Risk, Utility Concepts, and Policy Choices: Discussion

The problem of decision making under uncertainty poses significant challenges to economics. As the papers point out, at least some empirical evidence appears to refute axioms at the core of the neoclassical research program. While some may be dismayed by this empirical evidence, such refutations are an essential feature of progressive research. From a methodological viewpoint, the central question is not whether refutations occur, but how a research program absorbs and generalizes such contrary evidence (Lakatos). This session provides three key examples of current attempts to respond to the problems posed by uncertainty. Each paper addresses a different challenge: the adjustment of risk perceptions to a changing environment, the empirical shortcomings of the expected utility model, and the modeling of imperfect decision processes in an imperfect world. Each paper makes significant progress in identifying prospects for research. Smith and Desvousges (SD) address an area of central concern to environmental policy; the problem of choice in an environment of rapidly evolving information. As SD point out, much of environmental economics, and economic choice theory in general, has dealt with informationally static environments. This emphasis on statics stands in contrast to the actual policy setting where new sources of hazards are discovered daily, where detection instruments are increasingly sensitive to small concentrations, and where toxicological predictions are controversial and subject to change. SD focus on how risk perceptions change as individuals are exposed to new information. In their case study, the new information described radon gas levels in single family homes. Each participating household received two types of information: information on the physical radon level in a home and interpretative booklets that described health risks and the means to reduce exposure. To determine how information affected risk perceptions, different booklets were sent to different treatment groups. Each booklet varied with respect to the amount of qualitative or quantitative information and the extent to which individuals were commanded or cajoled into taking remedial action. SD adapted the bayesian model of Viscusi and O'Connor to model the evolution of risk perceptions. The bayesian approach asserts a simple linear relationship between an individual's prior risk perception, new information, and the subjective posterior risk assessment. Two interesting attributes of the bayesian model are not mentioned in the paper. First, the bayesian linearity restriction is econometrically testable and allows for the potential rejection of the bayesian framework. This is a weak test, however, since psychological research indicates that posterior beliefs are not bayesian but are proportional to bayesian posterior probabilities (Edwards). Second, parameter estimates from the linear model can be used to convert two elusive, qualitative concepts into quantitative measurements. The bayesian parameter estimates quantify both the subjective confidence that individuals place on the new information and the risk level that subjects perceive in narrative and graphical information. The bayesian analysis is certain to yield important new insights for the design and evaluation of public health warnings and hazard information systems. From this perspective, it is very interesting that the impact of information treatments does vary in a systematic and measurable way. For instance, at the twomonth updating, the simple fact sheet that included no explanatory information resulted in the highest posterior risk. Across the informaJohn P. Hoehn is an assistant professor, Department of Agricultural Economics, Michigan State University.