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.

经典博弈论解决了在给予适当激励的情况下个人如何做出决策，例如，是否贪婪地使用资源还是有节制地使用资源。然而，博弈论通常不会解决长期重塑资源的行为的后果。行动通常会随后改变公地（环境）和相关收益。在这个项目中，我们提出了一个统一的数学框架，使用博弈论、网络科学和环境的工具来建模和分析个人的激励、意见和环境的耦合演化。基于非线性动力系统。拟议的项目旨在研究与以下方面相关的基本问题：(a)何时以及如何在人群中出现合乎需要的行为，例如合作行为，以及(b)在复杂的系统中是否可以避免公地悲剧，例如公共行为。 ，在大流行期间，该项目的科学贡献将有可能对我们对公共卫生系统中合作和环境崩溃的理解产生变革性影响，在公共卫生系统中，个人的行为会影响资源，以及工程化的多主体系统，例如。，该项目的总体目标植根于具有举措的教育议程，例如为教育工作者提供夏季住宿研究经验，旨在让更广泛的公众了解博弈论和博弈论的核心概念。非线性系统，并推动对社会问题的系统思维视角。该项目的前提是个体行为是动态的，即根据选择或学习而演变，这种学习行为会对环境产生后续影响，从而对环境产生影响。未来的激励措施拟议的研究旨在开发一个数学框架来研究人口的福祉取决于行为所影响的环境。拟议的研究旨在实现以下科学贡献：1）新颖的模型。反馈游戏中的策略学习动态，与社会生物和技术系统（包括流行病和自治系统）不断发展相关；2）用于跟踪动态网络游戏中的理性行为的分散算法；3）动态干预机制的框架，以诱导理想的系统此类环境中的行为水平； 4）设计和分析实验，以揭示同伴效应和模糊性对合作感知风险的作用。这项工作将带来新颖的分析和可扩展的去中心化算法，适用于解决社会和技术多智能体系统中的现实问题。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。