Asaf Cohen
University of Michigan, MI·Mathematics & Statistics
When huge numbers of individuals each make strategic choices, as traders in a market, users of a communication network or animals in a population do, the group's behavior can be hard to predict. Mean field games are a mathematical framework for such settings.
- Fields
Mathematics & Statistics
- Topics
mean field games
stochastic games
game theory
- Grants
- 1 known grant, through Jul 2028
What they work on
- Rigorously connect the long-horizon behavior of finite-player stochastic games to their mean field game limits, identifying structural features that stay stable as the number of players grows.
- Quantify how far large-population games deviate from equilibrium over long time horizons.
- Develop a learning framework that guides agents toward mean field equilibrium while adapting to unknown parameters, and analyze its convergence rates and long-run regret.
- Describe metastable behavior and transitions between multiple mean field equilibria using large deviations theory and deep-learning-based numerical methods.
More about the research
They replace the effect of countless other players with an average "crowd" effect. Most past work looks at interactions over a fixed, finite time. Many real systems, however, run for long periods, so questions of stability, equilibrium selection and robustness matter more. This project studies the long-term dynamics of these games.
Why it matters
This is fundamental mathematics. It adds understanding of how stable collective behavior emerges and persists when many strategic agents interact over long periods. The results could eventually inform the design of more resilient systems such as communication networks and financial markets, and the study of ecological populations.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Ludovic Tangpi
Princeton University, NJ·Mathematics & Statistics·Early-career
Develop and analyze a new form of propagation of chaos for interacting particle systems that evolve backward in time, together with the associated functional inequalities, as the main tool for making the mean field game limit rigorous.
Igor Cialenco
Illinois Institute of Technology, IL·Mathematics & Statistics
Develop a tractable top-down stochastic model of groundwater levels and use it to study how prices of tradeable groundwater rights form as a Nash equilibrium among competing water users.
Simina Branzei
Purdue University, IN·Computer Science·Early-career
Analyze market dynamics in which computationally limited players with only local information interact over time, testing whether globally efficient allocations emerge and whether markets grow or shrink.
The school
University of Michigan: 124 labs on PI Pages, $2.1B a year on research, 963 research doctorates a year.
Reach out
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