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

该奖项的研究目标是建立基础技术，根据节点级代理的决策对复杂网络系统的演化动力学进行建模和分析。研究目标将通过将离散选择随机效用理论和基于网络科学的方法相结合来对复杂网络系统的演化特征进行建模。该研究计划包括三个部分：a)从网络结构数据推断代理的未观察到的收益；b)在战略交互场景中推断代理的未观察到的收益；c)通过影响节点级决策来确定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主系统级互联网，将用于验证。这项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产生的网络进化，并评估引导进化走向更高性能的机制，例如对节点故障和目标攻击的健壮性。这项研究的结果为准确的系统性能预测和预测复杂的网络系统未来的发展提供了希望。由此产生的方法将能够开发更好的网络代理模型，以便有效地设计联网系统上的进程和协议。这些办法还将使政策和奖励设计能够指导现有网络的重组，以提高绩效。研究成果将被整合到研究生和本科课程中。结果将通过工业伙伴、政策制定者、开放源码软件工具、期刊出版物和会议报告进行传播。

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.