Collaborative Research on Dependence Assessment for Decision and Risk Analysis

专家判断是重要决策的关键投入 问题，以及这些判断的启发和建模是 在决策分析、风险管理、 分析和专家系统。 随着问题变得越来越复杂 (e.g.,区域和全球环境变化的多系统建模），由于有许多相关变量，评估和建模有关变量之间关系的专家知识变得更加重要。 到目前为止，条件分布提供了唯一正式的，严格的，完全的 一种表示专家关于一组 相关随机变量 然而，这种方法成为 除非变量的数量非常多，否则是不可行的。 小，它可能会造成认知问题。 一个替代 一种方法涉及用以下形式表示联合分布： Copula，它是边际分布的函数， 对变量之间的相关性进行编码。 的总目标 拟议的研究是开发依赖程序， 在实际的专家知识编码情况下进行评估。 在 在这个为期一年的试点项目中，研究人员将主要关注数学发展和依赖性评估。 他们也将开始调查的行为问题，涉及到通过边际分布和依赖措施（与边际和条件分布相比）的专家知识的启发。 这一初步工作将为今后制定健全的评估程序和可执行的议定书以及使用这些评估的分析框架奠定基础。

Expert judgments are key inputs in important decision-making problems, and the elicitation and modeling of such judgments are formalized in methodologies such as decision analysis, risk analysis, and expert systems. As problems grow in complexity (e.g., multi-systems modeling for regional and global environmental change), with many variables of interest, assessing and modeling expert knowledge about relationships among variables becomes more crucial. Up to this point, conditional distributions have provided the only formal, rigorous, and fully general way to represent an expert's knowledge about a set of interrelated random variables. However, this approach becomes unwieldy and infeasible unless the number of variables is very small, and it may pose cognitive problems. An alternative approach involves representing joint distributions in terms of copulas, which are functions of the marginal distributions and encode the dependence among the variables. The overall goal of the proposed research is to develop procedures for dependence assessment in practical expert-knowledge encoding situations. In this one-year pilot project, the researchers will focus primarily on the mathematical development and dependence assessments. They will also begin an investigation into the behavioral issues involved in the elicitation of expert knowledge via marginal distributions and dependence measures (as compared to marginal and conditional distributions). This initial work will provide the basis for the future development of sound assessment procedures and implementable protocols as well as an analytical framework for using those assessments.