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Iterated Belief Revision, Reliability, and Inductive Kevin T. Kelly

Summary: Iterated Belief Revision, Reliability, and Inductive
Kevin T. Kelly
Department of Philosophy
Carnegie Mellon University
September 24, 2001
Belief revision theory concerns methods for reformulating an agent's epistemic state when
the agent's beliefs are refuted by new information. The usual guiding principle in the
design of such methods is to preserve as much of the agent's epistemic state as possible
when the state is revised. Learning theoretic research focuses, instead, on a learning
method's reliability or ability to converge to true, informative beliefs over a wide range of
possible environments. This paper bridges the two perspectives by assessing the reliability
of several proposed belief revision operators. Stringent conceptions of "minimal change"
are shown to occasion a limitation called inductive amnesia: they can predict the future if
and only if they cannot remember the past. Avoidance of inductive amnesia can therefore
function as a plausible and hitherto unrecognized constraint on the design of belief revision
0.1 Introduction
According to the familiar, Bayesian account of probabilistic updating, full beliefs change


Source: Andrews, Peter B. - Department of Mathematical Sciences, Carnegie Mellon University


Collections: Mathematics