Beyond analytic bounds: Re-evaluating predictive power in risky decision models

Abstract

Abstract Research in behavioral decision-making has produced many models of decision under risk. To improve our understanding of choice under risk, it is essential to perform rigorous model comparisons over large sets of decision settings to find which models are most useful. Recently, such large-scale comparisons have produced conflicting conclusions: A variant of cumulative prospect theory (CPT) was the best model in a study by He, Analytis, and Bhatia (2022), whereas variants of the model BEAST were the best in two choice prediction competitions. This study delves into these contradictions to identify and explore the underlying reasons. We replicate and extend the analysis by He et al., this time incorporating BEAST, which was previously excluded because it cannot be analytically estimated. Our results show that while CPT excels in systematically hand-crafted tasks, BEAST—originally designed for broader decision-making contexts—matches or even surpasses CPT’s performance when choice tasks are randomly selected, and predictions are made for new, unknown decision makers. This success of BEAST, very different from classical decision models—as it does not assume, for example, subjective transformations of outcomes and probabilities—puts into question previous conclusions concerning the underlying psychological mechanisms of choice under risk. Our results challenge the field to expand beyond established evaluating techniques and highlight the importance of an inclusive approach toward nonanalytic models, like BEAST, to achieve more objective insights into decision-making behavior.