The terms risk and uncertainty in the title of this chapter arise from a distinction that economists make between risky and uncertain situations, the former when probabilities of the outcomes in a situation are known (and not 0 or 1) and the latter when the probabilities are not well specified (Knight, 1921; Luce & Raiffa,
1957). Applicable rational models are EUT in the former case and SEUT in the latter. A related distinction often found in the behavioral literature is between precise and ambiguous probabilities, the former applying when probability distributions are described or well learned from the environment and the latter when they are not (Ellsberg, 1961). The term ambiguous is really a misnomer in this context, in that something is ambiguous if it can take on one of a number of well-defined meanings, which is not what is intended when decision researchers refer to ambiguity or ambiguous probability. The terms vague or imprecise would be more appropriate, but generally have not taken hold in this literature. See Budescu and Wallsten (1987) for further discussion.

A third distinction, this one made by philosophers and risk analysts, is between aleatory and epistemic uncertainty, the former when the uncertainty is due to stochastic factors in the environment and the latter when it is due to lack of knowledge (Hacking, 1975).

Busemeyer, Jerome R.; Wang, Zheng; Townsend, James T.; Eidels, Ami (2015–03–20). The Oxford Handbook of Computational and Mathematical Psychology (Oxford Library of Psychology) (Page 210). Oxford University Press. Kindle Edition.