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Learning Source Biases: Multi-sourced Misspecifications and Consequences

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  • Lin Hu
  • Matthew Kovach
  • Anqi Li

Abstract

We study how a decision maker (DM) learns about the biases of unfamiliar information sources. Absent any friction, a rational DM uses familiar sources as yardsticks to discern the true bias of a source. If the DM has misspecified beliefs, this process fails. We derive long-run beliefs, behavior, welfare, and corresponding comparative statics when the DM holds dogmatic, incorrect beliefs about the biases of several familiar sources. The distortion due to misspecified learning aggregates misspecifications associated with familiar sources independently of sources the DM learns about. This has implications for labor market discrimination, media bias, and project finance and oversight.

Suggested Citation

  • Lin Hu & Matthew Kovach & Anqi Li, 2023. "Learning Source Biases: Multi-sourced Misspecifications and Consequences," Papers 2309.08740, arXiv.org, revised Mar 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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