Eliciting Expert Knowledge in Conservation Science

MARTIN, TARA G., BURGMAN, MARK A., FIDLER, FIONA, KUHNERT, PETRA M., LOW-CHOY, SAMANTHA, MCBRIDE, MARISSA, & MENGERSEN, KERRIE (2012) Eliciting Expert Knowledge in Conservation Science. Conservation Biology, 26(1), pp. 29-38.

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Abstract

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.

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ID Code: 53429
Item Type: Journal Article
Refereed: Yes
Keywords: Bayesian priors, bias, decision making, elicitation, expert judgment, expert opinion, overconfidence
DOI: 10.1111/j.1523-1739.2011.01806.x
ISSN: 08888892
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Wiley
Deposited On: 06 Jan 2015 02:00
Last Modified: 06 Jan 2015 02:00

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