Evolutionary computation and agent-based modeling: biologically-inspired approaches for understanding complex social systems

Computational social science in general, and social agent-based modeling (ABM) simulation in particular, are challenged by modeling and analyzing complex adaptive social systems with emergent properties that are hard to understand in terms of components, even when the organization of component agents is know. Evolutionary computation (EC) is a mature field that provides a bio-inspired approach and a suite of techniques that are applicable to and provide new insights on complex adaptive social systems. This paper demonstrates a combined EC-ABM approach illustrated through the RebeLand model of a simple but complete polity system. Results highlight tax rates and frequency of public issue that stress society as significant features in phase transitions between stable and unstable governance regimes. These initial results suggest further applications of EC to ABM in terms of multi-population models with heterogeneous agents, multi-objective optimization, dynamic environments, and evolving executable objects for modeling social change. ⇤Funding for this study was provided by the Center for Social Complexity at George Mason University and by grant no. N00014-08-1-0378 from the O ce of Naval Research. The authors are solely responsible for any errors in this study. Special thanks to Mark Rouleau for initial development of the MASON RebeLand model with the first author, to Sean Luke for support with the MASON system, and to members of the Mason-HRAF Joint Project on Eastern Africa for comments and discussions.