CSIRO Ecosystem Sciences, Ecoscience Precinct, GPO Box 2583 Brisbane, Queensland 4001, Australia.
Conserv Biol. 2012 Feb;26(1):29-38. doi: 10.1111/j.1523-1739.2011.01806.x.
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.
由于问题的复杂性、相对缺乏数据以及许多保护决策的紧迫性,专业知识在保护的科学和实践中得到广泛应用。专业知识是关于特定主题的实质性信息,其他人并不广泛了解。专家是拥有这些知识的人,并且其解释通常受到尊重。我们将专家对特定情况下可能发生的事情的预测称为专家判断。一般来说,专家征集方法包括五个步骤:决定如何使用信息,确定要征集什么,设计征集过程,进行征集,以及将征集到的信息转化为可用于模型或直接做出决策的定量陈述。最后一步称为编码。在征集专家知识时需要考虑的一些因素包括如何与多个专家合作以及如何组合多个判断,尽量减少征集信息中的偏差,以及验证专家信息的准确性。我们强调了结构化的征集技术,如果采用这些技术,将提高专家判断的准确性和信息量,并确保准确捕捉不确定性。我们建议从四个方面来检查专家征集的全面性和有效性:研究设计和背景、征集设计、征集方法和征集结果。就像实证数据的可靠性取决于其获取的严格程度一样,专家知识的可靠性也是如此。