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Coping with ignorance: unforeseen contingencies and non-additive uncertainty

Author

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  • Paolo Ghirardato

    (Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA)

Abstract

In real-life decision problems, decision makers are never provided with the necessary background structure: the set of states of the world, the outcome space, the set of actions. They have to devise all these by themselves. I model the (static) choice problem of a decision maker (DM) who is aware that her perception of the decision problem is too coarse, as for instance when there might be unforeseen contingencies. I make a "bounded rationality'' assumption on the way the DM deals with this difficulty, and then I show that imposing standard subjective expected utility axioms on her preferences only implies that they can be represented by a (generalized) expectation with respect to a non-additive measure, called a belief function. However, the axioms do have strong implications for how the DM copes with the type of ignorance described above. Finally, I show that some decision rules that have been studied in the literature can be obtained as a special case of the model presented here (though they have to be interpreted differently).

Suggested Citation

  • Paolo Ghirardato, 2001. "Coping with ignorance: unforeseen contingencies and non-additive uncertainty," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 17(2), pages 247-276.
    • Handle: RePEc:spr:joecth:v:17:y:2001:i:2:p:247-276
    Note: Received: December 16, 1999; revised version: March 22, 2000
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    Cited by:

    1. Youichiro Higashi & Kazuya Hyogo, 2012. "Lexicographic expected utility with a subjective state space," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 49(1), pages 175-192, January.
    2. Galanis, Spyros, 2007. "Unawareness of theorems," Discussion Paper Series In Economics And Econometrics 51816, Economics Division, School of Social Sciences, University of Southampton.
    3. Luca Rigotti & Matthew Ryan & Rhema Vaithianathan, 2011. "Optimism and firm formation," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 46(1), pages 1-38, January.
    4. Vassili Vergopoulos, 2011. "Dynamic consistency for non-expected utility preferences," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 48(2), pages 493-518, October.
    5. Jean-Yves Jaffray & Meglena Jeleva, 2011. "How to deal with partially analyzable acts?," Theory and Decision, Springer, vol. 71(1), pages 129-149, July.
    6. Nabil I. Al-Najjar & Luciano De Castro, 2010. "Uncertainty, Efficiency and Incentive Compatibility," Discussion Papers 1532, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    7. Jürgen Eichberger & David Kelsey, 1999. "E-Capacities and the Ellsberg Paradox," Theory and Decision, Springer, vol. 46(2), pages 107-138, April.
    8. Peter Wakker, 2011. "Jaffray’s ideas on ambiguity," Theory and Decision, Springer, vol. 71(1), pages 11-22, July.
    9. Hiroyuki Nakata, 2011. "Equivalent comparisons of information channels," Theory and Decision, Springer, vol. 71(4), pages 559-574, October.

    More about this item

    Keywords

    Unforeseen contingencies; Underspecified decision problem; Belief functions; Choquet integrals; Pessimism index.;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • L22 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Organization and Market Structure

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