CAREER: Evolutionary Games in Dynamic and Networked Environments for Modeling and Controlling Large-Scale Multi-agent Systems

职业：动态和网络环境中的进化博弈，用于建模和控制大规模多智能体系统

• 批准号: 2239410
• 负责人: Ceyhun Eksin
• 金额: $ 50.35万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239410&HistoricalAwards=false
• 关键词: CAREER; Evolutionary; Games; Dynamic; Networked

Classical game theory addresses how individuals make decisions given suitable incentives, for example, whether to use resources rapaciously or with restraint. However, game theory does not typically address the consequences of the actions that reshape the resources over the long term. Indeed, individuals' actions often subsequently modify the commons (environment) and associated payoffs. In this project, we propose a unified mathematical framework to model and analyze the coupled evolution of individuals' incentives, opinions, and the environment using tools from game theory, network science, and nonlinear dynamic systems. Based on the mathematical framework, the proposed project is organized to study fundamental issues relating to (a) when and how desirable behavior, e.g., cooperative behavior, arise in the populations, and (b) whether tragedies of the commons can be averted in complex systems, e.g., during a pandemic. Scientific contributions of this project will have the potential to have a transformational impact on our understanding of the emergence of cooperation and environmental collapse in public health systems where individuals' actions affect the resources, and in engineered multi-agent systems, e.g., autonomous or energy systems, that involve self-interested entities. The overarching goals of the project are rooted in an educational agenda with initiatives, e.g., a summer residential research experience for educators, designed to expose the broader public to central concepts in game theory and nonlinear systems, and push for a systems-thinking perspective on societal problems.The premise of this project is that individual behavior is dynamic, i.e., evolves according to selection or learning, and such learning behavior has subsequent effects on the environment, and thus on the future incentives for learning. The proposed research is a concerted effort to develop a mathematical framework for studying population behavior when the population’s well-being depends on the environment that the behavior is affecting. The proposed research aims to achieve the following scientific contributions: 1) novel models of strategic learning dynamics in feedback-evolving games with relevance to socio-biological and -technological systems including epidemics and autonomous systems; 2) decentralized algorithms for tracking rational behavior in dynamic network games; 3) a framework for dynamic intervention mechanisms to induce desirable system-level behavior in such settings; 4) design and analysis of experiments to uncover the role of peer effects and ambiguity on perceived risks on cooperation. This effort will lead to novel analysis, and scalable decentralized algorithms applicable to addressing real-world problems in social and technological multi-agent systems.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.

经典博弈论研究的是个体如何在适当的激励下做出决策，例如，是贪婪地使用资源还是有节制地使用资源。然而，博弈论通常不解决长期重塑资源的行为的后果。事实上，个人的行为往往随后修改公地（环境）和相关的收益。在这个项目中，我们提出了一个统一的数学框架来模拟和分析个人的激励，意见和环境的耦合演化，使用博弈论，网络科学和非线性动力系统的工具。 根据数学框架，拟议的项目是组织研究有关的基本问题（a）何时以及如何可取的行为，例如，合作行为，在人群中出现，以及（B）在复杂系统中是否可以避免公地悲剧，例如，在大流行期间。这个项目的科学贡献将有可能对我们理解公共卫生系统中出现的合作和环境崩溃产生变革性的影响，在公共卫生系统中，个人的行动会影响资源，在工程多代理系统中，例如，自治或能源系统，涉及自我利益的实体。该项目的总体目标植根于一个教育议程，其中包括以下举措：为教育工作者提供暑期住宿研究体验，旨在让更广泛的公众了解博弈论和非线性系统的核心概念，并推动对社会问题的系统思维观点。该项目的前提是个人行为是动态的，即，根据选择或学习而进化，这种学习行为对环境产生后续影响，从而对未来的学习动机产生影响。拟议的研究是一项协调一致的努力，以发展一个数学框架，研究人口行为时，人口的福祉取决于环境的行为正在影响。 本研究的目标是实现以下科学贡献：1）与社会生物和技术系统（包括流行病和自治系统）相关的反馈进化游戏中的策略学习动态模型; 2）跟踪动态网络游戏中理性行为的分散算法; 3）动态干预机制的框架，以诱导此类设置中的理想系统级行为;（4）设计和分析实验，揭示同伴效应和模糊性对感知合作风险的作用。这一努力将导致新的分析，和可扩展的分散算法适用于解决社会和技术多智能体系统中的现实世界的问题。这个奖项反映了NSF的法定使命，并已被认为是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的支持。