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4 ANALYSIS
Pages 97-117

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From page 97...
... Risk analysis can be qualitative as well as quantitative; in fact, for some important elements of risk, no valid method of quantification is available. Analytic techniques are essential for understanding risk, and many useful volumes have been written about them (e.g., Raiffa, 1968; U.S.
From page 98...
... Methods for qualitative analysis include systematic clinical and field observation, logical inference from historical and comparative studies, inference from legal precedent, ethnographic interviewing, and the application of principles of ethics. Although the bulk of the effort in developing methods of risk analysis has been addressed to quantitative methods, critical aspects of risk frequently require qualitative evaluation.
From page 99...
... Effective decision support systems can allow analysts to access and evaluate data, in some cases in real time (e.g., for hurricane, flood, or pollutant spill evaluations) ; test predictive models; evaluate management and decision options; perform uncertainty analyses; and identify data and research needs to improve predictions.
From page 100...
... But they also present analysts with a tradeoff between the needs for simplicity and for verisimilitude. Incorporating more real-world components and processes can lead to more realistic representations, but complex models can require analysts to make many estimates, and may exceed analysts' ability to understand how the model operates and therefore to obtain meaningful insights.
From page 101...
... For both quantitative and qualitative risk analysis, technical adequacy is a necessary but not sufficient characteristic: analysis must also be relevant to the given risk decision. First, the questions to be addressed must be appropriate for the available analytic techniques and must be ones for which information exists.
From page 102...
... We do not comment on specific ones, but focus instead on how such techniques can be appropriately integrated into the process that results in a risk characterization. We focus especially on the class of techniques, including those of benefit-cost analysis and multiattribute utility analysis, that aims to reduce risk to a single dimension as an aid to priority setting and decision making.
From page 103...
... Government agencies may also use the techniques to routinize their decision processes and to meet legal tests regarding arbitrariness and capriciousness. There are two chief strengths of such analytical techniques: they require analysts to pay careful attention to several dimensions of risk and, in the course of deciding on how to aggregate across dimensions, the techniques may elicit careful deliberation about the relationships and tradeoffs among the dimensions.
From page 104...
... The danger lies in using judgments that are implicit in analytic techniques but are made without broad-based deliberation, as substitutes for that deliberation. It lies in acting as if values are not embedded in the analyses or as if some particular analytic technique can be assumed in advance to yield the best or most trustworthy understanding of a risk situation.
From page 105...
... We conclude that analytic techniques for simplifying risk should be treated like other analytic techniques used to inform risk decisions. That is, decisions about using them, refining them, and interpreting their results should be made as part of an appropriately broad-based analyticdeliberative process involving not only analytic experts, but also the public officials and interested and affected parties whose decisions the techniques are intended to inform.
From page 106...
... It would be worthwhile to experiment with the use these techniques in particular areas of risk decision making where they seem likely to be helpful and to carefully evaluate the effects of their use on understanding and on the decision-making process. It would also be worthwhile to experiment further with deliberative techniques for priority setting, in which an appropriately broad-based process considers information from analyses of the various dimensions of a risk and information from the application of analytic techniques that seek to simplify risk.
From page 107...
... Three hypothetical descriptions of risk can illustrate the prevalence and importance of the different types of uncertainty in risk characterization. Consider these three risks: a 1-in-100 chance of a river overflowing its levee in a given year with a given impact on life and property; a 1-in10,000 chance of a volcano erupting near the proposed waste repository at Yucca Mountain in the next 10,000 years, resulting in the release of a given quantity of radioactive material; and a 1-in-1,000,000 chance of an individual contracting a fatal cancer over his or her lifetime due to a chemical exposure.
From page 108...
... Rather, we focus on the role of uncertainty in risk characterization and the role that uncertainty analysis can play as part of an effective iterative process for assessing, deliberating, and understanding risks. In describing this role, we note the critical importance of social, cultural, and institutional factors in determining how uncertainties are considered, addressed, or ignored in the tasks that support risk characterization.
From page 109...
... The important uncertainties are those that create important differences in the assessed outcomes and may therefore affect preferences among the available decision options. Because risk characterization requires providing information about the full set of factors of concern to the interested parties, it must address uncertainty not only about the physical and biological impacts of the risk, but also about the social and political factors inherent to the risk.
From page 110...
... Formal value-of-information analysis provides a set of useful techniques for assessing these implications. These techniques involve estimating how risks would change with new information, such as additional experimental results, before that information exists.
From page 111...
... We emphasize, however, that determining whether a reduced uncertainty would make a difference in a decision often requires deliberation as well as analysis. Different participants in the decision process may not agree on how to interpret new information or on the appropriate criteria for making or revising risk decisions.
From page 112...
... Information that is available or provided on the occurrence of one supposedly representative event can cause analysts to ignore or undervalue large amounts of relevant information. Thus, representativeness has been attributed as the cause of many shortcomings or biases in "statistical thinking," such as failure to appreciate the difference in reliability between small and large samples of data and failure to make one's predictions of future events sufficiently dependent on the overall population mean rather than a few events presumed to be typical.
From page 113...
... demonstrates this for the case of a hypothetical low-probability event that usually presents risk a of 1 In 1 million.: The ability to deal with ignorance and surprise unforeseen or unforeseeable circumstances is inherently limited In an uncertainty analysis. Unfortunately, experience shows that it is often these unknown circumstances and surprise events that shake risk analyses and topple expectations, rather than the factors (important though they might be)
From page 114...
... Understanding depends not only on the inherent features of a risk, or even the experience and expertise of the analyst attempting to characterize it, but also on the social context of the risk analysis and the associated deliberative process (e.g., Brown, 1989; lasanoff, 1987a, 1987b, 1991; MacKenzie, 1990; Michael, 1992; Shapin, 1994; Thompson, Ellis, and Wildovsky., 1990; Wynne, 1980, 1987, 1995~. These factors affect the way information about uncertainty is created and utilized in evaluating risks and the degree to which analysts acknowledge uncertainty.
From page 115...
... When the stakes in a decision are high, accuracy or inaccuracy in science may be accentuated by participants for their own purposes. For example, in the early 1980s a debate over acceptable levels of polychlorinated biphenyls in the ground around leaking transformers (for example, on electric power poles)
From page 116...
... CONCLUSIONS Analytic techniques can be used for several aspects of risk characterization. Most familiar among these uses is to estimate the likelihood of particular adverse outcomes.
From page 117...
... Analysis conducted to simplify the multidimensionality of risk or to make sense of uncertainty can be misleading or inappropriate, can create more confusion that it removes, and can even exacerbate the conflicts it may have been undertaken to reduce. Because of the power of formal analytical techniques to shape understanding, decisions about using them for these purposes and about interpreting their results should not be left to analysts alone, but should be made as part of an appropriately broad-based analytic-deliberative process.


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