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DEALING WITH UNCERTAINTY ABOUT RISK IN RISK MANAGEMENT 44 Dealing With Uncertainty About Risk in Risk Management Chris G. Whipple Science tells us what we can know, but what we can know is little, and if we forget how much we cannot know we become insensitive to many things of great importance. Theology, on the other hand, induces a dogmatic belief that we have knowledge where in fact we have ignorance, and by doing so generates a kind of impertinent insolence towards the universe. Uncertainty, in the presence of vivid hopes and fears, is painful, but must be endured if we wish to live without the support of comforting fairy tales. Bertrand Russell, A History of Western Philosophy, 1945 Until the last 15 years or so, efforts to improve health and safety were directed primarily at risks of relatively certain magnitude. The social harm from accidents and diseases such as polio was all too easy to measure. Risks were managed by learning from mistakes; this is still an essential part of good risk management. But trial-and-error management is ill suited for many risks of current concernâfor example, risks with long latency periods or catastrophic potential. We now seek better ways to manage risks prospectively, methods that avoid the human costs of a trial-and-error approach. Where experience is not a guide, risk management is more difficult. We have been struggling with several such cases for the past decade: nuclear power, chemical carcinogens, and more recently, biotechnologies. One approach to uncertainty about such risks has been to try to reduce it through research. Substantial resources have been expended to understand these risks, and risk management has been improved by such studies. Despite this
DEALING WITH UNCERTAINTY ABOUT RISK IN RISK MANAGEMENT 45 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. effort, however, and particularly when direct human evidence is not available, large uncertainties about risk remain. Research may eventually resolve many questions that now trouble us, and in some cases postponing a decision to await research results may avoid uncertainty. But many risks are likely to remain uncertain indefinitely. Estimating the magnitude of risks that cannot be measured directly frequently requires the use of assumptions that cannot be tested empirically. Not only are such risks uncertain, but often the uncertainty cannot be characterized by a probability distribution. Although such distributions are useful for describing some uncertainties, they are often not feasible in risk assessment. Sometimes there is no reasonable method even to assign weights to the plausibility of alternative assumptions. Methods have been developed to elicit subjective descriptions of uncertainty; these, however, raise the question of whose estimates to accept. Recognition of these uncertainties has at times led to the view that risk assessment is a dubious enterprise, too uncertain to be relied upon for risk- management decisions. But low-level risks are inherently uncertain regardless of the approach taken to their study. This uncertainty is simply more apparent under some approaches to social risk management than others. Given the discomfort that uncertainty causes, it may be tempting to overstate what risk assessment can tell us. The limits to science are imprecise, as are the distinctions between that which is known and that which can reasonably be assumed. For these reasons, a technically accurate description of uncertainties is now considered an essential part of risk assessment. Risk assessors use assumptions to bridge gaps in knowledge. Often there are several alternative assumptions, each scientifically plausible and with no reasonable basis for choosing among them. For example, an assessor must decide which dose-response model to use in extrapolating from high-to low- dose risk. In such situations, recent practice endorses conservatism in risk estimation as protective of public health. The argument presented in this paper is that conservatism, defined as the systematic selection of assumptions leading to estimates of high risk, is not protective of human health in most situations. One way to deal with uncertainty is to categorize the smallest risks (often the most uncertain risks) as de minimis risks. De minimis risks are those judged to be too small to be of social concern, or too small to justify the use of risk- management resources for control (see Weinberg, in this volume). Properly applied, a de minimis risk concept can help set priorities for bringing regulatory attention to risk in a socially beneficial way. Although the de minimis approach ignores risks below some low limit, it too is in the longstanding tradition of risk- management methods that are intended to err on the side of safety in matters of uncertainty.