  Game-theoretic models relevant for computer science applications usually
feature a large number of players. The goal of this paper is to develop an
analytical framework for bounding the price of anarchy in such models. We
demonstrate the wide applicability of our framework through instantiations for
several well-studied models, including simultaneous single-item auctions,
greedy combinatorial auctions, and routing games. In all cases, we identify
conditions under which the POA of large games is much better than that of
worst-case instances. Our results also give new senses in which simple auctions
can perform almost as well as optimal ones in realistic settings.
