Learning in a Correlated World

We survey a growing literature on correlated learning—that is, how information from one choice changes beliefs about others when outcomes are correlated. The core modeling innovation is to represent the unknown mapping from choices to outcomes as the realized path of a stochastic process, most commonly the Brownian motion. We show how the framework has been applied to four canonical economic problems in which correlated learning is key but understudied: (a) search and experimentation, (b) communication, (c) innovation and market competition, and (d) attribute problems. We review emerging empirical and experimental evidence on correlated learning and outline new theoretical and methodological directions.