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Coarse Bayesian Updating
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AI
DOI：10.1093/restud/rdag016.png)
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Abstract
En 中文
Studies have shown that the standard law of belief updating-Bayes' rule-is descriptively invalid in various settings. In this paper, I introduce and analyse a generalization of Bayes' rule-Coarse Bayesian updating-accommodating much of the empirical evidence. I characterize the model axiomatically, show how it generates several well-known biases, and derive its main implications in static and dynamic settings. Each axiom expresses a property of Bayes' rule but, conditional on the others, stops just short of making the agent fully Bayesian. The model employs standard primitives, making it suitable for applications; I demonstrate this by applying it to a standard setting of decision under risk, leading to a close relationship with the Blackwell information ordering and comparative measures of cognitive sophistication and bias.
Keywords:
Belief updating
Bayes rule
Biased updating
Value of information
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