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Prince: An improved method for measuring incentivized preferences

Author

Listed:
  • Cathleen Johnson

    (West Virginia University)

  • Aurélien Baillon

    (Erasmus University Rotterdam)

  • Han Bleichrodt

    (Erasmus University Rotterdam)

  • Zhihua Li

    (University of Birmingham)

  • Dennie Dolder

    (Free University Amsterdam)

  • Peter P. Wakker

    (Erasmus University Rotterdam)

Abstract

This paper introduces the Prince incentive system for measuring preferences. Prince combines the tractability of direct matching, allowing for the precise and direct elicitation of indifference values, with the clarity and validity of choice lists. It makes incentive compatibility completely transparent to subjects, avoiding the opaqueness of the Becker-DeGroot-Marschak mechanism. It can be used for adaptive experiments while avoiding any possibility of strategic behavior by subjects. To illustrate Prince’s wide applicability, we investigate preference reversals, the discrepancy between willingness to pay and willingness to accept, and the major components of decision making under uncertainty: utilities, subjective beliefs, and ambiguity attitudes. Prince allows for measuring utility under risk and ambiguity in a tractable and incentive-compatible manner even if expected utility is violated. Our empirical findings support modern behavioral views, e.g., confirming the endowment effect and showing that utility is closer to linear than classically thought. In a comparative study, Prince gives better results than a classical implementation of the random incentive system.

Suggested Citation

  • Cathleen Johnson & Aurélien Baillon & Han Bleichrodt & Zhihua Li & Dennie Dolder & Peter P. Wakker, 2021. "Prince: An improved method for measuring incentivized preferences," Journal of Risk and Uncertainty, Springer, vol. 62(1), pages 1-28, February.
    • Handle: RePEc:kap:jrisku:v:62:y:2021:i:1:d:10.1007_s11166-021-09346-9
    DOI: 10.1007/s11166-021-09346-9
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    Cited by:

    1. Aurélien Baillon & Yoram Halevy & Chen Li, 2022. "Experimental elicitation of ambiguity attitude using the random incentive system," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 1002-1023, June.
    2. Kpegli, Yao Thibaut & Corgnet, Brice & Zylbersztejn, Adam, 2023. "All at once! A comprehensive and tractable semi-parametric method to elicit prospect theory components," Journal of Mathematical Economics, Elsevier, vol. 104(C).
    3. Ilke AYDOGAN & Loïc BERGER & Valentina BOSETTI & Ning LIU, 2022. "Three layers of uncertainty," Working Papers 2022-iRisk-01, IESEG School of Management.
    4. Ilke AYDOGAN & Loïc BERGER & Valentina BOSETTI, 2023. "Unraveling Ambiguity Aversion," Working Papers 2023-iRisk-01, IESEG School of Management.
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    6. Baillon, Aurélien & Bleichrodt, Han & Li, Chen & Wakker, Peter P., 2021. "Belief hedges: Measuring ambiguity for all events and all models," Journal of Economic Theory, Elsevier, vol. 198(C).
    7. Aurélien Baillon & Yoram Halevy & Chen Li, 2022. "Randomize at Your Own Risk: On the Observability of Ambiguity Aversion," Econometrica, Econometric Society, vol. 90(3), pages 1085-1107, May.
    8. Adrian Bruhin & Maha Manai & Luís Santos-Pinto, 2022. "Risk and rationality: The relative importance of probability weighting and choice set dependence," Journal of Risk and Uncertainty, Springer, vol. 65(2), pages 139-184, October.
    9. Raineau, Yann & Giraud-Héraud, Éric & Lecocq, Sébastien & Pérès, Stéphanie & Pons, Alexandre & Tempère, Sophie, 2023. "When health-related claims impact environmental demand: Results of experimental auctions with Bordeaux wine consumers," Ecological Economics, Elsevier, vol. 204(PA).
    10. Ronald Klingebiel & Feibai Zhu, 2023. "Ambiguity aversion and the degree of ambiguity," Journal of Risk and Uncertainty, Springer, vol. 67(3), pages 299-324, December.
    11. Bleichrodt, Han & Eichberger, Jürgen & Grant, Simon & Kelsey, David & Li, Chen, 2021. "Testing dynamic consistency and consequentialism under ambiguity," European Economic Review, Elsevier, vol. 134(C).
    12. Lotito Gianna & Maffioletti Anna & Santoni Michele, 2023. "Testing Source Influence on Ambiguity Reaction: Preference and Insensitivity," Working papers 083, Department of Economics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    13. Ilke Aydogan & Loïc Berger & Valentina Bosetti, 2023. "Unraveling Ambiguity Aversion," Post-Print hal-04370668, HAL.
    14. Géraldine Bocquého & Julien Jacob & Marielle Brunette, 2023. "Prospect theory in multiple price list experiments: further insights on behaviour in the loss domain," Theory and Decision, Springer, vol. 94(4), pages 593-636, May.
    15. Ilke Aydogan & Loïc Berger & Valentina Bosetti, 2023. "Unraveling Ambiguity Aversion," Post-Print hal-04071242, HAL.
    16. Jacob K Goeree & Bernardo Garcia-Pola, 2023. "A Non-Parametric Test of Risk Aversion," Papers 2308.02083, arXiv.org.
    17. Christina McGranaghan & Steven G. Otto, 2022. "Choice uncertainty and the endowment effect," Journal of Risk and Uncertainty, Springer, vol. 65(1), pages 83-104, August.
    18. repec:hal:wpaper:hal-04071242 is not listed on IDEAS
    19. Olivier L'Haridon & Craig S. Webb & Horst Zank, 2021. "An Effective and Simple Tool for Measuring Loss Aversion," Economics Discussion Paper Series 2107, Economics, The University of Manchester.
    20. Mohammed Abdellaoui & Enrico Diecidue & Emmanuel Kemel & Ayse Onculer, 2022. "Temporal Risk: Utility vs. Probability Weighting," Management Science, INFORMS, vol. 68(7), pages 5162-5186, July.
    21. Timo R. Lambregts & Paul Bruggen & Han Bleichrodt, 2021. "Insurance decisions under nonperformance risk and ambiguity," Journal of Risk and Uncertainty, Springer, vol. 63(3), pages 229-253, December.

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    More about this item

    Keywords

    Incentive compatibility; Random incentive system; BDM; Choice list; Matching;
    All these keywords.

    JEL classification:

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

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