# Understanding Emergent Behaviours in Multi-Agent Systems with Evolutionary Game Theory

arXiv: https://arxiv.org/abs/2205.07369 · HTML: https://mlsanity.com/paper/2205.07369/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 85 references that are in the map.
- First posted on arXiv 15 May 2022.

## Builds on (most cited first, 11 of 11)

- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Statistical physics of human cooperation](https://mlsanity.com/paper/1705.07161/) (2017)
- [When to (or not to) trust intelligent machines: Insights from an evolutionary game theory analysis of trust in repeated games](https://mlsanity.com/paper/2007.11338/) (2020)
- [The Windfall Clause: Distributing the Benefits of AI for the Common Good](https://mlsanity.com/paper/1912.11595/) (2019)
- [Voluntary safety commitments provide an escape from over-regulation in AI development](https://mlsanity.com/paper/2104.03741/) (2021)
- [Mediating Artificial Intelligence Developments through Negative and Positive Incentives](https://mlsanity.com/paper/2010.00403/) (2020)
- [Artificial Intelligence Development Races in Heterogeneous Settings](https://mlsanity.com/paper/2012.15234/) (2020)
- [Cost efficiency of institutional incentives in finite populations](https://mlsanity.com/paper/2103.01131/) (2021)
- [Institutional Incentives for the Evolution of Committed Cooperation: Ensuring Participation is as Important as Enhancing Compliance](https://mlsanity.com/paper/2110.13307/) (2021)
- [Exogenous Rewards for Promoting Cooperation in Scale-Free Networks](https://mlsanity.com/paper/1905.04964/) (2019)
- [Evolution of Coordination in Pairwise and Multi-player Interactions via Prior Commitments](https://mlsanity.com/paper/2009.11727/) (2020)

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

- [Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGI](https://mlsanity.com/paper/2603.13244/) (2026)

## Abstract (arXiv)

The mechanisms of emergence and evolution of collective behaviours in dynamical Multi-Agent Systems (MAS) of multiple interacting agents, with diverse behavioral strategies in co-presence, have been undergoing mathematical study via Evolutionary Game Theory (EGT). Their systematic study also resorts to agent-based modelling and simulation (ABM) techniques, thus enabling the study of aforesaid mechanisms under a variety of conditions, parameters, and alternative virtual games. This paper summarises some main research directions and challenges tackled in our group, using methods from EGT and ABM. These range from the introduction of cognitive and emotional mechanisms into agents' implementation in an evolving MAS, to the cost-efficient interference for promoting prosocial behaviours in complex networks, to the regulation and governance of AI safety development ecology, and to the equilibrium analysis of random evolutionary multi-player games. This brief aims to sensitize the reader to EGT based issues, results and prospects, which are accruing in importance for the modeling of minds with machines and the engineering of prosocial behaviours in dynamical MAS, with impact on our understanding of the emergence and stability of collective behaviours. In all cases, important open problems in MAS research as viewed or prioritised by the group are described.