# Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligence

arXiv: https://arxiv.org/abs/2403.07017 · HTML: https://mlsanity.com/paper/2403.07017/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 9 Mar 2024.

## Builds on (most cited first, 1 of 1)

- [Reinforcement Learning Produces Dominant Strategies for the Iterated Prisoner's Dilemma](https://mlsanity.com/paper/1707.06307/) (2017)

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

- [CoMAI: A Collaborative Multi-Agent Framework for Robust and Equitable Interview Evaluation](https://mlsanity.com/paper/2603.16215/) (2026)

## Abstract (arXiv)

Evolutionary Game Theory (EGT) and Artificial Intelligence (AI) are two fields that, at first glance, might seem distinct, but they have notable connections and intersections. The former focuses on the evolution of behaviors (or strategies) in a population, where individuals interact with others and update their strategies based on imitation (or social learning). The more successful a strategy is, the more prevalent it becomes over time. The latter, meanwhile, is centered on machine learning algorithms and (deep) neural networks. It is often from a single-agent perspective but increasingly involves multi-agent environments, in which intelligent agents adjust their strategies based on feedback and experience, somewhat akin to the evolutionary process yet distinct in their self-learning capacities. In light of the key components necessary to address real-world problems, including (i) learning and adaptation, (ii) cooperation and competition, (iii) robustness and stability, and altogether (iv) population dynamics of individual agents whose strategies evolve, the cross-fertilization of ideas between both fields will contribute to the advancement of mathematics of multi-agent learning systems, in particular, to the nascent domain of ``collective cooperative intelligence'' bridging evolutionary dynamics and multi-agent reinforcement learning.