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Betting on Own Knowledge: Experimental Test of Overconfidence

Author

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  • Pavlo R. Blavatskyy

Abstract

This paper presents a new incentive compatible method for measuring confidence in own knowledge. This method consists of two parts. First, an individual answers several general knowledge questions. Second, the individual chooses among three alternatives: 1) one question is selected at random and the individual receives a payoff if he or she has answered this question correctly; 2) the individual receives the same payoff with a probability equal to the percentage of correctly answered questions; 3) either the first or the second alternative is selected. The choice of the first (second) alternative reveals overconfidence (underconfidence). The individual is well calibrated if he or she chooses the third alternative. Experimental results show that subjects, on average, exhibit underconfidence about their own knowledge when the incentive compatible mechanism is used. Their confidence in own knowledge does not depend on their attitude towards risk/ambiguity.

Suggested Citation

  • Pavlo R. Blavatskyy, 2008. "Betting on Own Knowledge: Experimental Test of Overconfidence," IEW - Working Papers 358, Institute for Empirical Research in Economics - University of Zurich.
    • Handle: RePEc:zur:iewwpx:358
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    File URL: https://www.econ.uzh.ch/apps/workingpapers/wp/iewwp358.pdf
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    Cited by:

    1. Schlag, Karl H. & van der Weele, Joël J., 2015. "A method to elicit beliefs as most likely intervals," Judgment and Decision Making, Cambridge University Press, vol. 10(5), pages 456-468, September.
    2. repec:cup:judgdm:v:10:y:2015:i:5:p:456-468 is not listed on IDEAS

    More about this item

    Keywords

    Overconfidence; underconfidence; lottery; experiment; risk aversion;
    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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