title: The predictive accuracy of intertemporal-choice models.
abstract: How do people choose between a smaller reward available sooner and a larger reward available later? Past research has evaluated models of intertemporal choice by measuring goodness of fit or identifying which decision-making anomalies they can accommodate. An alternative criterion for model quality, which is partly antithetical to these standard criteria, is predictive accuracy. We used cross-validation to examine how well 10 models of intertemporal choice could predict behaviour in a 100-trial binary-decision task. Many models achieved the apparent ceiling of 85% accuracy, even with smaller training sets. When noise was added to the training set, however, a simple logistic-regression model we call the difference model performed particularly well. In many situations, between-model differences in predictive accuracy may be small, contrary to long-standing controversy over the modelling question in research on intertemporal choice, but the simplicity and robustness of the difference model recommend it to future use.
authors.affiliation: Stony Brook University, New York, USA.
authors.forename: Kodi B
authors.initials: KB
authors.lastname: Arfer
authors.forename: Christian C
authors.initials: CC
authors.lastname: Luhmann
mesh_terms: D000328:Adult; D002755:Choice Behavior; D003657:Decision Making; D065786:Delay Discounting; D005260:Female; D006801:Humans; D059032:Learning Curve; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008432:Mathematical Computing; D008875:Middle Aged; D015233:Models, Statistical; D011930:Reaction Time; D012984:Software; D060388:Support Vector Machine; D055815:Young Adult
keywords: intertemporal choice; machine learning; mathematical modeling; predictive validity