Unlearning What You Have Learned

Titelbaum, Michael (2007) Unlearning What You Have Learned. In [2007] LSE-Pitt Conference: Confirmation, Induction and Science (London, 8 - 10 March, 2007).

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.