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

In: Experiments in Economics Decision Making and Markets

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Listed:
  • John D. Hey
  • 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. 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 terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decisionmakers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule.

Suggested Citation

  • John D. Hey & Gianna Lotito & Anna Maffioletti, 2018. "The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity," World Scientific Book Chapters,in: Experiments in Economics Decision Making and Markets, chapter 8, pages 189-219 World Scientific Publishing Co. Pte. Ltd..
  • Handle: RePEc:wsi:wschap:9789813235816_0008
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    References listed on IDEAS

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    4. Wilcox, Nathaniel T., 2011. "'Stochastically more risk averse:' A contextual theory of stochastic discrete choice under risk," Journal of Econometrics, Elsevier, vol. 162(1), pages 89-104, May.
    5. Craig R. Fox & Amos Tversky, 1995. "Ambiguity Aversion and Comparative Ignorance," The Quarterly Journal of Economics, Oxford University Press, vol. 110(3), pages 585-603.
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    7. John Hey & Jinkwon Lee, 2005. "Do Subjects Separate (or Are They Sophisticated)?," Experimental Economics, Springer;Economic Science Association, vol. 8(3), pages 233-265, September.
    8. Ghirardato, Paolo & Maccheroni, Fabio & Marinacci, Massimo, 2004. "Differentiating ambiguity and ambiguity attitude," Journal of Economic Theory, Elsevier, vol. 118(2), pages 133-173, October.
    9. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
    10. 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.
    11. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.
    12. Yoram Halevy, 2007. "Ellsberg Revisited: An Experimental Study," Econometrica, Econometric Society, vol. 75(2), pages 503-536, March.
    13. Schmeidler, David, 1989. "Subjective Probability and Expected Utility without Additivity," Econometrica, Econometric Society, vol. 57(3), pages 571-587, May.
    14. David Ahn & Syngjoo Choi & Douglas Gale & Shachar Kariv, 2014. "Estimating ambiguity aversion in a portfolio choice experiment," Quantitative Economics, Econometric Society, vol. 5, pages 195-223, July.
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    Keywords

    Experimental Economics; Risk; Ambiguity; Markets; Auctions; Bargaining; Econometrics; Methodology;

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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