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A Parameter-Free Elicitation of the Probability Weighting Function in Medical Decision Analysis

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  • Han Bleichrodt

    (iMTA/iBMG, Erasmus University, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands)

  • Jose Luis Pinto

    (Department of Economics, Universitat Pompeu Fabra, Barcelona, Spain)

Abstract

An important reason why people violate expected utility theory is probability weighting. Previous studies on the probability weighting function typically assume a specific parametric form, exclude heterogeneity in individual preferences, and exclusively consider monetary decision making. This study presents a method to elicit the probability weighting function in rank-dependent expected utility theory that makes no prior assumptions about the functional form of the probability weighting function. We use both aggregate and individual subject data, thereby allowing for heterogeneity of individual preferences, and we examine probability weighting in a new domain, medical decision making. There is significant evidence of probability weighting both at the aggregate and at the individual subject level. The modal probability weighting function is inverse S-shaped, displaying both lower subadditivity and upper subadditivity. Probability weighting is in particular relevant at the boundaries of the unit interval. Compared to studies involving monetary outcomes, we generally find more elevation of the probability weighting function. The robustness of the empirical findings on probability weighting indicates its importance. Ignoring probability weighting in modeling decision under risk and in utility measurement is likely to lead to descriptively invalid theories and distorted elicitations.

Suggested Citation

  • Han Bleichrodt & Jose Luis Pinto, 2000. "A Parameter-Free Elicitation of the Probability Weighting Function in Medical Decision Analysis," Management Science, INFORMS, vol. 46(11), pages 1485-1496, November.
    • Handle: RePEc:inm:ormnsc:v:46:y:2000:i:11:p:1485-1496
    DOI: 10.1287/mnsc.46.11.1485.12086
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    1. Mark McCord & Richard de Neufville, 1986. ""Lottery Equivalents": Reduction of the Certainty Effect Problem in Utility Assessment," Management Science, INFORMS, vol. 32(1), pages 56-60, January.
    2. Lattimore, Pamela K. & Baker, Joanna R. & Witte, Ann D., 1992. "The influence of probability on risky choice: A parametric examination," Journal of Economic Behavior & Organization, Elsevier, vol. 17(3), pages 377-400, May.
    3. Peter Wakker & Anne Stiggelbout, 1995. "Explaining Distortions in Utility Elicitation through the Rank-dependent Model for Risky Choices," Medical Decision Making, , vol. 15(2), pages 180-186, June.
    4. Beattie, Jane & Loomes, Graham, 1997. "The Impact of Incentives upon Risky Choice Experiments," Journal of Risk and Uncertainty, Springer, vol. 14(2), pages 155-168, March.
    5. Yaari, Menahem E, 1987. "The Dual Theory of Choice under Risk," Econometrica, Econometric Society, vol. 55(1), pages 95-115, January.
    6. Camerer, Colin F & Hogarth, Robin M, 1999. "The Effects of Financial Incentives in Experiments: A Review and Capital-Labor-Production Framework," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 7-42, December.
    7. Joseph S. Pliskin & Donald S. Shepard & Milton C. Weinstein, 1980. "Utility Functions for Life Years and Health Status," Operations Research, INFORMS, vol. 28(1), pages 206-224, February.
    8. George Wu & Richard Gonzalez, 1996. "Curvature of the Probability Weighting Function," Management Science, INFORMS, vol. 42(12), pages 1676-1690, December.
    9. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
    10. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    11. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98, World Scientific Publishing Co. Pte. Ltd..
    12. Pamela K. Lattimore & Joanna R. Baker & A. Dryden Witte, 1992. "The Influence Of Probability on Risky Choice: A parametric Examination," NBER Technical Working Papers 0081, National Bureau of Economic Research, Inc.
    13. Wu, George & Gonzalez, Richard, 1998. "Common Consequence Conditions in Decision Making under Risk," Journal of Risk and Uncertainty, Springer, vol. 16(1), pages 115-139, April.
    14. Peter Wakker & Daniel Deneffe, 1996. "Eliciting von Neumann-Morgenstern Utilities When Probabilities Are Distorted or Unknown," Management Science, INFORMS, vol. 42(8), pages 1131-1150, August.
    15. Tversky, Amos & Wakker, Peter, 1995. "Risk Attitudes and Decision Weights," Econometrica, Econometric Society, vol. 63(6), pages 1255-1280, November.
    16. Harless, David W & Camerer, Colin F, 1994. "The Predictive Utility of Generalized Expected Utility Theories," Econometrica, Econometric Society, vol. 62(6), pages 1251-1289, November.
    17. Dubourg, W R & Jones-Lee, M W & Loomes, Graham, 1994. "Imprecise Preferences and the WTP-WTA Disparity," Journal of Risk and Uncertainty, Springer, vol. 9(2), pages 115-133, October.
    18. Heath, Chip & Tversky, Amos, 1991. "Preference and Belief: Ambiguity and Competence in Choice under Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 4(1), pages 5-28, January.
    19. Imran S. Currim & Rakesh K. Sarin, 1989. "Prospect Versus Utility," Management Science, INFORMS, vol. 35(1), pages 22-41, January.
    20. A.M. Stiggelbout & G.M. Kiebert & J. Kievit & J.W.H. Leer & G. Stoter & J.C.J.M. De Haes, 1994. "Utility Assessment in Cancer Patients," Medical Decision Making, , vol. 14(1), pages 82-90, February.
    21. Mohammed Abdellaoui, 2000. "Parameter-Free Elicitation of Utility and Probability Weighting Functions," Management Science, INFORMS, vol. 46(11), pages 1497-1512, November.
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