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An experimental test of prospect theory for predicting choice under ambiguity

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  • Amit Kothiyal
  • Vitalie Spinu
  • Peter Wakker

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Abstract

Prospect theory is the most popular theory for predicting decisions under risk. This paper investigates its predictive power for decisions under ambiguity, using its specification through the source method. We find that it outperforms its most popular alternatives, including subjective expected utility, Choquet expected utility, and three multiple priors theories: maxmin expected utility, maxmax expected utility, and a-maxmin expected utility. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Amit Kothiyal & Vitalie Spinu & Peter Wakker, 2014. "An experimental test of prospect theory for predicting choice under ambiguity," Journal of Risk and Uncertainty, Springer, vol. 48(1), pages 1-17, February.
  • Handle: RePEc:kap:jrisku:v:48:y:2014:i:1:p:1-17
    DOI: 10.1007/s11166-014-9185-0
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    3. Georgalos, Konstantinos & Paya, Ivan & Peel, David A., 2021. "On the contribution of the Markowitz model of utility to explain risky choice in experimental research," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 527-543.
    4. Konstantinos Georgalos, 2016. "Dynamic decision making under ambiguity," Working Papers 112111041, Lancaster University Management School, Economics Department.
    5. James Cox & Vjollca Sadiraj & Ulrich Schmidt, 2015. "Paradoxes and mechanisms for choice under risk," Experimental Economics, Springer;Economic Science Association, vol. 18(2), pages 215-250, June.
    6. Mengxing Wei & Ali al-Nowaihi & Sanjit Dhami, 2017. "Can quantum decision theory explain the Ellsberg paradox?," Discussion Papers in Economics 17/07, Division of Economics, School of Business, University of Leicester.
    7. 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.
    8. Robin Cubitt & Gijs Kuilen & Sujoy Mukerji, 2018. "The strength of sensitivity to ambiguity," Theory and Decision, Springer, vol. 85(3), pages 275-302, October.
    9. Olivier Armantier & Nicolas Treich, 2016. "The Rich Domain of Risk," Management Science, INFORMS, vol. 62(7), pages 1954-1969, July.
    10. Sebastian Oelrich, 2019. "Making regulation fit by taking irrationality into account: the case of the whistleblower," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 175-207, April.
    11. Zhihua Li & Julia Müller & Peter P. Wakker & Tong V. Wang, 2018. "The Rich Domain of Ambiguity Explored," Management Science, INFORMS, vol. 64(7), pages 3227-3240, July.
    12. 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).
    13. Lahno, Amrei M., 2014. "Social anchor effects in decision-making under ambiguity," Discussion Papers in Economics 20960, University of Munich, Department of Economics.
    14. Ali al-Nowaihi & Sanjit Dhami & Mengxing Wei, 2018. "Quantum Decision Theory and the Ellsberg Paradox," CESifo Working Paper Series 7158, CESifo.
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