goals (even when having different goals themselves), which can make initially small protests much larger. If the level of violence stays modest, protests can be an effective means to enforce policy change without social disruption (Shuman et al. 2022). In particular, the participation of many women increases the protest’s legitimacy (Chenoweth and Marks 2022), but this is no guarantee of success. If a regime is weakened by overspending, overextension, or conflicts between different domestic elites (Goldstone 2001; Skocpol 1979), or a revolt proceeds too rapidly for the rulers to organize an effective response (Kuran 1991), a revolt can turn into a revolution that topples the ruling elite, especially if part of the elite sides with the protesters or initiates the revolution. Most revolts are repressed before they get this far (Hale 2013), and even if they do, the resulting chaos often yields opportunity and legitimacy for a counterrevolution (Slater and Smith 2016). If protesters succeed in seizing power and want to establish a new regime, they need to be at least as well organized as their opponents, offer more inclusive institutions than the ancien régime, and have an ideology that appeals to the majority of the population

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to win their support over a long term period. Democratic tendencies in autocracies are often opposed by elites, who may organize coups if their interests are harmed. In a network of elite families in Haiti, the network measure of power centrality (Section 8.1.4) predicted how much effort elite families put in a coup (Naidu, Robinson, and Young 2021), in line with an evolutionary model (Gavrilets and Fortunato 2014). Some protest groups radicalized further and their contentions with other (e.g., elite) groups evolved into internal wars, for example, in Syria (2012). Over the past two centuries, internal wars were three times more frequent than interstate wars (Clauset 2020). Internal wars are more complex than interstate wars because the traditional partitioning between government, army, and citizens is blurred, non-state combat groups participate, and foreign groups support some of the participants (Van Creveld 2002). Resources in the hands of an extractive government and its elite (Fearon and Laitin 2003), in particular if they use violence to protect their interests, agitate relatively deprived men lacking job opportunities, and make them susceptible to radical ideas and recruitment into rebel or bandit groups. Once rebels, bandits, or protesters turned into either of these organize themselves, they look for looting opportunities (or revenge against others who stole from them). In general, the presence of lootable resources such as gold, diamonds, and oil increases the chances of internal war (Wimmer 2014), which is not conducive to developing inclusive institutions. Along with the immediate victims of violence, (internal) war leads to (mostly internal) displacement of a great many people. From the perspective of contending groups, internal wars resemble interstate wars (Tilly 1985). A radicalizing group establishes a framing and an ideology, including a narrative that dehumanizes opponents and makes violence appear to be the only viable option. On the basis thereof, solidarity is fostered. Subgroups look out for opportunities that coincide with opponents’ weaknesses, which occur when armies of extractive states weaken, lose international support, or both, as after the fall of Libya’s regime (2011). These temptations then agitate the rebels beyond their critical threshold, and violence breaks out. My inter-polity dynamics model illustrated in Figure 5.3 (p. 91) explains that rebel groups (obviously with extractive institutions) can gain a foothold against overstretching states with weak, as well as largely extractive, institutions, but that in most cases they cannot conquer the capital cities.26 An example is the Sahel region since 2012, where Islamist rebel/bandit groups conquered territories that contain gold or water from democratic but largely extractive states such as Mali, Niger, and Burkina Faso. In order to win, military technology and group size are crucial (Blattman and Miguel 2010), 26 In Figure 5.3B applied to a rebel group versus an extractive state, the latter has diameter 1 whereas the rebel group has a much smaller diameter with only the peak of the distribution and a smaller surface area than the state.

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as well as the concentrated application of force on opponents’ weak spots. Mountainous terrain is difficult to invade and therefore a favorite hideout. Multiple groups can act collectively against common enemies, as is currently the case in Congo, and the configuration of groups tends to cluster according to balance theory (p. 80; König et al. 2017). Armed rebel groups do fare better if they gain sympathy and support from (part of) the population; just as governments they need legitimacy and tax income, which they can obtain by providing public goods and exercising selective (versus indiscriminate) coercion (Kalyvas 2012). Many rebel groups tend to indiscriminately attack civilians who they believe to be siding with the government (Valentino 2014), such as schoolteachers in the Sahel (since 2012), which makes the state’s institutions disappear. When the state has nothing to offer to its population anymore, it loses its legitimacy. The vacancy created therein, rebels are quick to fill. Because ethnic groups have a more cohesive network and a more homogeneous group culture (of norms and language) than a multi-ethnic state, they can more easily organize themselves and exclude outsiders (Blattman and Miguel 2010). Ethnic divisions do not predict ethnic clashes, but contention can easily entail ethnic divisions, exploited by politicians who (tacitly) encourage their favorite ethnic group to commit violence against another ethnic group. Interethnic violence is often initiated and committed by small numbers of paramilitary or military individuals, typically a few percent of all males of an ethnic group, who are motivated by opportunities for looting and raping (Valentino 2014). In some cases, soldiers goad civilians at community meetings to participate in the killings, for example, in Rwanda (1994; Rogall 2021). In general, large portions of violence against civilians are encouraged by ruling elites of nation-states. If ethnic violence is not state supported, it can be avoided through ongoing interethnic interactions that increase mutual empathy, in line with the contact hypothesis (Mousa 2020; Paluck, Green, and Green 2019), especially when these interactions are institutionalized (in India in multiethnic civic associations; Varshney 2001) with positive (i.e., non-zero sum) interdependencies (Dhattiwala 2019), for example, through complementary (versus competitive) trade (Jha 2013). These conditions prevent the controversialness (p. 73) of issues of disagreement from passing beyond the critical threshold. To foster peace over a long period, skills and resources should be difficult to imitate and expropriate by other groups, respectively, and the benefits of trade should be shared by many (Jha 2013). By contrast, if resources are seized by a small subgroup, such as in the drug and oil trades, violence is likely. Although violence between individuals, protesters and states, multiple states, and between groups within states have different causes and dynamics, they all have a robust temporal pattern in common, namely a power law of

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interevent times of violence (p. 79). Interstate wars have a second robust pattern in common with each other. The wars with a certain severity (measured in numbers of deaths or wounded) are power law distributed (Johnson et al. 2013; Richardson 1948). Internal wars with relatively smaller groups are better modeled by an exponential, instead of power law, function. The power law of victims means that the enormous number of casualties of WW2 is no outlier, even though it may look like one. In other words, it is not beyond what one might expect to happen at some moment (Clauset 2018), even though Hitler and his army made it happen at that specific moment. Of note, there is no (known) relationship between the sizes of states and the frequency or severity of wars (Clauset 2020). A third pattern has also been found in the frequency of interstate wars (to be distinguished from events within a given war); since 1823 (the beginning of the data), the time intervals between the onsets of subsequent wars are centered around a mean interval with a Poisson distribution (Clauset 2020; Richardson 1944). The interevent times pattern seems to be robust and universal, but it is hard to say if it, as well as the power law of casualties, also holds true further into the past, before national states existed and weapons were industrially produced (i.e., before it was possible to kill millions in a short time). For the past two centuries, the most important takeaway is that despite institutional developments and the growth of populations, communication, education, production, transport, trade, average income, wealth, and the number of states, there has been no significant change in the frequency and severity of interstate wars.27 This is “a profound mystery for which we have no explanation” (Clauset 2020). A new development near the end of the 20th century was the entry of women in combat roles in a number of countries (King 2016). This extended to prestigious positions such as jet fighter pilot (even in India), but resulted in ambiguous status consequences. 6.13

Environmental degradation and new pandemics

Wars alone have an incredibly destructive effect on the natural environment, but they are eclipsed by the effects of industrial production itself and the transport of its products. The downsides of production have been known for centuries: medieval dyers, tanners, and metalworkers were exposed to toxic fumes, and the use of mercury had debilitating effects. Industrialization increased both the scale and variety of pollution, which caused bottom-up resistance early on, and sometimes resulted in (poorly enforced) regulations (Peplow 2020). Companies first reacted with denial, then they threatened that 27 It has been said that from 1950 onward, interstate wars decreased (Jackson and Nei 2015), but Clauset’s analysis suggests that this decrease is not significant. It is therefore not certain that after WW2, the increase of international alliances and trade lowered the chance of pertaining countries to get involved in interstate wars (opposite to Jackson and Nei 2015).

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closing down production would cost jobs, and subsequently, they promised that forthcoming technologies would solve the problems. “Yet time and again, technologies moved pollution out of sight, or traded one form for another” (ibid). To this day, companies react in similar ways: they downplay the severity of their pollution by telling lies and present themselves as impartial suppliers who satisfy free consumers’ choice (Supran 2021), thereby framing the public debate in their favor. By the 1970s, the problems with industrial production were already well researched and understood (Meadows et al. 1972; Rome 2015), yet still almost nothing was done, with tacit support by citizens who wanted to preserve their jobs and consumption habits. Governments continued to support, or at least give plenty of opportunities to, the fossil fuel industry, making it hard for non-fossil fuel innovations to attract investors and to gain a foothold in the market. The distribution of pollution broadly follows the wealth distribution and is concentrated amongst the wealthiest, with a second, comparatively small, peak among the poor who cook on indoor fires (Eisenstein 2017). The poor generally suffer most from environmental problems, including sweltering heat in cities and subsiding coastal areas. Many of them are too occupied trying to catch up materially to protest against these problems, though. On top of dealing with their own governments that allow companies to pollute, they are also constrained by wealthy countries that moved their most polluting industries to poor countries. Without massive and ongoing protests, species extinction and natural resource exhaustion have continued, pollution has mounted, and both CO2 production and temperatures have continued to increase (Motesharrei et al. 2016). During the 2010s, seven to nine million people died per year due to ultrafine particles in the air (Burnett et al. 2018), which have also damaged the brains of those who have to solve the problem (Zhang, Chen, and Zhang 2018). Higher temperatures make some parts of the world too hot to live in, cause storms and wildfires, and melt permafrost, which releases copious amounts of methane gas with a greenhouse effect 30 times stronger than CO2 , feeding back into further warming. Consequently, erosion is increased, the ocean’s CO2 absorption is decreased, oxygen needed by the fish we like to eat is diminished, and melting polar ice will eventually raise the sea level by ten meters (Lenton et al. 2019), inundating millions of coastal homes. Heat, erosion, overconsumption, collapsing ecosystems, pollution, and rising sea levels shrink the human niche, from which increasing numbers of people are excluded (Lenton et al. 2023).28 Several of these changes have positive feedback on and aggravate one another (Kemp et al. 2022). If and how humankind adapts to these changes remain to be 28 The human niche consists of the temperature zone where humans can survive and food can be produced, i.e., extreme temperatures remain rare and stay within bounds, and drinking water is available (Lenton et al. 2023). Outside of it, people depend on food and water brought in from elsewhere, as well as protection against extreme temperatures.

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seen. Large-scale international cooperation to curb the trends is likely to start when the catastrophe is already large and irrevocable, not at an earlier time when the catastrophe can still be avoided. Neither a lack of technology nor the majority’s willingness to cooperate (Ostrom 2010) stand in the way of solving environmental problems; the main obstacle is politically endorsed extreme inequality, with an ultra-wealthy elite that has invested heavily in fossil fuel technologies (Motesharrei et al. 2016; Adler 2015). If there were more equality, enforced by a global inclusive institution that capped excessive wealth and redistributed it to provide a decent living standard to everyone, the current energy consumption could be halved (Millward-Hopkins 2022) and disastrous levels of resource exploitation and environmental destruction could be prevented (Hickel et al. 2021). Without global institutions, however, solving environmental problems in time is difficult, even with benevolent leaders, because countries have different, sometimes opposing, interests, and our current international institutions cannot enforce norm maintenance in other countries (Buchholz and Sandler 2021). The way humans have handled the natural environment has made it a breeding ground for pandemics. Populations have suffered from infectious diseases since the agrarian transition, but in the decades after WW2, most people started to believe that the terrible pandemics of the past could be controlled or eradicated (e.g., smallpox and rinderpest). From the 1980s onward, however, both old and new infectious diseases (Ebola, HIV, and coronavirus) increased both in number and in the number of victims. Causes were, and still are, increased livestock production, more pathogen-carrying animals that do well in growing urban areas (rats and bats), and more contact with remaining wildlife (bush meat), with greater risks of diseases jumping from animals to humans. Once this occurs, they spread under the favorable conditions of a growing world population with decreasing antimicrobial resistance that is concentrated in larger urban areas, connected by more travel, and in a warmer climate (Heesterbeek et al. 2015; Lloyd-Smith et al. 2009). Incidental measures such as lockdowns do not make them disappear, and since there is no medicine for most of these diseases, the only way to control them is to address their entire ecosystem (Heesterbeek et al. 2015). In the past and present, pandemics affected poorer people the most. The long coronavirus lockdowns in Africa wreaked havoc among the majority (Josephson et al. 2021), and the damage done by missed months or years in school will continue to cause problems, among others, through soaring teenage pregnancies. Pandemics have long-term economic downsides and increase inequality.29 The only way in which they decrease inequality is by the higher death rate of poor people, who 29 The only exception to increasing inequality in Europe was the Black Death, but loss of wealth by the most wealthy was halted afterward, when institutions were adapted to prevent the fragmentation of patrimonies (Alfani 2022).

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then drop out of the distribution (Alfani 2022). Pandemics also increase distrust and protests. Many try to reduce their anxiety through cleanliness rituals and prayers (McNeill 1976). Others blame outgroups or deny the disease and protest against government policies. After the cholera pandemic in Europe in the 1830s, for example, “there were riots across the continent: doctors, nurses, and pharmacists were murdered, hospitals and medical equipment destroyed” (Bedford et al. 2019). When comparing agricultural with industrial societies, we saw more of almost everything in the latter, but also that in some fields, especially technology, “more is different” (Anderson 1972). Digital technologies were to make society yet more different. We also saw how the basic principles of cooperation, developed in forager societies, were complemented by record keeping, religion, and inclusive institutions in agrarian societies, technologies of communication in industrial society (and recently the internet; see the next chapter), and became further interconnected with culture (through ideologies such as nationalism), large-scale conflict, social movements, and inequality (seen as asymmetric exchange).

