DRU: What is a 'Better' Prediction System? Combining Statistical and Economic Metrics of Prediction Quality

DRU：什么是“更好”的预测系统？

• 批准号: 0729413
• 负责人: George Young
• 金额: --
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0729413&HistoricalAwards=false
• 关键词: DRU; What; Better; Prediction; System

What is a 'Better' Prediction System?Combining Statistical and Economic Metrics of Prediction QualityPIs: A. Small, III (PI), J. Evans, K. Keller, A. Kleit, A. ThompsonOrganizations: The Pennsylvania State University (lead), Howard UniversityThis research project concerns the ways that people use predictions and forecasts to make decisions. Almost everyone has had the experience of checking the weather forecast before deciding whether or not to pack an umbrella on an outing. More dramatically, emergency management officials in coastal regions who confront an on-coming hurricane will keep a close eye on the forecasts of hurricanes track and intensity, when deciding whether or not to order an evacuation of a large region. In these cases and in countless others, decision-makers rely on prediction systems, or forecasting models, to form ideas about what the future holds. Often, prediction systems take the form of computer simulation models that have been developed in advance by technical specialists. To develop a prediction system or forecasting model, a developer must have some idea of what it means for the model to do a 'good' job, or for one approach to work 'better' than another. Typically, the developers of prediction systems define quality in terms of statistical measures that are generic, in the sense that they have no necessary connection to the uses for which the system is being developed. For consumers of prediction systems, however, a 'good' prediction system is one that helps them to make better decisions, e.g., to avoid costly errors. A central goal of this project is to develop measures of quality for prediction systems that incorporate the needs and goals of users. These user-sensitive measures of quality provide a means for communicating priorities back to technical specialists, to help guide system development in genuinely useful directions. The project is led by a diverse team of economists, meteorologists (hurricanes, air pollution), geoscientists (climate thresholds), and statisticians. Theoretical work will complement applications in four areas of vital economic and social importance: the generation of electric power, hurricane evacuation decisions, protocols for issuing air-quality alerts, and thresholds in the climate system. Through interactions with stakeholders, the research will feed directly into decisions on how to improve weather and climate predictions, with potentially large economic benefits. The project involves graduate and undergraduate students at both Penn State and Howard University. Howard University is the leading HBCU (Historically Black Colleges and Universities) in the atmospheric sciences and has a nationally ranked graduate school. Collaboration between these two programs is providing a range of research experiences and advisor expertise to students from both schools.

什么是“更好”的预测系统？结合统计学和经济学的预测质量指标：A。Small，III（PI），J. Evans，K. Keller，A. Kleit，A.公司组织：宾夕法尼亚州立大学（牵头），霍华德大学这个研究项目关注的是人们使用预测和预测来做决定的方式。几乎每个人都有过这样的经历：在决定郊游是否带伞之前，先查看天气预报。更戏剧性的是，沿海地区的应急管理官员在面对即将到来的飓风时，在决定是否下令疏散大面积地区时，会密切关注飓风路径和强度的预测。在这些情况下以及无数其他情况下，决策者依靠预测系统或预测模型来形成关于未来的想法。通常，预测系统采用技术专家预先开发的计算机模拟模型的形式。为了开发一个预测系统或预测模型，开发人员必须对模型做得“好”或一种方法比另一种方法工作得“更好”意味着什么有一些想法。 一般来说，预测系统的开发者根据通用的统计措施来定义质量，因为它们与开发系统的用途没有必然的联系。然而，对于预测系统的消费者来说，“好的”预测系统是帮助他们做出更好决策的系统，例如，以避免代价高昂的错误。该项目的一个中心目标是为纳入用户需求和目标的预测系统制定质量衡量标准。这些对用户敏感的质量措施提供了一种将优先事项反馈给技术专家的手段，以帮助指导系统朝着真正有用的方向发展。该项目由经济学家、气象学家（飓风、空气污染）、地球科学家（气候阈值）和统计学家组成的多元化团队领导。理论工作将补充四个具有重要经济和社会意义的领域的应用：发电，飓风疏散决策，发布空气质量警报的协议以及气候系统中的阈值。通过与利益相关者的互动，该研究将直接为如何改善天气和气候预测的决策提供信息，并可能带来巨大的经济效益。 该项目涉及宾州州立大学和霍华德大学的研究生和本科生。霍华德大学是大气科学领域领先的HBCU（历史上的黑人学院和大学），并拥有全国排名第一的研究生院。这两个项目之间的合作为两所学校的学生提供了一系列的研究经验和顾问专业知识。