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

该奖项的研究目标是建立基于节点级代理决策的复杂网络系统演化动力学建模和分析的基础技术。通过将离散选择随机效用理论与基于网络科学的方法相结合，对复杂网络系统的进化特征进行建模，从而实现研究目标。研究计划包括三个部分：a)从网络结构数据中推断agent的未观察到的收益，b)推断agent在战略交互场景中的未观察到的收益，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示例，包括航空运输网络和自主系统级互联网，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进化，并评估将进化转向更高性能的机制，例如对节点故障和目标攻击的鲁棒性。本研究的结果为准确的系统性能预测和预测复杂网络系统未来的演变提供了希望。由此产生的方法将能够开发更好的网络替代模型，以便在网络系统上有效地设计过程和协议。这些办法还将使政策和奖励设计能够指导改组现有网络以改善业绩。研究成果将纳入研究生和本科课程。研究结果将通过工业合作伙伴、政策制定者、开源软件工具、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.