Abstract

Bayesian epistemology reconceives knowledge as well-calibrated credence under uncertainty rather than justified true belief. This reconception dissolves two sets of foundational problems. First, it eliminates classic epistemological puzzles: Gettier cases evaporate when we stop demanding certainty about truth-tracking, and Hume's problem of induction dissolves when we recognize that well-calibrated credences updating on observed regularities suffice for rational inference. Second, the same framework reveals the underlying structure of ethical argumentation. Moral reasoning exhibits a ubiquitous three-move pattern: present scenarios eliciting moral intuitions, demand logical consistency across variations, then revise when incoherence emerges. We demonstrate that this pattern maps precisely onto Bayesian reasoning—thought experiments function as evidence that updates moral credences, consistency demands implement rationality constraints analogous to probabilistic coherence, and revision constitutes Bayesian updating when response patterns reveal incoherence in one's credence distribution. This synthesis explains why thought experiments persuade, why consistency matters in ethics, and how moral reasoning proceeds rationally without requiring moral realism. The framework works across both domains because neither provides direct epistemic access to Truth, yet both permit rational inference through credence formation constrained by evidence and coherence requirements.