Mean Field Equilibria of Dynamic Games: Theory and Computation

该项目研究了一种被称为平均场平衡（MFE）的近似方法，可用于管理、设计和控制大型动态经济系统，如金融市场、电力市场、赞助搜索拍卖、在线市场和资源共享系统，如云计算平台。在平均场平衡中，个体假设其他参与者状态经验分布的波动由于规模大而被“平均”，从而优化保持其他参与者的状态分布固定。MFE简化了系统描述，使优化、设计和控制成为可能。这种平衡概念的简化提供了两全其美：它不仅更可信，而且提供了一种工具来理解参数和设计变化对这些复杂动态系统性能的影响。该项目开发的方法，以数学表征体制，其中MFE是一个可行的平衡概念，以及开发算法来计算和学习MFE。如果成功，研究结果可以在实践中使用，以利用复杂经济系统产生的计算和大数据可用性方面的巨大进步，以开发更结构化和原则性的设计变化影响预测。为了支持这一目标，该项目包括努力通过教育材料和课程设置使工程专业学生能够使用mfe技术。

This project studies an approximation methodology known as mean field equilibrium (MFE), which can be used for management, design, and control of large dynamic economic systems such as financial markets, power markets, sponsored search auctions, online marketplaces,and resource sharing systems such as cloud computing platforms. In a mean field equilibrium, individuals postulate that fluctuations in the empirical distribution of otherplayers' states have "averaged out" due to large scale, and thusoptimize holding the state distribution of other players fixed. MFE simplifies system description, making optimization, design, andcontrol possible. Such a simplification in the equilibrium conceptdelivers the best of both worlds: not only is it more believable, butit provides a tool to understand the impact of parameter and designchanges on the performance of these complex dynamic systems. Thisproject develops methods to mathematically characterize regimes where MFEis a viable equilibrium concept, as well as to develop algorithms tocompute and learn MFE.If successful, the research results could be employed in practiceto leverage the huge advances in computation and availability of big data generated by complex economic systems for developingmore structured and principled prediction of the impact of designchanges. In support of this goal, the project includes efforts to make MFEtechniques accessible to engineering students through educationalmaterials and course curriculum.