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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

Suggested Citation

  • Anna Conte & John Hey, 2013. "Assessing multiple prior models of behaviour under ambiguity," Journal of Risk and Uncertainty, Springer, vol. 46(2), pages 113-132, April.
  • Handle: RePEc:kap:jrisku:v:46:y:2013:i:2:p:113-132
    DOI: 10.1007/s11166-013-9164-x
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    References listed on IDEAS

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    Cited by:

    1. Eyal Ert & Stefan Trautmann, 2014. "Sampling experience reverses preferences for ambiguity," Journal of Risk and Uncertainty, Springer, vol. 49(1), pages 31-42, August.
    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. Attanasi, Giuseppe Marco & Gollier, Christian & Montesano, Aldo & Pace, Noémie, 2012. "Eliciting ambiguity aversion in unknown and in compound lotteries: A KMM experimental approach," IDEI Working Papers 744, Institut d'Économie Industrielle (IDEI), Toulouse.
    4. Bortolotti, Stefania & Soraperra, Ivan & Sutter, Matthias & Zoller, Claudia, 2017. "Too Lucky to Be True: Fairness Views under the Shadow of Cheating," IZA Discussion Papers 10877, Institute for the Study of Labor (IZA).
    5. Ali al-Nowaihi & Sanjit Dhami, 2016. "The Ellsberg paradox: A challenge to quantum decision theory?," Discussion Papers in Economics 16/08, Department of Economics, University of Leicester.
    6. Anna Conte & John D. Hey & Ivan Soraperra, 2014. "The Determinants of Decision Time," Jena Economic Research Papers 2014-004, Friedrich-Schiller-University Jena.
    7. Prokosheva, Sasha, 2016. "Comparing decisions under compound risk and ambiguity: The importance of cognitive skills," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 64(C), pages 94-105.
    8. Hudson, Paul & Botzen, W.J. Wouter & Feyen, Luc & Aerts, Jeroen C.J.H., 2016. "Incentivising flood risk adaptation through risk based insurance premiums: Trade-offs between affordability and risk reduction," Ecological Economics, Elsevier, vol. 125(C), pages 1-13.
    9. Smith, Robert Elliott, 2016. "Idealizations of Uncertainty, and Lessons from Artificial Intelligence," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 10, pages 1-40.
    10. repec:eee:jfinec:v:126:y:2017:i:3:p:471-489 is not listed on IDEAS
    11. Giuseppe Attanasi & Christian Gollier & Aldo Montesano & Noemi Pace, 2014. "Eliciting ambiguity aversion in unknown and in compound lotteries: a smooth ambiguity model experimental study," Theory and Decision, Springer, vol. 77(4), pages 485-530, December.
    12. L. A. Franzoni, 2016. "Optimal liability design under risk and ambiguity," Working Papers wp1048, Dipartimento Scienze Economiche, Universita' di Bologna.
    13. 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.
    14. Stephen Dimmock & Roy Kouwenberg & Olivia Mitchell & Kim Peijnenburg, 2015. "Estimating ambiguity preferences and perceptions in multiple prior models: Evidence from the field," Journal of Risk and Uncertainty, Springer, vol. 51(3), pages 219-244, December.
    15. Stefania Bortolotti & Ivan Soraperra & Matthias Sutter & Claudia Zoller, 2017. "Too Lucky to be True - Fairness Views under the Shadow of Cheating," CESifo Working Paper Series 6563, CESifo Group Munich.
    16. Huang, Yi-Chieh & Tzeng, Larry Y. & Zhao, Lin, 2015. "Comparative ambiguity aversion and downside ambiguity aversion," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 257-269.
    17. Anna Conte & Marco Scarsini & Oktay Sürücü, 2014. "An Experimental Investigation into Queueing Behavior," Jena Economic Research Papers 2014-030, Friedrich-Schiller-University Jena.

    More about this item

    Keywords

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

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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