Simple functional forms for utility require restrictive structural assumptions that are often contrary to observed behavior. Even so, they are widely used in applied economic research. I address this issue using a two-part adaptive experimental design to compare the predictions of a popular parametric model of decision making under risk to those of non-parametric bounds on indifference curves. Interpreting the latter as an approximate upper bound, I find the parametric model sacrifices very little in terms of predictive success. This suggests that, despite their restrictiveness, simple functional forms may nevertheless be useful representations of preferences over risky alternatives.

Revealed preference theory is brought to bear on the problem of recovering approximate parametric preferences from consistent and inconsistent consumer choices. We propose measures of the incompatibility between the revealed preference ranking implied by choices and the ranking induced by the considered parametric preferences. These incompatibility measures are proven to characterize well-known inconsistency indices. We advocate a recovery approach that is based on such incompatibility measures and demonstrate its applicability for misspecification measurement and model selection. Using an innovative experimental design, we empirically substantiate that the proposed revealed-preference-based method predicts choices significantly better than a standard distance-based method.