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The impact of statistical learning on violations of the sure-thing principle

Author

Listed:
  • Nicky Nicholls

    (Department of Economics, University of Pretoria)

  • Aylit Romm

    (School of Economic and Business Sciences, University of the Witwatersrand, Johannesburg, South Africa)

  • Alexander Zimper

    (Department of Economics, University of Pretoria)

Abstract

This paper experimentally tests whether violations of Savage's (1954) subjective expected utility theory decrease if the ambiguity of an uncertain decision situation is reduced through statistical learning. Because our data does not show such a decrease, existing models which formalize ambiguity within an Anscombe-Aumann (1963) framework--thereby reducing to expected utility theory in the absence of ambiguity--are violated. In contrast, axiomatic models of prospect theory can accommodate our experimental findings because they allow for violations of von Neumann and Morgenstern's (1947) independence axiom whenever uncertain decision situations transform into risky decision situations for which probabilities are known.

Suggested Citation

  • Nicky Nicholls & Aylit Romm & Alexander Zimper, 2013. "The impact of statistical learning on violations of the sure-thing principle," Working Papers 201364, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201364
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Alexander Zimper & Wei Ma, 2017. "Bayesian learning with multiple priors and nonvanishing ambiguity," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 64(3), pages 409-447, October.
    2. Groneck, Max & Ludwig, Alexander & Zimper, Alexander, 2016. "A life-cycle model with ambiguous survival beliefs," Journal of Economic Theory, Elsevier, vol. 162(C), pages 137-180.
    3. Michael H. Birnbaum & Ulrich Schmidt, 2015. "The Impact of Learning by Thought on Violations of Independence and Coalescing," Decision Analysis, INFORMS, vol. 12(3), pages 144-152.
    4. Daniel J. Benjamin & Mark Alan Fontana & Miles Kimball, 2020. "Reconsidering Risk Aversion," GRU Working Paper Series GRU_2020_026, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    5. Roxane Bricet, 2018. "Preferences for information precision under ambiguity," THEMA Working Papers 2018-09, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    6. Larry G. Epstein & Shaolin Ji, 2017. "Optimal Learning and Ellsberg’s Urns," Boston University - Department of Economics - Working Papers Series WP2017-010, Boston University - Department of Economics.
    7. Enrica Carbone & Konstantinos Georgalos & Gerardo Infante, 2019. "Individual vs. group decision-making: an experiment on dynamic choice under risk and ambiguity," Theory and Decision, Springer, vol. 87(1), pages 87-122, July.
    8. Larry G. Epstein & Shaolin Ji, 2022. "Optimal Learning Under Robustness and Time-Consistency," Operations Research, INFORMS, vol. 70(3), pages 1317-1329, May.
    9. Roxane Bricet, 2018. "The price for instrumentally valuable information," THEMA Working Papers 2018-10, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

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    More about this item

    Keywords

    Prospect Theory; Choquet Expected Utility Theory; Multiple Priors Expected Utility Theory; Sure Thing Principle; Independence Axiom;
    All these keywords.

    JEL classification:

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

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