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The Evolutionary Iron Law of Oligarchy

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The Evolutionary Iron Law of Oligarchy The evolutionary iron law of oligarchy by CEDRIC PERRET Thesis submitted in partial fulfilment of the requirements of Edinburgh Napier University, for the award of Doctor of Philosophy School of Computing Edinburgh Napier University SEPTEMBER 2019 Author’s declaration This thesis is a presentation of my original research work. Wherever contributions of others are involved, every effort is made to indic- ate this clearly, with due reference to the literature, and acknowledgement of collaborative research and discussions. SIGNED: .................................................... DATE: .......................................... i Acknowledgements First of all, I would like to thank my director of studies Dr. Simon Powers for his in- valuable support, help and mentorship during my PhD. Simon has been here for me all along, whether it is in research projects or for the organisation of workshop (and even if it means me randomly popping out in his office and interrupting his work on a daily basis). I want to salute its incredible researcher skills, modelling ability, deep knowledge of the literature and even of the philosophy of science which, all, made this PhD possible. More importantly, I want to thank him for being always careful of my well being, for supporting and motivate me when I needed it. I could not have asked for better supervisor. I would like to thank my second supervisor and head of team Pr. Emma Hart. Emma has always been there to support and guide me. For leading a team which aims to solve problems, she is the best problem solver I have ever known. I particularly want to thank her for a genuine care and for the energy she transmitted to me. I would like to thanks my PhD examiners: Pr. Thomas Currie and Pr. Andy Gard- ner, for their challenging but fair judgements, for giving me insightful comments and providing me with a lot of new research ideas to explore. I appreciated how much they were careful to make the viva an agreeable and a learning experience. I would like to thank Edinburgh Napier University for funding my PhD as well as my participation to several academic activities such as numerous international conferences and one expensive summer school. I also thank the Research Innovation Office of Edin- burgh Napier University for funding several of my projects, which has truly helped me develop my academic and organisational skills. Compared to other university, Napier has been outstanding at providing me with the financial support I need. In particular, I would like to thank the director of research Ben Paechter, who always have been open to discussion and who genuinely put a lot of effort to support PhD students. I would like to thank all my friends and colleagues for their help and support. In par- ticular, I would like to thank Dr. Andreas Steyven, Dr. Kevin Sim, Dr. Filippo Corro and Florian Perret for their huge help in programming whether it is on basic programming with java or how to use the freaking cluster. They have spent a lot of times answering my questions and they have saved me both time and few nervous breakdown. I also want to thank Andreas for answering my numerous questions on the PhD programme and the University regulations (and I salute his patience as most of the answers were in the guide furnished at the beginning of the PhD). I would like to thank Dr. Lois Burke for her huge help on English writing and for answering literally thousand of my questions ii ACKNOWLEDGEMENTS (including one question on this acknowledgment), even when they were asked at the most random times. I would like to thank Dr. Valentin Journé, (soon Dr.) Estelle Barbot and Dr. Maxime Dubart for their advises and opinions on modelling, data analysis and statistics. I would like to thank Dr. Christopher Stone for all these discussions on my research topic (and on any topics in general). I deeply thank Dr. Niaz Gharavi and Dr. Faqhrul Islam for providing me with the LaTeX template of this thesis, which saved me a lot of times. I would like to thank all the people that have welcome me in the field of comput- ing sciences and teach me enough so I can understand what we are talking during our team meeting. This includes most of the staff and PhD student of the School of Computing of Edinburgh Napier University, and in particular my research team: nature inspired intelligent systems (also called bio-inspired computing, also called Edinburgh Napier Robotics Evolution and Learning). I would like to thank Dr. Filippo Corro, who was initially my flatmate but who ends up being my private teacher in machine learning. I want to thank all my fellows PhD students at Edinburgh Napier University and in particular my colleagues of C51, for being joyful and supportive, despite life and work conditions of PhD students becoming more and more difficult. Thanks are due to all the persons that I have met