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Risky Curves: From Unobservable Utility to Observable Opportunity Sets

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Abstract

Most theories of risky choice postulate that a decision maker maximizes the expectation of a Bernoulli (or utility or similar) function. We tour 60 years of empirical search and conclude that no such functions have yet been found that are useful for out-of-sample prediction. Nor do we find practical applications of Bernoulli functions in major risk-based industries such as finance, insurance and gambling. We sketch an alternative approach to modeling risky choice that focuses on potentially observable opportunities rather than on unobservable Bernoulli functions.

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  • Daniel Friedman & Shyam Sunder, 2011. "Risky Curves: From Unobservable Utility to Observable Opportunity Sets," Cowles Foundation Discussion Papers 1819, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:1819
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    Cited by:

    1. Charness, Gary & Gneezy, Uri & Imas, Alex, 2013. "Experimental methods: Eliciting risk preferences," Journal of Economic Behavior & Organization, Elsevier, vol. 87(C), pages 43-51.
    2. Mariana Blanco & Dirk Engelmann & Alexander Koch & Hans-Theo Normann, 2010. "Belief elicitation in experiments: is there a hedging problem?," Experimental Economics, Springer;Economic Science Association, vol. 13(4), pages 412-438, December.
    3. Blanco, Mariana & Engelmann, Dirk & Normann, Hans Theo, 2011. "A within-subject analysis of other-regarding preferences," Games and Economic Behavior, Elsevier, vol. 72(2), pages 321-338, June.
    4. Christophe Bisière & Jean-Paul Décamps & Stefano Lovo, 2015. "Risk Attitude, Beliefs Updating, and the Information Content of Trades: An Experiment," Management Science, INFORMS, vol. 61(6), pages 1378-1397, June.
    5. Amar Cheema & Peter Leszczyc & Rajesh Bagchi & Richard Bagozzi & James Cox & Utpal Dholakia & Eric Greenleaf & Amit Pazgal & Michael Rothkopf & Michael Shen & Shyam Sunder & Robert Zeithammer, 2005. "Economics, Psychology, and Social Dynamics of Consumer Bidding in Auctions," Marketing Letters, Springer, vol. 16(3), pages 401-413, December.
    6. Shyam Sunder, 2006. "Determinants of Economic Interaction: Behavior or Structure," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 1(1), pages 21-32, May.
    7. Johannes G. Jaspersen & Marc A. Ragin & Justin R. Sydnor, 2019. "Predicting Insurance Demand from Risk Attitudes," NBER Working Papers 26508, National Bureau of Economic Research, Inc.
    8. Breaban, A.G., 2014. "Behavior and asset markets : Individual decisions, emotions and fundamental value trajectories," Other publications TiSEM a20e6a40-f15e-4331-83cb-c, Tilburg University, School of Economics and Management.

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    More about this item

    Keywords

    Expected utility; Risk aversion; St. Petersburg Paradox; Decisions under uncertainty; Option theory;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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