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Generating Ambiguity in the Laboratory

Author

Listed:
  • Jack Stecher

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Timothy Shields

    (George L. Argyros School of Business and Economics, Chapman University, Orange, California 92866)

  • John Dickhaut (deceased)

    (Formerly at George L. Argyros School of Business and Economics, Chapman University, Orange, California 92866)

Abstract

This article develops a method for drawing samples from a distribution with no finite quantiles or moments. The method provides researchers with a way to give subjects the experience of ambiguity. In any experiment, learning the distribution from experience is impossible for the subjects, essentially because it is impossible for the experimenter. We characterize our method, illustrate it in simulations, and then test it in a laboratory experiment. Our method does not withhold sampling information, does not assume that the subject is incapable of making statistical inferences, is replicable across experiments, and requires no special apparatus. We compare our method to the techniques used in related experiments that attempt to produce an ambiguous experience for the subjects. This paper was accepted by Peter Wakker, decision analysis.

Suggested Citation

  • Jack Stecher & Timothy Shields & John Dickhaut (deceased), 2011. "Generating Ambiguity in the Laboratory," Management Science, INFORMS, vol. 57(4), pages 705-712, April.
    • Handle: RePEc:inm:ormnsc:v:57:y:2011:i:4:p:705-712
    DOI: 10.1287/mnsc.1100.1307
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    References listed on IDEAS

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    3. Craig R. Fox & Amos Tversky, 1995. "Ambiguity Aversion and Comparative Ignorance," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(3), pages 585-603.
    4. Takashi Hayashi & Ryoko Wada, 2010. "Choice with imprecise information: an experimental approach," Theory and Decision, Springer, vol. 69(3), pages 355-373, September.
    5. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    6. Yoram Halevy, 2007. "Ellsberg Revisited: An Experimental Study," Econometrica, Econometric Society, vol. 75(2), pages 503-536, March.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Borgonovo, Emanuele & Marinacci, Massimo, 2015. "Decision analysis under ambiguity," European Journal of Operational Research, Elsevier, vol. 244(3), pages 823-836.
    2. Stefan T. Trautmann & Ferdinand M. Vieider & Peter P. Wakker, 2011. "Preference Reversals for Ambiguity Aversion," Management Science, INFORMS, vol. 57(7), pages 1320-1333, July.
    3. Qi Liu & Lei Lu & Bo Sun, 2017. "Incentive Contracting Under Ambiguity Aversion," International Finance Discussion Papers 1195, Board of Governors of the Federal Reserve System (U.S.).
    4. Yudistira Permana, 2020. "Explaining satisficing through risk aversion," Theory and Decision, Springer, vol. 89(4), pages 503-525, November.
    5. O'Callaghan, Patrick, 2016. "Measuring utility without mixing apples and oranges and eliciting beliefs about stock prices," MPRA Paper 69363, University Library of Munich, Germany.
    6. Surajeet Chakravarty & Todd R. Kaplan & Navonil Mustafee, 2020. "Altering Wait Time Information to Reduce A&E Overcrowding," Discussion Papers 2003, University of Exeter, Department of Economics.
    7. Pintér, Miklós, 2022. "How to make ambiguous strategies," Journal of Economic Theory, Elsevier, vol. 202(C).
    8. 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..
    9. Garcia, Thomas & Massoni, Sébastien & Villeval, Marie Claire, 2020. "Ambiguity and excuse-driven behavior in charitable giving," European Economic Review, Elsevier, vol. 124(C).
    10. Muraviev, Igor & Riedel, Frank & Sass, Linda, 2017. "Kuhn’s Theorem for extensive form Ellsberg games," Journal of Mathematical Economics, Elsevier, vol. 68(C), pages 26-41.
    11. Yudistira Permana & Saiqa Akbar & Anisa Nurpita, 2022. "Systemic risk and the financial network system: an experimental investigation," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(4), pages 631-651, December.
    12. John Dickhaut & Radhika Lunawat & Kira Pronin & Jack Stecher, 2011. "Decision making and trade without probabilities," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 48(2), pages 275-288, October.
    13. Takashi Hayashi & Ryoko Wada, 2010. "Choice with imprecise information: an experimental approach," Theory and Decision, Springer, vol. 69(3), pages 355-373, September.
    14. Yang, Chun-Lei & Yao, Lan, 2011. "Ellsberg Paradox and Second-order Preference Theories on Ambiguity: Some New Experimental Evidence," MPRA Paper 28531, University Library of Munich, Germany.
    15. Qi Liu & Lei Lu & Bo Sun, 2018. "Incentive contracting under ambiguity aversion," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 66(4), pages 929-950, December.

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    More about this item

    Keywords

    ambiguity; Ellsberg; Knightian uncertainty; laboratory experiments; decision analysis; theory;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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