Artifactual Uncertainty in Risk Analysis

The field of comparative risk analysis of electrical energy alternatives has traditionally been plagued by highly uncertain estimates of risk rates, and consequently by conflicting judgements of relative risk. To the extent that this uncertainty arises from traditional sources–imperfect observations or actual variance in the data–it can be brought within a Bayesian statistical framework which allows policy conclusions to be formulted and tested at different levels of confidence. It is shown that there are important methodological or "artifactual" sources of uncertainty, however, that cannot be treated by statistical means; these require conceptual advances for their resolution. By identifying these sources of uncertainty in simple thought experiments and examples, it is shown in what ways the concept of attributable risk, which is the policy-maker's chief concern, must be sharpened and refined to have unambiguous meaning. The conventional "multilinear" formula for calculating risk indices is challenged as a measure of attributable risk, and directions for further research to improve the methodological foundations of comparative risk analysis are identified.