Modeling Gain-Loss Asymmetries in Risky Choice: The Critical Role of Probability Weighting

Abstract

A robust empirical regularity in decision making is that the negative consequences of an option (i.e., losses) often have a stronger impact on people’s behavior than the positive consequences (i.e., gains). One common explanation for such a gain-loss asymmetry is loss aversion. To model loss aversion in risky decisions, prospect theory (Kahneman & Tversky, 1979) assumes a kinked value function (which translates objective consequences into subjective utilities), with a steeper curvature for losses than for gains. We highlight, however, that the prospect theory framework offers many alternative ways to model gain-loss asymmetries (e.g., via the weighting function, which translates objective probabilities into subjective decision weights; or via the choice rule). Our goal is to systematically test these alternative models against each other. In a reanalysis of data by Glöckner and Pachur (2012), we show that people’s risky decisions are best accounted for by a version of prospect theory that has a more elevated weighting function for losses than for gains but the same value function for both domains. These results contradict the common assumption that a kinked value function is necessary to model risky choices and point to the neglected role of people’s differential probability weighting in the gain and loss domains.


Back to Table of Contents