DATA-DRIVEN DYNAMIC DECISION MODELS

This article outlines a method for automatically generating models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. This is useful for designing empirically grounded agent-based simulations and for gaining direct insight into observed dynamic processes. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple approximations that explain most of the structure of complex stochastic processes. This method, implemented in C++ and R, scales well to large data sets. We apply our methods to empirical data from human subjects game experiments and international relations. We also demonstrate the method’s ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity. 1 MODEL REPRESENTATION This article describes a modeling method designed to understand data on dynamic decision-making. We have created a practical, easy-to-use software package implementing the method. Although our method is more broadly applicable, the motivation for the model representation was prediction of individual behavior in strategic interactions, i.e. games. Most behavioral game-theoretic treatments of repeated games us