Decision Making in Agent-Based Models

Agent-Based Models (ABM) are being increasingly applied to the study of a wide range of social phenomena, often putting the focus on the macroscopic patterns that emerge from the interaction of a number of agents programmed to behave in a plausible manner. This agent behavior, however, is all too often encoded as a small set of rules that produces a somewhat simplistic behavior. In this short paper, we propose to explore the impact of decision-making processes on the outcome of simulations, and introduce a type of agent that uses a more systematic and principled decision-making approach, based on casting the simulation environment as a Markov Decision Process. We compare the performance of this type of agent to that of more simplistic agents on a simple ABM simulation, and examine the interplay between the decision-making mechanism and other relevant simulation parameters such as the distribution and scarcity of resources. Our preliminary findings show that our novel agent outperforms the rest of agents, and, more generally, that the process of decision-making needs to be acknowledged as a first-class parameter of ABM simulations with a significant impact on the simulation outcome.