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The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity

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  • John Hey

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  • Gianna Lotito
  • Anna Maffioletti

Abstract

In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power, taking into account the relative parsimony of the various theories. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in testing contexts, is not the same as judging them on the basis of their explanatory and predictive power. We also find that the more elegant theoretical models do not perform as well as simple rules of thumb.
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Suggested Citation

  • John Hey & Gianna Lotito & Anna Maffioletti, 2010. "The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity," Journal of Risk and Uncertainty, Springer, vol. 41(2), pages 81-111, October.
  • Handle: RePEc:kap:jrisku:v:41:y:2010:i:2:p:81-111
    DOI: 10.1007/s11166-010-9102-0
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    References listed on IDEAS

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    1. Peter Klibanoff & Massimo Marinacci & Sujoy Mukerji, 2005. "A Smooth Model of Decision Making under Ambiguity," Econometrica, Econometric Society, vol. 73(6), pages 1849-1892, November.
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    More about this item

    Keywords

    Ambiguity; Bingo blower; Choquet expected utility; Decision field theory; Decision making; (Subjective) expected utility; (Gilboa and Schmeidler) MaxMin EU; (Gilboa and Schmeidler) MaxMax EU; (Ghirardato) alpha model; MaxMin; MaxMax; Minimum regret; Prospect theory; Uncertainty; D81; C91;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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