Ian Lustick, Brandon Alcorn, Miguel Garces, Alicia Ruvinsky
This paper suggests that computer-assisted agent-based modeling has the ability to move beyond abstract representations of political problems to theoretically sound virtualizations of real-world polities capable of producing probabilistic forecasts from distributions of stochastically perturbed model trajectories. In contrast to statistical approaches, this technique encompasses both prediction and explanation, with every distinctive trajectory traceable backward from the occurrence or non-occurrence of an event of interest through the branching points and mechanisms that led to it. In this paper we illustrate our technique for building a country-scale model from corroborated theories, focusing on the “Dynamic Political Hierarchy” module that integrates theories of cross-cutting cleavages, nested institutions, and dynamic loyalties. We present our forecasts for significant political events in Thailand for the year August 2010-July 2011. Drawing on this case we demonstrate how the challenges of internal validity can be met in complex formal models and conclude by emphasizing the importance of advances in visualization techniques for parsing large amounts of interrelated time-series data.
UPDATE: This paper has since been published in Volume 24 Number 3 of the Journal of Experimental & Theoretical Artificial Intelligence. Download Here