Unlearning what you have learned

Bayesian modeling techniques have proven remarkably successful at representing rational constraints on agents’ degrees of belief. Yet Frank Arntzenius’s “Shangri-La” example shows that these techniques fail for stories involving forgetting. This paper presents a formalized, expanded Bayesian modeling framework that generates intuitive verdicts about agents’ degrees of belief after losing information. The framework’s key result, called Generalized Conditionalization, yields applications like a version of Bas van Fraassen’s Reflection Principle for forgetting. These applications lead to questions about why agents should coordinate their doxastic states over time, and about the commitments an agent can make by assigning degrees of belief.
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