Collaborative Reseach: Model-Based Methods for Debiasing Individual Probability Assessments: Theory, Experiments, and Application to Mississippi River Delta Restoration

合作研究：基于模型的个体概率评估去偏方法：理论、实验以及在密西西比河三角洲恢复中的应用

• 批准号: 0962554
• 负责人: Benjamin Hobbs
• 金额: $ 15.28万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-03-15 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0962554&HistoricalAwards=false
• 关键词: Collaborative; Reseach; Model; Based; Methods

Experts are often asked to provide judgments to inform both private sector and public policy decisions. Such judgments may be used alone or with scientific models to estimate the probability of events such as changes in energy markets, levels of future carbon dioxide emissions, global temperature change, or the number of hurricanes to make landfall in the United States. Expert judgments are essential because past data may either be unavailable or not directly relevant due to changing conditions. From psychological research, however, we know that when making such probability judgments, people use mental short-cuts, or heuristics. The heuristics skew how people express judgments, resulting in unintentional biases in probabilities that systematically distort an individual's stated probabilities. These cognitive biases are the focus of the research, rather than intentional biases in which expressed probabilities are deliberately distorted in order to game the system. Minimizing cognitive biases in expert probabilities is essential when the probabilities are inputs to scientific models or to decisions that must be made without waiting for perfect information. The objective of this research is to develop mathematical models and statistical procedures with which an analyst can estimate the degree of bias for an individual and thereby quantify adjustments that would eliminate those biases. The research focuses on three cognitive biases: overprecision, the tendency to be too sure that a particular event will occur; partition dependence, in which judged probabilities depend inappropriately on how the range of the uncertain variable is divided; and carryover, an ordering effect in which an individual?s stated probabilities may be affected by previous judgments. The bias measurement and debiasing methods are to be developed and tested in experimental settings using a large group of participants. The experimental results will show the extent of the biases under various circumstances, and the effectiveness of the method for removing the bias. This research will enable experts to provide probabilities that better represent their beliefs and knowledge, undistorted by bias, when engaged in public- and private-sector risk analyses. Potential applications include decisions in which data scarcity, coupled with high stakes, make the use of expert judgments essential. These include many areas of business decision making, as well as high-stakes policy decisions concerning, for instance, climate change and terrorism risk.

专家经常被要求提供判断，为私营部门和公共政策决策提供信息。这些判断可以单独使用，也可以与科学模型一起使用，以估计事件的概率，如能源市场的变化，未来二氧化碳排放量的水平，全球气温变化，或飓风在美国登陆的次数。专家的判断是必不可少的，因为过去的数据可能无法获得，或者由于条件的变化而不直接相关。然而，从心理学研究中，我们知道，在做出这种概率判断时，人们会使用心理捷径，或称心理学。概率论扭曲了人们表达判断的方式，导致了概率上的无意偏差，从而系统地扭曲了个人陈述的概率。这些认知偏差是研究的重点，而不是故意的偏差，其中表达的概率被故意扭曲，以游戏系统。当概率是科学模型或决策的输入时，最大限度地减少专家概率中的认知偏差是至关重要的，而这些决策必须在不等待完美信息的情况下做出。本研究的目的是开发数学模型和统计程序，分析师可以估计个人的偏见程度，从而量化调整，消除这些偏见。这项研究主要集中在三个认知偏差：过度精确，倾向于过于肯定，一个特定的事件会发生;分区依赖，其中判断的概率不适当地取决于如何划分的不确定变量的范围;和结转，一个有序的影响，其中一个人？的陈述概率可能会受到先前判断的影响。偏倚测量和去偏倚方法将在实验环境中使用大量参与者进行开发和测试。实验结果将显示在各种情况下的偏差的程度，以及消除偏差的方法的有效性。这项研究将使专家们能够提供概率，更好地代表他们的信念和知识，不受偏见的扭曲，当从事公共和私营部门的风险分析。潜在的应用包括数据稀缺的决策，再加上高风险，使专家的判断必不可少。这包括许多商业决策领域，以及涉及气候变化和恐怖主义风险等高风险的政策决策。