7 DIGITAL SOCIETY

While industrial society has continued to expand in the 21st century, it has been complemented by, and enmeshed with, a more rapidly expanding digital world. Around the turn of the millennium, mobile phones, the internet, and the Web became widely dispersed, and as often occurs in industrial society, opportunities for some quickly became necessities for all (Smith 1776). Although not everyone around the world was readily connected online, cybercafés were founded in most urban areas within a few years and most people got a mobile phone. A range of online social foci was created where people could establish and maintain social contacts (most of these contacts were still geographically close, though), belong to groups, express themselves, access music and information, shop, and build a reputation—or lose it. Online record keeping based on consumer reviews established sellers’ reputations that made billions of transactions possible between people who had never met (Diekmann et al. 2014).1 In this new social ecosystem, the same cultural evolutionary patterns unfolded as before, but at a higher speed, with far more new information in a day and therefore scarcer attention for anything in particular (Lorenz-Spreen et al. 2019). In the words of Bellah (2011, p. 602):“Our rate of adaptation increased so greatly that we [were] having difficulty adapting to our adaptations”. At the onset of social media, protesters obtained an advantage by using digital technologies (a.o., mobile phones, social media) to organize mass 1 Whereas in offline, gossipers rarely overhear gossip about themselves, in online one can read what others write, for example, sellers reading consumer reviews. This often results in review reciprocity, and if there is a good chance of future encounters, criticism is often avoided to prevent retaliation, which decreases the reliability of online reputations (Livan, Caccioli, and Aste 2017). DOI: 10.4324/9781003460831-7

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protests (a.o., in Latin America; Valenzuela et al. 2016) but in most places they were soon overtaken by police who quickly learned to use the same technologies to track citizens and anticipate protests before they became a real threat (Tufekci 2017), in an arms race (McAdam 1983) that recurs throughout history. Meanwhile, a handful of tech companies benefited from online economies of scale, outcompeted many smaller ones, grew very large, collected massive amounts of data about billions of people, and designed sophisticated algorithms to nudge consumers toward purchasing their products, a process dubbed surveillance capitalism (Zuboff 2015). Nation-states and their elites have used surveillance since their inception (Foucault 1978), so, in cooperation with tech companies, they promptly seized the new opportunities offered by digital technologies to exert power and disseminate propaganda. The new technologies allowed them to do so much more efficiently and in a more concealed manner than in the past (Earl, Maher, and Pan 2022) when they eavesdropped on bakelite telephones and broadcasted propaganda on the radio. If they could afford it, nation-states’ forces also used the technologies for military intelligence and to expand their influence into other countries. In the digital arena, the distinction between autocratic and democratic states faded. Moreover, despite technological sophistication, the information collected has more bias and error than governments and companies are willing to acknowledge, due to bad input data, programming errors, and ill-supervised artificial intelligence. Consequently, people’s reputations in the eyes of corporate actors became co-determined by opaque and biased algorithms processing unreliable data. Furthermore, when data were handled insecurely, sensitive information about millions of individuals landed in the hands of criminals, who found a new niche to proliferate. Whereas people appreciate their offline privacy and respond to sensorial cues of being monitored (p. 35), most people do not understand how information about them is collected, spread, and (ab)used online, especially in instances where they are not pre-warned by cues that they can understand (Acquisti, Brandimarte, and Hancock 2022). Carefree, most people upload personal information to the internet that may become damaging to them, on top of ongoing surveillance, the mere scale of which they are largely incognizant. 7.1

(Self)deception and radicalization

Given the consequences of privacy infringements, as well as inequality and environmental degradation, one might expect wide-spread protests in favor of global public goods such as cybersecurity, climate protection, health, peace, and justice (Buchholz and Sandler 2021). Therefore, it seems paradoxical that in democratic countries in particular, with better-educated populations than ever before, protests against these problems are often drowned out by conspiracy theories, outcries of ethnocentrism and racism, as well as hatred

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toward anyone who does not strictly adhere to traditional gender roles and presentation.2 Many put the blame on false, but agitating, information on the web and social media, but are these really the culprits, or was there simply a shift from offline to online media with no substantial increase of false information? The impact and credibility of information are enhanced by redundancy of multiple, mutually consistent sources (Guilbeault and Centola 2021; SerraGarcia and Gneezy 2021), narratives of personal experiences, which sound more convincing than facts (Kubin et al. 2021), repetition (Rhodes, Leslie, and Tworek 2012), building upon earlier prejudice (Tarde 1890), besmirching opponents’ reputations (Yablokov 2022), and parasocial ties with leaders (not personally known by their followers; Horton and Wohl 1956). All these also hold true offline, however, except that transmission and repetition are cheaper online and therefore more frequent. Furthermore, false online information usually does not spread faster and farther than true information (Cinelli et al. 2020); in fact, this occurs only in exceptional cases (Vosoughi, Roy, and Aral 2018). The largest amounts of false information online and the most extreme hatred are produced, disseminated, and consumed by relatively small minorities (Grinberg et al. 2019; Johnson et al. 2019), including descending elites and populist upstarts who co-create it with, and transmit it to, their followers. Meanwhile, the majority is not (directly) exposed to a great deal of outright lies and hatred. Everyone does, however, receive seemingly innocuous deceptions, including offline forms from mainstream media; statements that are insinuated (but not said), biased information where relevant parts or context are left out, and correlation being confounded with causation (Watts, Rothschild, and Mobius 2021). Many people are overconfident about their ability to keep true and false information apart (84% in the USA; Kartal and Tyran 2022), and the more they overrate their ability, the less able they are (the Dunning-Kruger effect; Lyons et al. 2021), the less they understand, the stronger they are opposed to scientific knowledge, and the more impervious they are to corrective information (Light et al. 2022). On top of these shortcomings, they have a stronger confirmation bias, i.e., a preference for information in line with their preconceived ideas, and a higher distrust in mainstream media (Lyons et al. 2021). Many people are also overconfident about their ability to distinguish between true and false video messages, whereas they are no better at it than a coin flip (Serra-Garcia and Gneezy 2021). Deception, overconfidence, and extremism are of course much older than social media and played their roles in many conflicts before the internet. So far, we have noted commonalities, yet the online domain differs from the offline world in four important ways. First, for hundreds of thousands of years, humans have observed who were good hunters or mothers and paid more attention to role models than to layperson’s 2 A lack of large-scale protests in autocracies and weak democracies such as the Philippines is quite understandable because environmentalists were regularly killed.

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advice (Richerson and Boyd 2005). In contrast, online information has no tactile context, which makes distinguishing true from false information more difficult, and in democracies, laypersons can disseminate widely without gatekeepers, thereby increasing the volume of false information. Because even the most educated people do not understand, say, the biochemistry of vaccines, it is generally not expert knowledge that protects against falsehood but metaknowledge of how to identify reliable sources (Bruggeman 2017). Many people choose along their confirmation bias (Robertson et al. 2023), and in particular, overconfident people prefer social media to mainstream media as a source of information (Lyons et al. 2021). Second, people with rare extreme points of view can now find each other online, where offline interaction in large numbers was not previously possible. Hence, they can organize themselves online and exert relatively larger influence than is proportionate to their (initial) group size. Third, the lack of face-to-face interactions makes it much easier to insult and threaten without feeling fear or guilt. Fourth, numerous bots (Shao et al. 2018), of which ever more are AI-driven, adapt their personalized messages and recommendations in response to, or anticipating, readers’ (re)actions. These messages are sent out in vast numbers, and humans (for the most part) are unable to tell if they come from bots or humans (Cresci et al. 2015). Bots and social media combined do increase polarization, opportunities for populists, and distrust in democratic institutions (Lorenz-Spreen et al. 2023).3 We may therefore conclude that people are more easily confused and deceived online than offline. Believing falsehoods spread from fearmongering entails imagined, but truly felt, fear and grievances, and makes parts of the population feel uncertain, in particular when they (are made to) believe that evil forces (often projected on foreigners, ethnic minorities, and journalists) are conspiring against them.4 Some leaders and their cronies try to frame and reinforce these feelings and beliefs in a way that serves their own interests by promising grand solutions while hiding the socioeconomic destruction in their wake. These leaders also appeal to parts of the population who have grievances based on true experiences, for example, having been humiliated or lost their jobs. Social and socially constructed uncertainty, in particular about subsistence and status, as well as sudden changes such as an epidemic or job loss, increase fear and anger and draw deprived citizens from various backgrounds into groups (online and/or offline) where they feel accepted, and their grievances heard, sometimes rallied around a leader and at other times self-organized. If society at large is disinterested in their predicament, and their newly found group 3 Some effects of social media seem to be nonmonotonic over time, though. In upcoming democracies, social media increase knowledge about politics, which in longer established democracies is eclipsed by misinformation and reduced knowledge (Lorenz-Spreen et al. 2023). 4 I would conjecture that people who experience their lives as non-valuable (as Bellah (1976) and Sen (1999) meant) are more sensitive to conspiracy theories than people who have fulfilling lives or, in contrast, spend all their time making ends meet.

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members praise them for airing conspiracy theories online, they feel encouraged and turn first experiences into regular practice (Greve et al. 2022). Some scholars say that the lure of these groups is in their simple explanations of a complex world, but as conspiracy theories are often contrived and inconsistent and therefore difficult to comprehend, this argument is not convincing. The simplicity of conspiracy theories is mostly in the omission of the adverse consequences of the policies based on them, for example, opposing coronavirus vaccination. Once individuals become entrenched in conspiracy theories, inconsistency seems not to bother them anymore, and they may even shift back and forth between mutually inconsistent theories, depending on their audience (ibid), which is no simplistic sense making of the world. To such theorists, it seems not to matter a great deal which conspiracy theory they propose, as long as truth-based opinions are opposed. By means of conflicting with their opponents’ views, be it consistently or inconsistently, they signal their oppositional stance to one another, frame outgroup members in a consistently negative fashion, and foster ingroup solidarity. Thus, their logically inconsistent rejection of true information about social crises and outgroups is a politically consistent strategy that enables one to flexibly cut corners in a way that truth adepts cannot. The (self)deceivers are not completely insulated from true information, but, as anyone else, people interpret new information in the context of incumbent information, easily accept it if it is consistent with their (political meta) beliefs, and try to alter it if it clashes with their world view to make it (at least politically) consistent (Hunzaker 2016). One example is interpreting true information about the coronavirus as an indication of governments’ deception. If they were to take true information seriously, they would have to overhaul many false beliefs (Goldberg and Stein 2018), which is cognitively demanding, increases their uncertainty, and, perhaps worst of all, threatens their ingroup reputations. Hence, many people embedded in and strongly dependent on a single ingroup prefer to avoid, rather than engage with, cognitive dissonance (Festinger 1962). Motivated by false information and outgroup stereotypes, (groups of) citizens repress and coerce other (groups of) people who express themselves publicly, and sometimes they do so in cooperation with governments or elites (Earl, Maher, and Pan 2022). Whereas social media were intended to increase freedom of expression by circumventing gatekeepers, repression by peers in discussions about controversial topics actually leads to reduced freedom of expression. Analogous to the underestimation of social inequality (Kiatpongsan and Norton 2014), most people underestimate the magnitude of online repression, in particular repression by private actors (Earl, Maher, and Pan 2022). Deception turns into self-deception when counterevidence is persistently rationalized away without examining it (Trivers 2011). For example, when

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people in group Y are socialized to have a negative attitude toward people in group X, the former tend to rationalize their attitude by telling themselves that X-members must have done something wrong in the past, thereby creating a self-deception by means of a fictitious argument. Although collective self-deception is sometimes beneficial if it increases self-confidence in the face of a threat (e.g., the gri gri ritual; p. 79), it has downsides in other situations. By excluding outgroup knowledge in decision-making, knowledge diversity is reduced (Page 2007), which leads to worse decisions (Schulz et al. 2020). Further, ingroup members miss many opportunities for valuable interactions and get involved in avoidable, costly conflicts. Because evolutionary theory predicts that in the long run, self-damaging self-deceptions and their practitioners are outcompeted by well-informed individuals (p. 84), it remains an unanswered question why over the past centuries, their proportion of the population seems not to have declined, despite massively increased schooling since the 20th century. Although I have no ultimate answer to this question, we already have a proximate one, based on the model of radicalization and polarization (p. 73). To recap, when people are made to believe they are constrained by an outgroup, the latter’s controversialness increases, to which people respond by discussing outgroup-related issues more frequently (Elias and Scotson 1965); at a critical level of controversialness, the group radicalizes or both groups polarize. In Chapter 5, I presented an example of Republicans and Democrats in the USA, with graphical illustrations. The model demonstrates that beyond the critical level, radicalization of opinions and sentiments happens despite ongoing interactions with opponents (i.e., without echo-chamber effect), but also that radicalization is not necessarily caused by repulsion against opponents, and emerges primarily (and sometimes only) through positively interacting ingroup members with similar opinions who are sensitive to the controversialness of the issues discussed (Santos et al. 2021; Baumann et al. 2020). The model has two parameters (interaction strength and controversialness) that suggest potential solutions. We have seen that if there is a large inflow of people from different groups (with different beliefs) diluting incumbent ingroup interactions, or ingroup’s network is weakened otherwise, the transmission of hatred diminishes (p. 119). Historically, this sometimes happened accidentally, but online it can be implemented deliberately. Social media can be curtailed in a politically impartial manner without restricting the freedom of speech by restricting the number of times a message can be sent and the number of times a message can be relayed (Jackson, Malladi, and McAdams 2022), thereby reducing an ingroup’s interaction strength. Controversialness can be decreased if it is based on a real constraint when it is loosened by the outgroup, mitigated through changing circumstances, or when the outgroup or third parties offer substitutes, for example, through profitable exchange (p. 132) backed up by inclusive institutions. Then, feelings of injustice and

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anger diminish, and opposed groups can get back on speaking terms. This is different for fictional constraints because most self-deceptions are nonfalsifiable and supported by other self-deceptions and therefore largely impervious to true information. For example, when a member of X helps a member of Y, the latter recounts that although this X-member was friendly once, in essence they are all bad. Then, the hatred continues and corrective information about the putative constraint reinforces, instead of mitigates, commitment to one’s opinion. As Mark Twain said, “it’s easier to fool people than to convince them that they have been fooled.” 7.2

Polarization and democracy

During the 20th century, democracies were to some extent protected against autocracies by often winning against them in wars. Their overall fighting ability did not exceed autocracies’ ability, but their decisive advantage was their befriending other democracies from which they received help, whereas autocratic regimes preferred “smaller coalition size to avoid diluting the spoils of war” (Graham, Gartzke, and Fariss 2017). In the late 20th and early 21st century, along with external threats, democracies also came under threat by extremism from the inside, which contributes to the worldwide decline of democracy (Hyde 2020; Lorenz-Spreen et al. 2023). When global trade was deregulated, markets financialized, and multinational corporations could find employees everywhere, capital owners became more powerful (Crouch 2015)—again (Marx and Engels 1848)—and their reciprocity with other citizens became more asymmetric. The wealthy elites put their money in safe tax havens (Garcia-Bernardo, Janskỳ, and Tørsløv 2019) and became less willing to maintain costly inclusive institutions, which can be seen indirectly in the stalling and reducing of public spending in the 21st century (Benedetto, Hix, and Mastrorocco 2020). They also repressed the publication of critical information about them by threatening journalists, whistleblowers, academics, and other critics, with expensive lawsuits5 or worse. The global increase of inequality coincided with right-wing authoritarian populists attracting more votes. They made people believe that at the root of socioeconomic problems were not wealthy capitalists with their political influence, fraud, and tax evasion, but ethnic minorities and foreigners. By letting the wealthy elites largely off the hook (Crouch 2019), these right-wing populists obtained (at least passive) support from conservative politicians, thereby posing a larger threat to democracy. If under these circumstances, a populist party gets the upper hand, or a conservative party becomes more populist, it tends to weaken or dismantle the democratic institutions (i.e., independent judiciary, freedom of press, separation of powers, term length, 5 The jargon for this repression is Strategic Lawsuits Against Public Participation, or SLAPP.

