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

什么是“更好”的预测系统？结合预测质量的统计和经济指标类型：A. Small, III (PI)， J. Evans， K. Keller， A. Kleit， A. thompson组织：宾夕法尼亚州立大学（领导），霍华德大学本研究项目涉及人们使用预测和预测做出决策的方式。几乎每个人都有过这样的经历：在决定外出是否带伞之前，先看看天气预报。更引人注目的是，沿海地区的应急管理官员在面对即将到来的飓风时，将密切关注飓风的路径和强度预报，以决定是否下令疏散大片地区。在这些案例和无数其他案例中，决策者依靠预测系统或预测模型来形成对未来的看法。通常，预测系统采用由技术专家事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，开发人员必须知道模型做得“好”意味着什么，或者一种方法比另一种方法“更好”意味着什么。通常，预测系统的开发人员根据通用的统计度量来定义质量，在某种意义上，它们与正在开发的系统的用途没有必要的联系。然而，对于预测系统的消费者来说，一个“好的”预测系统可以帮助他们做出更好的决策，例如，避免代价高昂的错误。这个项目的一个中心目标是为结合用户的需要和目标的预测系统发展质量度量。这些用户敏感的质量度量提供了一种向技术专家传达优先级的方法，以帮助在真正有用的方向上指导系统开发。该项目由一个由经济学家、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。理论工作将补充在四个至关重要的经济和社会领域的应用：发电、飓风疏散决策、发布空气质量警报的协议和气候系统的阈值。通过与利益相关者的互动，这项研究将直接为如何改进天气和气候预测的决策提供依据，这可能带来巨大的经济效益。该项目涉及宾夕法尼亚州立大学和霍华德大学的研究生和本科生。霍华德大学是在大气科学方面领先的HBCU（历史上的黑人学院和大学），并拥有一个全国排名的研究生院。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。

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.