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The ambiguity triangle: uncovering fundamental patterns of behavior under uncertainty

Author

Listed:
  • Daniel R. Burghart
  • Thomas Epper
  • Ernst Fehr
Abstract
The probability triangle (also called the Marschak-Machina triangle) allows for compact and intuitive depictions of risk preferences. Here, we develop an analogous tool for choice under uncertainty - the ambiguity triangle - and show that indifference curves in this triangle capture preferences for unknown probabilities. In particular, the ambiguity triangle allows us to examine whether subjects adhere to the generalized axiom of revealed preference (GARP) and satisfy a non-parametric test for constant ambiguity attitudes. We find that more than 95% of subjects adhere to GARP and that about 60% satisfy our test for a constant ambiguity attitude. Yet, among these 60% of subjects there is substantial preference heterogeneity. We characterize this heterogeneity with finite-mixture estimates of a one-parameter extension of Expected Utility Theory wherein 48% of subjects are ambiguity averse, 22% are ambiguity seeking, and 30% are close to ambiguity neutral. The ambiguity triangle also highlights how variable ambiguity attitudes arise mainly because indifference curves are ’fanning-in’ across the triangle. This fanning-in property implies that aversion to ambiguity increases as the likelihood of receiving a good outcome increases. We capture this behavior with a simple parametric model that also allows for finite mixture characterizations of preference heterogeneity for these subjects. We show that for a substantial share of these subjects (43%) their fanning-in is so strong that, although they are initially ambiguity seeking, they become strongly ambiguity averse as the likelihood of receiving a good outcome increases.

Suggested Citation

  • 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.
    • Handle: RePEc:zur:econwp:196
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    File URL: https://www.zora.uzh.ch/id/eprint/111415/1/econwp196.pdf
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    References listed on IDEAS

    as
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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. Konstantinos Georgalos, 2016. "Dynamic decision making under ambiguity," Working Papers 112111041, Lancaster University Management School, Economics Department.
    3. Burghart, Daniel R., 2018. "Maximum probabilities, information, and choice under uncertainty," Economics Letters, Elsevier, vol. 167(C), pages 43-47.

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    More about this item

    Keywords

    Behavior under uncertainty; ambiguity preferences; heterogeneity; probability triangle; ambiguity triangle;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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