Techniques for mathematical analysis and optimization of agent-based models

Agent-based models are computer simulations in which entities (agents) interact with each other and their environment according to local update rules. Local interactions give rise to global dynamics. These models can be thought of as in silico laboratories that can be used to investigate the system being modeled. Optimization problems for agent-based models are problems concerning the optimal way of steering a particular model to a desired state. Given that agent-based models have no rigorous mathematical formulation, standard analysis is difficult, and traditional mathematical approaches are often intractable.

This work presents techniques for the analysis of agent-based models and for solving optimization problems with such models. Techniques include model reduction, simulation optimization, conversion to systems of discrete difference equations, and a variety of heuristic methods. The proposed strategies are novel in their application; results show that for a large class of models, these strategies are more effective than existing methods.