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A target-based foundation for the "hard-easy effect" bias

Author

Listed:
  • Robert Bordley

    () (Dept. of Industrial Engineering and Operations Management, University of Michigan, USA)

  • Marco LiCalzi

    () (Dept. of Management, Università Ca' Foscari Venice)

  • Luisa Tibiletti

    () (Dept. of Management, University of Torino)

Abstract

The "hard-easy effect" is a well-known cognitive bias on self-confidence calibration that refers to a tendency to overestimate the probability of success in hard-perceived tasks, and to underestimate it in easy-perceived tasks. This paper provides a target-based foundation for this effect, and predicts its occurrence in the expected utility framework when utility functions are S-shaped and asymmetrically tailed. First, we introduce a definition of hard-perceived and easy-perceived task based on the mismatch between an uncertain target to meet and a suitably symmetric reference point. Second, switching from a target-based language to a utility-based language, we show how this maps to an equivalence between the hard-perceived target/gain seeking and the easy-perceived target/loss aversion. Third, we characterize the agent's miscalibration in self-confidence. Finally, we derive sufficient conditions for the Òhard-easy effectÓ and the "reversed hard-easy effect" to hold.

Suggested Citation

  • Robert Bordley & Marco LiCalzi & Luisa Tibiletti, 2014. "A target-based foundation for the "hard-easy effect" bias," Working Papers 23, Department of Management, Università Ca' Foscari Venezia.
  • Handle: RePEc:vnm:wpdman:94
    as

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    File URL: http://virgo.unive.it/wpideas/storage/2014wp23.pdf
    File Function: First version, 2014
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    References listed on IDEAS

    as
    1. Abadir, Karim M., 2005. "The Mean-Median-Mode Inequality: Counterexamples," Econometric Theory, Cambridge University Press, vol. 21(02), pages 477-482, April.
    2. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    3. Marvin H. Berhold, 1973. "The Use of Distribution Functions to Represent Utility Functions," Management Science, INFORMS, vol. 19(7), pages 825-829, March.
    4. Marco LiCalzi, 2005. "A language for the construction of preferences under uncertainty," Game Theory and Information 0509002, EconWPA.
    5. Erio Castagnoli & Marco LiCalzi, 2005. "Expected utility without utility," Game Theory and Information 0508004, EconWPA.
    6. Stephen V. Burks & Jeffrey P. Carpenter & Lorenz Goette & Aldo Rustichini, 2013. "Overconfidence and Social Signalling," Review of Economic Studies, Oxford University Press, vol. 80(3), pages 949-983.
    7. R. Preston Mcafee & Hugo M. Mialon & Sue H. Mialon, 2010. "Do Sunk Costs Matter?," Economic Inquiry, Western Economic Association International, vol. 48(2), pages 323-336, April.
    8. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.
    9. Arvid Hoffmann & Sam Henry & Nikos Kalogeras, 2013. "Aspirations as reference points: an experimental investigation of risk behavior over time," Theory and Decision, Springer, vol. 75(2), pages 193-210, August.
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    11. Daniel Kahneman & Jack L. Knetsch & Richard H. Thaler, 1991. "Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias," Journal of Economic Perspectives, American Economic Association, vol. 5(1), pages 193-206, Winter.
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    Cited by:

    1. Barron, Kai & Gravert, Christina, 2018. "Confidence and Career Choices: An Experiment," Working Papers in Economics 715, University of Gothenburg, Department of Economics.
    2. Barron, Kai & Gravert, Christina, 2018. "Beliefs and actions: How a shift in confidence affects choices," MPRA Paper 84743, University Library of Munich, Germany.

    More about this item

    Keywords

    Expected utility; Hard-easy effect bias; Endowment effect bias; Sunk cost effect bias; Benchmarking procedure; Loss-gain asymmetry; van Zwet skewness conditions;

    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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