Data-driven dynamic decision models

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 algorithmbased 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 *

Betting and belief: Prediction markets and attribution of climate change

“…Additionally, the risk of failing to complete trades adds volatility, which also puts information in play. We have observed a similar phenomenon in a very different context, where adding stochastic noise to player decisions in iterated games improves the accuracy with which machine-learning algorithms can identify the players' strategies (Nay and Gilligan 2015). What is not clear to us is why increasing the number of traders slows convergence, even if the number of edges per trader remains fixed.…”