We present a computational model of mathematical reasoning according to which mathematics is a fundamentally stochastic process. That is, on our model, whether or not a given formula is deemed a theorem in some axiomatic system is not a matter of certainty, but is instead governed by a probability distribution. We then show that this framework gives a compelling account of several aspects of mathematical practice. These include: 1) the way in which mathematicians generate research programs, 2) the role of abductive reasoning in mathematics, 3) the way in which multiple proofs of a proposition can strengthen our degree of belief in that proposition, 4) the nature of the hypothesis that there are multiple formal systems that are isomorphic to physically possible universes, and 5) the prior distribution that a Bayes rational mathematician ought to have over possible mathematical systems. Thus, by embracing a model of mathematics as not perfectly predictable, we generate a new and fruitful perspective on the epistemology and practice of mathematics.
David H. Wolpert