Non-Bayesian updating in a social learning experiment

Roberta de Filippis, Antonio Guarino, Philippe Jehiel and Toru Kitagawa
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Roberta de Filippis: ABE / EBA - Autorité bancaire européenne / European Banking Authority, UCL - University College of London [London]
Toru Kitagawa: UCL - University College of London [London]

PSE-Ecole d'économie de Paris (Postprint) from HAL

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.

Keywords: Ambiguous belief updating; Multiple priors; Social learning; Experiment (search for similar items in EconPapers)
Date: 2022-01
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Citations: View citations in EconPapers (8)

Published in Journal of Economic Theory, 2022, 199, pp.105188. ⟨10.1016/j.jet.2021.105188⟩

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Journal Article: Non-Bayesian updating in a social learning experiment (2022)
Working Paper: Non-Bayesian updating in a social learning experiment (2022)
Working Paper: Non-Bayesian updating in a social learning experiment (2020)
Working Paper: Non-Bayesian updating in a social learning experiment (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:pseptp:halshs-03229978

DOI: 10.1016/j.jet.2021.105188

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