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honest elections, freedom for political opponents, privacy) that have historically restrained leaders from becoming autocratic, often with enthusiastic voter support (Bednar 2021). This has already happened in most democracies around the world to various degrees, and in 21 north-western democracies (where data are available), early 21st century support for social democratic parties declined and switched from lower to higher educated non-wealthy, while many lower-educated people, abandoned by complacent socialist parties, switched from left to populist right (Gethin, Martínez-Toledano, and Piketty 2022). One main vulnerability of democracy with respect to these internal forces against it is a design flaw: it allows citizens to use their freedom of speech and organization to destroy democracy from the inside. From an evolutionary point of view, it is clear that entities (such as democratic societies) that do not prevent self-destruction cannot survive, and disappear in the longer run. To prevent citizens from becoming receptive to fake news and radicalize, governments could foster a “cultural immune system” (Richerson and Boyd 2005) in schools that includes history lessons on power and discrimination, as well as skills to verify claims—especially one’s own—instead of judging intuitively, and to check the reputations of one’s sources. If children learn to pose critical questions, receptiveness to propaganda will diminish; in short, prebunking instead of debunking. However, most governments find too much truth too threatening. When inclusive institutions such as democracy are established, usually through struggle, they tend to disappear when left to their own devices (Guriev and Treisman 2022) and require a great deal of struggle to reestablish. This may imply permanent loss, but sometimes results in an oscillating pattern, which has occurred several times in the past. When traders from Venice (one of Italy’s polities) increased long-distance trade with Constantinople and the Levant from the 960s CE onward, non-elite members seized opportunities to become rich, and some incumbent rich became poor (Puga and Trefler 2014). This increased social mobility was accompanied by the creation of institutions to limit losses in trade and to facilitate co-investment by nonkin (colleganza, a.o.). The new rich claimed political influence (access to the newly created Great Council to keep the Doge in check), which they obtained. More inclusive institutions were created (a.o., the Law Merchant), and Venice grew economically (Acemoglu and Robinson 2012). As always, the wealth distribution became more skewed, here among others through newly obtained colonies (1204 CE), and the wealthy wanted to prevent newcomers from accessing politics and the most lucrative trade. They co-opted the wealthiest newcomers to preclude a revolution and institutionalized barriers to entry for everyone else to protect their interests and decrease social mobility (Serrata). Part of their power to do this was due to their positions in the intermarriage network, wherein the most central families (Section 8.1.4) were also politically

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the most powerful (Puga and Trefler 2014). The result of the reintroduction of extractive institutions was economic decline. Only when new kinds of activities were developed much later (a.o., textile, glass, leather, printing), participants excluded from long-distance trade could ameliorate their social positions through newly achieved economic success. The decline of democracy and inclusive institutions in the early 21st century emboldened autocrats, who became more extreme and violent—again. For example in Eswatini (Southern Africa), the king raised several rents and taxes (a.o., street vendor permits and ID-cards; 2020–2022) and kept healthcare and education severely underfunded to support an increasingly exuberant lifestyle of himself and his entourage that included 15 wives. Protests in favor of democracy, jobs, and women’s rights were rendered illegal, and the police beat, tortured, and killed protesters and bystanders alike. Experiencing increasing poverty due to excessive coronavirus lockdowns, citizens became more violent among one another. This small country is part of a broad trend of rising autocracy and state-supported violence, where every critic is labeled a “terrorist”. Because women play an important role in establishing democracy, autocrats are keen to especially reduce women’s rights, often appealing to traditional family values to legitimize their policy (Chenoweth and Marks 2022).6 The polarization model (p. 73) explains how a political elite with strong interactions discussing controversial issues such as women’s rights becomes more extreme. It also explains how in democracies, strong discussions among members of the dominant ethnic group about ethnic minorities can result in extremism, which in turn provokes extremism of and counter actions by opponents, for example, Hindus versus Muslims in India. Under increased equality and reinforced inclusive institutions, by contrast, “alternative facts” have less appeal, minorities look less threatening, and controversial issues can be resolved before polarization is reached (Bednar 2021). In this chapter, we have seen that online, cooperative, conflicting, and neutral ties have increased in vast numbers following our transition to a digital society, transmitting large volumes of true and false information at lightning speed, and throwing parts of the offline world out of balance in the process. Readers who feel intimidated by formal models may largely skip Chapter 8 and proceed to the conclusions (Chapter 9), but I hope without missing the first paragraphs of Section 8.2 on protest movements.

6 When an autocrat suppresses women’s rights, his wife and some other women from his close group seize the opportunity to ascend in society and wholeheartedly support the anti-feminist policy (Chenoweth and Marks 2022).

8 MODELS

For computationally oriented readers, the most interesting part of this book is still to come. After the short march through our long history, in previous chapters, I will flesh out a model of collective action that builds on the discussions of violence (p. 70) and protest groups (p. 94), and incorporate the 5r-package (Chapter 3). Subsequently, I explicate the three-step model of cultural evolution. I formalize it at the micro level, which complements the macro-level original (Thurner, Hanel, and Klimek 2018),1 and generalize it to culture-gene coevolution. I start with short treatments of social influence, polarization, social cohesion, and power. Notwithstanding some notational inheritance, the (sub) sections can be read in any order. 8.1

Social influence and cohesion

In this section, several key concepts and processes treated informally in earlier chapters are formalized: social influence, which sometimes leads to polarization, social cohesion, power, and power due to institutions in inter-polity dynamics. 8.1.1

Influence

If people converse, their different opinions and attitudes tend to converge (Friedkin and Johnsen 2011). If they keep discussing very long or their initial opinions were already similar, they will reach a perfect consensus. In Friedkin and Johnsen’s model, the change of individual i’s opinion between successive 1 A micro-level approach is also important to properly assess the speed of cultural change, which is much faster at the micro level than at the macro level, for example, the turnover of individual songs in the charts versus the change of musical genres. DOI: 10.4324/9781003460831-8

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time points, xi (t1 ) − xi (t0 ), due to interactions with others indexed j with opinions xj is ∑ xi (t1 ) − xi (t0 ) = ψ wij [xj (t0 ) − xi (t0 )]. (8.1) Social tie wij > 0 means that i receives information from j. Because in many cases, people are more responsive to proportions of their social environment rather than absolute numbers, absolute values of social ties aij ≥ 0, which may denote interaction frequencies, are row-normalized, turning frequencies (or other values) into re-scaled values that correspond to probabilities; ∑n ∑ wij = aij / j=1 aij , which implies j wij = 1. People have the strongest ties with close family, best friends, and perhaps a patron on whom they strongly depend. The parameter in the model, 0 ≤ ψ ≤ 1, indicates openness, or sensitivity, to social influence. For many people, sensitivity to influence increases with their identification with others, usually their ingroup. Accordingly, one could implement different sensitivities instead of one for all (which is a costly affair, at the expense of many degrees of freedom). Moreover, sensitivity tends to increase with uncertainty and diminishes with self-confidence. If for a given problem, an individual i already knows the solution and feels confident, or is wrong but stubborn, they stick to their initial opinion (ψi,x ≈ 0) even if there is social interaction (Jayles et al. 2017). If others let themselves be influenced, the stubborn can guide their group to a solution or a disaster, respectively (Couzin 2009; Becker et al. 2017a; Becker et al. 2017b). When controlling for sensitivity, we can see that people with more power (defined below) have a larger influence on where the consensus will be; they also have more power because they tend to be less sensitive to subordinates than the other way around. When the group discussion is about the framing of a certain issue, for example, when preparing a collective action, more powerful people thus have a larger influence on the framing. Bourdieu (1992) called this symbolic power, as a special category, but because it follows from the general concept, a special name for it is redundant (although it does not harm either). Adding random noise (i.e., randomness) to this model makes the opinions wiggly, but they converge to the same consensus as without noise. Also in the model below, noise has no interesting effect. 8.1.2

Polarization

Sometimes, conversing individuals do not converge toward a consensus but become more extreme instead. If this happens in two disagreeing camps, polarization results. Here, the social influence model is unsuited because it cannot explain increasing extremism beyond an initial range of opinions or attitudes. Baumann’s model (2020) can explain extremism and polarization but is different from and not a generalization of the social influence model; the

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polarization model predicts consensus for the same parameter settings as the social influence model (Figure 5.1A; p. 74), but only the latter predicts what the consensus will be. From the polarization model (Baumann et al. 2020), I remove the effects of assortment and activity level, which are redundant; they may accelerate polarization but do not lead to a qualitatively different outcome. Further, I replace the interaction strength parameter, K, by a matrix K that is isomorphic to the adjacency matrix W (that has cells wij , as in Eq. 8.1), at the expense of one degree of freedom because I only use two cell values. The modified model is ∑ dxi /dt = −xi + Kij wij tanh(ψxj ), (8.2) j

with ψ denoting controversialness. I use the same symbol as for sensitivity to social influence above, because controversialness can only have an effect when people find the issue valuable and are therefore sensitive to it and others’ opinions about it. Controversialness can increase or decrease due to a leader’s influence (e.g., ideology), or due to external influences (elaborated in Chapter 5 and on p. 73). Values of Kij = Kji are initialized with one baseline value K > 0. Then, values in one group, say, Republicans denoted Krr , are set higher than the baseline, Krr > Kdd = Kdr = K, where index d indicates Democrats.2 Note that in the influence model above, ψ ≤ 1, but here it is allowed to set ψ > 1. When abstracting away from the network and using one K value for everyone, the critical level where convergence of opinions changes into divergence can be inferred analytically, ψc ≈ 1/(2K), but with two different K-values, each group has its own tipping point, which have to be determined computationally. In all empirical cases, the critical level codepends on the network; if the modularity of the network is lowered (i.e., sparser within-group and denser intergroup connections), higher controversialness is necessary for polarization to emerge, which generalizes the contact hypothesis (Paluck, Green, and Green 2019). Figure 5.1B shows that radicalized individuals get diverse opinions, whereas in actuality, they often converge to an extremist consensus. For these cases, one may apply the good old influence model to each group separately once equilibrium has been reached in the polarization model. The polarization model can be generalized to multiple opinion dimensions and can express that opinions on one issue (e.g., striving toward equality) are correlated with opinions on another issue (e.g., tax on wealth), for example, due to common arguments for, or tightening norms in favor of, both. In multidimensional opinion space, correlated dimensions are non-orthogonal. Then, 2 In the Republicans-Democrats example in Figure 5.1A, ψ = Krr = Kdd = Kdr < 1.3, and in Figure 5.1B, Krr = ψ = 3; Kdd = Kdr = 2.

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if sensitivity to influence becomes high, social influence results in the alignment of different issues, in addition to polarization (Baumann et al. 2021). This means that, say, random initial opinions on different issues progressively cluster into associated opinions, which leaves large parts of the opinion space unoccupied, in the example because nobody in favor of equality also favors tax decrease for the rich. Clustering, or modularity, of culture emerges and results, among others, in ethnic and national cultures (compare with Axelrod’s model, p. 20). We already knew this, of course, but now we see that it also follows from the polarization model. 8.1.3

Social cohesion

By writing Equation 8.1 in matrix notation, the speed of convergence of opin∑ ions can be inferred. To this end, make a diagonal matrix D = diag( j wij ), with cells equal to 1 for every node with outdegree > 0 (else the node is irrelevant cohesion-wise and to be left out), and a Laplacian matrix L = D − W, with W the row-normalized adjacency matrix (Van Mieghem 2010). With the Laplacian, the influence model (for now without ψ) can be stated as dx/dt = −Lx.

(8.3)

The second-smallest eigenvalue of L is called algebraic connectivity (Fiedler 1973); it is minimally zero, for a disconnected graph where subgraphs cannot influence each other, while higher values indicate faster consensus (OlfatiSaber and Murray 2004). It is maximal in a clique (fully connected graph). To compare networks, one may normalize algebraic connectivity by dividing it by its maximum. To find out if for certain groups, their algebraic connectivity is high or low, one can compare with the average algebraic connectivity of randomized graphs with the same size and degree distribution. Along with (1) facilitating relatively quick consensus (Eq. 8.1), a cohesive network is supposed to bind a group together, and to reduce noise and biases in transmission, for which there should be: (2) redundancy, namely a minimum number of non-intersecting paths connecting arbitrary pairs of nodes (Moody and White 2003); (3) nexuses, or chords, connecting these paths midway (Figure 3.1, p. 33); (4) short average path lengths; and (5) reciprocity. An increase in any of the properties 1–4 is associated with an increase of algebraic connectivity, and therefore seems to be a suitable measure of social cohesion of (sub)groups. Mathematically exact results are often in terms of lower and upper bounds, and in most cases on networks with undirected, binary ties (Van Mieghem 2010). When calculating the eigenvalues of L, one may sometimes obtain complex eigenvalues, of which the sociological meaning is nebulous even though one can still solve Equation 8.3 numerically without difficulties. To avoid complex eigenvalues, one has to use a version of L with bidirectional ties, either by

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choosing an empirical threshold (i.e., lower bounds of interaction frequency or intensity) for the presence and bidirectionality of ties that must be meaningful in the domain of study, or by calculating the Laplacian in a particular complicated way (Chung 2005). The second option leads to overestimating cohesion when there is less reciprocity, though, hence it is better to present the reciprocity of the network up front than to hide it inside an intricate and rarely used Laplacian. Note that algebraic connectivity describes robustness, not resilience. Note also that cohesion has a sweet spot, because in large groups that are too dense, gossip becomes suffocating and counterproductive; also innovation is impeded (Burt 2008). Furthermore, cohesion is weakened by negative ties (denoting conflict; Harrigan et al. 2020), and is calculated only on the positive ties. Any kind of positive ties that contribute to social bonding can be used for the calculation, also ties that are not experienced as sympathy, for example, exchanges for material benefits. Cohesion is the relational part of group bonding, which also has an ideational part that comprises identification with the group and its goals (White and Harary 2001), and an emotional part: solidarity. In groups that exist for a long time, cohesion for the purpose of cooperation can be loosened for as long as most people are monitored by at least some others. Then, the group’s network can relax to a k-core (Seidman 1983) that connects everybody in the same (e.g., ethnic) group (such as the Turkana; Mathew and Boyd 2011). In a k-core, everyone has at least k ties with others who have at least k ties, but there can be topological bottlenecks and the number of non-intersecting network paths is typically much lower than k (in a connected group it is minimally 1), hence the network is more loosely connected. 8.1.4

Power centrality

In a network, individual i is more powerful if more people, indexed j, are influenced directly by i, if these people have stronger ties to i, and if j in their turn are more powerful, such that i influences more people at two steps removed (see the more extensive discussion on p. 55). On average, someone’s power does not reach further than two steps into their network, or three steps very weakly (Christakis and Fowler 2009; Pinheiro et al. 2014; Luo et al. 2017), also in macro-level networks of polities as nodes (Li et al. 2017). Bonacich’ (1987) power, or status, centrality measure can therefore be simplified to cp = AT 1 + b2 AT (AT 1),

(8.4)

with A the adjacency matrix with ties aij ≥ 0 (not row-normalized!) directed along deference (as in Eq. 8.1), AT the transpose of A, 1 the unit vector, and 0 ≤ b2 < 1 the relative magnitude of influence on people at distance 2. (One

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may add a third term with b3 = b22 .) This yields a vector that can be interpreted as a rank order of individuals in a network. Moreover, this measure is much more robust against missing data than when longer paths are included (Costenbader and Valente 2003). Bonacich defined b2 slightly smaller than 1/|λ1 |, with λ1 being the largest eigenvalue of the adjacency matrix; then, cp approximates Katz (1953) or eigenvector centrality. 8.1.5

Power in inter-polity dynamics

Power of large groups such as polities is more than the sum of individuals’ power and also depends on groups’ institutions. My inter-polity dynamics model is illustrated in Figure 5.3A (p. 91) with two polities, one extractive and the other inclusive. Due to their warfare, the territory taken by one goes at the expense of the other’s territory (Figure 5.3B). Let 0 ≤ x ≤ 1 be the territory size of an extractive polity and 1 − x of its opponent with inclusive institutions. In the function used for institution-based power (Kumaraswamy 1980), P(x, a) = abxa−1 (1 − xa )b−1 ,

(8.5)

b is fixed (b = 3.5) and 1.1 ≤ a ≤ 2 describes the degree of inclusiveness: ae = 1.1 for an extractive polity and ai = 2 for an inclusive one. In a war between them, the change of polity size, considering how much opponent’s territory is conquered, is dx/dt = (x − 1)P(x, ae ) − xP(1 − x, ai ).

