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Published November 1994 | Published
Journal Article Open

The Predictive Utility of Generalized Expected Utility Theories

Abstract

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).

Additional Information

© 1994 Econometric Society. Manuscript received February, 1992; final revision received February, 1994. Thanks to John Conlisk, Dave Grether, Bill Neilson, Nat Wilcox, and a co-editor and two anonymous referees for helpful comments, to Drazen Prelec and John Kagel for supplying their data, and especially to Teck-Hua Ho for collaboration in the project's early stages. Camerer's work was supported by NSF Grant SES-90-23531 and by the Russell Sage Foundation, where he visited during the 1991-1992 academic year.

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