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Assessing Multiple Prior Models of Behaviour under Ambiguity

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  • Ana Conte

    (University of Westminster, London, UK, and Max-Planck-Institute of Economics, Jena)

  • John D. Hey

    (University of York, UK)

Abstract

The recent spate of theoretical models of behaviour under ambiguity can be partitioned into two sets: those involving multiple priors (in which the probabilities of the various events are not known but probabilities can be attached to the various possible values for the probabilities) and those not involving multiple priors. This paper concentrates on the first set and provides an experimental investigation into recently proposed theories. Using an appropriate experimental interface, in which the probabilities on the various possibilities are explicitly stated, we examine the fitted and predictive power of the various theories. We first estimate subject-by-subject, and then we estimate and predict using a mixture model over the contending theories. The individual estimates suggest that 24% of our 149 subjects have behaviour consistent with Expected Utility, 56% with the Smooth Model (of Klibanoff et al, 2005), 11% with Rank Dependent Expected Utility and 9% with the Alpha Model (of Ghirardato et al 2004); these figures are close to the mixing proportions obtained from the mixture estimates. If we classify our subjects through the posterior probabilities (given all the evidence) of each of them being of the various types: using the estimates we get 25%, 50%, 20% and 5% (for EU, Smooth, Rank Dependent and Alpha); while using the predictions 22%, 53%, 22% and 3%. Interestingly the Smooth model seems to fare the best.

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Bibliographic Info

Paper provided by Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics in its series Jena Economic Research Papers with number 2011-068.

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Date of creation: 2011
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Handle: RePEc:jrp:jrpwrp:2011-068

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Keywords: Alpha Model; Ambiguity; Expected Utility; Mixture Models; Rank Dependent Expected Utility; Smooth Model;

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References

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  1. repec:hal:journl:halshs-00451982 is not listed on IDEAS
  2. Peter Klibanoff & Massimo Marinacci & Sujoy Mukerji, 2002. "A smooth model of decision making under ambiguity," ICER Working Papers - Applied Mathematics Series 11-2003, ICER - International Centre for Economic Research, revised Apr 2003.
  3. Ben Greiner, 2004. "The Online Recruitment System ORSEE 2.0 - A Guide for the Organization of Experiments in Economics," Working Paper Series in Economics 10, University of Cologne, Department of Economics.
  4. Andersen, Steffen & Fountain, John & Harrison, Glenn W. & Rutström, Elisabet E., 2009. "Estmating Aversion to Uncertainty," Working Papers 07-2009, Copenhagen Business School, Department of Economics.
  5. Uzi Segal, 1985. "The Ellsberg Paradox and Risk Aversion: An Anticipated Utility Approach," UCLA Economics Working Papers 362, UCLA Department of Economics.
  6. Quiggin, John, 1982. "A theory of anticipated utility," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 323-343, December.
  7. Thibault Gajdos & Takashi Hayashi & Jean-Marc Tallon & Jean-Christophe Vergnaud, 2006. "Attitude toward imprecise information," Cahiers de la Maison des Sciences Economiques v06081, Université Panthéon-Sorbonne (Paris 1).
  8. Ghirardato, Paolo & Maccheroni, Fabio & Marinacci, Massimo, 2004. "Differentiating ambiguity and ambiguity attitude," Journal of Economic Theory, Elsevier, vol. 118(2), pages 133-173, October.
  9. Tversky, Amos & Kahneman, Daniel, 1992. " Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
  10. Peter Moffatt & Simon Peters, 2001. "Testing for the Presence of a Tremble in Economic Experiments," Experimental Economics, Springer, vol. 4(3), pages 221-228, December.
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  12. Arie Preminger & David Wettstein, 2005. "Using the Penalized Likelihood Method for Model Selection with Nuisance Parameters Present only under the Alternative: An Application to Switching Regression Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(5), pages 715-741, 09.
  13. John Hey & Gianna Lotito & Anna Maffioletti, 2010. "The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity," Journal of Risk and Uncertainty, Springer, vol. 41(2), pages 81-111, October.
  14. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
  15. David Ahn & Syngjoo Choi & Douglas Gale & Shachar Kariv, 2008. "Estimating Ambiguity Aversion in a Portfolio Choice Experiment," Levine's Working Paper Archive 122247000000001989, David K. Levine.
  16. Anna Conte & John D Hey & Peter G Moffatt, 2007. "Mixture Models of Choice Under Risk," Discussion Papers 07/06, Department of Economics, University of York.
  17. Halevy, Yoram, 2005. "Ellsberg Revisited: an Experimental Study," Microeconomics.ca working papers halevy-05-07-26-11-51-13, Vancouver School of Economics, revised 25 Feb 2014.
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  19. John D Hey & Noemi Pace, . "The Explanatory and Predictive Power of Non Two-Stage-Probability Theories of Decision Making Under Ambiguity," Discussion Papers 11/22, Department of Economics, University of York.
  20. David Schmeidler, 1989. "Subjective Probability and Expected Utility without Additivity," Levine's Working Paper Archive 7662, David K. Levine.
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Citations

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Cited by:
  1. Attanasi, Giuseppe & Gollier, Christian & Montesano, Aldo & Pace, Noémie, 2012. "Eliciting ambiguity aversion in unknown and in compound lotteries: A KMM experimental approach," TSE Working Papers 12-338, Toulouse School of Economics (TSE).
  2. Anna Conte & M. Levati, 2014. "Use of data on planned contributions and stated beliefs in the measurement of social preferences," Theory and Decision, Springer, vol. 76(2), pages 201-223, February.
  3. Sujoy Mukerji & Robin Cubitt & Gijs van de Kuilen, 2014. "Discriminating between Models of Ambiguity Attitude: A Qualitative Test," Economics Series Working Papers 692, University of Oxford, Department of Economics.
  4. Anna Conte & John D. Hey & Ivan Soraperra, 2014. "The Determinants of Decision Time," Jena Economic Research Papers 2014-004, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.

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