Decision Flow Networks for Effective Classification in Service Systems

该奖项通过改进近乎实时地验证发布在社交媒体上的信息的过程，提高了在线通信的安全性。无论是通过网络攻击还是通过不准确的信息，错误信息都可能对公民社会产生毁灭性的影响，防止其传播被国会视为当今国家面临的主要安全挑战之一。因为今天的社交媒体网络是如此高度相连，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共享，在线媒体面临的挑战是平衡新闻核实的准确性和速度，以避免错误信息的流行。该项目将开发新的方法来指导内部(专有评分算法)和外部(第三方事实核查)资源，以便快速准确地发现和破坏错误信息。该研究为时敏分类问题开辟了新的方向，并在涉及二元决策的时敏决策树中具有广泛的应用。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尖端安全挑战。这个项目采用了一种新的范式，将决策框架与排队型性能测量相结合，研究了决策流网络。这一范例通过允许一系列决策影响知识流来扩展标准排队网络，反之亦然。决策在很大程度上取决于为网络提供服务的代理(例如，算法或事实核查人员)的随机处理时间和准确性。该奖项着眼于具有二元决策的决策流网络(例如，作业被归类为两种类别之一)，开发了新的运筹学模型，捕捉了代理知识和网络中信息流之间的相互依赖。这些模型集成了决策论和排队论，分析需要稳健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，并扩展了每个领域的边界。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.