SGER: Scale and Complexity in Computational Models of Social Interaction

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The research will investigate the computing challenges posed by extending large-scale multi-agent computational models to achieve both greater scale and more complex and realistic behaviors by individual agents. Individual agents in these models are represented by complex data structures that represent their internal states and behavioral repertoires. In agent populations of even modest size, the number of interaction histories (which determines the state of a model) is huge, so building realistic models poses significant computational hurdles. The primary aim of this research is to explore the quantity-complexity frontier: point at which there are tradeoffs between models with large numbers of simple agents and models with greater numbers of more complex agents. This project aims to develop innovative computational techniques to extend both the scale and the complexity of models by two to three orders of magnitude. Such advances are crucial to understanding whether phase changes occur when scale and/or complexity is significantly increased in multi-agent or social systems. It is also critical to future real-world applications of multi-agent systems modeling to problems such as understanding development and migration patterns (e.g. China), which pose problems of scale, and understanding peer-group effects on crime rates (which pose problems of modeling the complexity of individual agent behaviors).