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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 cemmap 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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    File URL: https://www.ifs.org.uk/uploads/cemmap/wps/CWP391818.pdf
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    References listed on IDEAS

    as
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    5. Hanany, Eran & Klibanoff, Peter, 2007. "Updating preferences with multiple priors," Theoretical Economics, Econometric Society, vol. 2(3), September.
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    Cited by:

    1. Marco Angrisani & Antonio Guarino & Philippe Jehiel & Toru Kitagawa, 2017. "Information redundancy neglect versus overconfidence: a social learning experiment," CeMMAP working papers CWP32/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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