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、电力和运输网络等基础关键基础设施，都是高度相互依赖的。虽然存在大量的孤立网络的建模和分析的研究，相互依存和多层网络的研究，特别是在存在的战略和自利的决策者，面临着许多开放的挑战。该提案将利用优化，博弈论，机器学习和图论的工具，为多层网络的研究建立一个分析框架。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运行以及确保相互依赖的关键基础设施产生影响。与拟议的研究相结合，教育计划将扩大对拟议研究领域的参与，并通过与俄亥俄州州立大学的外联方案合作，为各级受众提供应用和实践经验。该建议的核心是使用博弈论建模和分析来研究多层网络。该项目将提出并分析一类新的多网络游戏，用于研究互联网络上的分散决策和学习。该框架将考虑多智能体系统中实体可用的多模态信息和通信渠道，并确定多层网络环境中分散决策效率低下的潜在来源（与集中运营和单层网络相比）。它还将通过机制设计和有针对性的干预措施，提出并评估激励措施或干预措施的影响，以塑造这些网络上分散决策和分布式学习的成果。这些研究结果将被用来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措施的能力，并防止不必要的均衡或学习结果。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.