Talk: Simon Huttegger (UC Irvine)

Title:

Bayesian Randomness

Joint work with Sean Walsh (UCLA) and Francesca Zaffora Blando (CMU)

Abstract:

In this talk, we pursue two goals. First, we develop a Bayesian perspective on algorithmic randomness: a branch of computability theory concerned with characterizing the notion of a sequence displaying no effectively detectable patterns. Second, we argue that taking a Bayesian point of view on randomness leads to new insights for Bayesian epistemology, more specifically, for two pillars of Bayesian epistemology: convergence to the truth and merging of opinions. In particular, adopting such a perspective reveals that, for computable Bayesian agents, the sequences of observations, or data streams, along which convergence to the truth and merging of opinions occur are uniformly characterizable in an informative way: they are the algorithmically random data streams.