Nesting a Data-driven Agent-based Model within a Theoretical Framework

Miguel Garces, Brandon Alcorn

Traditionally agent-based modeling has been a field that focuses largely on abstract models that offer insights into real world phenomena, but rarely with enough complexity to play a major role within academic or policy discussions about particular problems in particular places. In this paper we outline an approach to building country virtualization models that combines traditional micro-level interactions in an agent-based model with macro-level theory to simulate complex political systems in a range of countries in Southeast Asia. We discuss the advantages, and potential downfalls, to adding complexity into agent-based models and present results that highlight some of the analytic advantages to having multiple levels of analysis present in a model and a distribution of counterfactual model results by which we can analyze and forecast political outcomes.

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