Overabundant Information and Learning Traps
We develop a model of learning from overabundant information: Agents have access to many sources of information, where observation of all sources is not necessary in order to learn the payoﬀ-relevant unknown. Short-lived agents sequentially choose to acquire a signal realization from the best source for them. All signal realizations are public. Our main results characterize two starkly diﬀerent possible long-run outcomes, and the conditions under which each obtains: (1) eﬃcient information aggregation, where signal acquisitions eventually achieve the highest possible speed of learning; (2) “learning traps,” where the community gets stuck using an suboptimal set of sources and learns ineﬃciently slowly. A simple property of the correlation structure separates these two possibilities. In both regimes, we characterize which sources are observed in the long run and how often.