Science, Policy, and Data-Driven Decisions in a Data Vacuum

Abstract

Environmental regulation invariably requires making decisions in the face of scientific uncertainty. However, making decisions in the near-absence of evidence—essentially, the most extreme uncertainty—is a special case because it most plainly exposes the defaults and preferences of those making the decisions, and because it may inspire creative ways of reducing the probability of error. Here, we relate the case of an Endangered Species Act listing of several rockfish species in Puget Sound, Washington, which illustrates a set of decisions the National Marine Fisheries Service made in the absence of critical information about those populations. Subsequent scientific effort and technological advances have been powerful tests of the listing decision, and have suggested different outcomes for each of the three species under evaluation. We discuss this case in the context of agency discretion and internal incentives to make or defer decisions. We then highlight the roles of technological change and institutional learning as they intersect with these incentives, and suggest structural means of enabling this kind of effective data use by administrative agencies more generally.