Bayesian networks and boundedly rational expectations

Ran Spiegler*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1243-1290
Number of pages48
JournalQuarterly Journal of Economics
Issue number3
StatePublished - 1 Aug 2016


Dive into the research topics of 'Bayesian networks and boundedly rational expectations'. Together they form a unique fingerprint.

Cite this