 Although this distinction between risk and uncertainty is decision-theoretically useful, from an epistemological point of view it is in need of clarification. Only very rarely are probabilities known with certainty. Strictly speaking, the only clear-cut cases of “risk” (known probabilities) seem to be idealized textbook cases that refer to devices such as dice or coins that are supposed to be known with certainty to be fair. In real-life situations, even if we act upon a determinate probability estimate, we are not fully certain that this estimate is exactly correct, hence there is uncertainty. It follows that almost all decisions are made “under uncertainty”. If a decision problem is treated as a decision “under risk”, then this does not mean that the decision in question is made under conditions of completely known probabilities. Rather, it means that a choice has been made to simplify the description of this decision problem by treating it as a case of known probabilities. This is often a highly useful idealization in decision theory. However, in practical applications it is important to distinguish between those probabilities that can be treated as known and those that are uncertain and therefore much more in need of continuous updating. Typical examples of the former are the failure frequencies of a technical component that are inferred from extensive and well-documented experience of its use. The latter case is exemplified by experts’ estimates of the expected failure frequencies of a new type of component.