Collaborative Research: Combining Expert Judgments for Environmental Risk Analysis

合作研究：结合专家判断进行环境风险分析

• 批准号: 0084372
• 负责人: James Hammitt
• 金额: $ 18.03万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-15 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0084372&HistoricalAwards=false
• 关键词: Collaborative; Research; Combining; Expert; Judgments

This collaborative project with Clemen (0084383) and Gelman (0084368) will develop and demonstrate improved methods for using expert judgment in environmental risk analysis. Risk-analytic models rely heavily on parameter estimates obtained using either informal or formal expert judgments. Such methods can be sensitive to two problems: biases in experts coding their knowledge into probability distributions (such as overconfidence bias), and the typically uncertain degree of dependence in judgments between experts (e.g., each expert's judgment combines his reading of a common scientific literature and his own experience and interpretations). The project will analyze the properties of two state-of-the-art methods for combining expert judgments: the "classical" method developed by Roger Cooke and colleagues at Delft University of Technology and the "copula" method developed by Robert Clemen and colleagues at Duke University. It will compare the methods mathematically and by evaluating their performance on synthetic and actual expert judgment data sets. In addition, the project will develop improved methods for combining distributions based on fully Bayesian methods for incorporating overconfidence and dependence among experts, and compare them with the two existing methods.The project will lead to better methods and an improved understanding of alternative methods for combining expert judgment in environmental risk analysis. Many parameters in a risk-analysis model cannot be measured directly, for physical or ethical reasons (e.g., one typically cannot trace pollutants far from their source nor conduct toxicity studies on humans). As a result, parameter estimates are often based on expert judgment. In most cases, the expert judgment is applied informally, as when the model builders use their own "best guess" estimates of parameter values. In some cases (such as risks associated with nuclear power), judgments (in the form of probability distributions) are elicited from a panel of experts. However, there is at present no standard method for combining judgments from multiple experts, and limited understanding of the properties of alternative methods. This research will lead to better understanding of alternative methods and, ultimately, to better use of expert judgment.

这个与Clemen（0084383）和Gelman（0084368）的合作项目将开发和演示在环境风险分析中使用专家判断的改进方法。 风险分析模型在很大程度上依赖于使用非正式或正式的专家判断获得的参数估计。 这种方法可能对两个问题敏感：专家将其知识编码为概率分布的偏差（如过度自信偏差），以及专家之间判断的依赖程度通常不确定（例如，每个专家的判断结合了他对普通科学文献的阅读和他自己的经验和解释）。 该项目将分析两种用于结合专家判断的最先进方法的特性：德尔夫特理工大学的罗杰·库克及其同事开发的“经典”方法和杜克大学的罗伯特·克莱门及其同事开发的“copula”方法。 它将在数学上比较这些方法，并通过评估它们在合成和实际专家判断数据集上的表现。 此外，该项目还将开发基于完全贝叶斯方法的改进分布组合方法，以纳入专家之间的过度自信和依赖，并将其与现有的两种方法进行比较。该项目将导致更好的方法，并提高对环境风险分析中结合专家判断的替代方法的理解。 由于物理或伦理原因，风险分析模型中的许多参数无法直接测量（例如，人们通常无法在远离污染物来源的地方追踪污染物，也无法对人类进行毒性研究）。 因此，参数估计通常基于专家判断。 在大多数情况下，专家的判断是非正式的，当模型构建者使用自己的参数值的“最佳猜测”估计。 在某些情况下（如与核电有关的风险），判断（以概率分布的形式）是从专家小组得出的。 然而，目前还没有标准的方法来结合多个专家的判断，并有限的了解替代方法的属性。 这项研究将导致更好地了解替代方法，并最终更好地利用专家的判断。