The Exemplar Confusion Model: An Account of Biased Probability Estimates in Decisions from Description

At the core of every decision-making task are two simple features; outcome values and probabilities. Over the past few decades, many models have developed from von Neumann’ and Morgenstern’s (1945) Expected Utility Theory to provide a thorough account of people’s subjective value and probability weighting functions. In particular, one such model that has been largely successful in both Psychology and Economics is Cumulative Prospect Theory (CPT; Tversky & Kahneman, 1992). While these models do fit people’s choice behavior well, few models have attempted to provide a psychological account for subjective value, probability weighting, and resulting choice behavior. In this paper, we focus on a memory confusion process as described in Hawkins et al.’s (2014) exemplar-based model for decisions from experience, the Exemplar Confusion (ExCon) model, and adapt it to account for biased probability estimates in decisions from description. Using Bayesian model selection techniques, we demonstrate that it is able to account for real choice data from a Rieskamp (2008) study using gains, losses, and mixed description-based gambles, and performs at least as well as CPT.