(8.6)

Starting with an extractive polity with size x = 1, Figure 5.3B shows that its territory is eaten away by the inclusive polity, for as long as the latter’s power exceeds its opponent’s, until dx/dt = 0, at x ≈ 0.4. In this dyadic model, only the effect of institutions is taken into account, not of weapons, army size, or alliances with other polities. 8.2

Cooperation for public goods

To introduce the model of collective action, it is best to start with a relatively simple example, namely a protest in favor of political change. If there was no network before, it will be established when relatively deprived or institutionally abandoned people share their grievances with others who share the same fate, and collectively frame (Eq. 8.1) or situationally comprehend their problems and goals. There may not (yet) be norms or reputations, in which case the 5r-package (p. 38) is incomplete. There may not (yet) be leaders, either. The outbreak of protests is usually explained by critical mass theory (Marwell and Oliver 1993), which takes recourse neither to the 5r-package nor to selective incentives, and applies to fledgling groups of people who hardly

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know each other. It uses strong rationality assumptions that are too strong for this case, but I argue that its main results can be inferred without these strong assumptions, by assuming that rationality is bounded by a lack of knowledge of how the situation will evolve (Simon 2000), and information about others is limited to network neighbors. The crucial step is to allow for randomness (Macy 1991), or “trembling hands” (Dion and Axelrod 1988) as game theorists say: a chance that in the heat of the moment, some individuals accidentally contribute to the public good, which might encourage others to join in. This chance increases when opponents (e.g., ruler forces) provoke citizens by severe mismanagement or deploy cruelty against innocent persons. It was conjectured for a long time that if agitating stimuli cause anger and arousal, a critical level may be reached—a “spark”—that entails a burst of collective protest (Lieberson and Silverman 1965). This is exactly what the model shows, with a novel insight with respect to critical mass and other binary decision theories that cooperation can break out without zealous initiative takers, leaders, or strong rationality (Bruggeman, Sprik, and Quax 2020). Individuals who happen to be sensitive to certain stimuli in a given situation become agitated when exposed to these stimuli. Their sensitivity can be a learned response, for example, to certain symbols, sometimes due to a process of radicalization, or be based on an awareness of possibilities or harmful consequences, or can be a (partly genetically inherited) physical response. On an evolutionary timescale, it is likely that cooperation on the basis of external stimuli predates the 5r-package by billions of years, when single cells started to respond to chemical signals in their environment. In the case of protests, the relevant stimuli are opponents’ provocations and the temptations due to their failures and weaknesses. Other examples are the sight of human suffering and the urgency of problems in general (see also Footnote 21, p. 122), and one could generalize by saying that the driving stimuli (variable T in the model) can be threat, turmoil, tension, trouble, temperature (in climate change), or temptation during any of the former,3 depending on the domain of application. T may also increase the controversialness of opponents and their issues, which, beyond its critical point (p. 73), increases radicalization. If actors want to legitimize actions to themselves that are widely seen as illegitimate, such as the onset of violence, they may create turmoil by and to themselves, for example, Russia’s media that mass-produced lies about Ukraine (2022) to convince their citizens that they had to fight to protect Russia against Ukrainian “Nazis”. Clever autocrats and their regime keep their threat under the critical level (when an additional provocation would “ignite” their opponents) 3 If in the Ising model, T is to stand for temptation, an increase of T might simultaneously increase C or decrease D.

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but make it high enough to sufficiently threaten their opponents (other polities or protesters) in order to get what they want without the cost of fighting. Once protest or violence breaks out in one place, it increases turmoil in other locales, and an initial protest or violent conflict can diffuse geographically, for example, the revolutions across Europe in 1848. If the participants wish to continue their collective action over a longer time span, agitating turmoil will not suffice, and they will have to flesh out the 5r or 6r-package in one way or another, to be added to the model later in this section. They will also need (more) resources, which increase, and can be expressed in terms of, the costs of cooperation. Collective actions end when goals are achieved, resources run out, opponents win, others intervene, or participants fully comprehend the situation and start freeriding on group members. Lab experiments have pointed out that a majority of people are conditional cooperators who follow the majority in their network environment (Chaudhuri 2011; Kurzban and Houser 2005). In risky situations with unknown benefits and costs such as protests against autocratic regimes, the proportion of conformists is even higher (Wu et al. 2014; Morgan et al. 2012). However, under uncertainty, people become less, not more, cooperative (Kappes et al. 2018), which increases the challenge to explain cooperation. Moreover, there are also unconditional defectors who do not cooperate regardless of circumstances or social pressure, in proportion p of the group. For the moment, everybody is assumed to be a conditional cooperator (p = 0); we will examine later what happens if p > 0. Apart from agitating stimuli, modeled at the aggregate level,4 individuals obtain information about the behavior of ingroup members who are spatially or socially close by. Indices i and j run over individuals, and rownormalized social ties wij (Eq. 8.1) mean that i notices j’s behavior, at least, or discusses with j; kith and kin have relatively stronger ties. In the simulations for this book, all ties are bidirectional, but not necessarily symmetric in strength. Presuming that preliminary interactions resulted in a shared intentionality (p. 31), individuals decide to contribute to the public good (C) or to defect (D), with 0 < D < C. If this inequality does not hold, there is no dilemma of collective action and the model does not apply. Behavioral variable σi can take the value σi = C or σi = −D. The minus sign looks weird, but will make sense in Equation 8.7 below. In the examples, C = 1 and D = 0.5 (versus D = 0 in game theory); a generalization including interpersonal variation and a relation with game-theoretic payoffs can be found in the Appendix. Important here is that for a fixed sum of D + C, increasing C while decreasing D makes the public good appear 4 In an aggressive confrontation of two groups, say e and f, e raises the turmoil Tfe for f while f raises the turmoil Tef for e, and oftentimes, Tfe ̸= Tef .

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more valuable, for example, through constraint (p. 72), ideology, or deception.5 The motivating effect corresponds to lowering individuals’ threshold of the number of cooperating network contacts, also in other binary decision models (Granovetter 1978; Watts 2002); this threshold can be calculated in a mean field approach (Bruggeman, Sprik, and Quax 2020). The public good will appear less appealing due to impromptu trouble and rising costs; then C decreases and D increases. If, accordingly, we drop the constraint that D + C is constant, and let C become (almost) equal to D, people become indifferent with respect to having or not having the public good and will cease to see the point of acting collectively. Another obstacle to collective action is a lack of consensus; then there is no shared intentionality. The less consensus there is on the common goal or public good, the more C and D become distributed around (C − D)/2 (Appendix). Then it is still possible that some people contribute if they find the public good highly valuable, whereas the majority will not, unless, with a great deal of luck, individuals’ thresholds happen to be arranged in successive order such that zealous initiative takers set in motion a cascade of followers (Granovetter 1978). So far there is not a single case where this assumption has been shown to hold true, but when the critical mass effect was observed, it has been assumed to be true without further testing. The turmoil assumption, by contrast, will be falsified if the model’s predictions turn out to be false, but the evidence from the first empirical test (below) was highly in favor of the model. For C = 1 and D = 0.5, the challenge of cooperation can be depicted in Figure 8.1A as a hill that participants have to overcome (continuous line). When moving from left (nobody cooperates) to right on the horizontal axis (NC /n is the average cooperation over n group members), there is a hill top where some are exploited by others who freeride, and far less than the maximum public good is achieved. To solve the model, the quantity on the vertical axis, H/n, is to be minimized; it may be loosely interpreted as negative likelihood or average dissatisfaction (Galam, Gefen, and Shapir 1982), whereby proceeding from lower to higher levels is unlikely whereas the other way around (if possible) is certain. The model is ∑ H=− wij σi σj . (8.7) i̸=j

Readers will understand how the hill in Figure 8.1A is obtained by doing the calculation for a group of n = 2 (dotted line) by hand (in this case, 5 Tilly (2002) argued that before participating in a protest, people consider its “WUNC”: the worthiness of the cause (i.e., its legitimacy and alignment with their interests), unity (i.e., solidarity), the number of people already participating (i.e., conditional cooperation), and participants’ commitment (in terms of C and D in the model), all of which, if positively correlated, would increase their C and decrease their D.

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wij = wji = 1). The larger the group is, the more rounded the hill becomes. For clarity, one might subscript Hg with respect to a goal or public good g on which there is consensus; a goal shift will change C and D. The model is an asymmetric version of Ising’s spinglass from physics (Stein and Newman 2013; Kobe 2000) used earlier in its usual symmetric form (with σ = 1 or −1) to generalize Schelling’s segregation model (Jones 1985; Stauffer and Solomon 2007) and to model groupthink (Callen and Shapero 1974), polarization (Weidlich 1971), voting, and social influence in general (Castellano, Fortunato, and Loreto 2009). Solving the model by minimizing H goes as follows (Metropolis algorithm; Barrat, Barthelemy, and Vespignani 2008). For a given level of T (turmoil or other agitating stimuli), within some range of T, individual i is randomly picked among the conditional cooperators. Hi is calculated (Eq. 8.7 only for i); then σi is flipped to its opposite, e.g., from defection to cooperation, and H′i with the flip is calculated. The flip is accepted if H′i < Hi or with a probability that increases with T [specifically, if for a random number 0 ≤ cr ≤ 1, cr < exp(−(H′i − Hi )/T)]. If accepted, it changes the local environment of all nodes connected to i. The procedure is repeated a great many times, called Monte Carlo steps, and the level of cooperation is averaged, and all nodes are set to the defective state, before repeating the procedure for the next level of T (here in steps of 0.01). Individuals’ modeled behavior thus depends on social ∑ influence ( wij σi σj ), expected payoff (through C and D), agitating stimuli (T), and a portion of randomness (cr ). If one wishes, network dynamics could also be incorporated, for example, by a small chance at each Monte Carlo step that a cooperator disconnects from a defector (Gallo and Yan 2015), a chance of triadic closure of cooperators, or an expanding group. Figure 8.1B demonstrates that at a low level of agitation, collective action will not start. At a critical level Tc , there is a burst of cooperation wherein almost everyone in the (sub)group participates, indicated by the continuous line. The sudden increase in cooperation is the same on the time axis with Monte Carlo steps taken as a proxy for time6 (the continuous line in Figure 8.2B). In clustered networks, cooperation starts from the bottom-up in small clusters (i.e., at fewer Monte Carlo steps), as in street protests (e.g., in Egypt during 2011; Steinert-Threlkeld 2017). The same happens if agitation is locally stronger in some clusters, which then start cooperating before the rest, for example, a cluster in face-to-face contact with turmoil-producing opponents. In general, Tc increases with (sub)network size at a decreasing rate. The effect of agitating stimuli is nonmonotonic (Figure 8.1B); beyond the critical level, cooperation gradually decreases, which means that very strong stimuli confuse rather than agitate. The decline of cooperation is to be interpreted 6 Taking Monte Carlo steps as a proxy for time is strictly speaking incorrect, but the question is if doing it correctly, at the price of complicated math, would yield any additional insight in the social process.

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A

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FIGURE 8.1 Ising model of cooperation. (A) Average “dissatisfaction” (H/n) with nor-

malized cooperation (NC /n) for a very large network (continuous line) and a dyad (dotted line). (B) Cooperation (NC /n) with agitation (T) in a group (n = 50; density = 0.5). Without unconditional defectors, there is a burst of cooperation, and with a proportion of unconditional defectors above the critical level, there is no burst (dotted line).

qualitatively, not quantitatively. Some autocrats use this declining effect when earlier attempts to curtail information exchange among protesters failed, and try to smother protests by inundating the population with false information (Tufekci 2017). Now we look at what happens when there are unconditional defectors. If their proportion p increases, cooperation (NC /n) obviously decreases, and Tc shifts to the right. When p reaches a critical level, pc , there is no burst anymore but a gradually increasing cooperation with T, with a maximum at a lower level of cooperation (dotted line in Figure 8.1B). The instability close to pc is visible in the fluctuations, varying over simulation runs. For our choice of values of C and D, pc = 1/3 in a mean field analysis, independent of network size and density (Bruggeman, Weenink, and Mak 2022). Whereas pc is size-invariant, simulations point out that Tc is (much) lower for (very) small networks. (To study small networks, simulations are better suited than the mean field approach.) When unconditional defectors are clustered together instead of homogeneously distributed, they hold back conditional cooperators to a lesser extent, and pc becomes larger, depending on the specific network configuration. A first empirical study of this model, applied to video data of street violence by small groups (n = 59), demonstrated that the threshold of unconditional defectors was predicted accurately (see Figure 8.2A) and that violence tends to start in small subgroups. Because the falsification attempt of the turmoil assumption failed, the preliminary conclusion (awaiting further

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FIGURE 8.2 Results of the Ising model. (A) Groups with (1) and without (0) a burst,

with the proportion of unconditional defectors on the vertical axis. The horizontal line is the predicted critical proportion of unconditional defectors (pc = 1/3). Data from Bruggeman, Weenink, and Mak (2022). (B) Level of cooperation over time without unconditional defectors, in a clique of n = 5 at T = 0.10 ≈ Tc . Cooperation without noise in reputations (continuous line), and with noise (dotted line); pt = 0.5; pe = 0.2.

falsification attempts) is that the Ising model predicts correctly if a sizzle will become a burst or a fizzle. The Ising model demonstrates that if there are not too many unconditional defectors, cooperation can start without the full-blown 5r-package, but cooperation is unlikely to continue for very long. We know, however, that often, discontented citizens organize themselves with leaders who inspire and coordinate, and they develop norms and a division of labor (Tarrow and Tilly 2009; Goldstone 2001; Hale 2013). In lab experiments, leaders and initiative takers (with a larger interest in the public good) increase cooperation of others (Güth et al. 2007), in line with evolutionary theory (Gavrilets and Fortunato 2014). In the model, they facilitate the start of cooperation by reducing Tc for the remainder of the group. Furthermore, their framing and ideology will make the benefits appear rosier and the costs look lower, which also reduces Tc . The model can assess the effect size of leaders and can also explain cooperation without them. The effect of leaders is of course far more general than in protests, as is the effect of norms. If Nm group members are willing to maintain a prosocial norm, an addi∑ tional term −h ξi σi (or multiple terms for multiple norms) is added to Equation 8.7; h increases with Nm and ξi increases with i’s norm internalization, for example, moral norms. Sufficiently strong norms (i.e., internalized or large

