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The Explanatory and Predictive Power of Non Two-Stage-Probability Theories of Decision Making Under Ambiguity

Author

Listed:
  • Noemi Pace

    () (Department of Economics, University Of Venice C� Foscari)

  • John D Hey

    (Department of Economics, University of York)

Abstract

Representing ambiguity in the laboratory using a Bingo Blower (which is transparent and not manipulable) and asking the subjects a series of allocation questions (which are more efficient than pairwise choice questions), we obtain data from which we can estimate by maximum likelihood methods (with explicit assumptions about the errors made by the subjects) a significant subset of the empirically relevant models of behaviour under ambiguity, and compare their relative explanatory and predictive abilities. Our results suggest that not all recent models of behaviour represent a major improvement in explanatory and predictive power, particularly the more theoretically sophisticated ones.

Suggested Citation

  • Noemi Pace & John D Hey, 2011. "The Explanatory and Predictive Power of Non Two-Stage-Probability Theories of Decision Making Under Ambiguity," Working Papers 2011_12, Department of Economics, University of Venice "Ca' Foscari".
  • Handle: RePEc:ven:wpaper:2011_12
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    Citations

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    Cited by:

    1. Konstantinos Georgalos, 2019. "An experimental test of the predictive power of dynamic ambiguity models," Journal of Risk and Uncertainty, Springer, vol. 59(1), pages 51-83, August.
    2. Anna Conte & John D. Hey, 2018. "Assessing multiple prior models of behaviour under ambiguity," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 7, pages 169-188, World Scientific Publishing Co. Pte. Ltd..
    3. Konstantinos Georgalos, 2016. "Dynamic decision making under ambiguity," Working Papers 112111041, Lancaster University Management School, Economics Department.
    4. Robin Cubitt & Gijs Kuilen & Sujoy Mukerji, 2018. "The strength of sensitivity to ambiguity," Theory and Decision, Springer, vol. 85(3), pages 275-302, October.
    5. Noemi Pace & Giuseppe Attanasi & Christian Gollier & Aldo Montesano, 2012. "Eliciting ambiguity aversion in unknown and in compound lotteries: A KMM experimental approach," Working Papers 2012_23, Department of Economics, University of Venice "Ca' Foscari".
    6. John D. Hey & Yudistira Permana & Nuttaporn Rochanahastin, 2018. "When and how to satisfice: an experimental investigation," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 5, pages 121-137, World Scientific Publishing Co. Pte. Ltd..
    7. Daniel R. Burghart & Thomas Epper & Ernst Fehr, 2015. "The ambiguity triangle: uncovering fundamental patterns of behavior under uncertainty," ECON - Working Papers 196, Department of Economics - University of Zurich.
    8. Tsang, Ming, 2020. "Estimating uncertainty aversion using the source method in stylized tasks with varying degrees of uncertainty," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 84(C).
    9. Christian A. Vossler & Dong Yan, 2019. "An Experimental Investigation of Updating under Ambiguity," Working Papers 2019-02, University of Tennessee, Department of Economics.
    10. 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.
    11. Crosetto, P. & Filippin, A., 2017. "Safe options induce gender differences in risk attitudes," Working Papers 2017-05, Grenoble Applied Economics Laboratory (GAEL).
    12. Giuseppe Attanasi & Christian Gollier & Aldo Montesano & Noemi Pace, 2014. "Eliciting ambiguity aversion in unknown and in compound lotteries: a smooth ambiguity model experimental study," Theory and Decision, Springer, vol. 77(4), pages 485-530, December.
    13. Daniel R. Burghart & Thomas Epper & Ernst Fehr, 2020. "The uncertainty triangle – Uncovering heterogeneity in attitudes towards uncertainty," Journal of Risk and Uncertainty, Springer, vol. 60(2), pages 125-156, April.
    14. Federico Echenique, 2019. "New developments in revealed preference theory: decisions under risk, uncertainty, and intertemporal choice," Papers 1908.07561, arXiv.org, revised Dec 2019.
    15. Daniela Di Cagno & Daniela Grieco, 2019. "Measuring and Disentangling Ambiguity and Confidence in the Lab," Games, MDPI, Open Access Journal, vol. 10(1), pages 1-22, February.
    16. Thomas Demuynck & Clément Staner, 2020. "An Efficient Revealed Preference Test for the Maxmin Expected Utility Model," Working Papers ECARES 2020-31, ULB -- Universite Libre de Bruxelles.

    More about this item

    Keywords

    Alpha Model; Ambiguity; Bingo Blower; Choquet Expected Utility; Contraction Model; Rank Dependent Expected Utility; Subjective Expected Utility; Vector Expected Utility.;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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