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What is your level of overconfidence? A strictly incentive compatible measurement of absolute and relative overconfidence

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  • D. Urbig
  • J. Stauf
  • U. Weitzel

Abstract

This study contributes to the ongoing discussion on the appropriate measurement of overconfidence, in particular, its strictly incentive compatible measurement in experiments. Despite a number of significant advances in recent research, several important issues remain to be solved. These relate to the strictness of incentive compatibility, the identification of well-calibrated participants, the trichotomous classification into over- or underconfident and well-calibrated participants, and the generalization to measuring beliefs about the performance relative to other people. This paper develops a measurement of overconfidence that is improved regarding all four of these issues. We theoretically prove that our method is strictly incentive compatible and robust to risk attitudes within the framework of Cumulative Prospect Theory. Furthermore, our method allows the measurement of various levels of overconfidence and the direct comparison of absolute and relative confidence. We tested our method, and the results meet our expectations, replicate recent results, and show that a population can be simultaneously overconfident, well-calibrated, and underconfident. In our specific case, we find that more than ninety-five percent of the population believe to be better than twenty-five percent; about fifty percent believe to be better than fifty percent; and only seven percent believe to be better than seventy-five percent.

Suggested Citation

  • D. Urbig & J. Stauf & U. Weitzel, 2009. "What is your level of overconfidence? A strictly incentive compatible measurement of absolute and relative overconfidence," Working Papers 09-20, Utrecht School of Economics.
  • Handle: RePEc:use:tkiwps:0920
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    File URL: https://dspace.library.uu.nl/bitstream/handle/1874/309572/09_20.pdf
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    References listed on IDEAS

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    1. Mohammed Abdellaoui & Frank Vossmann & Martin Weber, 2005. "Choice-Based Elicitation and Decomposition of Decision Weights for Gains and Losses Under Uncertainty," Management Science, INFORMS, vol. 51(9), pages 1384-1399, September.
    2. Jinkwon Lee, 2008. "The effect of the background risk in a simple chance improving decision model," Journal of Risk and Uncertainty, Springer, vol. 36(1), pages 19-41, February.
    3. Thomas Dohmen & Armin Falk & David Huffman & Uwe Sunde & Jürgen Schupp & Gert G. Wagner, 2005. "Individual Risk Attitudes: New Evidence from a Large, Representative, Experimentally-Validated Survey," Discussion Papers of DIW Berlin 511, DIW Berlin, German Institute for Economic Research.
    4. Baumann, Andrea O. & Deber, Raisa B. & Thompson, Gail G., 1991. "Overconfidence among physicians and nurses: The 'micro-certainty, macro-uncertainty' phenomenon," Social Science & Medicine, Elsevier, vol. 32(2), pages 167-174, January.
    5. Marion Eberlein & Sandra Ludwig & Julia Nafziger, 2011. "The Effects Of Feedback On Self‐Assessment," Bulletin of Economic Research, Wiley Blackwell, vol. 63(2), pages 177-199, April.
    6. Peter Wakker & Daniel Deneffe, 1996. "Eliciting von Neumann-Morgenstern Utilities When Probabilities Are Distorted or Unknown," Management Science, INFORMS, vol. 42(8), pages 1131-1150, August.
    7. Reinhard Selten & Michael Mitzkewitz & Gerald R. Uhlich, 1997. "Duopoly Strategies Programmed by Experienced Players," Econometrica, Econometric Society, vol. 65(3), pages 517-556, May.
    8. Dan Lovallo & Colin Camerer, 1999. "Overconfidence and Excess Entry: An Experimental Approach," American Economic Review, American Economic Association, vol. 89(1), pages 306-318, March.
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    Cited by:

    1. Sandra Ludwig & Julia Nafziger, 2011. "Beliefs about overconfidence," Theory and Decision, Springer, vol. 70(4), pages 475-500, April.
    2. 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.
    3. Aurélien Baillon & Philipp Koellinger & Theresa Treffers, 2014. "Sadder but wiser: The Effects of Affective States and Weather on Ambiguity Attitudes," Tinbergen Institute Discussion Papers 14-044/I, Tinbergen Institute.
    4. Chen, Si & Schildberg-Hörisch, Hannah, 2018. "Looking at the bright side: The motivation value of overconfidence," DICE Discussion Papers 291, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    5. Daniela Di Cagno & Daniela Grieco, 2019. "Measuring and Disentangling Ambiguity and Confidence in the Lab," Games, MDPI, vol. 10(1), pages 1-22, February.
    6. Chen, Si & Schildberg-Hörisch, Hannah, 2019. "Looking at the bright side: The motivational value of confidence," European Economic Review, Elsevier, vol. 120(C).
    7. Mohammed Abdellaoui & Han Bleichrodt & Cédric Gutierrez, 2023. "Unpacking Overconfident Behavior When Betting on Oneself," Post-Print hal-04383402, HAL.
    8. Koellinger, Ph.D. & Treffers, T., 2012. "Joy leads to Overconfidence, and a Simple Remedy," ERIM Report Series Research in Management ERS-2012-001-STR, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. Philipp Koellinger & Theresa Treffers, 2015. "Joy Leads to Overconfidence, and a Simple Countermeasure," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.

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    Keywords

    Belief elicitation; Overconfidence; Better than average; Incentive compatibility;
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