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enough Nm ) result in a start of cooperation without agitation (T = 0). Norms make it possible to start cooperating asynchronously instead of in a burst, as in many situations in daily life, depending on when (i.e., at which Monte Carlo step) the cooperative norm (individually indexed, hi ) applies to whom. Norms also absorb a portion of situational uncertainty, but they come at additional costs for the maintainers, over and above the costs of cooperation. If the maintainers organize themselves and maintain the norms collectively, the cost per individual can stay low (Oliver 1980) or can be fully compensated by side payments, for example, through resources from punished defectors, or tax. (Norms cannot prevent the downfall of cooperation at high levels of agitation, though.) The evolution of norms is formalized in Section 8.3. Instead of i having perfect knowledge of j’s behavior (σj ), i’s knowledge boils down to a reputation of j, rij , that is based on gossip with possibly biased or erroneous content (Hilbe et al. 2018). Only if there is neither bias nor noise, rij = σj . This may be modeled as follows. When j defects or cooperates, network contacts gossip about j, and gossip spreads into the network with at each transmission a chance pt that it does not make it to the next individual, i.e., that someone does not gossip about j, and a chance pe that the gossip is incorrectly transmitted or interpreted. Individuals i update j’s reputation on the basis of gossip that they receive through one or multiple information channels. The update heuristic that people tend to use is taking the (tie weighted) average of the gossips (Sommerfeld, Krambeck, and Milinski 2008) and, if they have a tie with j, their personal observations (Sommerfeld et al. 2007). Consistent with the influence model (Eq. 8.1), I weigh the average gossip i receives and i’s personal observations equally. In each Monte Carlo step, updates of rij are substituted for σj in Equation 8.7. If there is no cooperation at the beginning, any noise or bias in gossip is slanted toward cooperation, which helps to make it start more easily, as in prisoners’ dilemma games (Dion and Axelrod 1988). This is illustrated by the dotted line in Figure 8.2B; a clique of five at Tc is exposed to noise (pt = 0.5; pe = 0.2), and is compared to a noise-free model run (continuous line). In the noisy case, cooperation starts earlier but has relapses, and in the long run, it is lower on average; cooperation further declines when adding more noise. This pattern demonstrates that if there are redundant information channels with multifold transmission of gossip (Section 8.1.3), noise does not ruin cooperation. The Ising model has two levels, collective and individual, and no third micro level of individuals’ psychology (which could be attached if there is a model for it). However, the effects of prosocial emotions and of benefits and costs can all be expressed in the model by individualizing decision rules, or strategies, and the values of C and D (Appendix). This is important to model changing strategies in the course of collective action. If social situations are repeated in similar ways, individuals learn, and

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they learn more quickly in smaller groups where they have a larger influence on the outcomes and their payoffs (Burton-Chellew and West 2021). Without strong norms, many individuals tend to abandon their initial conformism and (with an increasing chance at subsequent Monte Carlo steps) turn into unconditional defectors (Andreozzi, Ploner, and Saral 2020). The Ising model can thus accommodate individuals and subgroups with changing strategies, dealt with by decision rule updates in subsequent Monte Carlo steps. Differences across network nodes can also be used to model global cooperation for climate protection. At the global level, network nodes are countries, each with different benefits and costs, while negotiating in the network of diplomatic relations, and T stands for temperature. When performing this exercise, making a distinction between rich and poor countries and assuming some freeriding countries, no surprising result comes out (it has to become really hot before climate measures will be taken collectively), but it is a proof of concept that the model can also be applied to pressing problems at the global level. When relating the Ising model to the polarization model, we can get a partial answer to the question of why violent conflicts between groups became more frequent in agricultural societies. Once people had more possessions and land, these resources became desired by others, who turned them into controversial discussion topics. The polarization model shows how discussions about such topics can result in radicalization. This process of radicalization also produces agitating stimuli that drive T toward its critical level, Tc , which makes competing groups highly sensitive to few additional stimuli (e.g., provocations by or weaknesses of their opponents) that trigger an outburst of violence. The initiative (or command) of a leader lowers Tc in the model, which can suffice to get collective violence started. (Leader’s order to attack could be modeled as a norm.) Across studies, the agitating stimuli are different, of course, but at an abstract level, the same Ising model can be used for various agitating stimuli, situations, and species. A general conflict participation function (De Dreu and Triki 2022) illustrates how the model relates to a broader literature. This function is defined as qi = f(E(P), E(r), V(I), V(O), T), where E(P) is i’s expected payoff (see Appendix), E(r) is i’s expected gain in reputation, which would increase C or decrease D for i, V(I) is i’s care for their ingroup, which has the same implications, V(O) is i’s care for the outgroup, with opposite relations with C and D, and T stands for (the perception of) threat, turmoil, tension, or temptation (e.g., to steal; Mathew 2022). These variables, or subsets thereof, are important in great many studies of conflicts of various kinds, including conflicts of interest without violence where the collective action is symbolic (e.g., collectively signaling threat), and suggest

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applications of the Ising model to other species varying from quorum sensing bacteria (Diggle et al. 2007) to herd bulls defending their group against approaching lions (Estes 1991), and weavers chasing away snakes from their nests. On top of the variables in the participation function, the Ising model also incorporates the effect of network topology, which often makes a difference. To recapitulate, the Ising model can explain collective action on the basis of agitating stimuli, a network, and expected payoffs (with or without leaders), to which norms, reputations, and psychological effects can be added, and thereby formally represent the 5r-package as well as the 6r-package with leaders. Additional coherence and explanatory power can be obtained by combining the Ising model with other models such as polarization and cultural dynamics (below). 8.2.1

Social balance

Social balance theory is laid out on p. 80. Its model is a Hamiltonian equation that is similar to the Hamiltonian of the Ising model above (Galam 1996; Minh Pham et al. 2020). Individuals i, j and k can have positive (σi = 1) or negative (σi = −1) opinions about something, as a binarized version of the social influence model, and can have positive (aij = 1), negative (aij = −1), or absent (aij = 0) ties. For simplicity, the number of ties stays fixed, ties are symmetric (aij = aji ), and tie weights are abstracted away from (but some balance models have them). With a weighing factor ν for the relative magnitude of the social balance effect (the default is ν = 1) H=−

∑ i̸=j

aij σi σj − ν

∑

aij ajk aki .

(8.8)

i̸=j̸=k

In subsequent Monte Carlo steps, a randomly picked node can flip its opinion if the changed sign decreases H, or if it does not, with a chance that increases with turmoil or uncertainty (i.e., Metropolis algorithm). Then a tie is randomly picked and the same procedure is followed. For the outcome it hardly matters if tie changes are more or less frequent than opinion changes (Minh Pham et al. 2020). Other effects can be added to the balance model, for example, a religious norm against con∑ flict, h, can be expressed by an extra term, h i̸=j (1 − aij ), in the Hamiltonian (ibid). Another effect is that negative ties, even in balanced net∑ works, are often experienced as unpleasant, for which a term −ζ aij can be added, with 0 < ζ ≤ ν. If directed ties are modeled among individuals (not polities), the cynical triad (the enemy of my enemy is my friend) is to be prevented (Isakov et al. 2019) by counting it positively in H.

Models 161

Note that high social pressure, or social tightness (p. 66), corresponds to low temperature (T) in the ingroup—a cold social climate, so to speak— opposite to social turmoil (i.e., high social “temperature”) that increases the tolerance towards diverse opinions.7 8.3

Cultural evolution

Cultural evolution proceeds through (1) combination and transmission of cultural elements, (2) selection of elements after use in interactions with others and the environment, and (3) change of the sociocultural environment through the influx of new elements and the disappearance of incumbent elements. 8.3.1

Combination

Individuals have access to cultural elements through their social network, and may also use written sources and resources from the physical (including the natural) environment. When representing access to cultural sources as network ties, including earlier access that resulted in memorized culture, the network of all actors as well as material and immaterial sources accessed is larger than the social network. This larger structure can be written as a tensor M(t) at time t, which is a multidimensional array expressing who combines which elements into what innovations. For simplicity, I consider network ties, cultural elements, and resources, including access to public goods, to be either present (value 1) or absent (0), but in some cases one may choose differently. Sum totals of elements at the individual level are abundances of elements at the group or macro level. If an actor i at t0 combines8 two or more cultural elements (and uses resources) σ indexed u, v, . . . to make a new one, σy , we write σy (t1 ) = Mα,i yuv... (t0 )σu (t0 )σv (t0 ) . . . ,

(8.9)

with t1 ≥ t0 . If i combines seeds with a certain kind of soil, it may take a season before the result comes to the surface (t1 ≫ t0 ). Greek letters denote actor’s ability to succeed the given action: α expresses i’s knowledge of how to combine elements (and use resources) σu , σv , and others to create σy . For α,i simplicity, either Mα,i yuv... (t) = 1 or Myuv... (t) = 0. For new institutions as combinations of incumbent or older norms as well as other information (e.g., on specific circumstances), notation h is used instead of σ, as in the previous section. Equation 8.9 implies that only when all cultural elements and resources 7 Psychologists have miniaturized social balance theory and apply it to networks of beliefs/attitudes inside the mind (where balance theory once started; Heider 1946), with H standing for cognitive dissonance. It is called attitudinal entropy theory and it predicts that belief change not only depends on the social environment but also on the network of prior beliefs (Dalege and van der Does 2022). 8 Time t0 can be stretched to an interval of tinkering, τ , starting at t0 .

162 Models

on the right-hand side are available to i and i is in the know can σy be created. Hence, the chance of successful combination decreases with the number of elements combined in one stroke, a risk that is often reduced by combining in a number of intermediate steps, called modularization (Simon 1962). Hence σ’s can be modules of cultural elements, and in engineering, recombining modules is a well-known practice to innovate. Groups of people can create or reproduce cultural elements collectively, in particular public goods. Cooperation and defection are culturally meaningful, and are therefore cultural elements that can be represented by our σ-notation. From this cultural point of view, there are σC = C and σD = −D, and public good σg (or a certain amount of it) can be produced if a sufficient number of individuals i cooperate, σC,i . We have seen above that this challenge can be ∑ modeled by minimizing Hg = − wij σx,i σy,j , to which social norms can be added. If an actor i takes information y from a source or role model j without combining different pieces of information, transmission from j to i takes place, or σy,j → σy,i . It can be seen as a special, or baseline, case of Equation 8.9, σy,i (t1 ) = Mβ,i y (t)σy,j (t0 ), with β indicating i’s prior knowledge and learning effort (and j’s teaching if it matters); hence β implies sensitivity to influence (ψ, above) and is a subset of α. Some cultural elements come in packages, for example, bureaucracy; σy can then be unpacked as a concatenation of subelements, as they are written on the right-hand side of Equation 8.9. The network pattern of transmission can be investigated through the concept of cohesion, discussed above.9 If during transmission, elements are modified intentionally or accidentally, Equation 8.9 applies. Combination and transmission (and use) may overlap in time and are separated only analytically. After each transmission, individuals relate the acquired information to, and sometimes combine it with, their prior information, or knowledge, if they already had some. A special case is someone’s network contact’s opinion influencing their own opinion, resulting in an opinion update. For this case, we generalize the binary values of σ ∈ {0, 1} to continuous values, σ ∈ [−∞, ∞]. If we use the social influence model (Eq. 8.1), we can predict i’s updated opinion σv′ ,i (t1 ) after interacting with actors j holding opinions σu,j (t0 ). Although the simple functional form of Equation 8.9 does not apply, we can predict 9 To measure multi-fold paths from i to j through which “complex contagions” pass, Guilbeault and Centola (2021) devised a complicated measure that boils down to taking the induced subgraph of paths from i’s network neighborhood (ego network) to j’s neighborhood under several constraints, e.g., the path length from i to j should not exceed the shortest path by more than a few ties. A major constraint is individuals’ thresholds of acceptance, but these are generally unknown. Instead, one could calculate the normalized algebraic connectivity of the subgraph that takes nexuses between relevant paths into account, which in turn enhance transmission. Hence, higher algebraic connectivity indicates a higher chance that a message can be transmitted and will be accepted, which refines the current binary (present/absent) complex path measure.

Models 163

σv′ ,i (t1 ) = σv,i (t0 ) + ψi

∑

wij [σu,j (t0 ) − σv,i (t0 )].

(8.10)

j

If constraint or controversialness is high (and consequently, ψ > 1) or interactions are very strong (K > 1), the polarization model has to be used instead. 8.3.2

Selection

Individuals use cultural elements when interacting with other individuals who also use cultural elements, and with elements from their physical environment. Based on these experiences, be they real or imagined, individuals keep or reject certain elements. Sometimes, imitating a role model’s behavior (transmission) and selecting it happen in the same moment. Oftentimes, learning a behavior happens before applying it in social situations. In general, keeping, rejecting, and forgetting or losing a cultural element σy can be expressed as σy,i (t1 ) = F(Mγ,i yu... (t0 ), σy,i (t0 ), σu,j (t0 ), . . .),

(8.11)

where γ describes i’s knowledge (gnosis) about using σy and F describes (stochastically) i’s decision after using σy , possibly in combination with other elements, in an interaction with j (and perhaps others) using σu (possibly y = u) or with elements in the physical environment. The interactions can be cooperative (beneficial), neutral, or conflicting (detrimental) for i, and can take place with multiple individuals simultaneously. Others’ influence on actor’s selection is an instance of social influence in general (Eq. 8.10), which shows, again, that selection can overlap with transmission. To flesh out F, we may argue that i’s decision about an element σy is based on an assessment of its value, or utility, in (typically) recent interactions with other individuals indexed j using cultural elements indexed z. Note that a utility function does not imply that people are capable to rationally maximize their utility; they only manage to optimize over small numbers of relatively simple comparisons. In the utility function, time indices and Mγ,i y are left out for brevity, and i’s utility of using σy is a summation over interactions with others indexed j using elements σz,j , ∑ Uy,i (σy,i , σz,j ) = wij Uy,i (σy,i , σz,j ). (8.12) j

An example is i using norm hy in interactions with others using norms hz (McFadden 2001; Young 2015), including hz = hy , where utility is codetermined by sanctions and/or feelings of guilt. In this example, i’s utility is maximal if i conforms to the prevailing norm(s), which comprises cases where (almost) everyone, including i, transitions from one norm to another in

164 Models

a relatively short period of time (compared to the typical duration of norms). Norm changes are relatively fast within groups, but may last long in a society with many groups that are weakly cross-connected (Young 2011). For example, the diffusion of table manners (using fork and knife instead of fingers) in Europe took a long time society wide (Elias 1939), but within households the transition was rapid. One reason to reject a new and seemingly superior norm by parts of society can be its inconsistency with incumbent local norms. If people are emotionally attached to a moral norm that they highly value, it may take very long, possibly multiple generations, before they (or their grandchildren) can be won over to accept an alternative norm. Some prefer to die with their beloved (e.g., religious) norms without giving in. Others change within several years due to changes of their network (Vaisey and Lizardo 2010). If the prevailing norm is imposed top-down by a powerful ruler or their representatives r favoring hx , the term(s) wir Uy,i (hy,i , hx,r ) will weigh heavily in the summation in Equation 8.12 (through relatively large wir ), whereas in an egalitarian network, the influence of each of i’s contacts will be similar and relatively modest. Conforming to a norm may sound simple enough for people to maximize their utility, but there can be external effects, captured by variable T (see the Ising model above), that enter decision-making as random noise (Young 2015). Traditionally, T is interpreted as an indicator of bounded rationality, with minimal bounds at T = 0. The probability that i will choose, or behave according to, norm hy in interactions with others using norm(s) hz is eUy,i (hy,i ,hz,j )/T P(hy,i ) = ∑ U (h ,h )/T , u,i u,i z,j ue

(8.13)

with index u running over all norm alternatives known to i (including hu = hy ), which might be few or one. Institutional entrepreneurs (i.e., politicians) will not only propose an alternative norm but also try to increase turmoil (T) or the temptation of a new norm to facilitate the transition, as Schelling noticed long ago (1960). Generalizing the norm example, the probability that i will select a cultural element σy in a comparison with elements σx is eUy,i (Yy )/T P(σy,i ) = ∑ U (Y )/T , x x,i xe

(8.14)

