Abstract Number:
3592
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Connor Thompson (1), Nathan Sandholtz (1)
Institutions:
(1) Brigham Young University, Provo, Utah
Co-Author:
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
sport|inverse optimization|fourth down|bootstrap|resampling|
Sponsors:
Section on Statistics in Sports
Tracks:
Miscellaneous
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