Data-Driven Dynamic Decision Models
This enables empirically grounded agent-based simulations and insights into observed dynamic processes, though it appears incremental as it builds on existing genetic algorithm and model representation techniques.
The authors developed a method to automatically generate interpretable dynamic decision models with strong predictive power, applying it to human game experiments and international relations data while demonstrating accurate recovery of known data-generating processes.
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.