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Estimating ambiguity preferences and perceptions in multiple prior models: Evidence from the field

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  • Stephen Dimmock
  • Roy Kouwenberg

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  • Olivia Mitchell
  • Kim Peijnenburg

Abstract

We develop a tractable method to estimate multiple prior models of decision-making under ambiguity. In a representative sample of the U.S. population, we measure ambiguity attitudes in the gain and loss domains. We find that ambiguity aversion is common for uncertain events of moderate to high likelihood involving gains, but ambiguity seeking prevails for low likelihoods and for losses. We show that choices made under ambiguity in the gain domain are best explained by the α-MaxMin model, with one parameter measuring ambiguity aversion (ambiguity preferences) and a second parameter quantifying the perceived degree of ambiguity (perceptions about ambiguity). The ambiguity aversion parameter α is constant and prior probability sets are asymmetric for low and high likelihood events. The data reject several other models, such as MaxMin and MaxMax, as well as symmetric probability intervals. Ambiguity aversion and the perceived degree of ambiguity are both higher for men and for the college-educated. Ambiguity aversion (but not perceived ambiguity) is also positively related to risk aversion. In the loss domain, we find evidence of reflection, implying that ambiguity aversion for gains tends to reverse into ambiguity seeking for losses. Our model’s estimates for preferences and perceptions about ambiguity can be used to analyze the economic and financial implications of such preferences. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Stephen Dimmock & Roy Kouwenberg & Olivia Mitchell & Kim Peijnenburg, 2015. "Estimating ambiguity preferences and perceptions in multiple prior models: Evidence from the field," Journal of Risk and Uncertainty, Springer, vol. 51(3), pages 219-244, December.
  • Handle: RePEc:kap:jrisku:v:51:y:2015:i:3:p:219-244
    DOI: 10.1007/s11166-015-9227-2
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    References listed on IDEAS

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    1. repec:eee:ecolet:v:174:y:2019:i:c:p:123-127 is not listed on IDEAS
    2. Anantanasuwong, Kanin & Kouwenberg, Roy & Mitchell, Olivia S & Peijnenburg, Kim, 2019. "Ambiguity Attitudes about Investments: Evidence from the Field," CEPR Discussion Papers 13518, C.E.P.R. Discussion Papers.
    3. repec:kap:jrisku:v:58:y:2019:i:1:d:10.1007_s11166-019-09299-0 is not listed on IDEAS
    4. repec:kap:theord:v:85:y:2018:i:3:d:10.1007_s11238-018-9657-9 is not listed on IDEAS
    5. Balafoutas, Loukas & Sutter, Matthias, 2019. "How Uncertainty and Ambiguity in Tournaments Affect Gender Differences in Competitive Behavior," IZA Discussion Papers 12348, Institute of Labor Economics (IZA).
    6. Loukas Balafoutas & Brent J. Davis & Matthias Sutter, 2017. "How uncertainty and ambiguity in tournaments affect gender differences in competitive behavior," Working Papers 2017-20, Faculty of Economics and Statistics, University of Innsbruck.
    7. repec:kap:jrisku:v:54:y:2017:i:1:d:10.1007_s11166-017-9253-3 is not listed on IDEAS
    8. Müller, Julia & Li, Zhihua & Wakker, Peter P. & Wang, Tong V., 2016. "The Rich Domain of Ambiguity Explored," Annual Conference 2016 (Augsburg): Demographic Change 145734, Verein für Socialpolitik / German Economic Association.
    9. repec:kap:expeco:v:22:y:2019:i:1:d:10.1007_s10683-018-9582-3 is not listed on IDEAS
    10. Robin Cubitt & Gijs Kuilen & Sujoy Mukerji, 2018. "The strength of sensitivity to ambiguity," Theory and Decision, Springer, vol. 85(3), pages 275-302, October.
    11. repec:kap:jrisku:v:54:y:2017:i:3:d:10.1007_s11166-017-9260-4 is not listed on IDEAS
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    13. repec:kap:jrisku:v:56:y:2018:i:1:d:10.1007_s11166-018-9273-7 is not listed on IDEAS
    14. Stephen G. Dimmock & Roy Kouwenberg & Peter P. Wakker, 2016. "Ambiguity Attitudes in a Large Representative Sample," Management Science, INFORMS, vol. 62(5), pages 1363-1380, May.

    More about this item

    Keywords

    Ambiguity; Decision-making under uncertainty; Multiple prior models; Alpha-MaxMin model; D81; C93; C23;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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