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Cognitive Biases: Mistakes or Missing Stakes?

Author

Listed:
  • Benjamin Enke

    (Harvard University and NBER)

  • Uri Gneezy

    (UC San Diego Rady School of Management)

  • Brian Hall

    (Harvard Business School)

  • David Martin

    (Harvard University)

  • Vadim Nelidov

    (University of Amsterdam and Tinbergen Institute)

  • Theo Offerman

    (University of Amsterdam and Tinbergen Institute)

  • Jeroen van de Ven

    (University of Amsterdam and Tinbergen Institute)

Abstract

Despite decades of research on heuristics and biases, evidence on the effect of large incentives on cognitive biases is scant. We test the effect of incentives on four widely documented biases: base-rate neglect, anchoring, failure of contingent thinking, and intuitive reasoning. In laboratory experiments with 1,236 college students in Nairobi, we implement three incentive levels: no incentives, standard lab payments, and very high incentives. We find that very high stakes increase response times by 40% but improve performance only very mildly or not at all. In none of the tasks do very high stakes come close to debiasing participants.

Suggested Citation

  • Benjamin Enke & Uri Gneezy & Brian Hall & David Martin & Vadim Nelidov & Theo Offerman & Jeroen van de Ven, 2023. "Cognitive Biases: Mistakes or Missing Stakes?," The Review of Economics and Statistics, MIT Press, vol. 105(4), pages 818-832, July.
    • Handle: RePEc:tpr:restat:v:105:y:2023:i:4:p:818-832
    DOI: 10.1162/rest_a_01093
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    References listed on IDEAS

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    Cited by:

    1. Pedro Gonzalez-Fernandez, 2024. "Belief Bias Identification," Papers 2404.09297, arXiv.org, revised Nov 2024.
    2. Uri Gneezy & Yoram Halevy & Brian Hall & Theo Offerman & Jeroen van de Ven, 2024. "How Real is Hypothetical? A High-Stakes Test of the Allais Paradox," Working Papers tecipa-783, University of Toronto, Department of Economics.
    3. Benjamin Patrick Evans & Sumitra Ganesh, 2024. "Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning," Papers 2402.00787, arXiv.org.

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