Complementary Information and Learning Traps
We develop a model of social learning from complementary information: Short-lived 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? Long-run 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.