Bayes Is Back

A core tenet of Bayesian epistemology is that Bayesian conditionalization is the rule of rational credal revision. But it has been pointed out in the recent literature that if learning can be nontransparent, then Bayesian conditionalization does not universally maximize expected accuracy. This result raises an explanatory challenge for any externalist Bayesian who does not want to give up on a connection between accuracy and epistemic rationality: Why is Bayesian conditionalization the rule of rational credal revision, despite the fact that it does not universally maximize expected accuracy? This article proposes an answer to this challenge. The article argues that an updating rule can be evaluated, not only on the basis of its expected accuracy in particular learning situations, but also on the basis of its total expected accuracy across different learning situations. The article proposes a notion of global evidential constancy, and shows that, out of all globally evidentially constant rules, Bayesian conditionalization has the highest total expected accuracy across all learning situations, including nontransparent ones. Bayes is back!