Mean Field Equilibria of Dynamic Games: Theory and Computation

该项目研究一种称为平均场均衡(MFE)的近似方法，该方法可用于管理、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、在线市场和云计算平台等资源共享系统。在平均场均衡中，个体假设由于大范围的原因，其他玩家状态的经验分布的波动已经“平均出来”，因此优化了保持其他玩家的状态分布固定。MFE简化了系统描述，使优化、设计和控制成为可能。这种对均衡概念的简化是两全其美的：它不仅更可信，而且提供了一种工具来了解参数和设计变化对这些复杂动态系统性能的影响。该项目开发了对MFE是可行的均衡概念的制度进行数学表征的方法，以及开发计算和学习MFE的算法。如果成功，研究成果可以用于实践，利用复杂经济系统在计算和大数据可用性方面的巨大进步，开发更有结构和原则性的设计更改影响预测。为了支持这一目标，该项目包括努力通过教育材料和课程课程使工科学生能够接触到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.