• DOI:10.1111/j.1523-1739.2011.01806.x
• Corpus ID: 24984910

Eliciting Expert Knowledge in Conservation Science

@article{Martin2012ElicitingEK,
  title={Eliciting Expert Knowledge in Conservation Science},
  author={Tara G. Martin and Mark A. Burgman and Fiona Fidler and Petra M. Kuhnert and Samantha Low-Choy and Marissa McBride and Kerrie Lee Mengersen},
  journal={Conservation Biology},
  year={2012},
  volume={26}
}

• T. Martin, M. Burgman, +4 authors K. Mengersen
• Published 1 February 2012
• Computer Science
• Conservation Biology

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.