Collaborative Research on Dependence Assessment for Decision and Risk Analysis

决策与风险分析的依赖性评估合作研究

• 批准号: 9320754
• 负责人: Robert Clemen
• 金额: $ 4.73万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-03-01 至 1995-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9320754&HistoricalAwards=false
• 关键词: Collaborative; Research; Dependence; Assessment; Decision

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.

专家判断是重要决策问题的关键输入，这种判断的启发和建模在决策分析、风险分析和专家系统等方法论中形式化。随着问题变得越来越复杂(例如，针对区域和全球环境变化的多系统建模)，涉及到许多感兴趣的变量，评估和建模有关变量之间关系的专家知识变得更加关键。到目前为止，条件分布提供了唯一正式的、严格的和完全通用的方法来表示专家关于一组相互关联的随机变量的知识。然而，这种方法变得笨拙和不可行，除非变量的数量非常少，而且它可能会造成认知问题。另一种方法涉及用联系数表示联合分布，联系数是边际分布的函数，并编码变量之间的相关性。拟议研究的总体目标是制定在实际专家-知识编码情况下进行相关性评估的程序。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。他们还将开始调查通过边际分布和依赖度量(与边际分布和条件分布相比)获取专家知识所涉及的行为问题。这项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。