title: Model reduction for agent-based social simulation: coarse-graining a civil violence model.
abstract: Agent-based modeling (ABM) constitutes a powerful computational tool for the exploration of phenomena involving emergent dynamic behavior in the social sciences. This paper demonstrates a computer-assisted approach that bridges the significant gap between the single-agent microscopic level and the macroscopic (coarse-grained population) level, where fundamental questions must be rationally answered and policies guiding the emergent dynamics devised. Our approach will be illustrated through an agent-based model of civil violence. This spatiotemporally varying ABM incorporates interactions between a heterogeneous population of citizens [active (insurgent), inactive, or jailed] and a population of police officers. Detailed simulations exhibit an equilibrium punctuated by periods of social upheavals. We show how to effectively reduce the agent-based dynamics to a stochastic model with only two coarse-grained degrees of freedom: the number of jailed citizens and the number of active ones. The coarse-grained model captures the ABM dynamics while drastically reducing the computation time (by a factor of approximately 20).
authors.affiliation: Department of Chemical and Biological Engineering and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zou
authors.forename: Vladimir A
authors.initials: VA
authors.lastname: Fonoberov
authors.forename: Maria
authors.initials: M
authors.lastname: Fonoberova
authors.forename: Igor
authors.initials: I
authors.lastname: Mezic
authors.forename: Ioannis G
authors.initials: IG
authors.lastname: Kevrekidis
mesh_terms: D003198:Computer Simulation; D005716:Game Theory; D015233:Models, Statistical; D012919:Social Behavior; D014754:Violence