Volume 119, Issue 7, July 2022
Simon M. Huttegger
Pages 380-403
Rethinking Convergence to the Truth
The Bayesian theorem on convergence to the truth states that a rational inquirer believes with certainty that her degrees of belief capture the truth about a large swath of hypotheses with increasing evidence. This result has been criticized as showcasing a problematic kind of epistemic immodesty when applied to infinite hypotheses that can never be approximated by finite evidence. The central point at issue—that certain hypotheses may forever be beyond the reach of a finite investigation no matter how large one’s reservoir of evidence—cannot be captured adequately within standard probability theory. As an alternative, I propose a nonstandard probabilistic framework that, by using arbitrarily small and large numbers, makes room for the type of fine-grained conceptual distinctions appropriate for a deeper analysis of convergence to the truth. This framework allows for the right kind of modesty about attaining truth in the limit.