Empirical Game-Theoretic Analysis: A Survey

TL;DR

This survey reviews the empirical game-theoretic analysis (EGTA) approach, highlighting its methodology, applications across diverse domains, and recent advances driven by machine learning to handle complex strategic situations.

Contribution

It provides a comprehensive overview of EGTA methodology, key concepts, and recent developments, especially in integrating machine learning techniques.

Findings

EGTA has been successfully applied to auctions, markets, and cybersecurity.

Recent machine learning advances have enhanced EGTA's capacity for complex game analysis.

The survey identifies key challenges and future research directions in EGTA.

Abstract

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.