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Under- versus overconfidence: an experiment on how others perceive a biased self-assessment

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Abstract

This paper reports findings of a laboratory experiment, which explores how reported self-assessment regarding the own relative performance is perceived by others. In particular, I investigate whether overconfident or underconfident subjects are considered as more likeable, and who of the two is expected to win in a tournament, thereby controlling for performance. Underconfidence beats overconfidence in both respects. Underconfident subjects are rewarded significantly more often than overconfident subjects, and are significantly more often expected to win. Subjects being less convinced of their performance are taken as more congenial and are expected to be more ambitious to improve, whereas overconfident subjects are rather expected to rest on their high beliefs. While subjects do not anticipate the stronger performance signal of underconfidence, they anticipate its higher sympathy value. The comparison to a non-strategic setting shows that men strategically deflate their self-assessment to be rewarded by others. Women, in contrast, either do not deflate their self-assessment or do so even in non-strategic situations, a behavior that might be driven by non-monetary image concerns of women.

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Notes

  1. Svenson (1981), e.g., shows that more than 50 % of participants (77 % in a Swedish and 88 % in a US study) report to drive more safely than the median, taking this result as evidence for overconfidence. Other studies demonstrating the better-than-average effect are Alicke (1985), Dunning et al. (1989) and Messick et al. (1985).

  2. In laboratory experiments Hoelzl and Rustichini (2005) show that people’s choice behavior (competition vs. lottery) is underconfident when the task is non-familiar. Krueger (1999) and Moore and Cain (2007) also find that people tend to be underconfident rather than overconfident when the task is (perceived as) difficult. Clark and Friesen (2009) test for overconfidence in people’s forecasts of their absolute and relative performance and observe a correct self-assessment or underconfidence more often than overconfidence.

  3. For example Niederle and Vesterlund (2007) show that overconfidence makes bad performing men selecting competitive payment schemes too often (regarding payoff maximization), and that underconfidence makes high performing women selecting competitive payment schemes too little.

  4. The type of overconfidence observed in this study is overplacement as termed by Larrick et al. (2007). See also Moore and Healy (2008) for a more precise distinction of the different types of overconfidence.

  5. This is in line with women’s shame of overestimation observed by the experimental study by Ludwig and Thoma (2012).

  6. Note that the accuracy of agents’ self-assessment is still observable in the control treatments, but the agents do not have a monetary incentive to be chosen.

  7. Also compare Charness et al. (2012) who use this argumentation to explain overconfidence in non-strategic competitive settings.

  8. Another study claiming that individuals inflate their reported self-assessment due to non-monetary image concerns is Burks et al. (2013). In a large survey with male truck drivers, they find a correlation between how much one cares about his image and overconfidence, consequently claiming that “overconfidence is a social signaling bias”.

  9. In an experimental study Montinari et al. (2012) observe that the ex ante low ability type is chosen more often, as he is expected to exert more effort when receiving a fixed wage. However, the reason is a higher expected reciprocity when hiring the low ability type, which is absent in my study, as agents do not receive a fixed wage and do not learn whether they’ve been chosen or not until the end of the experiment.

  10. See e.g., Balafoutas and Sutter (2012), Beyer (1990), Beyer and Bowden (1997), Charness et al. (2012), Datta Gupta et al. (2013), Dohmen and Falk (2011), Möbius et al. (2012), Niederle et al. (2013) and Niederle and Vesterlund (2007).

  11. Two agents receive the same rank to have the same initial position for their self-assessment.

  12. Answers in the follow-up questionnaire show that principals nevertheless expected the agents to state their true belief about their relative ranks.

  13. This choice actually became relevant for 20 out of 56 principals.

  14. On subjects’ screens the situations were listed in a different order and I varied whether the more or less self-confident agent was listed first, what actually did not influence results.

  15. Besides my interest in whether accuracy is favored over over- and underconfidence, I picked the 12 different situations to cover the most realistic outcomes. Using the SVM instead of showing the actual deviations to the principal decreases the number of sessions needed and allows for a cleaner data analysis.

  16. This situation became relevant in 8 out of 56 situations.

  17. Results go in the same direction when excluding hypothetical choices, but are less significant due to fewer observations. The separated data is listed in the Appendix.

  18. The results are robust to OLS regressions.

  19. To elicit risk preferences, individuals indicated on a scale ranging from 0 to 10 whether they are willing to take risks (or try to avoid risks). 0 represented a very weak willingness to take risks, while 10 represented a strong willingness to take risks. Dohmen et al. (2011) show that this general risk question is a good predictor of actual risk-taking behavior.

  20. The results are robust to OLS regressions.

  21. Seven out of 15 agents stating the worse rank win task 2 in PERF and 7 out of 12 in PERF-CON. 10 pairs of agents state the same rank.

  22. To calculate the simulated ranks, I ran Monte-Carlo simulations, in which I randomly drew 500,000 groups consisting of 15 participants out of the performance distribution of all agents (with replacement). I then calculated for any given performance level the rank within each simulated group. The simulated rank equals the mode of all 500,000 calculated ranks.

  23. I cannot confirm the highly debated finding that subjects are overconfident. In this experiment, 22 % of agents estimate their rank correctly, 35 % have a deviation of +/\(-\)1 rank, 25 % have a deviation of +/\(-\)2 ranks and only 9 % of subjects overestimate their rank by 3 or 4 ranks. In comparison also 9 % underestimate their rank by 3–5 ranks.

  24. Note that simulated ranks in PERF are lower than in SYMP. As participants on lower ranks are more likely to overestimate themselves, the difference across treatments might not be due to a treatment difference, but only due to a performance difference and the limited scale. To take care of this issue I conduct ordered probit regressions below.

  25. The results are robust when conducting OLS regressions.

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Acknowledgments

I would like to thank Martin Kocher, Sandra Ludwig, Ernesto Reuben, Klaus Schmidt, Marta Serra-Garcia, Sebastian Strasser, Ferdinand Vieider, participants of the MELESSA Brown Bag Seminar at the University of Munich for helpful comments and discussions. Financial support from Sonderforschungsbereich (SFB)/Transregio (TR) 15 is gratefully acknowledged. For providing laboratory resources I kindly thank MELESSA of the University of Munich.

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  1. Ludwig-Maximilians-Universität (University of Munich), Munich, Germany

    Carmen Thoma

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Correspondence to Carmen Thoma.

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Appendix

See Table 9.

Table 9 Principals’ choices in SYMP separated
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Thoma, C. Under- versus overconfidence: an experiment on how others perceive a biased self-assessment. Exp Econ 19, 218–239 (2016). https://doi.org/10.1007/s10683-015-9435-2

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