Empirical game theoretic models: computational issues

This paper discusses computational issues raised by a generic solution and estimation methodology applicable to a broad range of empirical game theoretic models with incomplete information. By combining the use of Monte Carlo simulation techniques with that of smooth kernel estimation of empirical distribution functions, the authors develop a numerical algorithm of unparalleled performance and flexibility applicable, in particular, to models for which no operational solutions currently exist. An illustration to a set of procurement data from the French aerospace industry is used to illustrate the operation of this algorithm.