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Subjective beliefs formation and elicitation rules: experimental evidence

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Abstract

Since they have been increasingly used in economics, elicitation rules for subjective beliefs are under scrutiny. In this paper, we propose an experimental design to compare the performance of such rules. Contrary to previous works in which elicited beliefs are compared to an objective benchmark, we consider a pure subjective belief framework (confidence in own performance in a cognitive task and a perceptual task). The performances of elicitation rules are assessed according to the accuracy of stated beliefs in predicting success. For the perceptual task we also compare stated beliefs to Signal Detection Theory predictions. We find consistent evidence in favor of the Lottery Rule which provides more accurate beliefs and is not sensitive to risk aversion. Furthermore the Free Rule, a simple rule with no incentives, elicits relevant beliefs and even outperforms the Quadratic Scoring Rule. Beside this comparison, we propose a belief formation model where we distinguish between two stages in the beliefs: beliefs for decision making and confidence beliefs. Our results give support to this model.

Suggested Citation

  • Guillaume Hollard & Sébastien Massoni & Jean-Christophe Vergnaud, 2010. "Subjective beliefs formation and elicitation rules: experimental evidence," Documents de travail du Centre d'Economie de la Sorbonne 10088, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  • Handle: RePEc:mse:cesdoc:10088
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    Cited by:

    1. Zahra Murad & Martin Sefton & Chris Starmer, 2016. "How do risk attitudes affect measured confidence?," Journal of Risk and Uncertainty, Springer, vol. 52(1), pages 21-46, February.
    2. Noémi Berlin & Marie-Pierre Dargnies, 2012. "Linking Beliefs to Willingness to Compete," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00755660, HAL.
    3. Jean-Pierre Benoit & Juan Dubra & Don Moore, 2013. "Does the better –than- average effect show that people are Overconfident?: two experiments," Documentos de Trabajo/Working Papers 1301, Facultad de Ciencias Empresariales y Economia. Universidad de Montevideo..
    4. Karl Schlag & James Tremewan & Joël Weele, 2015. "A penny for your thoughts: a survey of methods for eliciting beliefs," Experimental Economics, Springer;Economic Science Association, vol. 18(3), pages 457-490, September.
    5. Stefan T. Trautmann & Gijs Kuilen, 2015. "Belief Elicitation: A Horse Race among Truth Serums," Economic Journal, Royal Economic Society, vol. 125(589), pages 2116-2135, December.
    6. Jean-Pierre Benoît & Juan Dubra & Don A. Moore, 2015. "Does The Better-Than-Average Effect Show That People Are Overconfident?: Two Experiments," Journal of the European Economic Association, European Economic Association, vol. 13(2), pages 293-329, April.
    7. Theo Offerman & Asa B. Palley, 2016. "Lossed in translation: an off-the-shelf method to recover probabilistic beliefs from loss-averse agents," Experimental Economics, Springer;Economic Science Association, vol. 19(1), pages 1-30, March.
    8. Egan, Daniel & Merkle, Christoph & Weber, Martin, 2014. "Second-order beliefs and the individual investor," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 652-666.
    9. Aurélien Baillon & Laure Cabantous & Peter Wakker, 2012. "Aggregating imprecise or conflicting beliefs: An experimental investigation using modern ambiguity theories," Journal of Risk and Uncertainty, Springer, vol. 44(2), pages 115-147, April.
    10. David Owens Jr. & Zachary Grossman Jr. & Ryan Fackler Jr., 2014. "The Control Premium: A Preference for Payoff Autonomy," American Economic Journal: Microeconomics, American Economic Association, vol. 6(4), pages 138-161, November.

    More about this item

    Keywords

    Belief elicitation; confidence; signal Detection Theory; methodology; incentives; experimental economics;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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