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

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  • Gravel, Nicolas
  • Marchant, Thierry
  • Sen, Arunava

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

  • Gravel, Nicolas & Marchant, Thierry & Sen, Arunava, 2018. "Conditional expected utility criteria for decision making under ignorance or objective ambiguity," Journal of Mathematical Economics, Elsevier, vol. 78(C), pages 79-95.
    • Handle: RePEc:eee:mateco:v:78:y:2018:i:c:p:79-95
    DOI: 10.1016/j.jmateco.2018.08.001
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    Cited by:

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    2. Sharma, Tridib & Vadovic, Radovan, 2010. "Axiom of Monotonicity: An Experimental Test," MPRA Paper 22863, University Library of Munich, Germany.
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    5. Szwagrzak, Karol, 2021. "Weighing Sample Evidence," Working Papers 3-2021, Copenhagen Business School, Department of Economics.
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    7. Flores-Szwagrzak, Karol, 2022. "Learning by Convex Combination," Working Papers 16-2022, Copenhagen Business School, Department of Economics.
    8. 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;
    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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