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Are the Unskilled Really That Unaware? Understanding Seemingly Biased Self-Assessments

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  • Marian Krajc

Abstract

The so-called unskilled-and-unaware problem was experimentally identified a decade ago: The unskilled are seemingly afflicted by a double curse because they also seem unaware of their (relative) lack of skills. Numerous authors have elaborated on this problem – experimentally as well as theoretically. In this paper, we report on the results of three experiments (one field, two laboratory) through which we test a theoretical model and some informal extensions. Specifically, we examine the impact of general information and specific information (feedback) on the quality of self-assessment (“calibration”) in various tasks and under various conditions. Overconfidence behavior initially prevails in almost all settings. We find a strong positive effect of general information on calibration, and show that calibration improves more when feedback is provided. In our experiments, it is the unskilled who improve their calibration the most.

Suggested Citation

  • Marian Krajc, 2008. "Are the Unskilled Really That Unaware? Understanding Seemingly Biased Self-Assessments," CERGE-EI Working Papers wp373, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    • Handle: RePEc:cer:papers:wp373
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    Cited by:

    1. Feld, Jan & Sauermann, Jan & de Grip, Andries, 2017. "Estimating the relationship between skill and overconfidence," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 68(C), pages 18-24.
    2. Brookins, Philip & Lucas, Adriana & Ryvkin, Dmitry, 2014. "Reducing within-group overconfidence through group identity and between-group confidence judgments," Journal of Economic Psychology, Elsevier, vol. 44(C), pages 1-12.
    3. Murad, Zahra & Starmer, Chris, 2021. "Confidence snowballing and relative performance feedback," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 550-572.
    4. Gignac, Gilles E. & Zajenkowski, Marcin, 2020. "The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data," Intelligence, Elsevier, vol. 80(C).
    5. Meeran, Sheik & Goodwin, Paul & Yalabik, Baris, 2016. "A parsimonious explanation of observed biases when forecasting one’s own performance," International Journal of Forecasting, Elsevier, vol. 32(1), pages 112-120.
    6. Dunkel, Curtis S. & Nedelec, Joseph & van der Linden, Dimitri, 2023. "Reevaluating the Dunning-Kruger effect: A response to and replication of Gignac and Zajenkowski (2020)," Intelligence, Elsevier, vol. 96(C).
    7. Krawczyk, Michał & Wilamowski, Maciej, 2019. "Task difficulty and overconfidence. Evidence from distance running," Journal of Economic Psychology, Elsevier, vol. 75(PB).
    8. Michal Krawczyk, 2010. "Incentives and Timing in Relative Performance Judgments. A Field Experiment," Framed Field Experiments 00692, The Field Experiments Website.
    9. Jan R. Magnus & Anatoly A. Peresetsky, 2021. "A statistical explanation of the Dunning-Kruger effect," Working Papers w0286, New Economic School (NES).
    10. Sawler, James, 2021. "Economics 101-ism and the Dunning-Kruger effect: Reducing overconfidence among introductory macroeconomics students," International Review of Economics Education, Elsevier, vol. 36(C).
    11. Pallavi Kompella & Brant Gracia & Lucy LeBlanc & Shelly Engelman & Chinmayee Kulkarni & Niral Desai & Viviana June & Stephen March & Sarah Pattengale & Gabriel Rodriguez-Rivera & Seung Woo Ryu & Isabe, 2020. "Interactive youth science workshops benefit student participants and graduate student mentors," PLOS Biology, Public Library of Science, vol. 18(3), pages 1-10, March.

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    More about this item

    Keywords

    Calibration; judgement errors; unskilled; unaware; metacognition; experiment;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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