Non-Bayesian updating in a social learning experiment

Roberta De Filippis, Antonio Guarino, Philippe Jehiel () and Toru Kitagawa

Journal of Economic Theory, 2022, vol. 199, issue C

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)
JEL-codes: D01 D81 D90 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Related works:
Working Paper: 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:eee:jetheo:v:199:y:2022:i:c:s0022053121000053

DOI: 10.1016/j.jet.2021.105188

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