IDEAS home Printed from https://ideas.repec.org/p/hal/pseptp/halshs-03229978.html

Non-Bayesian updating in a social learning experiment

Author

Listed:
  • Roberta de Filippis

    (ABE / EBA - Autorité bancaire européenne / European Banking Authority, UCL - University College of London [London])

  • Antonio Guarino

    (UCL - University College of London [London])

  • Philippe Jehiel

    (UCL - University College of London [London], 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 nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - 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 nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Toru Kitagawa

    (UCL - University College of London [London])

Abstract

In our laboratory experiment, subjects, in sequence, have to predict the value of a good. The second subject in the sequence makes his prediction twice: first ("first belief"), after he observes his predecessor's prediction; second ("posterior belief"), after he observes his private signal. We find that the second subjects weigh their signal as a Bayesian agent would do when the signal confirms their first belief; they overweight the signal when it contradicts their first belief. This way of updating, incompatible with Bayesianism, can be explained by the Likelihood Ratio Test Updating (LRTU) model, a generalization of the Maximum Likelihood Updating rule. It is at odds with another family of updating, the Full Bayesian Updating. In another experiment, we directly test the LRTU model and find support for it.

Suggested Citation

  • Roberta de Filippis & Antonio Guarino & Philippe Jehiel & Toru Kitagawa, 2022. "Non-Bayesian updating in a social learning experiment," PSE-Ecole d'économie de Paris (Postprint) halshs-03229978, HAL.
    • Handle: RePEc:hal:pseptp:halshs-03229978
    DOI: 10.1016/j.jet.2021.105188
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kawakami, Hajime, 2023. "Doob’s consistency of a non-Bayesian updating process," Statistics & Probability Letters, Elsevier, vol. 203(C).
    2. Cheng, Xiaoyu, 2022. "Relative Maximum Likelihood updating of ambiguous beliefs," Journal of Mathematical Economics, Elsevier, vol. 99(C).
    3. Wenbo Zou & Xue Xu, 2023. "Ingroup bias in a social learning experiment," Experimental Economics, Springer;Economic Science Association, vol. 26(1), pages 27-54, March.
    4. Cheng, Ing-Haw & Hsiaw, Alice, 2022. "Distrust in experts and the origins of disagreement," Journal of Economic Theory, Elsevier, vol. 200(C).
    5. Kathleen Ngangoué, M., 2021. "Learning under ambiguity: An experiment in gradual information processing," Journal of Economic Theory, Elsevier, vol. 195(C).
    6. Yves Breitmoser & Justin Valasek & Justin Mattias Valasek, 2023. "Why Do Committees Work?," CESifo Working Paper Series 10800, CESifo.
    7. Breitmoser, Yves & Valasek, Justin, 2023. "Why do committees work?," Discussion Paper Series in Economics 18/2023, Norwegian School of Economics, Department of Economics.
    8. Elchin Suleymanov, 2025. "Robust Maximum Likelihood Updating," Papers 2504.17151, arXiv.org, revised Dec 2025.
    9. Cavatorta, Elisa & Guarino, Antonio & Huck, Steffen, 2024. "Social learning with partial and aggregate information: Experimental evidence," Games and Economic Behavior, Elsevier, vol. 146(C), pages 292-307.
    10. Minnich, Aljoscha & Roggenkamp, Hauke & Lange, Andreas, 2024. "Ambiguity attitudes and surprises: Experimental evidence on communicating new information within a large population sample," Journal of Economic Behavior & Organization, Elsevier, vol. 228(C).
    11. Duffy, John & Hopkins, Ed & Kornienko, Tatiana, 2021. "Lone wolf or herd animal? Information choice and learning from others," European Economic Review, Elsevier, vol. 134(C).
    12. Shishkin, Denis & Ortoleva, Pietro, 2023. "Ambiguous information and dilation: An experiment," Journal of Economic Theory, Elsevier, vol. 208(C).
    13. Marco Angrisani & Antonio Guarino & Philippe Jehiel & Toru Kitagawa, 2021. "Information Redundancy Neglect versus Overconfidence: A Social Learning Experiment," American Economic Journal: Microeconomics, American Economic Association, vol. 13(3), pages 163-197, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:pseptp:halshs-03229978. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Caroline Bauer (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.