LEARNING FROM OTHERS - A WELFARE ANALYSIS

This paper considers a smooth and noisy version of the statistical pre diction model studied in the herding/informational cascades literature and compares market and optimal learning. The latter is characterized by defining a decentralized welfare benchmark as the solution to an i nfinite horizon team problem. Market behavior involves herding, in the sense that agents put too little weight on their private information for any given precision of public information, and yields underinvestm ent in the production of public information. However, both market and optimal learning involve slow learning. Examples of the model include learning by doing, reaching consensus, and consumer learning about qua lity.