Updating for Externalists

Abstract

The character I'll call 'the externalist' says that your evidence could fail to tell you what evidence you do or not do have. In that case, it could be rational for you to be uncertain about what your evidence is. This is a kind of uncertainty which orthodox Bayesian epistemology has difficulty modeling. If externalism is correct, then the orthodox Bayesian learning norms of conditionalization and reflection are inconsistent with each other. I will recommend that an externalist Bayesian reject conditionalzation. In its stead, I will provide a new theory of rational learning for the externalist. I'll defend this theory by arguing that its advice will be followed by anyone whose learning dispositions maximize expected accuracy. I then explore some of the theory's consequences for the rationality of epistemic akrasia, peer disagreement, undercutting defeat, and uncertain evidence. (Presented at the Pitt-CMU Graduate Conference, March, 2019, and Princeton University, April, 2019.)

Year
Links