Asuman Ozdaglar

Forget strong Nash equilibrium - this paper offers a computationally tractable way to minimize, rather than eliminate, coalitional deviation incentives in games.

Global AI aggregators can actually *worsen* learning compared to specialized, local ones, especially when they update too quickly.

Learning from ranked preferences alone can be surprisingly difficult: even with access to the full ranking of actions, standard online learning guarantees break down unless the environment is sufficiently stable.