The games agents play - in markets, conflicts, or most other contexts - often defy strict game-theoretic analysis. Games may be unmanageably large (combinatorial or infinite state or action spaces), and present severely imperfect information, which could be further complicated by partial dynamic revelation. Moreover, the game may be specified procedurally, for instance by a simulator, rather than in an explicit game form.

With colleagues and students over the past few years, I have been developing a body of techniques for strategic analysis, adopting the game-theoretic framework but employing it in domains where direct "model-and-solve" cannot apply. This empirical game-theoretic methodology embraces simulation, approximation, statistics and learning, and search. Through applications to canonical auction games, and rich trading scenarios, we demonstrate the value of empirical methods for extending the scope of game-theoretic analysis. This perspective also sheds insight into behavioral models and bases for predicting joint action in complex multiagent scenarios.