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Information Theory and Biased Beliefs

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  • Little, Andrew T.

Abstract

Beliefs can be incorrect or biased in many ways. I propose a unifying microfoundation for many well-documented biases using ideas from information theory. The common theme is that subjective beliefs are the solution to an optimization problem where one goal is accuracy, formalized by minimizing Kullback-Leibler divergence from the objective belief. Correct beliefs are a special case where accuracy is the only goal. Other goals or constraints produce biases such as motivated beliefs, partition dependence, anchoring, overconfidence, confirmation bias, base-rate neglect, and conservatism.

Suggested Citation

  • Little, Andrew T., 2022. "Information Theory and Biased Beliefs," OSF Preprints vfqy2_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:vfqy2_v1
    DOI: 10.31219/osf.io/vfqy2_v1
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