Social learning and rational expectations

Abstract

This paper argues that some of the pathologies identified by the social learning literature are not robust. Incorrect herds need indivisibilities and signals of bounded precision to arise. In smooth models convergence to the correct action and full revelation of information obtains. However, in the presence of noise convergence is slow. Two robust properties of learning from others are identified. The first, a self-correcting property, responsible for the convergence (self-enhancing facet) at a slow rate (self-defeating facet). The second, the existence of an information externality responsible for herding and underinvestment in public information and relevant from the welfare point of view. The results imply that convergence to full-information equilibria in rational expectations market models may be slow. Nevertheless, this does not apply to models in which learning is mostly from the environment. Furthermore, appropriate market mechanisms may speed up convergence even when learning is from others.