The Effect of Foregone Outcomes on Choices From Experience An Individual-Level Modeling Analysis

We examined the relative weight given to obtained and foregone outcomes (i.e., outcomes from the non-chosen options) in repeated choices using cognitive modeling. Previous modeling studies have yielded mixed results. When participants' choices are analyzed by models that predict the next choice ahead in a sequence of decisions, the results imply that people give less weight to foregone than to obtained outcomes. In contrast, in simulation models of n trials ahead, the results imply that, on average, people give equal weight to foregone and obtained outcomes. Using datasets of experience-based binary choices with fixed (stationary) payoff distributions (Erev & Haruvy, in press) and dynamic (nonstationary) payoff distributions (Rakow & Miler, 2009), we employed generalization tests at the individual level to examine whether the findings derived from the one-step-ahead method are due to overfitting. The results of trial-ahead model fitting implied that for the nonstationary tasks only, foregone outcomes received lower weight. However, when this dataset was assessed via generalization criteria at the individual level, equal weighting of foregone and obtained outcomes was the best assumption. This implies that overfitting is implicated in the superior fit of models that assume discounting of foregone outcomes.