An Affective Probability Weighting Function for Risky Choice with Nonmonetary Outcomes

The assumption of an inverse S-shaped probability weighting function allows cumulative prospect theory to explain several well-established regularities in risky choice between monetary lotteries. Empirical evidence indicates that in choices between options with nonmonetary outcomes, the shape of the weighting function is strongly influenced by the negative emotions often associated with these outcomes. In its current form, however, cumulative prospect theory is silent with respect to how to formally integrate the influence of affective processes on the shape of the weighting function. Here, we propose an affective probability weighting function in which the two main features of the weighting function, probability sensitivity and elevation, gradually change with the affective value of the nonmonetary outcomes. We test our proposition in a model competition with three data sets. The results show that the affective probability weighting function improves the ability of (cumulative) prospect theory to predict choices between options with nonmonetary outcomes. We observed approximately linear probability weighting for the least affective nonmonetary outcomes and probability neglect for the worst or multiple outcomes. These findings demonstrate that integrating the effect of affective processes in formal decision models is crucial for advancing the understanding of choices between nonmonetary risky options-and thus ensuring the generalizability of the models beyond choices between monetary lotteries.