 We begin by recalling the notion of a probability function for the propositional language \(\mathcal{L}\). (In mathematics, probability functions are usually defined for a \(\sigma\)-algebra of subsets of a given set \(\Omega\), and required to satisfy countable additivity; cf.   Section 4.3.   In logical contexts, however, it is often more natural to define probability functions ‘immediately’ for the logic’s object language (Williamson 2002). Because this language is finitary—all its formulas have finite length—, it also suffices to require finite additivity.) A probability function (for \(\mathcal{L}\)) is a function \(P: \mathcal{L}\to \mathbb{R}\) satisfying the following constraints: