081018 (M10) Direct inference, probability, and a conceptual gulf in risk communication

Abstracts and Reviews application of the hockey stick model to the Iraqi data shows, however, that the statistical upper limit of the threshold based on the hockey stick model could be as high as 255 ppm. Furthermore, the maximum likelihood estimate of the threshold using a different parametric model is virtually zero. These and other analyses demonstrate that threshold estimates based on parametric models exhibit high statistical variability and model dependency, and are highly sensitive to the precise definition of an abnormal response. Consequently, they are not a reliable basis for setting a reference dose (Rfd) for methylmercury. Benchmark analyses and statistical analyses useful for deriving NOAELs are also presented. We believe these latter analyses - particularly the benchmark analyses - generally form a sounder basis for determining RIDs than the type of hockey stick analyses presented by cox et al. However, the acute nature of the exposures, as well as other appropriate for determining acceptable human exposures to methylmercury.

Keywords: Methylmercury, risk assessment, benchmark. 081017 (M10) Characterizing Perception of Ecological Risk. McDaniels T., Axelrod L.J., Slovic P., University of British Columbia, Westwater Research Center and School of Community and Regional Planning, Vancouver, Decision Research, Oregon, Risk Analysis,

Vol. 15, Number 5, 1995, pp. 575-588. Relatively little attention has been paid to the role of human perception in ecological risk management. This paper attempts to characterize perceived ecological risk, using the psychometric paradigm developed in the domain of human health risk perception. The research began by electing a set of scale characteristics and risk items (e.g. technologies, actions, events, beliefs) from focus group participants. Participants in the main study were 68 university students who completed a survey instrument that elicited ratings for each of 65 items on 30 characteristic scales and one scale regarding general risk to natural environments. The results are presented in terms of mean responses over individuals for each scale and item combination. Factor analysis show that five factors characterize the judgment data. These have been termed impact on species, human benefits, impact on humans, avoidability, and knowledge of impacts. The factor results correspond with initial expectations and provide a plausible characterization of judgments regarding ecological risk. Some comparisons of mean responses for selected individual items are presented. Keywords: risk perception, ecological risk analysis.

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081018 (M10) Direct Inference, Probability, and a Conceptual Gulf in Risk Communication. Walker V.R., Hofstra University School of Law, Hempstead, New York, Risk Analysis, Vol. 15, Number

5, 1995, pp. 603-609. Differences in the conceptual frameworks of scientists and nonscientists may create barriers to risk communication. This article examines two such conceptual problems. First, the logic of "direct inference" from group statistics to probabilities about specific individuals suggests that individuals might be acting rationally in refusing to apply to themselves the conclusions of regulatory risk assessments. Second, while regulators and risk assessment scientists often use an "objectivist" or "relative frequency" interpretation of probability statements, members of the public are more likely to adopt a"subjectivist" or "degree of confidence" interpretation when estimating their personal risks, and either misunderstand or significantly discount the relevance of risk assessment conclusions. If these analysis of inference and probability are correct, there may be a conceptual gulf at the center of risk communication that cannot be bridged by additional data about the magnitude of group risk. Suggestions are made for empirical studies that might help regulators deal with the conceptual gulf. Keywords: direct inference, probability, risk communication, risk perception, uncertainty, variability, personal risk. 081019 (M10) On the Selection of Distributions for Stochastic Variables. Seiler F.A., Alvarez J.L., Risk Analysis, Vol. 16,

Number 1, 1996, pp. 5-18. One of the main steps in an uncertainty analysis is the selection of appropriate distribution functions for all stochastic variables. In this paper, criteria for such selection are reviewed, the most important among them being any a priori knowledge about the nature of a stochastic variable, and the Central Limit Theorem of probability theory applied to sums and products of stochastic variables. In application of these criteria, it is shown that many of the popular selections, such as the uniform distribution for a poorly known variable, require far more knowledge than is actually available. However, the knowledge available is usually sufficient to make use of other, more appropriate distributions. Next, functions of stochastic variables and the selection