γ,i where Yy = Myx... , σy,i , σx,j , . . ., which are the arguments in F(. . .) in Equation 8.11. Putting time back in, i’s use of σy,i (t0 ) results in σy,i (t1 ) = 1 or 0. The outcome σy,i (t1 ) = 0 may also be due to i forgetting the cultural element or how to use it; then Mγ,i y (t > t0 ) = 0. Individuals also use culture to select (i.e., establish, maintain, or reject) their network ties. An example is i who does not want to cooperate the next

Models 165

time when interacting with j who defected and cuts the formerly cooperative tie with j, Mγ,i C,D(j) (t > t0 ) = 0. Perhaps i continues observing j to glean certain information σu ; then, Mβ,i u (t > t0 ) = 1. Network and culture thus coevolve. 8.3.3

Environment

The social and physical environments change as a consequence of new, modified, transmitted, used, lost, and discarded elements, as well as demographic and network changes. If i dies, Mi (t > t0 ) = 0 for all other super and subscripts at once. If u’s knowledge is not yet transmitted to others or recorded, it is lost. Along with deaths, new individuals are born who are socialized into their sociocultural environment. Taking all sorts of cultural and network changes together, M(t1 ) = M(t0 ) + Z(M(t0 ), σu (t0 ), σv (t0 ), σy (t0 ), . . .),

(8.15)

where Z describes the changes in M. Equation 8.15 is the grand bookkeeper of everything described by Equations 8.9 and 8.11, which also registers changes due to external causes (not explicated as arguments for brevity), for example, the effect of a natural disaster. A transformed environment constrains and enables subsequent combinations and interactions. 8.3.4

Generalizations

Equations 8.9, 8.11, and 8.15 describe the coevolution of culture and the network, each changing in response to the other, continually driven out of equilibrium by the entry of new combinations and the overturn of people. This system of equations cannot be solved analytically, and requires simulation modeling in specific domains and time periods. An example is the stochastic actor-based model (Snijders 2001) wherein actors’ traits coevolve with the network; in this model, there is no innovation. Another example is the widely known epidemic model (with the three states: susceptible, infected, recovered) with a rate β (equal for everyone) for the transmission of a disease (or cultural element) from infected (or knowing) to susceptible individuals, and a rate γ of recovery (forgetting or rejecting the cultural element); the two parameters thus have similar meanings as in the three-step model. These and other models of parts of evolution are special instances of our general three-step model. When adding genetic evolution to the model, we obtain culturally influenced partner selection at step 1. After τ = 9 months, a new genetic package σi (t0 + τ ) of i, the child of j and k, results from their creative interaction σi (t0 + τ ) = Mα ijk (t0 )σj (t0 )σk (t). Humans do not transmit genetic material to one another but bacteria do. Moreover, certain species reproduce asexually. At step 2, interactions with elements from the natural or sociocultural environment, σx , lead to survival, sometimes death (i.e., natural selection), or

166 Models

to (epi)genetic changes, σi′ (t1 ) = Mϵ,i ix (t0 )σi (t0 )σx (t0 ), and copying genes may lead to mutations (more so at post-reproductive age), σi′ (t1 ) = Mϵ,i i (t0 )σi (t0 ). There are also the combined effects of genes, cultural elements, and environmental conditions on individuals’ (i.e., their genes’) survival. Step 3 accounts births and mortalities, as well as relevant environmental changes such as climate. We now have a general and coherent model of the coevolution of genes and culture, meshed with cooperation, conflict, and social influence. At the macro level, it correctly predicts the power law size distributions of bursts and crises in evolutionary processes (Figure 6.2, p. 110), which has been corroborated for the amounts of change of countries’ GDPs, business failures, and issued patents (Thurner, Klimek, and Hanel 2010). It also predicts that at some moment, the number of cultural elements will rapidly grow, way beyond earlier small growth spurts, which we know as the Industrial Revolution (Appendix). We can use the formal models of collective action and cultural dynamics to clarify the debate about cultural—not genetic—group selection that some adhere to (Richerson et al. 2016) and others reject (Pinker 2012). Suppose there are groups of two kinds, indexed 1 and 2, with prosocial norms h1 and h2 , respectively, that every respective individual internalizes to the same degree, and one norm is stronger than the other, h1 > h2 . The norms have been invented (step 1) before our thought experiment, and are here taken as given. Their unequal strength can be due to random chance or a social process not of interest here. When producing public goods such as food and ∑ ∑ defense (H = − wij σi σj − h σi ; see the Ising model above), groups with norm h1 will cooperate at lower levels of agitating stimuli (T) than groups with norm h2 . Because lower levels are reached earlier, norm h1 groups will produce more public goods (step 1), everything else equal, which individuals in these groups can use in their interactions with others or in the physical environment (step 2). Consequently, individuals in norm h1 groups share an increased survival chance, net of other effects on survival, which implies that, along with Darwinian selection at the individual level, there is a selection effect at the group level (manifested in the bookkeeping of step 3). The formal model thus helps to disentangle Pinker’s (2012) “hairy blob” and demonstrates that if there are many groups, such that other effects can be statistically controlled, and relatively inert norms compared to intergroup migration, as well as long enough time, group selection will take place. 8.4

Appendix

Model of collective action. There is no assumption that actors know their payoffs in advance, but they will certainly get payoffs when the dust has settled. I opt for linear functions, as in much evolutionary game theory (Perc et al.

Models 167

2017). The payoffs are defined to obtain a meaningful interpretation of the Ising model, but they play no role in its calculations. At each Monte Carlo step, a randomly chosen individual i chooses between C and D in the presence of NC ≥ 0 cooperators. The payoffs for cooperation, PC , and defection, PD , are PC = θ(NC + 1)/n − 1,

(8.16)

with a cost of 1, and PD = θNC /n + Q,

(8.17)

with θ ≥ 1 a synergy or enhancement factor. Factor Q does not occur in game theory (yet)10 ; it establishes that if the outcomes of defection versus cooperation become to be perceived as equally valuable (D approximates C), or in other words, the local and global minima in Figure 8.1A become (almost) identical, PC approximates PD . This is done by defining Q = (θ/n − 1)(1 − R); R = (C − D)/(C + D); θ = θ0 + R,

(8.18)

with a base rate θ0 ≥ 1. The values of C = 1 and D = 0.5 in the examples can be generalized and related to the usual symmetric Ising model with σ = {1, −1} through a mapping {C, −D} → {σ0 + ∆, σ0 − ∆}, with a bias σ0 = (C − D)/2 with respect to 0, and with σ values symmetrical at each side of σ0 at an offset ∆ = (C + D)/2 (Bruggeman, Sprik, and Quax 2020). In the examples, σ0 = 0.25 and ∆ = 0.75. These quantities are in the payoffs through R = σ0 /∆, and the focal point (Schelling 1960) can now be defined as σ0 +∆. By means of a mean field analysis, one can prove that the threshold of unconditional defectors equals pc = σ0 /∆, and therefore R = pc (Bruggeman, Weenink, and Mak 2022). In conflicts with other groups, constraint (p. 72) and radicalization will increase σ0 . Payoffs may differ between individuals, corresponding to individualized σ0,i (with Ci > Di ). If contributions to the public good also vary across individuals, the payoffs are to be redefined. Individualized payoffs come at a high price because they consume many degrees of freedom, but one may compare groups wherein everyone is assumed to have the same. Either way, payoffs become PC = θ(E + e)/n − e,

(8.19)

with an individual contribution e > 0 when the others together have contributed E, and PD = θE/n + Q,

(8.20)

10 This payoff function with Q improves an earlier function without it (in Bruggeman, Sprik, and Quax 2020).

168 Models

with θ as above, Q = (θ/n − e)(1 − R), and R = (C − D)/e(C + D). These quantities are individualized or per group, but for clarity, the index has been omitted. If for everyone e = 1, the individualized payoffs recover the equalfor-everyone payoffs defined above. Model of cultural evolution. The proof of the phase transition in innovations, historically known as the Industrial Revolution, is from Thurner cum suis (2018, pp. 283–284), with the text adapted. For simplicity, all principles (i.e., cultural production rules) to make new cultural elements combine two incumbent ones; if in actuality there are more than two elements involved, the invention is analytically decomposed into intermediate steps. We abstract away from specific actors (u) and their individual knowledge (α), hence the size of M (defined above) is only determined by the number of elements that could exist, N.11 The number of principles to make elements out of pairs of elements is the density, 0 ≤ r ≤ 1, of cells in M that equal 1, times N. The model starts at a time t0 hundreds of thousands of years ago when there were n0 different elements (a relatively small number but n0 ≫ 1), which we follow over time, nt , far into the future, n∞ . As a matter of fact, part of the novel cultural elements are principles, hence r also increases over time, but for now we ignore the relation between nt and rt . With given elements and knowledge at t0 , how many elements can be invented at the next time t1 ? This depends on the proportion of pairs that could be made from n0 , 21 n0 (n0 − 1), to the total number of pairs in M, 12 N(N − 1), times rN, or n0 (n0 − 1) rN2 rn0 (n0 − 1) rn2 = ≈ 0. 2 N(N − 1) N−1 N

(8.21)

If we subtract what has already been invented, n0 /N, we get the size of the adjacent possible12 at t0 , n0 ) rn2 ( . (8.22) ∆n0 = 0 1 − N N When we fast forward to a later time t ≫ t0 , the number of elements at the next time t + 1 equals nn+1 = nt + ∆nt , which implies a recurrence equation: nt+1 ) 2 r ( 1− (nt+1 − n2t ). (8.23) ∆nt+1 = N N Let us define ct = ∆nt+1 /∆nt , and c its asymptotic value in the limit t → ∞. Applied to Equation 8.23, and using n2t+1 − n2t = ∆n2t + 2nt ∆nt , ( n∞ ) n∞ . c = 2r 1 − (8.24) N N 11 In actuality, some elements can be made by several principles, and some principles can be used to make different elements, but let us make the argument not more complicated than necessary. 12 Everything that can be created at t + 1 on the basis of culture available at t is called the adjacent possible (Hanel, Kauffman, and Thurner 2005).

Models 169

Further, n∞ =

∞ ∑ t=0

ct n0 =

n0 . 1−c

When combining the last two equations, ) ( n0 ) n0 ( n0 − 1 = 0. + 2r c3 − 2c2 + c 1 + 2r N N N

(8.25)

(8.26)

This can be solved with Cardano’s method, which yields two real solutions ( ) √ 2c N 1± 1− . (8.27) n∞ = 2 r Through rescaling, n → n/N, the size of culture, N, drops out of the equation, which demonstrates that the result only depends on r and n0 . After substituting variables, the two solutions become mathematically equivalent to the equations of a van der Waals gas, and point out a phase transition from the low to the high solution (Hanel, Kauffman, and Thurner 2007). The transition is continuous (i.e., smooth) with increasing r (i.e., ongoing innovation) when n0 is already large, which we presumed not to be the case. If n0 is relatively small compared to the current number of cultural elements, which seems a reasonable assumption, the model predicts that when r reaches a critical level (rc ), nt jumps from the low to the high solution in Equation 8.27. The sudden phase transition in r is of course (somewhat) smooth in the temporal dimension (see Figure 6.2). In the derivation, M has no internal structure, only a density, but in a simulation one could apply a (data based) topology, which might yield multiple smaller jumps, based on keystone inventions such as the wheel or computer, instead of one big jump. A random tensor M is used in the simulation shown in Figure 6.2 (p. 110), with r > rc . In this simulation, for each ordered triple (y, u, v) in M there is a chance p1 that combining elements σu and σv (a productive pair, currently available) will result in innovation σy (step 1), and a smaller chance p2 < p1 that a randomly picked element is disliked and discarded (step 2). Each change in steps 1 and 2 is automatically registered (i.e., the environment is updated; step 3). The consequence of randomness in steps 1 and 2 is a highly skewed distribution of the amounts of change in the numbers of cultural elements from one moment to the next, which replicates empirical patterns (Thurner, Klimek, and Hanel 2010).

9 CONCLUSIONS

Every day, people utter sentences that have never been said before and do things that have never been done in quite the same way, both producing and responding to a social world of bewildering complexity. Yet, we can discover (dynamic) patterns in this complexity and can find explanations for them. The words that people use in their sentences and most else of what they know they learned from or with the help of others, who in turn learned from others, who learned from others, eventually connecting all humans who ever lived in one giant sociocultural network. The origins of this network date back to our human ancestors, even before they spoke language. Sociologists are supposed to explain the social world, but they have become specialists in specific modern phenomena, and bemoan that the only connection between them is a common knowledge of a handful of 19th century sociologists. However, these founders of the discipline are of little help in understanding the phenomena with the largest impact on the largest number of people—culture, cooperation, and conflict—as well as our 300,000year history before and after their own times. Therefore, I have turned the inward search for coherence in classical sociology books into an outward reaching approach to the subject matter of the discipline—humankind— that is coherent of itself through the sociocultural (as well as genetic) network. This network has anonymous African founders instead of famous European ones, and billions of contributors all over the world, including the people who examine it. Following in this outward approach, I have searched the work of scholars while disregarding their disciplines, benefiting from great many authors’ local expertise in scientific and geographic areas. DOI: 10.4324/9781003460831-9

Conclusions 171

We have seen that cultural dynamics in the giant network can be explained by combination, transmission, use, and selection of information, all of which result in change of the environment. This environment includes the network itself, as well as the physical environment, with feedback loops reaching back to cultural dynamics. The three general principles of cultural dynamics— combination, selection, and environment—contribute to the coherence of sociology in a new way and provide ample room for a large variety of approaches and theories, as no single one could possibly answer all questions. I have combined, used, and selected insights from multiple disciplines to answer a number of these question, and posed others that I could not answer. When cross-fertilizing multiple disciplines, parsimony and clarity are crucial. There should be no more concepts than necessary, and typologies are to be reduced to only the most important. If there are categories, for example consensus versus polarization, there should be a theory that explains how they come about, instead of just retrospective rationalizations. Whereas new sociological theory tends to introduce new neologisms and typologies, I have done the opposite; this book has fewer typologies and concepts than previous theory when comparing for scope and historical depth. For instance, I have not used the word civilization, not only to resist conceptual overcrowding, but also to challenge the false pretense of a distinction between “civilized” and so-called primitive or barbarian cultures (two words that I have intentionally avoided). Moreover, I have not introduced neologisms. Using relatively few concepts, we have examined how humans and their ancestors lived as foragers, and how they solved individual and collective problems by means of culture and cooperation, including conflicts with one another and with other groups. Cooperation was accomplished through individuals’ embeddedness in a cohesive network wherein they (re)acted on the basis of prosocial norms, reputations, and prosocial psychology, for as long as the long-term benefits exceeded the costs (and if they were often wrong thereof, Darwinian selection would settle the score). As is still the case today, people have always cooperated according to these same principles, sometimes with leadership as an additional principle. A portion of foragers’ problems were trade-offs, which they attempted to optimize through heuristics, many of which they learned culturally. We in modern society also face many tradeoffs, like the choice between specializing and generalizing in education, and use heuristics to solve our trade-offs and other problems. Foragers lived in small, egalitarian camp groups embedded in larger, occasional groups that all together formed ethnic groups. Although the dynamics, overlaps, and sizes of groups changed in agrarian and industrial societies, and inequality mounted considerably, the embedded group structure remained present throughout history, enabling and constraining cooperation, cultural dynamics, and conflict. Cooperation and the use and selection of culture are done mostly within groups, whereas innovation is largely done by cross-fertilizing ideas across

