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Bayesian Networks and Boundedly Rational Expectations

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  • Ran Spiegler

Abstract

I present a framework for analyzing decision making under imperfect understanding of correlation structures and causal relations. A decision maker (DM) faces an objective long-run probability distribution p over several variables (including the action taken by previous DMs). The DM is characterized by a subjective causal model, represented by a directed acyclic graph over the set of variable labels. The DM attempts to fit this model to p, resulting in a subjective belief that distorts p by factorizing it according to the graph via the standard Bayesian network formula. As a result of this belief distortion, the DM’s evaluation of actions can vary with their long-run frequencies. Accordingly, I define a "personal equilibrium" notion of individual behavior. The framework enables simple graphical representations of causal-attribution errors (such as coarseness or reverse causation), and provides tools for checking rationality properties of the DM’s behavior. I demonstrate the framework’s scope of applications with examples covering diverse areas, from demand for education to public policy. JEL Code: D03.

Suggested Citation

  • Ran Spiegler, 2016. "Bayesian Networks and Boundedly Rational Expectations," The Quarterly Journal of Economics, Oxford University Press, vol. 131(3), pages 1243-1290.
  • Handle: RePEc:oup:qjecon:v:131:y:2016:i:3:p:1243-1290.
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    References listed on IDEAS

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    Cited by:

    1. Ignacio Esponda & Demian Pouzo, 2014. "Berk-Nash Equilibrium: A Framework for Modeling Agents with Misspecified Models," Papers 1411.1152, arXiv.org, revised May 2016.
    2. repec:aea:aejmic:v:9:y:2017:i:2:p:54-75 is not listed on IDEAS

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    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles

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