Updating ambiguous beliefs in a social learning experiment - Roberta De Filippis; Antonio Guarino; Philippe Jehiel; Toru Kitagawa (Centre for Microdata Methods and Practice (cemmap), 2017)
We present a social learning experiment in which subjects predict the value of a good in sequence. We elicit each subject's belief twice: first ("first belief"), after he observes his predecessors' prediction; second, after he also observes a private signal. Our main result is that subjects update on their signal asymmetrically. They weigh the private signal as a Bayesian agent when it confirms their first belief and overweight it when it contradicts their first belief. This way of updating, incompatible with Bayesianism, can be explained by ambiguous beliefs (multiple priors on the predecessor's rationality) and a generalization of the Maximum Likelihood Updating rule. Our experiment allows for a better understanding of the overweighting of private information documented in previous studies.
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