Quitting Certainties
Michael G. Titelbaum · 2012
Abstract Subjective Bayesianism is one of the most popular tools of contemporary epistemology, using probability mathematics to provide comprehensive rational constraints both for an agent’s degrees of belief at a given time and for the evolution of those degrees of belief over time. Yet Conditionalization (the traditional Bayesian updating rule) has trouble modeling cases involving memory loss and context-sensitivity, because these cases involve the loss of certainties over time. This book proposes a new Bayesian modeling framework, the Certainty-Loss Framework (or CLF), that yields correct verdicts about rational requirements in such cases. The framework resolves a variety of outstanding problems for Bayesianism, including the Sleeping Beauty Problem concerning self-locating beliefs and difficulties squaring Bayesianism with Everettian interpretations of quantum mechanics. CLF is developed within a carefully-articulated formal modeling methodology that focuses our attention on the boundaries of our models’ applicability and the precise relation between formal systems and norms. The result is a framework that merges the advantages of formal modeling with the complexities of everyday epistemic life.