# Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games

arXiv: https://arxiv.org/abs/2206.11114 · HTML: https://mlsanity.com/paper/2206.11114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)
- [A Generalised Method for Empirical Game Theoretic Analysis](https://mlsanity.com/paper/1803.06376/) (2018)
- [Symmetric Decomposition of Asymmetric Games](https://mlsanity.com/paper/1711.05074/) (2017)

## Built on by (most cited first, 1 of 1)

- [Collective Learning Mechanism based Optimal Transport Generative Adversarial Network for Non-parallel Voice Conversion](https://mlsanity.com/paper/2504.13791/) (2025)

## Abstract (arXiv)

Evolutionary game theory has been a successful tool to combine classical game theory with learning-dynamical descriptions in multiagent systems. Provided some symmetric structures of interacting players, many studies have been focused on using a simplified heuristic payoff table as input to analyse the dynamics of interactions. Nevertheless, even for the state-of-the-art method, there are two limits. First, there is inaccuracy when analysing the simplified payoff table. Second, no existing work is able to deal with 2-population multiplayer asymmetric games. In this paper, we fill the gap between heuristic payoff table and dynamic analysis without any inaccuracy. In addition, we propose a general framework for m versus n 2-population multiplayer asymmetric games. Then, we compare our method with the state-of-the-art in some classic games. Finally, to illustrate our method, we perform empirical game-theoretical analysis on Wolfpack as well as StarCraft II, both of which involve complex multiagent interactions.