On the distinction between aleatory and epistemic uncertainty and its implications on reliability and risk analysis

During the past decades, the scientific community has provided different frameworks for classifying uncertainty and its sources. The most common distinction-or at least, the one that is widely accepted in the reliability and risk analysis community-is the distinction between aleatory and epistemic uncertainty. The former is considered irreducible because it is due to the natural variability of random phenomena. The latter is attributed to the lack of knowledge, thus, it is reducible as long as we are able to gather more information. In this paper, we propose a discussion about this distinction from an historical point of view and we introduce some alternative theories to deal with uncertainty.