# Long-term Dynamics and Learning in Large Population Games

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Regents of the University of Michigan - Ann Arbor (MI) · $199,625

## Abstract

This project advances the mathematical foundations of mean field games, a powerful framework for modeling the collective behavior of large populations of strategic agents. While most existing research has focused on finite-horizon interactions, this project investigates the long-term dynamics of these systems, where questions of stability, equilibrium selection, and robustness are especially critical. Many real-world systems - such as communication networks, financial markets, and ecological populations - evolve over extended periods and require both coordination and long-run predictability. By analyzing how stable behavioral patterns emerge and persist in such settings, this research contributes to a deeper scientific understanding and supports the development of resilient technologies. 

The investigator aims to rigorously connect the long-horizon behavior of finite-agent stochastic games to their mean field counterparts. The project explores structural features of these games that remain stable as the number of agents grows, quantifying the long-run deviation from equilibrium. A learning framework is also developed to guide agents toward equilibrium behavior while adapting to unknown parameters, with particular attention to convergence rates and long-run regret. Furthermore, the project examines systems with multiple mean field equilibria, developing probabilistic tools and numerical methods based on large deviations and deep learning, to describe metastable behaviors an

## Key facts

- **NSF award ID:** 2505998
- **Awardee organization:** Regents of the University of Michigan - Ann Arbor (MI)
- **SAM.gov UEI:** GNJ7BBP73WE9
- **PI:** Asaf Cohen
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** —
- **Estimated total:** $199,625
- **Funds obligated:** $199,625
- **Transaction type:** Standard Grant
- **Period:** 08/15/2025 → 07/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2505998

## Citation

> US National Science Foundation, Award 2505998, Long-term Dynamics and Learning in Large Population Games. Retrieved via AI Analytics 2026-06-09 from https://api.ai-analytics.org/grant/nsf/2505998. Licensed CC0.

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