Resolving Inconsistencies in Utility Measurement Under Risk: Tests of Generalizations of Expected Utility

• Published in: Management science Vol. 53; no. 3; pp. 469 - 482
• Main Authors: Bleichrodt, Han, Abellan-Perpinan, Jose Maria, Pinto-Prades, Jose Luis, Mendez-Martinez, Ildefonso
• Format: Journal Article
• Language: English
• Published: Linthicum, MD INFORMS 01-03-2007; Institute for Operations Research and the Management Sciences
• Series: Management Science
• Subjects: Analysis; Applied sciences; Business management; Business studies; Decision analysis; Decision theory. Utility theory; Exact sciences and technology; Expected utility; health; Loss aversion; Management science; Measurement techniques; Mensuration; nonexpected utility; Operational research and scientific management; Operational research. Management science; Organizational effectiveness; Parametric models; Probability; Prospect theory; Risk; Risk aversion; Risk management; Statistical median; Studies; Term weighting; Utility functions; Utility measurement; Utility theory; Weighting functions; United States; Prospect theory; Probabilistic approach; Utility theory; Health; Risk factor; Risk theory; nonexpected utility; utility measurement; Expected utility
• ISSN: 0025-1909; 1526-5501
• DOI: 10.1287/mnsc.1060.0647

This paper explores inconsistencies that occur in utility measurement under risk when expected utility theory is assumed and the contribution that prospect theory and some other generalizations of expected utility can make to the resolution of these inconsistencies. We used five methods to measure utilities under risk and found clear violations of expected utility. Of the theories studied, prospect theory was the most consistent with our data. The main improvement of prospect theory over expected utility was in comparisons between a riskless and a risky prospect (riskless-risk methods). We observed no improvement over expected utility in comparisons between two risky prospects (risk-risk methods). An explanation for the latter observation may be that there was less distortion in probability weighting in the interval [0.10, 0.20] than has commonly been observed.