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Experimental Elicitation of Ambiguity Attitude using the Random Incentive System

Author

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  • Baillon, Aurélien
  • Halevy, Yoram
  • Li, Chen

Abstract

We demonstrate how the standard usage of the random incentive system in ambiguity experiments is not incentive compatible if the decision maker is ambiguity averse. We propose a slight modification of the procedure in which the randomization takes place before decisions are made and the state is realized and prove that if subjects evaluate the experimental environment in that way (first - risk, second - uncertainty), incentive compatibility may be restored.

Suggested Citation

  • Baillon, Aurélien & Halevy, Yoram & Li, Chen, 2014. "Experimental Elicitation of Ambiguity Attitude using the Random Incentive System," Microeconomics.ca working papers yoram_halevy-2014-26, Vancouver School of Economics, revised 21 Jul 2015.
  • Handle: RePEc:ubc:pmicro:yoram_halevy-2014-26
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    File URL: http://faculty.arts.ubc.ca/yhalevy/RIS_Ambiguity.pdf
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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. Robin Cubitt & Gijs Kuilen & Sujoy Mukerji, 2018. "The strength of sensitivity to ambiguity," Theory and Decision, Springer, vol. 85(3), pages 275-302, October.
    3. Calford, Evan, 2016. "Mixed Strategies in Games with Ambiguity Averse Agents," MPRA Paper 74909, University Library of Munich, Germany.
    4. Yaron Azrieli & Christopher P. Chambers & Paul J. Healy, 2018. "Incentives in Experiments: A Theoretical Analysis," Journal of Political Economy, University of Chicago Press, vol. 126(4), pages 1472-1503.
    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. Oechssler, Jörg & Rau, Hannes & Roomets, Alex, 2016. "Hedging and Ambiguity," Working Papers 0621, University of Heidelberg, Department of Economics.
    7. Calford, Evan M., 2020. "Uncertainty aversion in game theory: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 720-734.
    8. Charness, Gary & Gneezy, Uri & Halladay, Brianna, 2016. "Experimental methods: Pay one or pay all," Journal of Economic Behavior & Organization, Elsevier, vol. 131(PA), pages 141-150.
    9. Ilke Aydogan & Loic Berger & Valentina Bosetti & Ning Liu, 2018. "Three layers of uncertainty: an experiment," Working Papers 623, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    10. Brown, Alexander L. & Healy, Paul J., 2018. "Separated decisions," European Economic Review, Elsevier, vol. 101(C), pages 20-34.
    11. Othon M. Moreno & Yaroslav Rosokha, 2016. "Learning under compound risk vs. learning under ambiguity – an experiment," Journal of Risk and Uncertainty, Springer, vol. 53(2), pages 137-162, December.
    12. Kfir Eliaz & Pietro Ortoleva, 2016. "Multidimensional Ellsberg," Management Science, INFORMS, vol. 62(8), pages 2179-2197, August.
    13. Oechssler, Jörg & Roomets, Alex, 2014. "Unintended hedging in ambiguity experiments," Economics Letters, Elsevier, vol. 122(2), pages 243-246.
    14. Cettolin, Elena & Riedl, Arno, 2019. "Revealed preferences under uncertainty: Incomplete preferences and preferences for randomization," Journal of Economic Theory, Elsevier, vol. 181(C), pages 547-585.
    15. Roxane Bricet, 2018. "The price for instrumentally valuable information," THEMA Working Papers 2018-10, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    16. Ghirardato, Paolo & Pennesi, Daniele, 2020. "A general theory of subjective mixtures," Journal of Economic Theory, Elsevier, vol. 188(C).

    More about this item

    Keywords

    Uncertianty; Design of Experiments;

    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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