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Learning Source Biases: Multisource Misspecifications and Their Impact on Predictions

Author

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  • Junnan He
  • Lin Hu
  • Matthew Kovach
  • Anqi Li

Abstract

We study how a Bayesian decision maker (DM) learns about the biases of novel information sources to predict a random state. Absent frictions, the DM uses familiar sources as yardsticks to accurately discern the biases of novel sources. We derive the distortion of the DM's long-run prediction when he holds misspecified beliefs about the biases of several familiar sources. The distortion aggregates misspecifications across familiar sources independently of the number and nature of the novel sources the DM learns about. This has implications for labor market discrimination, media bias, and project finance and oversight.

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  • Junnan He & Lin Hu & Matthew Kovach & Anqi Li, 2023. "Learning Source Biases: Multisource Misspecifications and Their Impact on Predictions," Papers 2309.08740, arXiv.org, revised Sep 2024.
    • Handle: RePEc:arx:papers:2309.08740
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    References listed on IDEAS

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    1. Kfir Eliaz & Ran Spiegler, 2020. "A Model of Competing Narratives," American Economic Review, American Economic Association, vol. 110(12), pages 3786-3816, December.
    2. Mira Frick & Ryota Iijima & Yuhta Ishii, 2022. "Dispersed Behavior and Perceptions in Assortative Societies," American Economic Review, American Economic Association, vol. 112(9), pages 3063-3105, September.
    3. Baum, Matthew A. & Gussin, Phil, 2008. "In the Eye of the Beholder: How Information Shortcuts Shape Individual Perceptions of Bias in the Media," Quarterly Journal of Political Science, now publishers, vol. 3(1), pages 1-31, March.
    4. Esponda, Ignacio & Pouzo, Demian & Yamamoto, Yuichi, 2021. "Asymptotic behavior of Bayesian learners with misspecified models," Journal of Economic Theory, Elsevier, vol. 195(C).
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