Keywords: expert judgment, imprecise data, environmental risk assessment, probability bounds, uncertainty, expert elicitation, environmental risks, cognitive bias, linguistic misunderstanding, recycled water, water pollution, reasoning, communication, expert knowledge
Facilitated expert
Keywords: expert judgment, imprecise data, environmental risk assessment, probability bounds, uncertainty, expert elicitation, environmental risks, cognitive bias, linguistic misunderstanding, recycled water, water pollution, reasoning, communication, expert knowledge
Facilitated expert judgment of environmental risks: acquiring and analysing imprecise data
Expert judgment is essential for most practical, science–based risk analyses and forecasts. Typically, experts are asked to provide judgments in precise quantitative language. This requirement is a significant impediment to accurate and well–calibrated judgments because many scientists routinely misinterpret formal statistical language and concepts. Cognitive biases compound these conceptual and linguistic misunderstandings, compromising potentially vital information. We designed a protocol to remedy this problem, using a trained facilitator who deployed a range of techniques to elicit knowledge for a case study of recycled water contamination. The elicitation generated a range of imprecise information about the parameter, which we translated into constraints for probability bounds analysis. This approach allows experts to use forms of reasoning and communication best suited to them. We show how to represent and combine expert knowledge, and discuss the use of probability bounds analysis to supplement expert elicitation by framing risk problems in heuristic and mathematical terms.