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We study how a Bayesian decision maker (DM) learns about the biases of novelinformation sources to predict a random state. Absent frictions, the DM usesfamiliar sources as yardsticks to accurately discern the biases of novelsources. We derive the distortion of the DM's long-run prediction when he holdsmisspecified beliefs about the biases of several familiar sources. Thedistortion aggregates misspecifications across familiar sources independentlyof the number and nature of the novel sources the DM learns about. This hasimplications for labor market discrimination, media bias, and project financeand oversight.

Learning Source Biases: Multisource Misspecifications and Their Impact on Predictions