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Expected utility theory and prospect theory: one wedding and a decent funeral

Published online by Cambridge University Press:  14 March 2025

Glenn W. Harrison  and
E. Elisabet Rutström
Glenn W. Harrison*
Affiliation:
Department of Economics, College of Business Administration, University of Central Florida, Orlando, FL, USA
E. Elisabet Rutström*
Affiliation:
Department of Economics, College of Business Administration, University of Central Florida, Orlando, FL, USA
*
e-mail: [email protected]
e-mail: [email protected]
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Abstract

Choice behavior is typically evaluated by assuming that the data is generated by one latent decision-making process or another. What if there are two (or more) latent decision-making processes generating the observed choices? Some choices might then be better characterized as being generated by one process, and other choices by the other process. A finite mixture model can be used to estimate the parameters of each decision process while simultaneously estimating the probability that each process applies to the sample. We consider the canonical case of lottery choices in a laboratory experiment and assume that the data is generated by expected utility theory and prospect theory decision rules. We jointly estimate the parameters of each theory as well as the fraction of choices characterized by each. The methodology provides the wedding invitation, and the data consummates the ceremony followed by a decent funeral for the representative agent model that assumes only one type of decision process. The evidence suggests support for each theory, and goes further to identify under what demographic domains one can expect to see one theory perform better than the other. We therefore propose a reconciliation of the debate over two of the dominant theories of choice under risk, at least for the tasks and samples we consider. The methodology is broadly applicable to a range of debates over competing theories generated by experimental and non-experimental data.

Keywords

Expected utility theoryProspect theoryMixture models

JEL classification

C12: Hypothesis Testing: General C91: Laboratory, Individual Behavior D81: Criteria for Decision-Making under Risk and Uncertainty C51: Model Construction and Estimation
Type
Research Article
Information
Experimental Economics , Volume 12 , Issue 2 , June 2009 , pp. 133 - 158
Copyright
Copyright © Economic Science Association 2008

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Footnotes

We thank the U.S. National Science Foundation for research support under grants NSF/IIS 9817518, NSF/HSD 0527675 and NSF/SES 0616746; Ryan Brossette, Harut Hovsepyan, David Millsom and Bob Potter for research assistance; and Steffen Andersen, Vince Crawford, Curt Eaton, John Hey, Peter Kennedy, Jan Kmenta, Peter Wakker, two referees, and numerous seminar participants for helpful comments. Supporting data and instructions are stored at the ExLab Digital Library at http://exlab.bus.ucf.edu.

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