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Assessing multiple prior models of behaviour under ambiguity

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  • Anna Conte
  • John Hey

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Abstract

The recent spate of theoretical models of behaviour under ambiguity can be partitioned into two sets: those involving multiple priors and those not involving multiple priors. This paper provides an experimental investigation into the first set. Using an appropriate experimental interface we examine the fitted and predictive power of the various theories. We first estimate subject-by-subject, and then 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, 11% with Rank Dependent Expected Utility and 9% with the Alpha Model; these figures are close to the mixing proportions obtained from the mixture estimates where the respective posterior probabilities of each of them being of the various types are 25%, 50%, 20% and 5%; and using the predictions 22%, 53%, 22% and 3%. The Smooth model appears the best. Copyright Springer Science+Business Media New York 2013

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Bibliographic Info

Article provided by Springer in its journal Journal of Risk and Uncertainty.

Volume (Year): 46 (2013)
Issue (Month): 2 (April)
Pages: 113-132

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Handle: RePEc:kap:jrisku:v:46:y:2013:i:2:p:113-132

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Web page: http://www.springerlink.com/link.asp?id=100299

Related research

Keywords: Alpha model; Ambiguity; Expected utility; Mixture models; Rank dependent expected utility; Smooth model; D81; C91; C23;

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  1. Steffen Andersen & Glenn Harrison & Morten Lau & E. Rutström, 2009. "Elicitation using multiple price list formats," Experimental Economics, Springer, vol. 12(3), pages 365-366, September.
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  9. 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.
  10. 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.
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  15. Ghirardato, Paolo & Maccheroni, Fabio & Marinacci, Massimo, 2004. "Differentiating ambiguity and ambiguity attitude," Journal of Economic Theory, Elsevier, vol. 118(2), pages 133-173, October.
  16. 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.
  17. 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.
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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," LERNA Working Papers 12.21.378, LERNA, University of Toulouse.
  2. Anna Conte & M. Vittoria Levati, 2011. "Use of data on planned contributions and stated beliefs in the measurement of social preferences," Jena Economic Research Papers 2011-039, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.
  3. 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.
  4. 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.

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