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: Institute for Fiscal Studies
Toru Kitagawa: Institute for Fiscal Studies and University College London

No CWP60/20, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Date: 2020-12-14
New Economics Papers: this item is included in nep-exp
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Related works:
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 (2022)
Working Paper: Non-Bayesian updating in a social learning experiment (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:ifs:cemmap:60/20

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