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Assessing multiple prior models of behaviour under ambiguity

In: Experiments in Economics Decision Making and Markets

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  • Anna Conte
  • John D. Hey

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.

Suggested Citation

  • Anna Conte & John D. Hey, 2018. "Assessing multiple prior models of behaviour under ambiguity," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 7, pages 169-188, World Scientific Publishing Co. Pte. Ltd..
  • Handle: RePEc:wsi:wschap:9789813235816_0007
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    1. John D. Hey & Noemi Pace, 2018. "The explanatory and predictive power of non two-stage-probability theories of decision making under ambiguity," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 6, pages 139-167, World Scientific Publishing Co. Pte. Ltd..
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    7. John D. Hey & Gianna Lotito & Anna Maffioletti, 2018. "The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 8, pages 189-219, World Scientific Publishing Co. Pte. Ltd..
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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. d’Albis, Hippolyte & Attanasi, Giuseppe & Thibault, Emmanuel, 2020. "An experimental test of the under-annuitization puzzle with smooth ambiguity and charitable giving," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 694-717.
    3. Robin Cubitt & Gijs Kuilen & Sujoy Mukerji, 2018. "The strength of sensitivity to ambiguity," Theory and Decision, Springer, vol. 85(3), pages 275-302, October.
    4. 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.
    5. Robin Cubitt & Gijs van de Kuilen & Sujoy Mukerji, 2020. "Discriminating Between Models of Ambiguity Attitude: a Qualitative Test," Journal of the European Economic Association, European Economic Association, vol. 18(2), pages 708-749.
    6. Noemi Pace & Giuseppe Attanasi & Christian Gollier & Aldo Montesano, 2012. "Eliciting ambiguity aversion in unknown and in compound lotteries: A KMM experimental approach," Working Papers 2012_23, Department of Economics, University of Venice "Ca' Foscari".
    7. L. A. Franzoni, 2016. "Optimal liability design under risk and ambiguity," Working Papers wp1048, Dipartimento Scienze Economiche, Universita' di Bologna.
    8. 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.
    9. Tsang, Ming, 2020. "Estimating uncertainty aversion using the source method in stylized tasks with varying degrees of uncertainty," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 84(C).
    10. 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.
    11. Enrica Carbone & Konstantinos Georgalos & Gerardo Infante, 2019. "Individual vs. group decision-making: an experiment on dynamic choice under risk and ambiguity," Theory and Decision, Springer, vol. 87(1), pages 87-122, July.
    12. Anna Conte & John D. Hey & Ivan Soraperra, 2014. "The Determinants of Decision Time," Jena Economic Research Papers 2014-004, Friedrich-Schiller-University Jena.
    13. Bali, Turan G. & Brown, Stephen J. & Tang, Yi, 2017. "Is economic uncertainty priced in the cross-section of stock returns?," Journal of Financial Economics, Elsevier, vol. 126(3), pages 471-489.
    14. Nartea, Gilbert V. & Bai, Hengyu & Wu, Ji, 2020. "Investor sentiment and the economic policy uncertainty premium," Pacific-Basin Finance Journal, Elsevier, vol. 64(C).
    15. 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.
    16. 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.
    17. Anna Conte & Marco Scarsini & Oktay Sürücü, 2014. "An Experimental Investigation into Queueing Behavior," Jena Economic Research Papers 2014-030, Friedrich-Schiller-University Jena.
    18. Ali al-Nowaihi & Sanjit Dhami, 2016. "The Ellsberg paradox: A challenge to quantum decision theory?," Discussion Papers in Economics 16/08, Division of Economics, School of Business, University of Leicester.
    19. 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.
    20. 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.
    21. Ali al-Nowaihi & Sanjit Dhami & Mengxing Wei, 2018. "Quantum Decision Theory and the Ellsberg Paradox," CESifo Working Paper Series 7158, CESifo.
    22. 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.

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    More about this item

    Keywords

    Experimental Economics; Risk; Ambiguity; Markets; Auctions; Bargaining; Econometrics; Methodology;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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