Decision Flow Networks for Effective Classification in Service Systems

用于服务系统有效分类的决策流网络

• 批准号: 1826353
• 负责人: Seyed M. R. Iravani
• 金额: $ 43.99万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826353&HistoricalAwards=false
• 关键词: Decision; Flow; Networks; Effective; Classification

This award advances the security of online communications by improving the process of verifying information posted to social media outlets in near real-time. Whether through cyber-attacks or inaccuracies, misinformation can have devastating effects on civil society and preventing its spread is considered by Congress to be one of the major security challenges facing the Nation today. Because today's social media networks are so highly connected, information can spread through social media at great speed. As information is made public and shared online, the challenge facing online media outlets is to balance the accuracy and speed of news verification in order to avoid epidemics of misinformation. This project will develop new methods to direct internal (proprietary scoring algorithms) and external (third-party fact-checking) resources in order to detect and disrupt misinformation quickly and accurately. The research opens new directions in time-sensitive classification problems and has wide application to time sensitive decision-trees involving binary decisions. This project will provide opportunities for graduate students to learn about cutting-edge security challenges through interactions with online media firms. Employing a new paradigm that integrates a decision-making framework with queueing-type performance measures, this project studies decision flow networks. This paradigm extends standard queueing networks by allowing a sequence of decisions to impact knowledge flow and vice versa. Decisions depend heavily on the random processing times and accuracy of agents (e.g., algorithms or fact-checkers) who provide service to the network. Focusing on decision flow networks with binary decision (e.g., jobs are classified into one of two categories), this award develops novel operations research models that capture the interdependence between the agent's knowledge and the flow of information in the network. These models integrate decision theory and queueing theory, and the analysis require a mix of robust optimization and stochastic dynamic programming techniques. This novel approach makes a rich contribution to the existing literature and extends the boundaries of each of these fields.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项通过改善验证信息的信息近乎实时验证发布到社交媒体的信息，从而提高了在线通信的安全性。 无论是通过网络攻击还是不准确性，错误信息可能会对公民社会产生毁灭性的影响，并认为国会被认为是当今国家面临的主要安全挑战之一。 由于当今的社交媒体网络是如此高度联系，因此信息可以以很大的速度通过社交媒体传播。 随着信息公开并在线共享，在线媒体媒体面临的挑战是平衡新闻验证的准确性和速度，以避免流行不良信息。 该项目将开发新的方法来指导内部（专有评分算法）和外部（第三方事实检查）资源，以快速，准确地检测和破坏错误信息。 该研究开辟了时间敏感分类问题的新方向，并在涉及二进制决策的时间敏感决策中有广泛的应用。 该项目将为研究生提供机会，通过与在线媒体公司的互动来了解最先进的安全挑战。 该项目采用新的范式将决策框架与队列类型的绩效指标相结合，该项目研究了决策流网络。 该范式通过允许一系列决策来影响知识流，反之亦然，扩展了标准排队网络。决策在很大程度上取决于为网络提供服务的代理（例如算法或事实检查器）的随机处理时间和准确性。 该奖项专注于具有二进制决策的决策流网络（例如，作业被归类为两个类别之一），开发了新型操作研究模型，这些研究模型捕获了代理商的知识与网络中信息流之间的相互依存关系。 这些模型整合了决策理论和排队理论，分析需要强大的优化和随机动态编程技术。 这种新颖的方法为现有文献做出了丰富的贡献，并扩展了每个领域的界限。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准来通过评估来支持的。