Bayesian Networks and Boundedly Rational Expectations *
提出了一个分析决策者在相关性结构和因果关系理解不完美时如何决策的框架，用有向无环图表示主观因果模型，并定义了个人均衡概念，可用于解释因果归因错误并检验理性。
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.