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Parameter-Free Elicitation of Utility and Probability Weighting Functions

Parameter-Free Elicitation of Utility and Probability Weighting Functions This paper proposes a two-step method to successively elicit utility functions and decision weights under rank-dependent expected utility theory and its “more descriptive” version: cumulative prospect theory. The novelty of the method is that it is parameter-free, and thus elicits the whole individual preference functional without imposing any prior restriction. This method is used in an experimental study to elicit individual utility and probability weighting functions for monetary outcomes in the gain and loss domains. Concave utility functions are obtained for gains and convex utility functions for losses. The elicited weighting functions satisfy upper and lower subadditivity and are consistent with previous parametric estimations. The data also show that the probability weighting function for losses is more “elevated” than for gains. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Management Science INFORMS

Parameter-Free Elicitation of Utility and Probability Weighting Functions

Abdellaoui, Mohammed
Management Science , Volume 46 (11): 16 – Nov 15, 2000
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Publisher
INFORMS
Copyright
Copyright © INFORMS
Subject
Research Article
ISSN
0025-1909
eISSN
1526-5501
DOI
10.1287/mnsc.46.11.1497.12080
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a two-step method to successively elicit utility functions and decision weights under rank-dependent expected utility theory and its “more descriptive” version: cumulative prospect theory. The novelty of the method is that it is parameter-free, and thus elicits the whole individual preference functional without imposing any prior restriction. This method is used in an experimental study to elicit individual utility and probability weighting functions for monetary outcomes in the gain and loss domains. Concave utility functions are obtained for gains and convex utility functions for losses. The elicited weighting functions satisfy upper and lower subadditivity and are consistent with previous parametric estimations. The data also show that the probability weighting function for losses is more “elevated” than for gains.

Journal

Management Science – INFORMS

Published: Nov 15, 2000

Keywords: Keywords : decision making ; expected utility ; rank-dependent expected utility ; cumulative prospect theory ; probability weighting function

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