Self-adapting simulated artificial societies - Gower-Winter, Brandon (Department of Computer Science, 2024)
Agent-Based Models (ABM) are computational models that utilize autonomous agents to interact and adapt to the environments in which they occupy. They are used in fields ranging from Economics to Ecology. More recently, ABM are being used in Computational Archaeology to aid in explaining the complex social phenomena that gave rise to ancient societies all over the world. Despite their potential, ABM are limited by the fact their agents are rarely adaptive despite adaptability often touted as one of Agent-Based Modelling's greatest strengths. In this work we remedy this by investigating whether Machine Learning (ML) algorithms can be used as adaptive mechanisms for Agent-based Models simulating complex social phenomena. We aim to do this by comparing ML agents, developed using Reinforcement Learning and two Evolutionary Algorithms as adaptive-mechanisms, to rule-based agents typically found in contemporary literature. To achieve this, we create NeoCOOP, an Agent-Based Model designed to simulate the complex social phenomena that arise from resource sharing agents in ancient societies. By conducting scenario experimentation, we examined the adaptive capacity of our four agent-types by measuring their ability to maintain both population and resources levels in a virtual re-creation of Ancient Egypt during the Predynastic Period. Our results indicate that our ML agents (Utility and IE) perform better or on par with even complex rule-based agents (Traditional and RBAdaptive). The IE agent-type ranked first and was the most adaptive agent-type. The Utility and RBAdaptive agents jointly ranked second and the Traditional agent ranked last. Overall, the findings of this work clearly show that adaptive-agents are more suited to modelling the dynamics of complex environments than their rule-based counterparts. More specifically, our results demonstrate that ML algorithms are particularly well suited as these adaptive mechanisms given that they not only allowed our agents to maintain high population and resource levels, they facilitated the emergence of additional emergent phenomena such as resource acquisition strategy specialization. It is our hope that the findings presented in this work pushes the state of the art such that future research endeavours seek to use truly adaptive-agents in their complex Archaeological ABM