172 Conclusions

groups. Collective conflicts are mostly between groups. Because forager societies were small, it was relatively easy for us to comprehend the dynamics of culture, cooperation, and conflict before we stepped up to more recent and far more complex societies. The transition to agrarian subsistence was slow, overlapped with ongoing foraging, and was followed by gradually increasing urbanization, specialization, and inequality, increasing scales of cooperation, warfare, and polities, and by new institutions such as moralistic religions. All these phenomena involved and fed back into culture. Recently, scholars started to quantitatively investigate the interconnections between these phenomena with large data, for example, the role of weapons and warfare in the increasing scale of polities and the origin of moralistic religions, with several surprising findings. New micro-level models will have to be developed to explain the patterns found. The transition from sustainable agricultural societies (the extinction of several animal species notwithstanding) to an unsustainable, global industrial society was relatively sudden, concomitant with a rapid expansion of the cultural tree, which is predicted and explained mathematically by the evolutionary model. It shows that if the knowledge of principles on how to combine incumbent elements into new elements stays below a critical level, there is a gradual increase of cultural elements. It also shows, however, that if the knowledge of principles surpasses the critical level, there is a sudden surge of cultural elements and knowledge (i.e., a discontinuous phase transition). The evolutionary model thereby demonstrates that for the Industrial Revolution, no genius inventors were necessary; there happened to be a tipping point of knowledge in the network of inventors, technicians, and entrepreneurs who got most of their ideas from other inventors, technicians, and entrepreneurs. The surge of inventions was followed by the rapid rise of urbanization driven by mass migration, which further increased inequality and large-scale production of weapons used for warfare and colonization. All the while, the world’s geography became neatly partitioned into national states. These combined trends also resulted in the progressive transformation, depletion, pollution, and destruction of the natural environment, turning it into a hotbed of contagious diseases and debilitating heat, with disasters ranging from unbearable drought to inundations. Citizens in this deteriorating environment became better educated than people had ever been before, but many were deceived—especially online—by misinformation posted by other citizens, politicians, or (AI-driven) bots drawing their attention away from the most pressing problems—inequality and environmental degradation—toward non-problems, giving autocrats opportunities to increase their power. The contrast is striking: in small forager societies, individuals knew a large proportion of their population and understood much of what was going on, whereas in modern, complex society, individuals know

Conclusions 173

a tiny fraction of the population directly and most people understand very little of what is going on, confused by more false information than they can handle. Although the prospects for sapiens are in decline, our scientific understanding of our species is improving. Much larger datasets are available than just a few decades ago in all scientific fields. Statistical models of data can help the analysis thereof, but these models have become more complicated without increasing predictive power (Salganik 2023), and since the 1950s, scientific papers yield ever less novelty (Park, Leahey, and Funk 2023). What is missing in sociology is a theory that integrates existing findings and offers new opportunities for cross-fertilization. Evolutionary theory can do just that. It gives a coherent outlook on society; explaining the making, diffusion, use, and demise of culture, while integrating it with genetic evolution, cooperation, conflict, and (nearly) all other social phenomena. Many current problems such as extreme inequality, environmental degradation, lack of cooperation, discrimination, warfare, and other forms of conflict are largely incomprehensible without a historical perspective, and a theory of cultural evolution makes history coherent. By cross-fertilizing scientific literatures, it was possible to make general theories of culture and cooperation. For a general theory of conflict, in contrast, there is still much work to be done. We do know that once people identify with a certain group and internalize its norms, many will cling tenaciously to their group and its culture as if they are indispensable body parts—social and cultural limbs—and easily go along with in/outgroup distinctions and constraints framed by others, even when these distinctions are arbitrary and the constraints are fictitious (e.g., gender). Although we understand how this works, we do not understand why, in part because people can—and occasionally do—learn to see through (initially believed) falsehood. Some people are then forced to migrate to different groups and/or places. A general pattern in collective violent conflicts has become clear, however, and we can explain that too. The onset of conflicts can be explained in terms of constraint, which makes an outgroup and pertaining issues controversial for an ingroup, increases ingroup interactions, and, beyond a critical level of controversialness, leads to radicalization. Through ingroup interactions, a shared, yet radical, intentionality is achieved against the outgroup, sometimes through lengthy discussions and at other times through rapid visual contact in close proximity. Then, if agitation, such as outgroup provocations or the temptation of a (fantasized) victory, passes a critical threshold, collective violence commences. The Ising model helps us to predict when collective violent action will break out and when it will not. We also know that in the course of a conflict, the interevent times of violent acts are power law distributed, and the network of the ingroup, outgroup, and third parties evolves toward a more balanced state. The combination of incumbent theories plus the Ising model

174 Conclusions

thus leads us to a new theory of conflict that interconnects in multiple ways with the theories of cultural evolution and cooperation. All findings in this book could be told informally in natural language, but some of them could only be obtained through formal modeling. After all, “formality, when it works, is not the opposite of the informal substance but a refined version of it” (Stinchcombe 2001, p. 3). The refinements pertain to the intricacy of the patterns to be explained, the complexity of many interacting people, and the effects of random chance. An example of intricacy is the conceptualization of social cohesion as algebraic connectivity. It takes into account the multiplicity of independent paths through the network connecting all pairs of individuals in it, the nexuses between these paths, and paths’ lengths. An example of complexity is the tipping point where the general tendency of discussing individuals toward convergence (i.e., the contact hypothesis) turns into its opposite of polarization. An example of randomness is the exchange model, where random chance suffices to explain realistic distributions of inequality, which implies that extraordinary intelligence is not necessary to become rich. Another example is in the behavior that leads to the tipping point where agitated individuals burst into collective (violent) action, which can be prevented when the proportion of defectors surpasses a critical threshold; then, the burst of violence fizzles out. The Ising model can be applied more generally than to violence alone; it can also express the principles of cooperation in peaceful collective actions of many sorts and can be incorporated in the three-step model of cultural evolution when innovation is done collectively. The three-step model, in turn, was generalized to a model of the coevolution of genes and culture, which correctly predicts the power law size distributions of bursts and crises in evolutionary processes, as well as a tipping point where the number of cultural elements rapidly increases (what we know as the Industrial Revolution). At tipping points, qualitatively new properties of groups emerge, which happen to be largely insensitive to details at the individual level. Computational models of tipping points can therefore be based on simplified representations of participants and thereby remain tractable, while being refined where it matters, namely, in the treatment of great many interactions and randomness. Formal models can be made in different ways, of course, and new models, data, and relationships between social phenomena will undoubtedly be made, harvested, and discovered, respectively. If someone writes Another Sociology of Humankind accordingly, I hope that my book will provide a head start. Certainly, cultural evolution provides the most general and parsimonious theory of cultural dynamics. It will be very difficult to invent something better.

GLOSSARY

The most important concepts are italicized in the main text and defined here. Brokerage. A broker is someone in-between others who are otherwise separated by geographic barriers, distance, secrecy, deceit, or cleavages in the network. A broker’s contacts are only connected indirectly through the broker (Burt 1992). By bridging between others who produce or desire different things, plus some luck (which sometimes means competitors’ bad luck), a broker can establish transactions, make a profit, and accumulate wealth. Brokerage in knowledge networks means bridging in-between and combining ideas. The network pattern is the same as brokerage in trade networks (Burt 2004; Bruggeman 2016). Of note, a knowledge broker can in principle also combine different pieces of information from the same source, or network node, for example, from an individual knowing different things without having combined them yet. Also in business, selling an item to the person from whom one bought it is sometimes possible; pawnbrokers even make a living out of it.1 Cohesion is the bonding of a group, which is stronger if there are more nodeindependent network paths, a shorter average length of these paths (hops through the network), and more nexuses in between. People can also bond on the basis of shared goals or morality (i.e., identification with their group, its goals, or rules), and they can bond emotionally; all of these are called solidarity. In their network ties, goals, and emotions, people are 1 Brokerage can be measured by betweenness centrality for network paths on which an actor sits astride, restricted to actor’s direct contacts (Bruggeman 2016), because people further away do not contribute to actor’s brokerage opportunities (Burt 2007). This truncation also makes the measure more robust against missing data (Costenbader and Valente 2003). For brokerage from a single source, one could use degree of pertaining ties.

176 Glossary

not always equally bonded with others. They can even identify and feel bonded with others who may not be aware of this, or who are unwilling to reciprocate. Conflict, constraint, violence, and war. Conflict means disagreement between interdependent actors, which often starts at incompatible interests. Chances of violence increase if an actor believes to be constrained by alter, who (supposedly) threatens actor or precludes access to critical resources and thereby reduces the actor’s (or actor’s children’s) life expectancy. Violence is the intentionally infliction of injury, manifested in reduced physical or psychological health. The above definitions include internal group conflict and self-inflicted violence. In networks, violence and other conflicts are represented by negatively valued social ties. I define war as armed violence between at least two groups the size of at least a few hundred people. Note that by this definition, foragers also fought wars (Boyd and Richerson 2022). Cooperation is making an effort at a cost in terms of calories, time, or resources to provide a benefit to someone else. In case of collective goods, it is defined as contributing to the entire group including oneself. Critical threshold. When a “driving” variable (e.g., agitation) passes a critical threshold, also called a tipping point, the network (or system in general) goes from its current state to a qualitatively different state, for example, from inaction to collective action, or from consensus to polarization. Cultural elements are chunks of information, such as ideas, applications, norms, symbols, meanings, styles, and interpretations of human made objects. Meanings of fuzzy concepts can be modeled by Gaussian distributions; then, the usual math of cultural transmission applies (with an extra variance term; Boyd and Richerson 1985, Ch. 3). In the 1970s, before the discovery of epigenetics, when genes were believed to be immutable during one’s lifetime, cultural elements were conceptualized analogously as crisp and called memes (Dawkins 1976), the same as our name for online messages today. However, now we know that both cultural elements and genes are mutable (Landecker and Panofsky 2013). Culture is defined as “those aspects of thought, speech, action (meaning behavior), and artifacts which [are] learned and transmitted” (Cavalli-Sforza and Feldman 1981, p. 10). The notion of transmission I use interchangeably with that of diffusion. Gender is a socially constructed typology of people based on socially interpreted sex characteristics and is tied to institutionalized expectations. The categories of man and woman are universal, but locally, people may define the two categories loosely or tightly, with or without overlap. Groups. Without defining what a group really is, I use two approaches to determine them: one based on network data and the other on people’s

Glossary 177

identifications. The results of the two approaches tend to be strongly but imperfectly associated, and there are exceptions. The network approach is based on modularity (Simon 1962), which means that groups are relatively dense areas in a network with sparse intergroup connections, compared to a randomized version of the network with the same distribution of ties (Newman 2018). Groups can be determined by community detection algorithms (Fortunato and Newman 2022), and because groups are temporal processes where people enter and leave (Kasarda and Janowitz 1974), detection can also be done longitudinally (Fortunato 2010; Bruggeman et al. 2012). Aggregates such as industries, fields, and societies can be defined as supergroups of interdependent groups at lower levels (see hierarchy). The embedded group structure can be retrieved by tuning a resolution parameter from fine to coarse grain (Reichardt and Bornholdt 2006; Traag and Bruggeman 2009). Community detection has a margin of error that depends on the definition and measurement of the social ties and on missing data. For example, at any moment in time, people have many dormant ties that could be activated but are almost forgotten (Fischer and Offer 2020) and are therefore easily missed by surveys, which can lead to groups being determined incorrectly. The other approach to groups is based on people’s identifications. Arguably, the most important ones are family and ethnicity; people identify with an ethnic group if they believe they have common ancestors (Weber 1922), which makes it a super family. In most cases, co-ethnics have a common language too, with framings, institutions, and practices cast in that language. Ethnicity is not simply a trait that people have, but a social practice of classification and framing that can be under or overcommunicated, and varies across time, relations, and situations (Brubaker 2006). Putative boundaries are traversed by imitation, admixture, and migration, and differences between individuals from the same group are often larger than between-group averages. People in groups with different languages become different in other cultural domains, as well as diverging biologically (but not genetically) because language affects brain wiring (Robinson, Fernald, and Clayton 2008). Hierarchy. Intuitively, a hierarchy is an upside down tree. Most people first think of authority hierarchy, but authority is just one kind of hierarchical relation. There are many more kinds, for example, embeddedness of groups (i.e., the subset relation), which has nothing to do with authority.2 A hierarchy means that from every entity (groups or people), there is a unique path, following the steps of the relation, to the top (or root) 2 Mathematically, the subset and authority relations are partial orderings (Halmos 1960), here defined for the subset relation, which means that if every individual in group A is also in group B, A is a subset of B. A partial ordering on the subset relation is antisymmetric (if group A is a subset of group B, B is not a subset of A), transitive (if A is a subset of B and B is

178 Glossary

element (see Figure 1.1A). In large social groups (e.g., polities), however, there are, respectively, groups or individuals misfitting the overall embeddedness or authority structure, forming competing or otherwise interdependent structures. Agricultural and industrial societies have multiple, partly overlapping, hierarchical (or hierarchy-like) ordering relations based on location, religion, military rank, and other features considered important, making the hierarchical structure somewhat messy. Compared with the ideal type of a tree, the real type (a term that Norbert Elias used in his lectures) of hierarchies tends to be messier, although still visible. Interpretation, pattern, and principle. An interpretation is an assignment of meaning, specifically, a mental or symbolic representation of certain information in its context. Examples include a conversation, behavior, event, value, feeling, or object, based on the knowledge and intentions of the (interacting) actor(s) interpreting. “A pattern is a regularity observed across events, [behavior] or artifacts, with or without exceptions. A principle is a generalization that brings together different patterns under a single denominator and which is usually said to explain the regularities” (Bod 2018). Principles may not be causal, e.g., grammar rules, and many have exceptions. Causal principles are called mechanisms (Hedström and Ylikoski 2010). Mechanisms, if empirically supported, also help us to correct our over-interpretation bias when we make more meaning than can be reasoably based on given (gappy and noisy) information. An interpreted pattern, with the putative principles that explain it, is called a model (Bod 2018). Frames are interpretations used to simplify and organize perceptions and intentions in order to comprehend the world (Goffman 1974, p. 40). Kinship is an institution that assigns people (believed to be) from common ancestors or affines to categories with specific rights, prohibitions, and obligations (Lévi-Strauss 1965). For it to endure, it has to be internally coherent and perceived as valuable by the majority of practitioners, even when they do not obey the rules (ibid). Legitimacy is defined relationally: the behavior or trait of (a group of) alter(s) or thing(s) is legitimate in given circumstances in the eyes of an audience if it meets expectations. These expectations might have been agreed upon earlier, and are taken for granted without questions asked (Schoon 2022), or are based on accepted general moral rules (Weber 1922; Bellah 2011), for example, the idea that larger contributions to the group merit larger rewards. —————————————-

a subset of C, A is a subset of C), and reflexive (a group is a subset of itself). Partial orderings are also hierarchical if from each node there is a unique path to a unique top node.

