Efficient Nash Computation in Large Population Games with Bounded Influence
We introduce a general representation of large-population games in which each player's influence on the others is centralized and limited, but may otherwise be arbitrary. This representation significantly generalizes the class known as congestion games in a natural way. Our main results are provably correct and efficient algorithms for computing and learning approximate Nash equilibria in this general framework.