Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)
Gabriel Y. Weintraub, Lanier Benkard, Benjamin Van Roy
We propose a mean-field approximation that dramatically reduces the computational complexity of solving stochastic dynamic games. We pro- vide conditions that guarantee our method approximates an equilibrium as the number of agents grow. We then derive a performance bound to assess how well the approximation performs for any given number of agents. We apply our method to an important class of problems in ap- plied microeconomics. We show with numerical experiments that we are able to greatly expand the set of economic problems that can be analyzed computationally.