Bayesian Networks and Boundedly Rational Expectations *
Bayesian Networks and Boundedly Rational Expectations *
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贝叶斯网络和有界理性预期 *
DOI：
10.1093/qje/qjw011
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Spiegler R
中科院分区：
文献类型：
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作者：
Spiegler R
我提出了一个框架，用于在对相关结构和因果关系不完全理解的情况下分析决策。决策者（DM）面临着一个客观的长期概率分布在几个变量（包括以前DM采取的行动）。DM的特征在于主观因果模型，由变量标签集上的有向无环图表示。DM试图拟合这个模型顶部，导致主观信念扭曲，通过标准贝叶斯网络公式根据图对其进行因子分解。由于这种信念扭曲，DM对行为的评价可能会随着行为的长期频率而变化。因此，我定义了个人行为的“个人平衡”概念。该框架使简单的图形表示的因果归因错误（如粗糙或反向因果关系），并提供了工具，检查DM的行为的合理性属性。我展示了框架的应用范围与例子涵盖不同领域，从教育需求到公共政策。
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 distributionpover 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 top, resulting in a subjective belief that distortspby 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.