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Random Expected Utility

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  • Faruk Gul
  • Wolfgang 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 UCLA Department of Economics in its series Levine's Bibliography with number 122247000000000834.

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Date of creation: 04 Jan 2005
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Handle: RePEc:cla:levrem:122247000000000834

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  1. Faruk Gul & Wolfgang Pesendorfer, 2006. "Random Expected Utility," Econometrica, Econometric Society, vol. 74(1), pages 121-146, 01.
  2. 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.
  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. Stephen A. Clark, 1995. "The random utility model with an infinite choice space," Economic Theory, Springer, vol. 7(1), pages 179-189.
  5. 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.
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Cited by:
  1. Glenn W Harrison, 2008. "Neuroeconomics: A Critical Reconsideration," Levine's Working Paper Archive 122247000000001915, David K. Levine.
  2. Blavatskyy, Pavlo R., 2008. "Stochastic utility theorem," Journal of Mathematical Economics, Elsevier, vol. 44(11), pages 1049-1056, December.
  3. Andrew Caplin & Daniel Martin, 2013. "A Testable Theory of Imperfect Perception," Levine's Working Paper Archive 786969000000000649, David K. Levine.
  4. Oyarzun, Carlos & Sarin, Rajiv, 2012. "Mean and variance responsive learning," Games and Economic Behavior, Elsevier, vol. 75(2), pages 855-866.
  5. John K. Dagsvik, 2006. "Axiomatization of Stochastic Models for Choice under Uncertainty," Discussion Papers 465, Research Department of Statistics Norway.
  6. Wilcox, Nathaniel, 2007. "Stochastically more risk averse: A contextual theory of stochastic discrete choice under risk," MPRA Paper 11851, University Library of Munich, Germany.
  7. F. Gul & W. Pesendorfer, 2002. "Random Expected Utility," Princeton Economic Theory Working Papers 497768e9b9fc18361ac0810b3, David K. Levine.
  8. Pavlo Blavatskyy, 2010. "Reverse common ratio effect," Journal of Risk and Uncertainty, Springer, vol. 40(3), pages 219-241, June.
  9. Stefania Minardi & Andrei Savochkin, 2013. "Preferences With Grades of Indecisiveness," Carlo Alberto Notebooks 309, Collegio Carlo Alberto.
  10. Blavatskyy, Pavlo, 2013. "Which decision theory?," Economics Letters, Elsevier, vol. 120(1), pages 40-44.
  11. Douglas Bernheim & Antonio Rangel, 2007. "Beyond Revealed Preference Choice Theoretic Foundations for Behavioral Welfare Economics," Discussion Papers 07-031, Stanford Institute for Economic Policy Research.
  12. Pavlo Blavatskyy, 2009. "Preference reversals and probabilistic decisions," Journal of Risk and Uncertainty, Springer, vol. 39(3), pages 237-250, December.
  13. 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.

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