Epistemic accuracy and subjective probability

De Finetti suggested that scoring rules-namely, loss functions by which a forecaster is virtually charged depending on the degree of inaccuracy of his predictions-could be employed also to provide a compelling argument for probabilism. However, De Finetti's choice of a specific scoring rule for this purpose (Brier's quadratic rule) appears somewhat arbitrary, and the general pragmatic flavour of the argument-which makes it a variant of the well-known Dutch Book Theorem-has been deemed unsuitable for an epistemic justification of probabilism. In this paper we suggest how Brier's rule may be justified on epistemic grounds by means of a strategy that is different from the one usually adopted for this purpose (e.g., in Joyce 1998), taking advantage of a recent characterization result concerning distance functions between real-valued vectors (D'Agostino and Dardanoni 2008).