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Solving an environmental problem requires stakeholder understanding and agreement. What appears to be simple problems to define become complex when data determine outcomes. This paper will describe the seven step decision logic developed for and used by US EPA to determine the problem and the decision rule for establishing compliance and acceptable uncertainty in the data used in informed decision making. Advances made in Kriging and Bayesian statistical inference tools to quantify estimates of data distributions and associated uncertainty will be described. But, making informed decisions pivot on tradeoff between cost of obtaining more information and cost of action. Raifa's foundational work on statistical decision theory is the basis for creating quantitative cost driven methodology directly applicable to cost-risk-benefit environmental decision making and actions. The paper presents various real world applications of these tools.
This is a preprint submission to PeerJ Preprints. It derived from talks presented by the authors at the Daniel Goodman Memorial Symposium, held in Bozeman, MT on March 20th and 21st, 2014. The symposium was held in honor of the late Dr. Daniel Goodman, professor of biology and ecology at Montana State University, and focused on the theme of "Decision-making under uncertainty: Risk assessment and the best available science" in the context of applied ecology and environmental science.
Expanded bibliography to help locate techncial products