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Non-Bayesian updating in a social learning experiment

Author

Listed:
  • Roberta De Filippis

    (Institute for Fiscal Studies)

  • Antonio Guarino

    (Institute for Fiscal Studies)

  • Philippe Jehiel

    (Institute for Fiscal Studies)

  • Toru Kitagawa

    (Institute for Fiscal Studies and University College London)

Abstract

In our laboratory experiment, subjects, in sequence, have to predict the value of a good. We elicit the second subject?s belief twice: first (?first belief?), after he observes his predecessor?s action; second (?posterior belief?), after he observes his private signal. Our main result is that the second subjects weigh the private 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 multiple priors on the predecessor?s rationality and a generalization of the Maximum Likelihood Updating rule. In another experiment, we directly test this theory and find support for it.

Suggested Citation

  • Roberta De Filippis & Antonio Guarino & Philippe Jehiel & Toru Kitagawa, 2018. "Non-Bayesian updating in a social learning experiment," CeMMAP working papers CWP39/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:39/18
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Cheng, Ing-Haw & Hsiaw, Alice, 2022. "Distrust in experts and the origins of disagreement," Journal of Economic Theory, Elsevier, vol. 200(C).
    3. Kawakami, Hajime, 2023. "Doob’s consistency of a non-Bayesian updating process," Statistics & Probability Letters, Elsevier, vol. 203(C).
    4. Cheng, Xiaoyu, 2022. "Relative Maximum Likelihood updating of ambiguous beliefs," Journal of Mathematical Economics, Elsevier, vol. 99(C).
    5. 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.
    6. Kathleen Ngangoué, M., 2021. "Learning under ambiguity: An experiment in gradual information processing," Journal of Economic Theory, Elsevier, vol. 195(C).
    7. Yves Breitmoser & Justin Valasek & Justin Mattias Valasek, 2023. "Why Do Committees Work?," CESifo Working Paper Series 10800, CESifo.
    8. Breitmoser, Yves & Valasek, Justin, 2023. "Why do committees work?," Discussion Paper Series in Economics 18/2023, Norwegian School of Economics, Department of Economics.
    9. 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).
    10. Shishkin, Denis & Ortoleva, Pietro, 2023. "Ambiguous information and dilation: An experiment," Journal of Economic Theory, Elsevier, vol. 208(C).

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    More about this item

    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

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