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Bayesian Networks and Boundedly Rational Expectations

Listed author(s):
  • Ran Spiegler

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.

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File URL: http://hdl.handle.net/10.1093/qje/qjw011
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Article provided by Oxford University Press in its journal The Quarterly Journal of Economics.

Volume (Year): 131 (2016)
Issue (Month): 3 ()
Pages: 1243-1290

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Handle: RePEc:oup:qjecon:v:131:y:2016:i:3:p:1243-1290.
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