Glossary 179

Network. A network is a set of nodes (individuals or groups) connected by ties (edges), which can stand for any kind of interaction or interdependence of interest, depending on the goal of the researcher(s) (see interpretation). Important network phenomena are assortment (p. 19), transitivity (p. 19), cohesion, the embedded structure of groups, brokerage, power, cultural transmission, cooperation, and conflict. Kadushin (2012) has written a non-technical introduction, and Newman (2018) has written a solid handbook. Niche. A (cultural) niche is a set of (cultural) resources and conditions that make it possible to thrive (Levins 1968). If niches of multiple actors (animals, people, or groups) intersect (i.e., overlap in a 2D-drawing), they compete, but, up to some point, the effect size of cooperation or mutualism may still be larger. Norm and institution. A social norm is an if-then rule for behavior (e.g., if meeting a familiar person, then greet accordingly) created for situations where the lack of the rule leads to (possibly imaginary) negative consequences for others (e.g., decreasing social cohesion), with a sanction when disobeying (e.g., negative gossip and its ramifications). Institutions are often regarded as equivalent to norms (Young 2011) or seen as bundles of norms (e.g., bureaucracy). People maintain institutions, and if they do so in an organized way, for example, the United Nations, the organization itself is often called an institution in everyday language. See also Footnote 4 in Chapter 2. Patronage is an enduring reciprocal, yet unequal relationship wherein resources or services are exchanged. For example, in a patrimony, a landowner (patron) extracts money (patrimonies) from their land by renting it to peasants (clients) who get a chance to grow their own food. Patrons tend to have multiple clients, who in turn can become patrons of their own clients; patronage then evolves into a hierarchy-like structure with multiple levels and gang plank ties connecting people in different branches of the tree (see hierarchy). When patrons, each with their own network, compete, they may try to reinforce their position by coopting clients, thereby preventing them from renegading to another patron.3 Polity. A polity is a population with an independent territory ruled by a patron (autocracy), who may cooperate and sometimes compete with other patrons (oligarchy), or by a corporate entity (possibly somewhat inclusive). The frontiers can sometimes be precise (e.g., a river) but before modern national states they were often fuzzy. The notion of polity is more

3 Martin (2009) argued that patronage is no hierarchy because it is not transitive, but I believe it is weakly transitive; if A dominates B and B dominates C, A weakly and indirectly dominates C (see power and Section 8.1.4).

180 Glossary

general than that of state, which in turn has specialized officials or bureaucrats. It assumes neither monopolies of tax or violence, which are often contested, nor centralized governance. Power is the chance to influence others, possibly against their interests (Weber 1922). The acquisition of support and resources by powerful actors is elaborated on p. 55 and formalized in Section 8.1.4, while their conflicts with others are elaborated in Chapter 5. Religion is a system of practices, or specifically rituals that are framed in “a general order of existence” and cause intense feelings and moods (Bellah 2011, p. xiv). These feelings bind the practitioners into a community (Durkheim 1912). Bellah, following Geertz, deliberately omitted belief in supernatural being(s) from the definition, even though many religious people hold such beliefs. Adherents to different religions have in common a belief that gods have a human-like psychology but perform acts that defy the laws of physics, as well as a conviction that their faith is superior to all other faiths (Boyer 2008). Ritual. A ritual is a sequence of symbolic actions that are causally opaque (Whitehouse and Lanman 2014), each time executed in (nearly) the same order, possibly with verbal or stylistic variations. It may have a rhythm (McNeill 1995), such as song and dance, but prayers (except in the case of poems) usually do not. In collective (interaction) rituals, practitioners perform in sync (Durkheim 1912; Gelfand et al. 2020), in opposition, or in alternating fashion. Shared intentionality of a group is a goal-oriented consensus accompanied by feelings of solidarity. Social inequality is an ordering relation among individuals (see hierarchy) that can be based on possessions, power, or characteristics such as gender or ethnicity. The associations between these variables tend to be strong, but there are exceptions. Value. People have an interest in cultural elements and perceive them as valuable when they appear to be useful, complementary, appropriate, timely, rare, or, in contrast, widely used when there are positive externalities. They may use nonvaluable elements if not using them leads to punishment, for example, wearing a uniform that they dislike. Some values, e.g., of breathing, are genetically determined, whereas most values on the cultural spectrum are largely or entirely socially determined. New cultural elements may appeal to only a few first users, thereby increasing their valuation of them; the first user may then set in motion a process of diffusion that raises many people’s value assessment of these elements. At the same time, substitutable incumbent elements may be depreciated in comparison with the new entrants. Many values thus coevolve with pertaining culture.

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INDEX

5r-package, 38–41, 63, 77, 95, 111, 151, 160 6r-package, 40, 63, 64, 160 absorptive capacity, 12, 108 actor, 30 adaptation, 4, 18, 20, 46, 66, 68, 137 adjacent possible, 168 Africa, 15–18, 23, 47, 49, 61, 62, 65, 66, 68, 86, 87, 91, 93, 102, 104, 116, 120, 122, 131, 135, 145 agitation, 33, 77, 78, 95, 96, 130, 131, 152, 153, 155, 156, 158 agrarian transition, 25, 45, 47, 66 agriculture, 6, 18, 19, 23, 27, 45–48, 52–58, 61, 68–70, 84, 85, 87, 98, 103, 104, 126 algebraic connectivity, 32, 149, 162 America, 17, 46, 52, 59, 61, 64–66, 86, 101–103, 105, 112–115, 117, 120, 126, 127, 138 Asia, 15–17, 46, 48, 87, 88, 103, 116, 120, 122, 126 assortment, 19, 124 attractiveness, 43, 69 Australia, 16, 17 autocracy, 30, 62, 63, 85, 90, 99, 120, 125, 131, 138, 143–145, 153, 156 balance theory, 31, 80–82, 118, 132, 160 brain, 38

brokerage, 21, 50–52, 56, 94, 102, 104, 106, 107, 127, 175 bubonic plague, 93, 98, 119, 127, 135 bureaucracy, 67, 88, 89, 93, 102–105, 111, 117, 120, 122 capitalism, 101, 107, 120–122, 125, 127, 138, 143 carrying capacity, 20, 113, 114 charisma, 55, 114 China, 17, 46, 49, 59, 60, 62, 63, 65, 75, 88, 89, 99, 102, 104, 106, 119–121, 123, 126, 128 class, 53, 57, 115 club goods, 36, 39 coevolution, 7, 38, 67 cognitive dissonance, 141 cohesion, 32–34, 37, 38, 41, 78, 80, 106, 132, 149, 150, 162, 175 colonization, 23, 65, 93, 101–104, 106, 114, 116, 117, 120, 122, 144 combination, 11, 161, 165 communism, 115, 118–121 computational models, 9, 146, 174 conditional cooperation, 41, 95, 153, 154 conflict, 26, 64, 70, 98, 102–105, 118, 120, 130, 147, 151, 176 conflict resolution, 70, 80, 92 conformism, 20, 59, 95 consensus, 32, 124, 125, 146

226 Index

constraint in conflict, 72, 73, 94 controversialness, 73, 132, 142 cooperation, 25, 28, 30, 50, 55, 63, 104, 105, 111, 112, 118, 123, 135, 138, 141, 151–158, 162, 164, 166, 167 core design principles, 40 critical mass, 5, 95, 151 critical threshold, 66, 176 cultural element, 2, 176 cultural environment, 14, 165 cultural evolution, 2, 11, 161 culture, definition, 2 democracy, 73, 86, 90, 115, 118–122, 131, 138, 140, 143, 145 demography, 17, 20, 45–48, 53, 66, 93, 98, 101, 102, 109, 113, 114, 126, 130, 135 deprivation, 55, 94, 116, 131, 151 diffusion, 3, 176 diseases, 18, 22, 46, 48, 49, 93, 98, 101, 128, 135, 165 division of labor, 48 Dunning-Kruger effect, 139 dyad, 30, 34, 35, 41, 55, 81, 83, 86, 111 dyadic cooperation, 41 eigenvalue centrality, 151 embedded groups, 20, 21 emergence, 7 emotions, 11, 12, 14, 19, 31, 32, 36, 39 empathy, 6, 26, 37, 39, 41, 52, 132 ethnicity, 21, 32, 39, 86, 90, 105, 106, 116, 119, 122, 124, 132, 143, 145, 149, 150, 177 Eurasia, 49, 59, 61, 62, 64, 66, 87, 93, 102, 104, 112 Europe, 16, 46–48, 64, 68, 84, 98, 100, 102, 103, 107, 113, 114, 116, 117, 119–122, 126 evolution, 7 evolution of cooperation, 38 extractive institution, 85, 117, 122, 131, 135, 145 family, 20, 34 fascism, 119 fitness, 4 focal point, 31, 41, 167 framing, 31, 33, 59, 63, 74, 94, 96, 106, 107, 119, 131, 134, 140, 151, 157, 177, 178

gender, 8, 26–28, 48, 53, 54, 60, 69, 71, 86, 101, 103, 108, 111, 115, 116, 118, 121, 124, 133, 139, 145, 176 genes, 8, 49, 50, 67 genetic evolution, 6, 165 globalization, 112, 118, 120, 121, 125–128, 133–135, 137, 138 gossip, 19, 21, 34, 39, 137 group, 18, 20, 176 group selection, 39, 92, 166 heuristic, 19, 24, 25, 34, 95, 158, 171 hierarchy, 28, 51, 104, 125, 177 honor, 43, 59, 71, 78 human dispersion, 17 human origin, 15 identity, 44 ideology, 28, 54, 59, 64, 75, 85, 104, 105, 114, 117–122, 129–131, 154, 157 imperialism, 98, 116 inclusive institution, 85, 114, 121, 122, 127, 128, 131, 140, 142–144 India, 49, 50, 62, 82, 89, 99, 100, 103, 116, 126, 128, 132, 133, 145 industrial revolution, 107–110, 116, 123, 166 inequality, 28, 50, 51, 53, 57, 61–63, 69, 113, 115, 118, 121, 123, 126–130, 135, 138, 141, 143, 145 influence, 20, 55, 81, 146, 150 innovation, 3 institution, 19, 50, 52, 85, 107, 117, 122, 124, 129, 132, 135, 161, 164 inter-polity dynamics, 151 inter-polity dynamics model, 87, 90, 91, 131 internet, 137 interpretation, 178 invention, 3 Ising model of conflict, 160 Ising model of cooperation, 122, 151, 152, 154, 156–160, 167 Islamic world, 50, 62, 89, 93, 99, 103, 111, 117, 122, 131 kinship, 21, 34, 50, 178 language, 66 leadership, 27, 39, 40, 52, 56, 59, 60, 63–65, 75, 76, 78, 84, 91, 94–96, 105–107, 110, 119, 121, 125, 135, 140, 144, 157, 159

Index 227

learning curve, 48, 123 legitimacy, 28, 35, 43, 59, 77, 84–86, 94, 105, 106, 117, 119–121, 132, 152, 178 lifetime uncertainty, 80 matrilineal, 54 meaning, 11, 31, 33, 176, 178 memes, 176 Mesopotamia, 46, 48, 60–62, 66, 89 migration, 16, 17, 39, 49, 53, 59, 66, 80, 91, 93, 94, 114, 119, 121, 128, 131, 177 modularity, 6, 21, 125, 126, 148, 149, 162, 176 money, 49, 65 monitoring, 18, 34, 35, 37, 87, 89, 104, 111, 138, 150 monogamy, 18 morale, 75, 77, 78 morality, 75, 76 morals, 19, 59, 60, 94, 105 nationalism, 105, 106, 113, 114, 117, 119, 122, 126, 132 natural environment, 17, 20, 23, 45–47, 66, 68, 133, 135, 161, 163 Neanderthal, 15, 68, 69 network, 8, 18, 20–22, 30, 32, 34, 48, 49, 51, 55, 61–64, 73, 80, 82, 94, 105, 107, 108, 118, 119, 121, 124, 126, 129, 144, 146, 147, 149, 150, 152, 155, 156, 158–161, 164, 179 niche, 23, 48, 179 norm, 5, 6, 11, 13, 18–20, 26, 27, 31, 32, 35–44, 59, 60, 63–66, 78, 95, 106, 108, 113, 114, 121, 132, 135, 148, 151, 157, 159–164, 166 organization, 100, 110–112, 114, 118, 123–125, 138 patrilineal, 54 patrilocal, 54 patrimony, 135, 179 patronage, 51–56, 63, 81, 85, 98, 104, 107, 127, 179 pattern, 1, 178 polarization model, 147, 148, 159, 163 polity, 49, 65–67, 87–93, 99, 106, 151, 179 polity decline, 93 populism, 139, 140, 143, 144 power, 28, 40, 43, 52, 55, 150

power centrality, 123, 131, 150 power law, 24, 79, 94, 133, 166 power, symbolic, 147 prestige, 11, 43 principle, 1, 178 protest, 114, 122, 130–132, 134, 136–138, 145, 146, 151–153 public good, 30, 31, 35, 37, 40, 63, 85, 86, 89, 90, 132, 138, 151, 152, 154, 157, 161, 162, 166, 167 punishment, 18, 26, 27, 34–37, 63, 66, 79, 85, 158, 163 racism, 7, 68, 74, 103, 115, 122, 138 radicalization, 76, 148, 159 random exchange model, 129 randomness, 4, 5, 14, 16, 19, 20, 24, 39, 41, 74, 77, 82, 88, 89, 118, 129, 149, 152, 154, 155, 164, 177 rationality, 10, 13, 14, 24, 95, 152 reciprocity, 35 religion, 58, 60, 63, 117, 126, 180 reputation, 35, 42, 43, 50, 56, 71, 72, 86, 111, 112, 137–139, 141, 144, 157, 158 revolution, 96, 104, 105, 107, 117–120, 130, 144 ritual, 16, 26, 32, 55, 59, 60, 79, 95, 96, 136, 180 Russia, 54, 88, 103, 116, 118, 120, 122, 126, 152 sanctioning, 35 science, 18, 99, 108, 109, 126 second-order dilemma, 34, 35, 40 selection, 13, 163 self-deception, 79, 141 serendipity, 2, 12 sexual selection, 69 shared intentionality, 31, 33, 37, 40, 41, 55, 63, 64, 77, 94, 153, 154 skin pigmentation, 68 slavery, 47, 52, 54, 85, 86, 93, 101–104, 112, 114, 117 social capital, 51 social complexity indicators, 66, 90, 92 social foci, 19, 41, 137 socialism, 114, 127, 144 solidarity, 19, 32, 33, 59, 122, 131, 175 specializing, 48, 62, 65, 67, 90, 103, 111 state, 67, 92, 93, 99, 102, 104, 106, 107, 113, 117, 120–122, 126, 131, 132, 138, 145, 180

228 Index

status, 11, 43, 44, 56, 62, 83, 90, 133, 140, 150 subset, 177 tax, 56, 60, 62, 65, 85–89, 92, 94, 95, 102, 117, 121, 126, 127, 132, 143, 145, 148, 158, 180 teams, 109, 123 thick reputations, 43 tie strength, 51, 54, 55, 83, 147 tightness, 66, 105, 161 tipping point, 66, 172, 176 trade-off, 12, 24, 25, 46, 48, 57, 68, 112, 125 transitivity, 19 transmission, 11, 33, 65, 88, 108, 117, 119, 139, 149, 158, 162, 165

triad, 41, 80, 83, 155 trust, 42 urbanization, 46–49, 113, 114, 121, 122, 135 USA, 73, 104, 106, 116, 120, 121, 123, 125, 126, 139 value, 13, 31, 42, 59, 154, 163, 180 variation, 4 violence, 26, 47, 48, 56, 57, 115, 118, 120–122, 130–132, 145, 146, 152, 153, 176 waqf, 99, 111 war, 56, 59, 79, 87, 90, 103, 118, 120, 122, 125, 131, 133, 143, 176 WUNC, 154