To resolve the major controversy about why prosocial behaviors persist in large‐scale human societies, we propose that two questions need to be answered. First, how do social interactions in small‐scale and large‐scale societies differ? By reviewing the exchange and collective‐action dilemmas in both small‐scale and large‐scale societies, we show they are not different. Second, are individual decision‐making mechanisms driven by self‐interest? We extract from the literature three types of individual decision‐making mechanism, which differ in their social influence and sensitivity to self‐interest, to conclude that humans interacting with non‐relatives are largely driven by self‐interest. We then ask: what was the key mechanism that allowed prosocial behaviors to continue as societies grew? We show the key role played by new social interaction mechanisms—change in the rules of exchange and collective‐action dilemmas—devised by the interacting individuals, which allow for self‐interested individuals to remain prosocial as societies grow.

Recent activity in the field of artificial intelligence (AI) has given rise to large language models (LLMs) such as GPT-4 and Bard. These are undoubtedly impressive achievements, but they raise serious questions about appropriation, accuracy, explainability, accessibility, responsibility, and more. There have been pusillanimous and self-exculpating calls for a halt in development by senior researchers in the field and largely self-serving comments by industry leaders around the potential of AI systems, good or bad. Many of these commentaries leverage misguided conceptions, in the popular imagination, of the competence of machine intelligence, based on some sort of Frankenstein or Terminator-like fiction: however, this leaves it entirely unclear what exactly the relationship between human(ity) and AI, as represented by LLMs or what comes after, is or could be.

A manifest trend is that larger and more productive human groups shift from distributed to centralized decision-making. Voluntary theories propose that human groups shift to hierarchy to limit scalar stress, i.e. the increase in cost of organization as a group grows. Yet, this hypothesis lacks a mechanistic model to investigate the organizational advantage of hierarchy and its role on its evolution. To fill this gap, we describe social organization by the distribution of individuals’ capacity to influence others. We then integrate this formalization into models of social dynamics and evolutionary dynamics. First, our results demonstrate that hierarchy strongly reduces scalar stress, and that this benefit can emerge solely because leaders and followers differ in their capacity to influence others. Second, the model demonstrates that this benefit can be sufficient to drive the evolution of leader and follower behaviours and ultimately, the transition from small egalitarian to large hierarchical groups.

Artificial life is concerned with understanding the dynamics of human societies. A defining feature of any society is its institutions. However, defining exactly what an institution is has proven difficult, with authors often talking past each other. This article presents a dynamic model of institutions, which views them as political game forms that generate the rules of a group's economic interactions. Unlike most prior work, the framework presented here allows for the construction of explicit models of the evolution of institutional rules. It takes account of the fact that group members are likely to try to create rules that benefit themselves. Following from this, it allows us to determine the conditions under which self-interested individuals will create institutional rules that support cooperation—for example, that prevent a tragedy of the commons. The article finishes with an example of how a model of the evolution of institutional rewards and punishments for promoting cooperation can be created. It is intended that this framework will allow artificial life researchers to examine how human groups can themselves create conditions for cooperation. This will help provide a better understanding of historical human social evolution, and facilitate the resolution of pressing societal social dilemmas. Artificial life is concerned with understanding the dynamics of human societies. A defining feature of any society is its institutions. However, defining exactly what an institution is has proven difficult, with authors often talking past each other. This article presents a dynamic model of institutions, which views them as political game forms that generate the rules of a group's economic interactions. Unlike most prior work, the framework presented here allows for the construction of explicit models of the evolution of institutional rules. It takes account of the fact that group members are likely to try to create rules that benefit themselves. Following from this, it allows us to determine the conditions under which self-interested individuals will create institutional rules that support cooperation—for example, that prevent a tragedy of the commons. The article finishes with an example of how a model of the evolution of institutional rewards and punishments for promoting cooperation can be created. It is intended that this framework will allow artificial life researchers to examine how human groups can themselves create conditions for cooperation. This will help provide a better understanding of historical human social evolution, and facilitate the resolution of pressing societal social dilemmas. Artificial life is concerned with understanding the dynamics of human societies. A defining feature of any society is its institutions. However, defining exactly what an institution is has proven difficult, with authors often talking past each other. This article presents a dynamic model of institutions, which views them as political game forms that generate the rules of a group's economic interactions. Unlike most prior work, the framework presented here allows for the construction of explicit models of the evolution of institutional rules. It takes account of the fact that group members are likely to try to create rules that benefit themselves. Following from this, it allows us to determine the conditions under which self-interested individuals will create institutional rules that support cooperation—for example, that prevent a tragedy of the commons. The article finishes with an example of how a model of the evolution of institutional rewards and punishments for promoting cooperation can be created. It is intended that this framework will allow artificial life researchers to examine how human groups can themselves create conditions for cooperation. This will help provide a better understanding of historical human social evolution, and facilitate the resolution of pressing societal social dilemmas.