Empirical Game-Theoretic Analysis: A Survey
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Abstract (arXiv)
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.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2024.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 8.
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- 2024A Survey on Self-play Methods in Reinforcement Learningcited by 26
- 2025Cross-environment Cooperation Enables Zero-shot Multi-agent Coordinationcited by 14
- 2026Discovering Multiagent Learning Algorithms with Large Language Modelscited by 3
- 2025Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?cited by 2
- 2026Verification Required: The Impact of Information Credibility on AI Persuasioncited by 1
- 2026Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learningcited by 1
- 2026Draining the Energy Commons: Self-Defeating Over-Appropriation as a Coordination Failure in Agentic LLM Collectivescited by 1
- 2025Ranking Joint Policies in Dynamic Games using Evolutionary Dynamicscited by 0
- 2025Graphical Models for Decision-Making: Integrating Causality and Game Theorycited by 0
- 2026Causal Foundations of Collective Agencycited by 0
- 2026Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Gamescited by 0
- 2026Efficient Nash Equilibrium Computation for Cybersecurity Gamescited by 0
- 2026Building Socio-Affective Artificial Intelligence for Interactive Multi-Agent Simulations22 Sep 2026
- 2026Efficient Nash Equilibrium Computation for Cybersecurity Games16 Sep 2026
- 2026Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games30 Jul 2026
- 2026Draining the Energy Commons: Self-Defeating Over-Appropriation as a Coordination Failure in Agentic LLM Collectives24 Jul 2026
- 2026Causal Foundations of Collective Agency30 Apr 2026
- 2026Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning27 Feb 2026
- 2026Discovering Multiagent Learning Algorithms with Large Language Models18 Feb 2026
- 2026Verification Required: The Impact of Information Credibility on AI Persuasion1 Feb 2026
- 2025Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?4 Aug 2025
- 2025Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination17 Apr 2025
- 2025Graphical Models for Decision-Making: Integrating Causality and Game Theory16 Apr 2025
- 2025Ranking Joint Policies in Dynamic Games using Evolutionary Dynamics20 Feb 2025
1 more
- 2024A Survey on Self-play Methods in Reinforcement Learning2 Aug 2024
- 2024A Survey on Self-play Methods in Reinforcement Learningcited by 26
- 2025Cross-environment Cooperation Enables Zero-shot Multi-agent Coordinationcited by 14
- 2026Discovering Multiagent Learning Algorithms with Large Language Modelscited by 3
- 2025Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?cited by 2
- 2026Verification Required: The Impact of Information Credibility on AI Persuasioncited by 1
- 2026Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learningcited by 1
- 2025Ranking Joint Policies in Dynamic Games using Evolutionary Dynamicscited by 0
- 2025Graphical Models for Decision-Making: Integrating Causality and Game Theorycited by 0
- 2026Causal Foundations of Collective Agencycited by 0
- 2026Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Gamescited by 0
- 2026Efficient Nash Equilibrium Computation for Cybersecurity Gamescited by 0
- 2026Building Socio-Affective Artificial Intelligence for Interactive Multi-Agent Simulationscited by 0
- 2026Building Socio-Affective Artificial Intelligence for Interactive Multi-Agent Simulations22 Sep 2026
- 2026Efficient Nash Equilibrium Computation for Cybersecurity Games16 Sep 2026
- 2026Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games30 Jul 2026
- 2026Causal Foundations of Collective Agency30 Apr 2026
- 2026Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning27 Feb 2026
- 2026Discovering Multiagent Learning Algorithms with Large Language Models18 Feb 2026
- 2026Verification Required: The Impact of Information Credibility on AI Persuasion1 Feb 2026
- 2025Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?4 Aug 2025
- 2025Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination17 Apr 2025
- 2025Graphical Models for Decision-Making: Integrating Causality and Game Theory16 Apr 2025
- 2025Ranking Joint Policies in Dynamic Games using Evolutionary Dynamics20 Feb 2025
- 2024A Survey on Self-play Methods in Reinforcement Learning2 Aug 2024