Nonlinear decision weights or moment-based preferences? A model competition involving described and experienced...

The predictive power of cumulative prospect theory and expected utility theory is typically compared using decisions from description, where lotteries' outcome values and probabilities are explicitly stated. In decisions from experience, individuals sample (in the sampling paradigm without cost) from the return distributions to learn outcome values and their relative frequencies; here cumulative prospect theory and expected utility theory require the calculation of probabilities from experience. Individuals, however, may be more attuned to the experienced moments of outcome distributions, rather than the probabilities. We therefore test the mean-variance-skewness model, and retrieve the proportion of expected utility theory (over income), cumulative prospect theory, and mean-variance-skewness populations using a latent-class hierarchical Bayesian model across six large datasets. For simple lotteries (with 1-2 outcomes), we find a mixture of cumulative prospect theory and mean-variance-skewness populations in decisions from both description and experience. For more complex lotteries (with 2-3 outcomes), all participants are classified as cumulative prospect theory types in decisions from description, but as mean-variance-skewness types in decisions from experience. This suggests that in decisions from experience with more complex return distributions, preferences for skewness are more predictive than nonlinear probability weighting.