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Are the unskilled really that unaware? An alternative explanation

Author

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  • Krajc, Marian
  • Ortmann, Andreas
Abstract
In a series of articles and manuscripts (e.g., [Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one's own incompetence lead to inflated self-assessment. Journal of Personality and Social Psychology, 77, 1121-1134; Dunning, D., Johnson, K., Ehrlinger, J., & Kruger, J. (2003). Why people fail to recognize their own incompetence. Current Directions in Psychological Science, 12, 83-87; Ehrlinger, J., Johnson, K., Banner, M., Kruger, J., & Dunning, D. (2008). Why the unskilled are unaware: Further exploration of (absent) self-insight among the incompetent. Organizational Behavior and Human Decision Processes, 105, 98-121.]), Dunning, Kruger and their collaborators argued that the unskilled lack the metacognitive ability to realize their incompetence. We propose that the alleged unskilled-and-unaware problem - rather than being one of biased judgements - is a signal extraction problem that differs for the skilled and the unskilled. Specifically, the unskilled face a tougher inference problem than the skilled.

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  • Krajc, Marian & Ortmann, Andreas, 2008. "Are the unskilled really that unaware? An alternative explanation," Journal of Economic Psychology, Elsevier, vol. 29(5), pages 724-738, November.
    • Handle: RePEc:eee:joepsy:v:29:y:2008:i:5:p:724-738
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    References listed on IDEAS

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    1. Andreas Ortmann & Ralph Hertwig, 2002. "The Costs of Deception: Evidence from Psychology," Experimental Economics, Springer;Economic Science Association, vol. 5(2), pages 111-131, October.
    2. Ehrlinger, Joyce & Johnson, Kerri & Banner, Matthew & Dunning, David & Kruger, Justin, 2008. "Why the unskilled are unaware: Further explorations of (absent) self-insight among the incompetent," Organizational Behavior and Human Decision Processes, Elsevier, vol. 105(1), pages 98-121, January.
    3. Rydval, Ondrej & Ortmann, Andreas, 2004. "How financial incentives and cognitive abilities affect task performance in laboratory settings: an illustration," Economics Letters, Elsevier, vol. 85(3), pages 315-320, December.
    4. Richard Sabot & John Wakeman-Linn, 1991. "Grade Inflation and Course Choice," Journal of Economic Perspectives, American Economic Association, vol. 5(1), pages 159-170, Winter.
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    6. Ralph Hertwig & Andreas Ortmann, 2005. "The Cognitive Illusion Controversy: A Methodological Debate in Disguise That Matters to Economists," Springer Books, in: Rami Zwick & Amnon Rapoport (ed.), Experimental Business Research, chapter 0, pages 113-130, Springer.
    7. Chu, Yun-Peng & Chu, Ruey-Ling, 1990. "The Subsidence of Preference Reversals in Simplified and Marketlike Experimental Settings: A Note," American Economic Review, American Economic Association, vol. 80(4), pages 902-911, September.
    8. Rami Zwick & Amnon Rapoport (ed.), 2005. "Experimental Business Research," Springer Books, Springer, number 978-0-387-24244-6, December.
    9. Amnon Rapoport & Rami Zwick (ed.), 2005. "Experimental Business Research," Springer Books, Springer, number 978-0-387-24243-9, December.
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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. 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.
    3. 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.
    4. 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.
    5. Jan R. Magnus & Anatoly A. Peresetsky, 2021. "A statistical explanation of the Dunning-Kruger effect," Working Papers w0286, New Economic School (NES).
    6. Schlösser, Thomas & Dunning, David & Johnson, Kerri L. & Kruger, Justin, 2013. "How unaware are the unskilled? Empirical tests of the “signal extraction” counterexplanation for the Dunning–Kruger effect in self-evaluation of performance," Journal of Economic Psychology, Elsevier, vol. 39(C), pages 85-100.
    7. Murad, Zahra & Starmer, Chris, 2021. "Confidence snowballing and relative performance feedback," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 550-572.
    8. 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.
    9. Ryvkin, Dmitry & Krajč, Marian & Ortmann, Andreas, 2012. "Are the unskilled doomed to remain unaware?," Journal of Economic Psychology, Elsevier, vol. 33(5), pages 1012-1031.
    10. Pavlo Blavatskyy, 2009. "Betting on own knowledge: Experimental test of overconfidence," Journal of Risk and Uncertainty, Springer, vol. 38(1), pages 39-49, February.
    11. 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).
    12. 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).
    13. Krawczyk, Michał & Wilamowski, Maciej, 2019. "Task difficulty and overconfidence. Evidence from distance running," Journal of Economic Psychology, Elsevier, vol. 75(PB).
    14. 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).
    15. 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;

    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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