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Information Redundancy Neglect versus Overconfidence: A Social Learning Experiment

Author

Listed:
  • Marco Angrisani

    (USC - University of Southern California)

  • Antonio Guarino

    (UCL - University College of London [London])

  • Philippe Jehiel

    (PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, UCL - University College of London [London])

  • Toru Kitagawa

    (UCL - University College of London [London])

Abstract

We study social learning in a continuous action space experiment. Subjects, acting in sequence, state their belief about the value of a good, after observing their predecessors' statements and a private signal. We compare the behavior in the laboratory with the Perfect Bayesian Equilibrium prediction and the predictions of bounded rationality models of decision making: the redundancy of information neglect model and the overconfidence model. The results of our experiment are in line with the predictions of the overconfidence model and at odds with the others'.

Suggested Citation

  • Marco Angrisani & Antonio Guarino & Philippe Jehiel & Toru Kitagawa, 2019. "Information Redundancy Neglect versus Overconfidence: A Social Learning Experiment," PSE Working Papers halshs-02183322, HAL.
    • Handle: RePEc:hal:psewpa:halshs-02183322
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02183322
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Melissa Newham & Rune Midjord, 2018. "Herd Behavior in FDA Committees: A Structural Approach," Discussion Papers of DIW Berlin 1744, DIW Berlin, German Institute for Economic Research.
    2. De Filippis, Roberta & Guarino, Antonio & Jehiel, Philippe & Kitagawa, Toru, 2022. "Non-Bayesian updating in a social learning experiment," Journal of Economic Theory, Elsevier, vol. 199(C).
    3. Lazarina Butkovich & Nina Butkovich & Saba Devdariani & Charles R. Plott & Han Seo, 2020. "Fake News, Information Herds, Cascades, and Economic Knowledge," Public Finance Review, , vol. 48(6), pages 806-828, November.
    4. Kai Barron & Steffen Huck & Philippe Jehiel, 2024. "Everyday Econometricians: Selection Neglect and Overoptimism When Learning from Others," American Economic Journal: Microeconomics, American Economic Association, vol. 16(3), pages 162-198, August.
    5. Cao, Qian & Li, Jianbiao & Niu, Xiaofei, 2019. "The role of overconfidence in overweighting private information: Does gender matter?," EconStor Preprints 203448, ZBW - Leibniz Information Centre for Economics.
    6. Christoph March & Anthony Ziegelmeyer, 2018. "Excessive Herding in the Laboratory: The Role of Intuitive Judgments," CESifo Working Paper Series 6855, CESifo.
    7. March, Christoph & Ziegelmeyer, Anthony, 2020. "Altruistic observational learning," Journal of Economic Theory, Elsevier, vol. 190(C).
    8. Aislinn Bohren & Daniel Hauser, 2018. "Social Learning with Model Misspeciification: A Framework and a Robustness Result," PIER Working Paper Archive 18-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Jul 2018.

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    More about this item

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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