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

Author

Listed:
  • Nicky Nicholls

  • Aylit Romm

  • Alexander Zimper

Abstract

This paper experimentally tests whether violations of Savage’s ( 1954 ) sure-thing principle (STP) decrease through statistical learning. Our subjects repeatedly had to bet on the drawings from an urn with an unknown proportion of differently colored balls. The control group was thereby subjected to learning through mere thought only. In addition, the test group received more and more statistical information over the course of the experiment by observing the color of the ball actually drawn after each bet. We expected that statistical learning would decrease the decision makers’ ambiguity, thereby implying a stronger decrease of STP violations in the test than in the control group. However, our data surprisingly shows that learning by mere thought rather than statistical learning leads to a decrease in STP violations. Copyright Springer Science+Business Media New York 2015
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Suggested Citation

  • Nicky Nicholls & Aylit Romm & Alexander Zimper, 2015. "Erratum to: The impact of statistical learning on violations of the sure-thing principle," Journal of Risk and Uncertainty, Springer, vol. 50(2), pages 117-117, April.
    • Handle: RePEc:kap:jrisku:v:50:y:2015:i:2:p:117-117
    DOI: 10.1007/s11166-015-9214-7
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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. Walter, Johannes & Biermann, Jan & Horton, John, 2024. "Advised by an Algorithm: Learning with Different Informational Resources," VfS Annual Conference 2024 (Berlin): Upcoming Labor Market Challenges 302407, Verein für Socialpolitik / German Economic Association.
    3. Phoebe Koundouri & Nikitas Pittis & Panagiotis Samartzis & Konstantinos Georgalos, 2025. "Comparative Ignorance as an Explanation of Ambiguity Aversion and Ellsberg Choices: A Survey with a New Proposal for Bayesian Training," DEOS Working Papers 2572, Athens University of Economics and Business.
    4. 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.
    5. 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.
    6. Roxane Bricet, 2018. "Preferences for information precision under ambiguity," Thema Working Papers 2018-09, THEMA (Théorie Economique, Modélisation et Applications), CY Cergy-Paris University, ESSEC and CNRS.
    7. 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.
    8. 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.
    9. Roxane Bricet, 2018. "The price for instrumentally valuable information," Thema Working Papers 2018-10, THEMA (Théorie Economique, Modélisation et Applications), CY Cergy-Paris University, ESSEC and CNRS.
    10. Larry G. Epstein & Shaolin Ji, 2022. "Optimal Learning Under Robustness and Time-Consistency," Operations Research, INFORMS, vol. 70(3), pages 1317-1329, May.
    11. Fabian Herweg & Svenja Hippel & Daniel Müller & Fabio Römeis, 2024. "Axiom Preferences and Choice Mistakes under Risk," CESifo Working Paper Series 11166, CESifo.

    More about this item

    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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