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

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Abstract

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)
Creators:	Creators Email ORCID 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
Subjects:	Specific Sciences > Probability/Statistics General Issues > Decision Theory General Issues > Confirmation/Induction
Date:	2007
URI:	https://philsci-archive.pitt.edu/id/eprint/3120

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