Algorithmic randomness for Doob's martingale convergence theorem in continuous time

We study Doob's martingale convergence theorem for computable continuous time martingales on Brownian motion, in the context of algorithmic randomness. A characterization of the class of sample points for which the theorem holds is given. Such points are given the name of Doob random points. It is shown that a point is Doob random if its tail is computably random in a certain sense. Moreover, Doob randomness is strictly weaker than computable randomness and is incomparable with Schnorr randomness

A Generalization of Resource-Bounded Measure, With Application to the BPP vs. EXP Problem

We introduce resource-bounded betting games, and propose a generalization of Lutz's resourcebounded measure in which the choice of next string to bet on is fully adaptive. Lutz's martingales are equivalent to betting games constrained to bet on strings in lexicographic order. We show that if strong pseudo-random number generators exist, then betting games are equivalent to martingales, for measure on E and EXP. However, we construct betting games that succeed on certain classes whose Lutz measures are important open problems: the class of polynomial-time Turing-complete languages in EXP, and its superclass of polynomial-time Turing-autoreducible languages. If an EXP-martingale succeeds on either of these classes, or if betting games have the "finite union property" possessed by Lutz's measure, one obtains the non-relativizable consequence BPP 6= EXP. We also show that if EXP 6= MA, then the polynomial-time truth-table-autoreducible languages have Lutz measure zero, whereas if EXP = BPP..