Overdriving the Headlights: Empirical Data Limit Risk Analyses

by
Scott Ferson, W. Troy Tucker, and Lev R. Ginzburg

and

Risk Perception and the Problems we Make for Ourselves

by
W. Troy Tucker, Scott Ferson, and Lev R. Ginzburg

Wednesday, March 3, 2005
4:05-4:30 PM Social gathering, light snacks
4:30–6:30 PM Program

Conference Room, CDM
One Cambridge Place, 50 Hampshire Street,
Cambridge, MA

RSVP Required to Korin Scheible at CDM, ScheibleKA@cdm.com by noon the day of the meeting to facilitate security sign in.

Overdriving the Headlights: Empirical Data Limit Risk Analyses

Scott Ferson, W. Troy Tucker, and Lev R. Ginzburg
Applied Biomathematics

Summary: Even though there may be little relevant empirical information, Monte Carlo simulation requires an analyst to select a precise statistical distribution for every variable in an assessment. Moreover, even when there is no information about correlations among the variables, analysts must make some assumptions about their dependencies. Typically, analysts assume independence even between variables that are mechanistically related. By making assumptions merely for the sake of mathematical convenience that do not have empirical justification, risk assessments based on Monte Carlo simulation yield results that cannot be considered reliable. Although many have argued that two-dimensional or second-order probabilistic risk assessments can account for uncertainties about distribution shapes and parameters, and dependencies and model structure, it is easy to show that the results obtained from such analyses can be grossly misleading. Probability bounds analysis, on the other hand, allows an analyst to relax inappropriately precise statements about statistical distributions as well as untenable independence assumptions. It bounds the probabilistic results in a rigorous way and characterizes their reliability. It can even comprehensively account for many kinds of model uncertainty that may attend a risk calculation. Several numerical examples involving the real-world ecological and human health risk calculations are described and graphically contrasted with results from precise Monte Carlo simulations and second-order simulations.

Risk Perception and the Problems we Make for Ourselves

Scott Ferson, W. Troy Tucker, and Lev R. Ginzburg
Applied Biomathematics

Summary: The failures of risk communication are often blamed on public ignorance of technical issues or mistrust of industry or government. We suggest that often neither ignorance nor mistrust is fundamentally responsible for the difficulty. Instead, humans seem wired by natural evolution to use a mental calculus for reckoning risk and making decisions that can be substantially different from probability theory. We suggest that several important biases of risk perception recognized by psychometricians can be interpreted as adaptive strategies for responding to incertitude, variation, and multiple dimensions of risk. In particular, we deduce evolutionary reasons why (i) people routinely misestimate risks, (ii) people are insensitive to prior probabilities, (iii) the notion of independence is so difficult to correctly interpret, and (iv) people concentrate on the worst case (and ignore how unlikely it is). If these biases are fundamental to human perception and not removable by general education or specific training, perhaps risk analysts should make their calculations and arguments more natural, interesting, and compelling to humans. We describe such an approach to risk assessment and communication based on a practical review of recent findings in evolutionary psychology and neurobiology. Implications for medical decision-making in the context of uncertainty are explored.

Biographies:

Scott Ferson Ph.D., is a senior scientist at Applied Biomathematics, a research firm specializing in methods for ecological and environmental risk analysis. His research focuses on developing reliable mathematical and statistical tools for ecological and human health risks assessments and on methods for uncertainty analysis when empirical information is very sparse. Ferson earned his doctorate in ecology and evolution from Stony Brook University. He is an author of Risk Assessment for Conservation Biology, editor of the collected volume Quantitative Methods for Conservation Biology and the author of RAMAS Risk Calc Softtware 4.0: Risk Assessment with Uncertain Numbers. He has written more than 75 other scholarly publications, including several software packages, in environmental risk analysis and uncertainty propagation. His research has addressed quality assurance for Monte Carlo assessment, exact methods for detecting clusters in small date sets, backcalculation methods for use in remediation planning, and distribution-free methods of risk analysis appropriate for use in information-poor situations.

W. Troy TuckerPh.D., is a human ecologist and anthropologist. He received his doctorate from the University of New Mexico in 1998. He has more than ten years experience in mathematical modeling and in collecting and analyzing data. Past projects include a quantitative statistical study of the demography of New Mexican men (supported by grants from NSF, NIH, and the W.T. Grant Foundation) and smaller quantitative demographic studies among a hunter-gatherer population in Venezuela, an agricultural village in Tanzania, and swidden agriculturalists in Madagascar. At Applied Biomathematics, his research has focused on risk perception (in cooperation with Pfizer), developing case studies and methods to test probabilistic deconvolution and probability bounds (under two grants from NIH), and developing and testing methods for the incorporation of human demography into ecological risk analyses (under a grant from NSF). He also has several years experience conducting probabilistic human health and ecological risk assessments for a Superfund site in Massachusetts and Connecticut (subcontracted to the US Army Corps of Engineers and US EPA).

Lev R. Ginzburg , Ph.D . , has been professor of ecology and evolutions at Stony Brook University since 1977. He founded Applied Biomathematics in 1982. Dr. Ginzburg’s scholarly research in trophic interactions in food chains has sparked a controversial revision of the fundamental equations used for modeling food chain dynamics. He has published widely on theoretical and applied ecology, genetics, and risk analysis and has produced eight books and more then 100 scientific papers. In 1982 Dr. Ginzburg was primary author of one of the seminal papers inaugurating the field of ecological risk analysis.