Overabundant Information and Learning Traps
This addresses a fundamental issue in social learning and information economics, with potential implications for AI and decision-making systems.
The paper tackles the problem of social learning from overabundant information sources, showing that communities can either achieve efficient information aggregation or get stuck in learning traps with inefficient learning, depending on signal correlation structures.
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.