Expert opinions in forecasting: The role of the Delphi technique
Expert opinions in forecasting: The role of the Delphi technique
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DOI：
10.1007/978-0-306-47630-3_7
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发表时间：
2001-01-01
期刊：
影响因子：
--
通讯作者：
Wright, G
中科院分区：
文献类型：
--
作者：
Rowe, G;Wright, G
由于缺乏适当的或可用的信息来使用统计程序，在预测任务中往往需要专家意见。但是，如何从专家那里得到最好的预测呢？一种解决方案是使用结构化的组技术，如德尔菲，用于引出和组合专家判断。在使用德尔菲技术时，控制匿名小组成员之间的信息交换经过若干轮（迭代），将最后一轮的估计值的平均值作为小组判断。这里提出了一些原则，以表明如何进行结构化的群体，以获得良好的专家判断。这些原则，适用于德尔菲小组的行为，指出了使用多少和什么类型的专家（5到20名具有不同领域知识的专家）;使用多少轮（一般是两三个）;使用哪种反馈（平均估计数加上每位专家的理由）;如何总结最终预测（对所有专家的估计进行同等权重）;如何用词提问（以一种平衡的方式，用简洁的定义，没有情绪化的术语和不相关的信息）;以及使用什么样的回答模式（频率，而不是概率或几率，可行时进行连贯性检查）。德尔菲小组比单个专家和传统小组准确得多，比统计小组（由不相互作用的个人组成，他们的判断是汇总的）更准确。研究表明，德尔菲小组与传统小组相比，优势为五比一，一个平局，与统计小组相比，优势为十二比二，两个平局。我们预计，通过遵循这些原则，预测者可能能够使用结构化的群体，有效地利用专家的意见。
Expert opinion is often necessary in forecasting tasks because of a lack of appropriate or available information for using statistical procedures. But how does one get the best forecast from experts? One solution is to use a structured group technique, such as Delphi, for eliciting and combining expert judgments. In using the Delphi technique, one controls the exchange of information between anonymous panelists over a number of rounds (iterations), taking the average of the estimates on the final round as the group judgment. A number of principles are developed here to indicate how to conduct structured groups to obtain good expert judgments. These principles, applied to the conduct of Delphi groups, indicate how many and what type of experts to use (five to 20 experts with disparate domain knowledge); how many rounds to use (generally two or three); what type of feedback to employ (average estimates plus justifications from each expert); how to summarize the final forecast (weight all experts' estimates equally); how to word questions (in a balanced way with succinct definitions free of emotive terms and irrelevant information); and what response modes to use (frequencies rather than probabilities or odds, with coherence checks when feasible). Delphi groups are substantially more accurate than individual experts and traditional groups and somewhat more accurate than statistical groups (which are made up of noninteracting individuals whose judgments are aggregated). Studies support the advantage of Delphi groups over traditional groups by five to one with one tie, and their advantage over statistical groups by 12 to two with two ties. We anticipate that by following these principles, forecasters may be able to use structured groups to harness effectively expert opinion.