Decision-Centric Foundations for Modeling and Analysis of Complex Networked Systems

该奖项的研究目标是建立建模和分析复杂网络系统的进化动力学在节点级代理的决策方面的基础技术。研究目标将通过集成离散选择随机效用理论和基于网络科学的方法来模拟复杂网络系统的演化特征来实现。该研究计划包括三个部分：a）从网络结构数据中推断智能体的未观察到的收益，B）推断智能体在策略交互场景中的未观察到的收益，以及c）确定通过影响节点级决策来提高网络性能的方法。复杂网络系统的具体例子，包括航空运输网络和自治系统级互联网，将用于验证。该研究将产生新的方法来模拟由独立或竞争实体做出的决策所导致的网络演进，并评估引导演进走向更高性能的机制，例如对节点故障和有针对性攻击的鲁棒性。这项研究的结果为准确的系统性能预测，并预测如何复杂的网络系统将在未来的发展前景。由此产生的方法将使网络更好的代理模型的发展，有效地设计网络系统的流程和协议。这些方法还将有助于政策和激励措施的设计，以指导现有网络的结构调整，提高业绩。研究成果将纳入研究生和本科生课程。研究结果将通过工业伙伴、决策者、开放源码软件工具、期刊出版物和会议介绍加以传播。

The research objective of this award is to establish foundational techniques for modeling and analyzing the evolutionary dynamics of complex networked systems in terms of node-level agents' decisions. The research objective will be achieved through an integration of discrete-choice random-utility theory and network science-based approaches to model the evolutionary characteristics of complex networked systems. The research plan consists of three parts: a) inferring agents' unobserved payoffs from network structure data, b) inferring agents' unobserved payoffs in strategic interaction scenarios, and c) determining ways to improve network performance by influencing node-level decisions. Specific examples of complex networked systems, including air-transportation networks and autonomous system level Internet, will be used for validation. The research will result in novel approaches to model network evolution resulting from decisions made by independent or competing entities, and to evaluate mechanisms for steering the evolution towards higher performance, such as robustness to node failure and targeted attacks. The results of this research hold promise for accurate system performance prediction, and for forecasting how the complex networked systems would evolve in the future. The resulting approaches would enable the development of better surrogate models of networks for efficient design of processes and protocols on networked systems. The approaches will also enable policy and incentive design to guide the restructuring of existing networks for improved performance. The outcomes of the research will be integrated into graduate and undergraduate courses. The results will be disseminated through industrial partners, policy makers, open-source software tools, journal publications and conference presentations.