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Non-Bayesian Updating in a Social Learning Experiment

De Filippis, R; Guarino, A; Jehiel, P; Kitagawa, T; (2018) Non-Bayesian Updating in a Social Learning Experiment. (Cemmap Working Paper CWP60/20). The Institute for Fiscal Studies: London, UK. Green open access

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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.

Type: Working / discussion paper
Title: Non-Bayesian Updating in a Social Learning Experiment
Open access status: An open access version is available from UCL Discovery
DOI: 10.47004/wp.cem.2020.6020
Publisher version: https://doi.org/10.47004/wp.cem.2020.6020
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10067007
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