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Unraveling Ambiguity Aversion

Author

Listed:
  • Ilke Aydogan

    (IÉSEG School Of Management [Puteaux])

  • Loïc Berger

    (CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux], EIEE - European Institute on Economics and the Environment, CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna])

  • Valentina Bosetti

    (Bocconi University [Milan, Italy], EIEE - European Institute on Economics and the Environment, CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna])

Abstract

We report the results of two experiments designed to better understand the mechanisms driving decision-making under ambiguity. We elicit individual preferences over different sources of uncertainty (risk, compound risk, model ambiguity, and Ellsberg ambiguity), which entail different degrees of complexity, from subjects with different sophistication levels. We show that (1) ambiguity aversion is robust to sophistication, but the strong relationship that has been previously reported between attitudes toward ambiguity and compound risk is not. (2) Ellsberg ambiguity attitude can be partly explained by attitudes toward complexity for less sophisticated subjects, but not for more sophisticated ones. Overall, and regardless of the subject's sophistication level, the main driver of Ellsberg ambiguity attitude is a specific treatment of unknown probabilities. These results leave room for using ambiguity models in applications with prescriptive purposes.

Suggested Citation

  • Ilke Aydogan & Loïc Berger & Valentina Bosetti, 2023. "Unraveling Ambiguity Aversion," Post-Print hal-04071242, HAL.
    • Handle: RePEc:hal:journl:hal-04071242
    DOI: 10.1162/rest_a_01358
    Note: View the original document on HAL open archive server: https://hal.science/hal-04071242
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    Cited by:

    1. Kerwin, Jason & Pandey, Divya, 2023. "Navigating Ambiguity: Imprecise Probabilities and the Updating of Disease Risk Beliefs," IZA Discussion Papers 16478, Institute of Labor Economics (IZA).

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    Keywords

    Ambiguity aversion; complexity; reduction of compound risk; model uncertainty;
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