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DEALING WITH UNCERTAINTY ABOUT RISK IN RISK MANAGEMENT 46 original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the retained, and some typographic errors may have been accidentally inserted. Please use the print version of this publication as the authoritative version for attribution. RISK VERSUS UNCERTAINTY Risk, as it is generally understood by health and safety risk analysts, measures the probability and severity of loss or injury. Uncertainty , on the other hand, refers to a lack of definite knowledge, a lack of sureness; doubt is its closest synonym. At times, these terms are confused. Risk and uncertainty are related in that both preclude knowledge of future states and both may be described by probabilities. It is important, however, to distinguish whether a lack of predictabiity arises from insufficient knowledge (uncertainty) or from a well-understood probabilistic process (risk). The risk associated with a bet on a fair coin toss is known precisely; the risk has no uncertainty, although the outcome of the toss is uncertain. Conversely, the outcome of the administration of an experimental drug is also uncertain, but in such a case the inability to predict may be due more to a lack of information than to what also may be an inherently probabilistic response to the drug. The predictability of the result of a large number of trials helps to clarify the distinction between risk and uncertainty. For a fair coin toss, we can predict that about half of the results will be heads. For an experimental drug given to a large population, the number of people adversely affected may not be predictable except within a broad range. In the case of an experimental drug, the estimated probability that an average individual will experience an adverse effect (or equivalently, the number of people in an exposed population experiencing an adverse effect) might be described by use of a probability distribution. A probability distribution applied to a probability is called a second-story probability. Such a distribution describes the likelihood that the probability of an adverse effect is a particular value. Decision analysts and theorists of subjective probability frequently note that the second-story probability representation is unnecessarily complex; such measures can be mathematically collapsed into a single probability. That is, the probability of a probability is a probability. For individual decision making, it may be immaterial what combination of probabilistic processes and information gaps gives rise to an estimate of the likelihood of some outcome; it is sufficient to describe the likelihood of an outcome by a probability. However, in the case of social risk management by a regulatory agency, it is often useful to distinguish between risk and uncertainty. Risk Assessment Policy The recent report Risk Assessment in the Federal Government: Managing the Process (National Research Council, 1983) endorsed the concept that scientific questions about the degree of risk posed by a specified exposure or activity should be separated, to the extent feasible, from the policy questions
