Embedding Artificial Intelligence in Agent-Based Models
Agent-Based Models (ABMs) have been increasingly used to study social phenomena, from the emergence of social norms to population dynamics or cultural transmission processes. Key to this method of computational simulation is the tension for explaining how macroscopic phenomena emerge from the interaction of agents behaving in a plausible manner. However, the behavior is too often encoded as a simple set of condition-action rules. We consider this kind of rule-based behaviour too simplistic, specifically when simulating human action and exploring the impact of decision-making processes on the simulation outcome. Therefore, we experiment with a novel type of agent whose decisions are based on casting the surrounding world as a Markov Decision Process, a decision-making model widely used in Artificial Intelligence. The performance of this type of agents is assessed on a simple ABM simulation and compared to that of traditional rule-based agents. We then analyse the interplay between decision-making processes and relevant simulation parameters such as the distribution of resources. Finally, we present the results of applying the insights thus gained to a concrete case study within the Simulpast project concerning the dynamics of Hunter-Gatherer populations in North Gujarat, India, in the mid and late Holocene.
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Embedding Artificial Intelligence in Agent-Based Models. Guillem Frances, Xavier Rubio, Carla Lancelotti, Alexis Torrano, Alex Albore. Presented at The 80th Annual Meeting of the Society for American Archaeology, San Francisco, California. 2015 ( tDAR id: 396991)
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