CAREER: Decision Making, Learning, and Incentive Design in Multilayer Networks

网络在社会的各个方面都很普遍。它们在信息传递、社会互动、经济交易和疾病传播等方面发挥着核心作用。此外，许多网络，包括通信、电力和交通网络等潜在的关键基础设施，都是高度相互依存的。虽然对孤立网络的建模和分析已有大量研究，但对相互依赖和多层网络的研究，特别是在战略和自利益决策者存在的情况下，面临着许多开放的挑战。该提案将利用优化、博弈论、机器学习和图论等工具，为多层网络的研究构建一个分析框架。研究结果将对复杂网络环境中自组织多代理系统的设计和操作以及相互依赖的关键基础设施的安全产生影响。与拟议的研究相结合，该教育计划将扩大拟议研究领域的参与，并通过与俄亥俄州立大学的外展项目合作，为各级受众提供应用和实践经验。该提案的核心是使用博弈论建模和分析来研究多层网络。本项目将提出并分析一类新的多网络博弈，用于研究互联网络上的分散决策和学习。该框架将解释多代理系统中实体可用的信息和通信渠道的多模态，并确定多层网络环境中分散决策效率低下的潜在来源（与集中操作和单层网络相比）。它还将通过机制设计和有针对性的干预，提出和评估激励措施或干预措施对这些网络上分散决策和分布式学习的结果的影响。这些发现将用于展示多层网络的整体研究，而不是单独关注每个网络，如何提高我们设计和评估经济和监管干预的能力，并防止不必要的平衡或学习结果。该奖项反映了美国国家科学基金会的法定使命，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。

Networks are prevalent in all aspects of society. They play a central role in information transmission, social interactions, economic transactions, and the spread of diseases, to name a few. Moreover, many networks, including this underlying critical infrastructure such as communication, power, and transportation networks, are highly interdependent. While there exists considerable research on the modeling and analysis of isolated networks, the study of interdependent and multilayer networks, particularly in the presence of strategic and self-interested decision makers, confronts many open challenges. This proposal will leverage tools from optimization, game theory, machine learning, and graph theory towards building an analytical framework for the study of multilayer networks. The findings will have impact on the design and operation of self-organizing multi-agent systems in complex network environments, as well as in securing interdependent critical infrastructure. Integrated with the proposed research, the education plan will broaden participation in the proposed research areas and provide audiences at all levels with applied and hands-on experiences through collaboration with outreach programs at the Ohio State University. At the core of this proposal is the study of multilayer networks using game-theoretic modeling and analysis. This project will propose and analyze a new class of multi-network games for the study of decentralized decision-making and learning over interconnected networks. This framework will account for the multi-modality of information and communication channels available to entities in multi-agent systems and identify the potential sources of inefficiency in decentralized decision making in multilayer network environments (as compared to both centrally operated and single layer networks). It will also propose and evaluate the impact of incentives or interventions to shape the outcomes of decentralized decision making and distributed learning on these networks, through mechanism design and targeted interventions. These findings will be used to show how the holistic study of multilayer networks, as opposed to focusing on each network individually, enhances our ability to design and evaluate economic and regulatory interventions, and prevent unwanted equilibria or learning outcomes.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.