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.

该奖项通过改进近乎实时地验证发布到社交媒体的信息的过程来提高在线通信的安全性。 无论是通过网络攻击还是不准确，错误信息都可能对公民社会产生破坏性影响，防止其传播被国会认为是当今国家面临的主要安全挑战之一。 由于今天的社交媒体网络是如此的高度连接，信息可以通过社交媒体以极快的速度传播。 随着信息在网上公开和分享，网络媒体机构面临的挑战是平衡新闻核实的准确性和速度，以避免错误信息的泛滥。 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，以便快速准确地检测和破坏错误信息。 该研究开辟了新的方向，在时间敏感的分类问题，并有广泛的应用，时间敏感的决策树，涉及二元决策。 该项目将为研究生提供机会，通过与在线媒体公司的互动了解前沿的安全挑战。 本计画以整合决策架构与绩效衡量的新范式，研究决策流网路。 这种模式扩展了标准的决策网络，允许一系列决策影响知识流，反之亦然。决策在很大程度上取决于代理的随机处理时间和准确性（例如，算法或事实检查器），其向网络提供服务。 关注具有二元决策的决策流网络（例如，工作被分为两类），该奖项开发了新颖的运筹学模型，捕捉代理人的知识和网络中信息流之间的相互依赖关系。 这些模型集成了决策理论和决策理论，分析需要鲁棒优化和随机动态规划技术的混合。 这一新颖的方法对现有文献做出了丰富的贡献，并扩展了这些领域的边界。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。