Munich Personal RePEc Archive

Learning from Private and Public Observation of Other's Actions

Amador, Manuel and Weill, Pierre-Olivier (2006): Learning from Private and Public Observation of Other's Actions.

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We study how a continuum of agents learn about disseminated information by observing others’ actions. Every period each agent observes a public and private noisy signal centered around the aggregate action taken by the population. The public signal represents an endogenous aggregate variable such as a price or a quantity. The private signal represents the information gathered through private communication and local interactions. We identify conditions such that the average learning curve is S-shaped: learning is slow initially, intensifies rapidly, and finally converges slowly to the truth. We show that increasing public information always slows down learning in the long run and, under some conditions, reduces welfare. Lastly, optimal diffusion of information requires that agents “strive to be different”: agents need to be rewarded for choosing actions away from the population average.

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