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Random Expected Utility

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  • F. Gul
  • W. Pesendorfer

Abstract

We develop and analyze a model of random choice and random expected utility. A decision problem is a finite set of lotteries that describe the feasible choices. A random choice rule associates with each decision problem a probability measure over choices. A random utility function is a probability measure over von Neumann-Morgenstern utility functions. We show that a random choice rule maximizes some random utility function if and only if it is mixture continuous, monotone (the probability that a lottery is chosen does not increase when other lotteries are added to the decision problem), extreme (lotteries that are not extreme points of the decision problem are chosen with probability 0), and linear (satisfies the independence axiom). Copyright The Econometric Society 2006.

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Bibliographic Info

Paper provided by David K. Levine in its series Princeton Economic Theory Working Papers with number 497768e9b9fc18361ac0810b33ef8396.

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Date of creation: 02 May 2002
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Handle: RePEc:cla:princt:497768e9b9fc18361ac0810b33ef8396

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  1. Stephen A. Clark, 1995. "The random utility model with an infinite choice space," Economic Theory, Springer, vol. 7(1), pages 179-189.
  2. Faruk Gul & Wolfgang Pesendorfer, 2005. "Random Expected Utility," Levine's Bibliography 122247000000000834, UCLA Department of Economics.
  3. Gil Kalai & Ariel Rubinstein & Ran Spiegler, 2001. "Rationalizing Choice Functions by Multiple Rationales," Discussion Paper Series dp278, The Center for the Study of Rationality, Hebrew University, Jerusalem.
  4. Fishburn, Peter C., 1992. "Induced binary probabilities and the linear ordering polytope: a status report," Mathematical Social Sciences, Elsevier, vol. 23(1), pages 67-80, February.
  5. Barbera, Salvador & Pattanaik, Prasanta K, 1986. "Falmagne and the Rationalizability of Stochastic Choices in Terms of Random Orderings," Econometrica, Econometric Society, vol. 54(3), pages 707-15, May.
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Cited by:
  1. Andrew Caplin & Daniel Martin, 2011. "A Testable Theory of Imperfect Perception," NBER Working Papers 17163, National Bureau of Economic Research, Inc.
  2. Jack Vromen, 2011. "Neuroeconomics: two camps gradually converging: what can economics gain from it?," International Review of Economics, Springer, vol. 58(3), pages 267-285, September.
  3. Glenn W Harrison, 2008. "Neuroeconomics: A Critical Reconsideration," Levine's Working Paper Archive 122247000000001915, David K. Levine.
  4. Pavlo Blavatskyy, 2009. "Preference reversals and probabilistic decisions," Journal of Risk and Uncertainty, Springer, vol. 39(3), pages 237-250, December.
  5. Faruk Gul & Wolfgang Pesendorfer, 2005. "Random Expected Utility," Levine's Bibliography 122247000000000834, UCLA Department of Economics.
  6. B. Douglas Bernheim & Antonio Rangel, 2008. "Beyond Revealed Preference: Choice Theoretic Foundations for Behavioral Welfare Economics," NBER Working Papers 13737, National Bureau of Economic Research, Inc.
  7. Blavatskyy, Pavlo R., 2008. "Stochastic utility theorem," Journal of Mathematical Economics, Elsevier, vol. 44(11), pages 1049-1056, December.
  8. Yoram Halevy, 2007. "Ellsberg Revisited: An Experimental Study," Econometrica, Econometric Society, vol. 75(2), pages 503-536, 03.
  9. Wilcox, Nathaniel T., 2011. "'Stochastically more risk averse:' A contextual theory of stochastic discrete choice under risk," Journal of Econometrics, Elsevier, vol. 162(1), pages 89-104, May.
  10. John K. Dagsvik, 2006. "Axiomatization of Stochastic Models for Choice under Uncertainty," Discussion Papers 465, Research Department of Statistics Norway.
  11. Pavlo Blavatskyy, 2010. "Reverse common ratio effect," Journal of Risk and Uncertainty, Springer, vol. 40(3), pages 219-241, June.

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