Memory Learning: A Computational Approach to Estimating Memory Bias in Human Decision Making

Oregon Convention Center 

In this paper we introduce a novel inverse decision problem formulation which we call "Memory Learning". Given a data set of human decisions and their consequences (i.e. rewards), we consider the situation in which the decisions appear to be "sub-optimal" according to a statistical analysis of the data. Our proposed method seeks to explain these deviations by learning a reweighting of observations, or 'memories', such that the analytical model trained on the reweighted observations matches the observed human behavior. We interpret the reweighting of the observations as a representation of the memory bias inherent in the decision-maker's choices. To bridge the gap between theoretical models and real-world decisions we explore various strategies for learning optimal weightings, employing both analytical and simulation methods. Finally, we introduce a unique iterative resampling approach to apply our method to the well-studied fourth down decision in professional football. Remarkably, our research reveals that our Memory Learning approach outperforms traditional classification methods in predicting coach decisions.

sport

inverse optimization

fourth down

bootstrap

resampling 

Section on Statistics in Sports