Empirical Game Theoretic Analysis: A Survey

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 are accelerating progress in EGTA, and promise to significantly expand our capacities for reasoning about complex game situations.

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Publication Details

Journal
Journal of Artificial Intelligence Research
Published
2025-02-18
DOI
https://doi.org/10.1613/jair.1.16146
Citations
13
Primary Topic
Game Theory and Applications
Type
article
Field-Weighted Citation Impact
27.39

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article

Empirical Game Theoretic Analysis: A Survey

Michael P. Wellman, Karl Tuyls, Amy Greenwald
13 citations
Journal of Artificial Intelligence Research
Game Theory and Applications
27.39
article

Empirical Game Theoretic Analysis: A Survey

Michael P. Wellman, Karl Tuyls, Amy Greenwald
article en
13 citations

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 are accelerating progress in EGTA, and promise to significantly expand our capacities for reasoning about complex game situations.

Journal of Artificial Intelligence ResearchVol. 82
John Brown University (US), University of Michigan (US), Michigan United (US)
Brown University, DeepMind
Openalex Percentile: Top 0%
Game Theory and Applications
27.39
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