Non-Bayesian updating in a social learning experiment - Roberta De Filippis; Antonio Guarinno; Philippe Jehiel; Toru Kitagawa (Centre for Microdata Methods and Practice (cemmap), 2018)
In our laboratory experiment, subjects, in sequence, have to predict the value of a good. We elicit the second subject's belief twice: first ("first belief"), after he observes his predecessor's action; second ("posterior" belief.), after he observes his private signal. Our main result is that the second subjects weigh the private 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 multiple priors on the predecessor's rationality and a generalization of the Maximum Likelihood Updating rule. In another experiment, we directly test this theory and find support for it.
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