Data-Driven Dynamic Decision Models

TL;DR

This paper presents a scalable, interpretable, data-driven approach for modeling dynamic decision processes using genetic algorithms, applicable to empirical data and agent-based simulations.

Contribution

It introduces a novel method combining efficient model representation and genetic algorithms to generate simple, interpretable models of complex stochastic processes.

Findings

Successfully applied to human game experiment data

Accurately recovered known data-generating processes

Scales well to large datasets

Abstract

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.