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