Path Dependence and Learning from Neighbors

Abstract

We study the long-run properties of a class of locally interactive learning systems. A finite set of players at fixed locations play a two-by-two symmetric normal form game with strategic complementarities, with one of their “neighbors” selected at random. Because of the endogenous nature of experimentation, or “noise,” the systems we study exhibit a high degree of path dependence. Different actions of a pure coordination game may survive in the long-run at different locations of the system. A reinterpretation of our results shows that the local nature of search may be a robust reason for price dispersion in a search model.