# Empirical Game-Theoretic Analysis: A Survey

arXiv: https://arxiv.org/abs/2403.04018 · HTML: https://mlsanity.com/paper/2403.04018/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2024.

## Built on by (most cited first, 13 of 13)

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- [Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination](https://mlsanity.com/paper/2504.12714/) (2025)
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- [Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning](https://mlsanity.com/paper/2603.00374/) (2026)
- [Draining the Energy Commons: Self-Defeating Over-Appropriation as a Coordination Failure in Agentic LLM Collectives](https://mlsanity.com/paper/2607.22188/) (2026)
- [Ranking Joint Policies in Dynamic Games using Evolutionary Dynamics](https://mlsanity.com/paper/2502.14724/) (2025)
- [Graphical Models for Decision-Making: Integrating Causality and Game Theory](https://mlsanity.com/paper/2504.13210/) (2025)
- [Causal Foundations of Collective Agency](https://mlsanity.com/paper/2605.00248/) (2026)
- [Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games](https://mlsanity.com/paper/2607.27536/) (2026)
- [Efficient Nash Equilibrium Computation for Cybersecurity Games](https://mlsanity.com/paper/2609.19399/) (2026)
- [Building Socio-Affective Artificial Intelligence for Interactive Multi-Agent Simulations](https://mlsanity.com/paper/2609.26927/) (2026)

## 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.
