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.
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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-pp.ucl.ac.uk/id/eprint/10067007 |
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