A Study of Expert Judgment in a Natural Setting

自然环境下的专家判断研究

• 批准号: 8822319
• 负责人: Thomas Stewart
• 金额: $ 10.4万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-05-01 至 1991-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8822319&HistoricalAwards=false
• 关键词: Study; Expert; Judgment; Natural; Setting

This proposal focuses on how weather forecasters make use of imperfect information in making predictions. The investigators were seen to provide an unusual and appropriate mix of skills needed to test several important theoretical questions about how experts assess the quality of information and important applied questions about how weather forecasts could be made more reliable. The project involves two experiments, using expert weather forecasters as the primary subject group, that will (a) compare the accuracy of forecasts made in limited vs. full information conditions and (b) test the consistency and reliability of information acquisition by examining how different forecasters make use of the same limited information base. The use of operating weather forecasters is viewed as particularly appropriate because, despite the fact that meterorologists often are viewed as the paragon of excellence in real-world probability judgments, their decision making processes and their ability to interpret and make inferences on the basis of incomplete information has not been thoroughly studied. The results will be general interest as a study of how experts make use of incomplete information and how human-computer interactions should be designed to provide assistance to decision makers facing complex forecasting demands under conditions of limited information.

这项建议的重点是天气预报员如何利用不完善的 信息进行预测。 调查人员被认为 提供了一个不寻常的和适当的技能组合需要测试几个 关于专家如何评估质量的重要理论问题 关于天气预报的信息和重要的应用问题 可以变得更可靠。 该项目包括两个实验， 使用专家天气预报员作为主要学科组， 将（a）比较有限与全面预测的准确性 信息条件和（B）测试的一致性和可靠性 通过检查不同的预报员如何利用 有限的信息基础。 工作天气的使用 预测者被认为是特别合适的，因为尽管 气象学家常被视为卓越的典范 在现实世界的概率判断中，他们的决策过程 以及他们解释和推理的能力 对不完整的信息没有进行深入研究。 结果 作为一项研究专家如何利用 不完整的信息和人机交互应该如何 旨在为面临复杂问题的决策者提供帮助 在信息有限的情况下预测需求。