Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213
Proc Natl Acad Sci U S A. 2014 May 20;111(20):7176-84. doi: 10.1073/pnas.1319946111. Epub 2014 May 12.
The elicitation of scientific and technical judgments from experts, in the form of subjective probability distributions, can be a valuable addition to other forms of evidence in support of public policy decision making. This paper explores when it is sensible to perform such elicitation and how that can best be done. A number of key issues are discussed, including topics on which there are, and are not, experts who have knowledge that provides a basis for making informed predictive judgments; the inadequacy of only using qualitative uncertainty language; the role of cognitive heuristics and of overconfidence; the choice of experts; the development, refinement, and iterative testing of elicitation protocols that are designed to help experts to consider systematically all relevant knowledge when they make their judgments; the treatment of uncertainty about model functional form; diversity of expert opinion; and when it does or does not make sense to combine judgments from different experts. Although it may be tempting to view expert elicitation as a low-cost, low-effort alternative to conducting serious research and analysis, it is neither. Rather, expert elicitation should build on and use the best available research and analysis and be undertaken only when, given those, the state of knowledge will remain insufficient to support timely informed assessment and decision making.
从专家那里以主观概率分布的形式获取科学和技术判断,可以为支持公共政策决策的其他形式的证据提供有价值的补充。本文探讨了进行这种启发式调查的合理性,以及如何最好地进行这种调查。讨论了一些关键问题,包括在哪些主题上存在、以及不存在具有知识的专家,这些知识为做出明智的预测判断提供了依据;仅使用定性不确定性语言的不足;认知启发式和过度自信的作用;专家的选择;为帮助专家在做出判断时系统地考虑所有相关知识而设计的启发式协议的制定、完善和迭代测试;对模型功能形式不确定性的处理;专家意见的多样性;以及在何时以及何时没有意义将来自不同专家的判断进行组合。虽然将专家启发式调查视为替代认真研究和分析的低成本、低努力的方法可能很诱人,但事实并非如此。相反,专家启发式调查应该建立在并利用现有的最佳研究和分析之上,并且只有在这些研究和分析的基础上,知识状况仍然不足以支持及时知情的评估和决策时,才应进行这种调查。