Probability, rational belief and belief change

A simple model of rational belief holds that: (i) an instantaneous snapshot of an ideally rational belief system corresponds to a probability distribution; and (ii) rational belief change occurs by Bayesian conditionalization. But a priori probability distributions of the Kolmogorov sort cannot distinguish between propositions that are simply true from propositions that are necessarily true. Further, propositions accepted by Bayesian conditionalization become necessary truths of the updated distribution. Thus on this model, once you have accepted a proposition, it is impossible to change your mind. These problems are not avoided by Jeffrey conditionalization nor by adopting infinitesimal probability values. In contrast, conditional probability distributions are able to distinguishpropositionsthataresimply truefrompropositions that are necessarily true. However, Bayesian conditionalization as a model of belief change still makes the newly accepted proposition necessarily true, and henc...

• Type: Conference
• Year: 2004
• Where: NMR
• Authors: Charles G. Morgan
• Keywords: Artificial Intelligence, Bayesian Conditionalization, NMR 2004, Probability Distributions, Rational Belief