@article{discovery10118620,
       publisher = {Elsevier},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
          volume = {199},
           month = {January},
            year = {2022},
           title = {Non-Bayesian updating in a social learning experiment},
         journal = {Journal of Economic Theory},
        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.},
          author = {De Filippis, R and Guarino, A and Jehiel, P and Kitagawa, T},
             url = {https://doi.org/10.1016/j.jet.2021.105188},
        keywords = {Ambiguous belief updating, Multiple priors, Social learning, Experiment}
}