Updating ambiguous beliefs in a social learning experiment - Roberta De Filippis; Antonio Guarino; Philippe Jehiel; Toru Kitagawa (Centre for Microdata Methods and Practice (cemmap), 2016)
We present a novel experimental design to study social learning in the laboratory. Subjects have to predict the value of a good in a sequential order. We elicit each subject's belief twice: first ("prior belief"), after he observes his predecessors' action; second ("posterior belief"), after he observes a private signal on the value of the good. We are therefore able to disentangle social learning from learning from a private signal. Our main result is that subjects update on their private signal in an asymmetric way. They weigh the private signal as a Bayesian agent would do when the signal confirms their prior belief; they overweight the signal when it contradicts their prior belief. We show that 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.
Keywords: 330