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Unlearning What You Have Learned

Titelbaum, Michael (2007) Unlearning What You Have Learned. In: UNSPECIFIED.


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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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Item Type: Conference or Workshop Item (UNSPECIFIED)
Titelbaum, Michael
Keywords: Bayesianism, forgetting
Subjects: Specific Sciences > Probability/Statistics
General Issues > Decision Theory
General Issues > Confirmation/Induction
Depositing User: Michael Titelbaum
Date Deposited: 03 Jan 2007
Last Modified: 07 Oct 2010 15:14
Item ID: 3120

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