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Conditional expected utility criteria for decision making under ignorance or objective ambiguity

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  • Nicolas Gravel

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Thierry Marchant

    (UGENT - Universiteit Gent = Ghent University = Université de Gand)

  • Arunava Sen

    (Indian Statistical Institute [New Delhi])

Abstract

We provide an axiomatic characterization of a family of criteria for ranking completely uncertain and/or ambiguous decisions. A completely uncertain decision is described by the set of all its consequences (assumed to be finite). An ambiguous decision is described as a set of possible probability distributions over a set of prizes. Every criterion in the family compares sets on the basis of their conditional expected utility , for some "likelihood" function taking strictly positive values and some utility function both having the universe of alternatives as their domain.

Suggested Citation

  • Nicolas Gravel & Thierry Marchant & Arunava Sen, 2018. "Conditional expected utility criteria for decision making under ignorance or objective ambiguity," Post-Print hal-01988972, HAL.
    • Handle: RePEc:hal:journl:hal-01988972
    DOI: 10.1016/j.jmateco.2018.08.001
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-01988972v1
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    Cited by:

    1. Anwesha Banerjee & Nicolas Gravel, 2020. "Contribution to a public good under subjective uncertainty," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 22(3), pages 473-500, June.
    2. Li, Chen & Wakker, Peter P., 2024. "A simple and general axiomatization of average utility maximization for infinite streams," Journal of Economic Theory, Elsevier, vol. 216(C).
    3. Sharma, Tridib & Vadovic, Radovan, 2010. "Axiom of Monotonicity: An Experimental Test," MPRA Paper 22863, University Library of Munich, Germany.
    4. Alekseev, Aleksandr & Sokolov, Mikhail V., 2021. "How to measure the average rate of change?," Mathematical Social Sciences, Elsevier, vol. 113(C), pages 43-59.
    5. Panagiotis Christias & Ioannis N. Daliakopoulos & Thrassyvoulos Manios & Mariana Mocanu, 2020. "Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support," Mathematics, MDPI, vol. 8(5), pages 1-26, May.
    6. Szwagrzak, Karol, 2021. "Weighing Sample Evidence," Working Papers 3-2021, Copenhagen Business School, Department of Economics.
    7. Paolo Brunori & Alain Trannoy & Caterina Francesca Guidi, 2021. "Ranking populations in terms of inequality of health opportunity: A flexible latent type approach," Health Economics, John Wiley & Sons, Ltd., vol. 30(2), pages 358-383, February.
    8. Flores-Szwagrzak, Karol, 2022. "Learning by Convex Combination," Working Papers 16-2022, Copenhagen Business School, Department of Economics.
    9. Amélie Vrijdags, 2013. "Min- and Max-induced rankings: an experimental study," Theory and Decision, Springer, vol. 75(2), pages 233-266, August.

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    More about this item

    Keywords

    Ignorance; Ambiguity; Conditional Probabilities; Expected Utility; Ranking Sets; axioms JEL classification numbers: D80; D81;
    All these keywords.

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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