Annie Liang, University of Pennsylvania
We develop a model of social learning from complementary information: Shortlived agents sequentially choose from a large set of (flexibly correlated) information sources for prediction of an unknown state, and information is passed down across periods. Will the community collectively acquire the best kinds of information? Longrun outcomes fall into one of two cases: (1) efficient information aggregation, where the community eventually learns as fast as possible; (2) “learning traps,” where the community gets stuck observing suboptimal sources and information aggregation is inefficient. Our main results identify a simple property of the underlying informational complementarities that determines which occurs. In both regimes, we characterize which sources are observed in the long run and how often.