  In the empirical approach to game-theoretic analysis (EGTA), the model of the
game comes not from declarative representation, but is derived by interrogation
of a procedural description of the game environment. The motivation for
developing this approach was to enable game-theoretic reasoning about strategic
situations too complex for analytic specification and solution. Since its
introduction over twenty years ago, EGTA has been applied to a wide range of
multiagent domains, from auctions and markets to recreational games to
cyber-security. We survey the extensive methodology developed for EGTA over the
years, organized by the elemental subproblems comprising the EGTA process. We
describe key EGTA concepts and techniques, and the questions at the frontier of
EGTA research. Recent advances in machine learning have accelerated progress in
EGTA, and promise to significantly expand our capacities for reasoning about
complex game situations.
