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Understanding the nonadditive probability decision model

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  • Mukerji, S.

Abstract

The pioneering research of Schmeidler [19] [20] and others identified behavioral axioms that underlie preferences consistent with the maximization of Choquet expected utility. However, these theories do not clarify the link between the epistemics of the decision maker's (DM) problem and his choice. This paper shows that if the DM is aware that his anticipation and perception of future contingencies is incomplete, then his subjective beliefs will be described by a nonadditive probability specification. Further, if the DM acts with a certain notion of caution given the incompleteness in his understanding of the environment, his preferences over acts may have a Choquet expected utility representation. The model developed here thus provides a justification of such beliefs and preferences based on "procedural rationality". The formalism also allows a simple characterization of how belief representation may change as the DM acquires a clearer picture of the contingency space underlying the uncertain environment.
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Suggested Citation

  • Mukerji, S., 1995. "Understanding the nonadditive probability decision model," Discussion Paper Series In Economics And Econometrics 9517, Economics Division, School of Social Sciences, University of Southampton.
  • Handle: RePEc:stn:sotoec:9517
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    Cited by:

    1. Spyros Galanis, 2013. "Unawareness of theorems," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 52(1), pages 41-73, January.
    2. Marie-Louise Vierø, 2009. "Exactly what happens after the Anscombe–Aumann race?," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 41(2), pages 175-212, November.
    3. Larry Epstein & Massimo Marinacci, 2005. "Coarse Contingencies," RCER Working Papers 515, University of Rochester - Center for Economic Research (RCER).
    4. Ayala Arad & Gabrielle Gayer, 2012. "Imprecise Data Sets as a Source of Ambiguity: A Model and Experimental Evidence," Management Science, INFORMS, vol. 58(1), pages 188-202, January.
    5. Mukerji, Sujoy & Tallon, Jean-Marc, 2004. "Ambiguity aversion and the absence of wage indexation," Journal of Monetary Economics, Elsevier, vol. 51(3), pages 653-670, April.
    6. Roman Kozhan, 2011. "Non-additive anonymous games," International Journal of Game Theory, Springer;Game Theory Society, vol. 40(2), pages 215-230, May.
    7. 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.
    8. MacLaren, D., 1995. "Uncertainty AVersion and Technical Barriers to Trade: An Australian Example," Department of Economics - Working Papers Series 498, The University of Melbourne.
    9. Chateauneuf, Alain & Dana, Rose-Anne & Tallon, Jean-Marc, 2000. "Optimal risk-sharing rules and equilibria with Choquet-expected-utility," Journal of Mathematical Economics, Elsevier, vol. 34(2), pages 191-214, October.
    10. 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.
    11. Jürgen Eichberger & David Kelsey, 1999. "E-Capacities and the Ellsberg Paradox," Theory and Decision, Springer, vol. 46(2), pages 107-138, April.
    12. Adriana Castaldo & Massimo Marinacci, 2001. "Random correspndences as bundles of random variables," ICER Working Papers - Applied Mathematics Series 12-2001, ICER - International Centre for Economic Research.
    13. 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.
    14. Marcello Basili & Carlo Zappia, 2010. "Ambiguity and uncertainty in Ellsberg and Shackle," Cambridge Journal of Economics, Oxford University Press, vol. 34(3), pages 449-474.

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