The Predictive Utility of Generalized Expected Utility Theories

Many alternative theories have been proposed to explain violations of expected utility (EU) theory observed in experiments. Several recent studies test some of these alternative against each other. Formal tests used to judge the theories usually count the of responses consistent with the theory, ignoring systematic variation in responses are inconsistent. We develop a maximum-likelihood estimation method which uses the information in the data, creates test statistics that can be aggregated across studies, enables one to judge the predictive utility-the fit and parsimony-of utility theories. of 23 data sets, using several thousand choices, suggest a menu of theories which the least parsimony for the biggest improvement in fit. The menu is: mixed, prospect theory, EU, and expected value. Which theories are best is highly to whether gambles in a pair have the same support (EU fits better) or not (EU poorly). Our method may have application to other domains in which various theories different subsets of choices (e. g. , refinements of Nash equilibrium in noncooperative).