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Updating ambiguous beliefs 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

We present a novel experimental design to study social learning in the laboratory. Subjects have to predict the value of a good in a sequential order. We elicit each subject’s belief twice: first (“prior belief”), after he observes his predecessors’ action; second (“posterior belief”), after he observes a private signal on the value of the good. We are therefore able to disentangle social learning from learning from a private signal. Our main result is that subjects update on their private signal in an asymmetric way. They weigh the private signal as a Bayesian agent would do when the signal confirms their prior belief; they overweight the signal when it contradicts their prior belief. We show that this way of updating, incompatible with Bayesianism, can be explained by ambiguous beliefs (multiple priors on the predecessor’s rationality) and a generalization of the Maximum Likelihood Updating rule.

Suggested Citation

  • Roberta De Filippis & Antonio Guarino & Philippe Jehiel & Toru Kitagawa, 2016. "Updating ambiguous beliefs in a social learning experiment," CeMMAP working papers CWP18/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:18/16
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    References listed on IDEAS

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    Cited by:

    1. Alice Hsiaw & Ing-Haw Cheng, 2016. "Distrust in Experts and the Origins of Disagreement," Working Papers 110R3, Brandeis University, Department of Economics and International Businesss School, revised Mar 2018.
    2. 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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    Keywords

    Social learning;

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