Evolutionary Agent-Based Modeling of Past Societies’ Angelos Chliaoutakis and Georgios Chalkiadakis 1 Abstract. In this work, we extend a generic agent-based model for simulating ancient societies, by blending, for the first time, evo- lutionary game theory with multiagent systems’ self-organization. Our approach models the evolution of social behaviours in a popula- tion of strategically interacting agents corresponding to households in the early Minoan era. To this end, agents participate in repeated games by means of which they exchange utility (corresponding to resources) with others. The results of the games contribute to both the continuous re-organization of the social structure, and the pro- gressive adoption of the most successful agent strategies. Agent pop- ulation is not fixed, but fluctuates over time. The particularity of the domain necessitates that agents in our games receive non-static pay- offs, in contrast to most games studied in the literature; and that the evolutionary dynamics are formulated via assessing the perceived fit- ness of the agents, defined in terms of how successful they are in ac- cumulating utility. Our results show that societies of strategic agents that self-organize via adopting the aforementioned evolutionary ap- proach, demonstrate a sustainability that largely matches that of self- organizing societies of more cooperative agents; and that strategic cooperation is in fact, in many instances, an emergent behaviour in this domain. 1 Introduction Over the past two decades, archaeology has utilized agent-based models (ABM) for simulating ancient societies [2]. This is due to the ABMs’ ability to represent individuals or societies, and encompass uncertainty inherent in archaeological theories. At the same time, in- corporating ideas from multiagent systems (MAS) research in ABMs can enhance agent sophistication, and contribute on the application of strategic principles for selecting among agent behaviours [4]. To this end, a recently developed ABM with autonomous, utility- based agents explores alternative hypotheses regarding the social or- ganization of ancient societies, by employing MAS ideas and al- gorithms [1]. The model incorporates different social organization paradigms and subsistence technologies (e.g., types of farming). Moreover, it employs a self-organization approach that allows the exploration of the historical social dynamics–i.e., the evolution of social relationships in a given society, while being grounded on archaeological evidence. However, the various social organization paradigms explored in that work assume a cooperative attitude on behalf of the agents. Specifically, agents were assumed to be willing to provide resources out of their stock in order to help agents in need, and such transfers drive the evolution of the social structure. In real- ity though, people are often driven by more individualistic instincts 1 School of Electronic and Computer Engineering, Technical University of Crete, Greece, email: {angelos, gehalk}@intelligence.tuc.gr and exhibit more egotistic societal behaviour. Therefore, if one is to model societal transformation accurately, agent behaviour has to be analysed from a strategic perspective as well. Assuming that agent in- teractions are based on rational decision-making, and also influenced by their very effect on the society as a whole, then the evolution of the social dynamics can be studied via a game-theoretic approach. The “mathematics” of evolution are the subject of evolutionary game the- ory (EGT) [3], which takes an interest in the replicator dynamics by which strategies evolve. In this work, we adopt such an approach for the first time, and pro- vide an alternative “social self-organization” approach to that of [1]: here, social self-organization is driven by the interactions of strategic agents operating within a given social organization group, and the effects these interactions have on agent utility. As such, our ABM employs a self-organization social paradigm where the evolution of the social organization structure is driven by the interaction of agent strategies in an evolutionary game-theoretic sense [3]. This allows us to study the evolution and adaptation of strategic behaviours of agents operating in the artificial ancient community, and the effect these have on the society as a whole. We are not aware of any archae- ological ABM that explicitly adopts an evolutionary game-theoretic approach. By contrast, our work here shows how EGT can be utilized within an archaeological ABM. 2 A utility-based ABM We build on top of the ABM developed in [1] for simulating an arti- ficial ancient society of agents evolving in a 2D grid environmental topology. The agents correspond to households, which are consid- ered to be the main social unit of production in Minoan societies for the period of interest (3,100-1,100 BCE) [5], each containing up to a maximum number of individuals (household inhabitants). House- holds are utility-based autonomous agents who can settle, or occa- sionally re-settle in order to improve their utility, and cultivate in a specific environmental location. The total number of agents in the system changes over time, as the annual levels of births and deaths is based on the amount of energy consumed by the household agent during the year. This in turn de- pends on the energy harvested, that is, the agent’s utility. These rates, produce a population growth rate of 0.1%, when households con- sume adequate resources. This corresponds to estimated world-wide population growth rates during the Bronze Age. The ABM incorporates a self-organization social paradigm, where agents within a settlement continuously re-assess their relations with others, and this affects the way resources are ultimately distributed among the community members, leading to “social mobility” in their relations. Self-organization gives rise, naturally, to implicit agent hi- Organization Structure ECAI 2016 G.A. Kaminka et al. (Eds.) © 2016 The Authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/978-1-61499-672-9-1577 1577