and discussed with during confer- ences and seminars, and the persons who invited me to meet their team or laboratories. In particular, I would like to thank all the participants and the organisers of the Complex System Summer School 2018 in Santa Fe for giving me one of my best experience in research and in my PhD. I want to thank Scotland and Scottish people for welcoming me in their country and for introducing me to their local remedies against the 6 months of dark winter (Famous Grouse, Buckfast and Cowgate). I also thank my companions all along this journey and in particular my flatmates Filippo Corro and Melanie Joud who endure my long PhD speeches and my list of complaints. Finally, I would like to deeply thank all my friends and family for their support during my PhD. I particularly thank all the friends that took some of their time to visit me. Enfin, je veux remercier mes parents, ma soeur et mon frère qui m’ont toujours supporté et qui ont toujours essayé de s’interesser a ma thèse même si, honnêtement, ca leur parle pas trop 1. 1This part is in french because my mom does not speak English and she already made several complaints about me not translating my papers and thesis for her. iii Abstract Social hierarchy is a pervasive element of modern societies, yet almost absent before the advent of agriculture during the Neolithic transition. Despite evidence supporting hierarchy as a product of evolution, it is hard to explain the mechanisms which drove this evolution. For instance, the evolution of followers appears as a paradox because followers receive fewer resources than leaders. The “iron law of oligarchy” proposes that the key to the Neolithic transition lies in the role of leaders in collective decision-making. First, leaders would emerge in response to an increase in group size because leaders speed up decision-making and facilitate coordination. Then, leaders would use their newly acquired influence to bias opinions and group decisions to impose inequality that benefits themselves. This theory has the benefit of explaining the origin of both beneficial and despotic sides of leaders. Yet, its investigation has been limited because of the lack of a formal description of (i) how individuals change with time and (ii) how individuals take collective decisions. Thus, we propose the evolutionary iron law of oligarchy, which reinterprets the iron law in evolutionary terms. We reduce leaders and followers to their capacity to influence and we claim that describing the evolution of this trait under the environmental changes observed at the Neolithic transition is sufficient to explain the emergence of helpful and despotic leaders. To investigate this claim, we build individual-centred models simulating consensus formation — how individuals take collective decisions — and evolutionary dynamics — how individuals change with time. On one hand, our results show that the evolutionary iron law of oligarchy is a viable scenario, which can unify previous theories explaining either the beneficial or despotic side of leaders. On the other hand, we developed a mechanistic model of the iron law of oligarchy which can apply across a range of scenarios, and which show under which conditions the iron law of oligarchy would apply. Finally, our results demonstrate that the iron law of oligarchy goes beyond political sciences and is underwritten by the laws of Darwinian evolution. Understanding the factors driving the emergence of hierarchy and despotism will open new perspectives to design better forms of governance. iv TABLEOF CONTENTS AUTHOR’S DECLARATION i ACKNOWLEDGEMENTS ii ABSTRACT iv TABLEOF CONTENTS v LISTOF FIGURES viii 1 INTRODUCTION 1 1.1 Social hierarchy is surprisingly widespread in human societies..... 1 1.2 From egalitarian hunter-gatherers to hierarchical agriculturists..... 2 1.3 Coercive explanations for the emergence of hierarchy........... 5 1.4 Functional explanations for the emergence of hierarchy ......... 7 1.5 An evolutionary investigation of the iron law of oligarchy......... 8 1.6 A main limit to evolutionary models of hierarchy: how to describe hier- archy at the individual level?.......................... 10 1.7 Social organisation as a distribution of influence.............. 12 1.8 Summary and research questions....................... 13 1.9 Publications ................................... 17 1.10 Code availability................................. 17 2 THEORIES OF THE EVOLUTIONARY ORIGINS OF HIERARCHY 18 2.1 Archaeological and anthropological evidence on the Neolithic transition 19 2.1.1 The Neolithic transition and the emergence of social hierarchy . 19 2.1.2 The explanandum: Leaders of post-Neolithic societies...... 19 2.1.2.1 Features of leaders of post-Neolithic societies . 19 2.1.2.2 What does this thesis not talk about? Other notions of leadership in the literature................. 21 2.1.3 The explanans: The environmental changes brought by the ad- vent of agriculture ..........................
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