  We develop a model of social learning from overabundant information:
Short-lived agents sequentially choose from a large set of (flexibly
correlated) information sources for prediction of an unknown state. Signal
realizations are public. We demonstrate two starkly different long-run
outcomes: (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 learns inefficiently. Our main results
identify a simple property of the signal correlation structure that separates
these outcomes. In both regimes, we characterize which sources are observed in
the long run and how often.
