Non-Bayesian updating in a social learning experiment

Roberta de Filippis (1, 2) , Antonio Guarino (2) , Philippe Jehiel (2, 3, 4) , Toru Kitagawa (2)

1 ABE / EBA - Autorité bancaire européenne / European Banking Authority
2 UCL - University College of London [London]
3 PSE - Paris School of Economics
4 PJSE - Paris Jourdan Sciences Economiques

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.

Mots clés: Ambiguous belief updating; Multiple priors; Social learning; Experiment

Domaines: Economies et finances

Roberta de Filippis, Antonio Guarino, Philippe Jehiel, Toru Kitagawa. Non-Bayesian updating in a social learning experiment. Journal of Economic Theory, 2022, 199, pp.105188. ⟨10.1016/j.jet.2021.105188⟩. ⟨halshs-03229978⟩