# A Generalised Method for Empirical Game Theoretic Analysis

arXiv: https://arxiv.org/abs/1803.06376 · HTML: https://mlsanity.com/paper/1803.06376/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

## Built on by (most cited first, 15 of 15)

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- [Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games](https://mlsanity.com/paper/2206.11114/) (2022)
- [Principal Trade-off Analysis](https://mlsanity.com/paper/2206.07520/) (2022)
- [Ranking Joint Policies in Dynamic Games using Evolutionary Dynamics](https://mlsanity.com/paper/2502.14724/) (2025)
- [A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis](https://mlsanity.com/paper/2606.10412/) (2026)
- [Efficient Nash Equilibrium Computation for Cybersecurity Games](https://mlsanity.com/paper/2609.19399/) (2026)

## Abstract (arXiv)

This paper provides theoretical bounds for empirical game theoretical analysis of complex multi-agent interactions. We provide insights in the empirical meta game showing that a Nash equilibrium of the meta-game is an approximate Nash equilibrium of the true underlying game. We investigate and show how many data samples are required to obtain a close enough approximation of the underlying game. Additionally, we extend the meta-game analysis methodology to asymmetric games. The state-of-the-art has only considered empirical games in which agents have access to the same strategy sets and the payoff structure is symmetric, implying that agents are interchangeable. Finally, we carry out an empirical illustration of the generalised method in several domains, illustrating the theory and evolutionary dynamics of several versions of the AlphaGo algorithm (symmetric), the dynamics of the Colonel Blotto game played by human players on Facebook (symmetric), and an example of a meta-game in Leduc Poker (asymmetric), generated by the PSRO multi-agent learning algorithm.