eprintid: 10118620
rev_number: 24
eprint_status: archive
userid: 608
dir: disk0/10/11/86/20
datestamp: 2021-01-18 14:25:45
lastmod: 2022-08-08 13:59:01
status_changed: 2021-01-18 14:25:45
type: article
metadata_visibility: show
creators_name: De Filippis, R
creators_name: Guarino, A
creators_name: Jehiel, P
creators_name: Kitagawa, T
title: Non-Bayesian updating in a social learning experiment
ispublished: pub
divisions: UCL
divisions: B03
divisions: C03
divisions: F24
keywords: Ambiguous belief updating, Multiple priors, Social learning, Experiment
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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: 2022-01
date_type: published
publisher: Elsevier
official_url: https://doi.org/10.1016/j.jet.2021.105188
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1841068
doi: 10.1016/j.jet.2021.105188
lyricists_name: Guarino, Antonio
lyricists_name: Jehiel, Philippe
lyricists_name: Kitagawa, Toru
lyricists_id: AGUAR93
lyricists_id: PJEHI72
lyricists_id: TKITA87
actors_name: Guarino, Antonio
actors_id: AGUAR93
actors_role: owner
full_text_status: public
publication: Journal of Economic Theory
volume: 199
article_number: 105188
citation:        De Filippis, R;    Guarino, A;    Jehiel, P;    Kitagawa, T;      (2022)    Non-Bayesian updating in a social learning experiment.                   Journal of Economic Theory , 199     , Article 105188.  10.1016/j.jet.2021.105188 <https://doi.org/10.1016/j.jet.2021.105188>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10118620/1/CWP6020-Non-Bayesian-updating-in-a-social-learning-experiment.pdf