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


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.

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