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What is the actual shape of perception utility?

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  • Kontek, Krzysztof

Abstract

Cumulative Prospect Theory (Kahneman, Tversky, 1979, 1992) holds that the value function is described using a power function, and is concave for gains and convex for losses. These postulates are questioned on the basis of recently reported experiments, paradoxes (gain-loss separability violation), and brain activity research. This paper puts forward the hypothesis that perception utility is generally logarithmic in shape for both gains and losses, and only happens to be convex for losses when gains are not present in the problem context. This leads to a different evaluation of mixed prospects than is the case with Prospect Theory: losses are evaluated using a concave, rather than a convex, utility function. In this context, loss aversion appears to be nothing more than the result of applying a logarithmic utility function over the entire outcome domain. Importantly, the hypothesis enables a link to be established between perception utility and Portfo-lio Theory (Markowitz, 1952A). This is not possible in the case of the Prospect Theory value function due its shape at the origin.

Suggested Citation

  • Kontek, Krzysztof, 2011. "What is the actual shape of perception utility?," MPRA Paper 31715, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:31715
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    References listed on IDEAS

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    1. Mohammed Abdellaoui & Han Bleichrodt & Olivier L’Haridon, 2008. "A tractable method to measure utility and loss aversion under prospect theory," Journal of Risk and Uncertainty, Springer, vol. 36(3), pages 245-266, June.
    2. 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.
    3. Mohammed Abdellaoui & Han Bleichrodt & Corina Paraschiv, 2007. "Loss Aversion Under Prospect Theory: A Parameter-Free Measurement," Management Science, INFORMS, vol. 53(10), pages 1659-1674, October.
    4. Amos Tversky & Daniel Kahneman, 1991. "Loss Aversion in Riskless Choice: A Reference-Dependent Model," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 1039-1061.
    5. Harry Markowitz, 1952. "The Utility of Wealth," Journal of Political Economy, University of Chicago Press, vol. 60(2), pages 151-151.
    6. Michael H. Birnbaum & Jeffrey P. Bahra, 2007. "Gain-Loss Separability and Coalescing in Risky Decision Making," Management Science, INFORMS, vol. 53(6), pages 1016-1028, June.
    7. George Wu & Alex B. Markle, 2008. "An Empirical Test of Gain-Loss Separability in Prospect Theory," Management Science, INFORMS, vol. 54(7), pages 1322-1335, July.
    8. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
    9. Nathalie Etchart-Vincent, 2009. "The shape of the utility function under risk in the loss domain and the "ruinous losses" hypothesis: some experimental results," Economics Bulletin, AccessEcon, vol. 29(2), pages 1393-1402.
    10. Helga Fehr-Duda & Manuele Gennaro & Renate Schubert, 2006. "Gender, Financial Risk, and Probability Weights," Theory and Decision, Springer, vol. 60(2), pages 283-313, May.
    11. John W. Payne & Dan J. Laughhunn & Roy Crum, 1980. "Translation of Gambles and Aspiration Level Effects in Risky Choice Behavior," Management Science, INFORMS, vol. 26(10), pages 1039-1060, October.
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    More about this item

    Keywords

    Prospect Theory; value function; perception utility; loss aversion; gain-loss separability violation; neuroscience; Portfolio Theory; Decision Utility Theory.;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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