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 other players' states have "averaged out" due to large scale, and thus optimize holding the state distribution of other players fixed. MFE simplifies system description, making optimization, design, and control possible. Such a simplification in the equilibrium concept delivers the best of both worlds: not only is it more believable, but it provides a tool to understand the impact of parameter and design changes on the performance of these complex dynamic systems. This project develops methods to mathematically characterize regimes where MFE is a viable equilibrium concept, as well as to develop algorithms to compute and learn MFE.
If successful, the research results could be employed in practice to leverage the huge advances in computation and availability of big data generated by complex economic systems for developing more structured and principled prediction of the impact of design changes. In support of this goal, the project includes efforts to make MFE techniques accessible to engineering students through educational materials and course curriculum.
