科研之友
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摘要
Unless an anticipation problem is routine and short-term, and objective data are plentiful, expert judgment will be needed. Risk assessment is analogous to anticipating the future, in that models need to be developed and applied to data. Since objective data are often scanty, expert knowledge elicitation (EKE) techniques have been developed for risk assessment that allow models to be developed and parametrized using expert judgment with minimal cognitive and social biases. Here, we conceptualize how EKE can be developed and applied to support anticipation of the future. Accordingly, we begin by defining EKE as a complete process, which involves considering experts as a source of data, and comprises various methods for ensuring the quality of this data, including selecting the best experts, training experts in the normative aspects of anticipation, and combining judgments from several experts, as well as eliciting unbiased estimates and constructs from experts. We detail various aspects of the papers that constitute this special issue and analyse them in terms of the stages of the EKE future-anticipation process that they address. We also identify the remaining gaps in our knowledge. Our conceptualization of EKE with the aim of supporting anticipation of the future is compared and contrasted with the extant research on judgmental forecasting.
- 出版日期2017-3
