# Cooperative Resilience in Artificial Intelligence Multiagent Systems

arXiv: https://arxiv.org/abs/2409.13187 · HTML: https://mlsanity.com/paper/2409.13187/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 61 references that are in the map.
- First posted on arXiv 20 Sep 2024.

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Open Problems in Cooperative AI](https://mlsanity.com/paper/2012.08630/) (2020)
- [Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents](https://mlsanity.com/paper/2404.16698/) (2024)
- [Self-Discover: Large Language Models Self-Compose Reasoning Structures](https://mlsanity.com/paper/2402.03620/) (2024)
- [A multi-agent reinforcement learning model of common-pool resource appropriation](https://mlsanity.com/paper/1707.06600/) (2017)
- [Melting Pot 2.0](https://mlsanity.com/paper/2211.13746/) (2022)
- [Reinforcement Learning for Feedback-Enabled Cyber Resilience](https://mlsanity.com/paper/2107.00783/) (2021)
- [Can LLM-Augmented autonomous agents cooperate?, An evaluation of their cooperative capabilities through Melting Pot](https://mlsanity.com/paper/2403.11381/) (2024)
- [Towards Resilient Artificial Intelligence: Survey and Research Issues](https://mlsanity.com/paper/2109.08904/) (2021)
- [On resilient control of dynamical flow networks](https://mlsanity.com/paper/1701.03614/) (2017)

## Built on by (most cited first, 8 of 8)

- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration](https://mlsanity.com/paper/2410.15048/) (2024)
- [Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization](https://mlsanity.com/paper/2603.12933/) (2026)
- [Enhancing Social Robots through Resilient AI](https://mlsanity.com/paper/2510.21469/) (2025)
- [Learning Incentive Structures for Cooperative Resilience in Multi-Agent Systems under Social Dilemmas](https://mlsanity.com/paper/2601.22292/) (2026)
- [Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game](https://mlsanity.com/paper/2606.28456/) (2026)
- [Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions](https://mlsanity.com/paper/2607.24589/) (2026)
- [Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks](https://mlsanity.com/paper/2609.11018/) (2026)

## Abstract (arXiv)

Resilience refers to the ability of systems to withstand, adapt to, and recover from disruptive events. While studies on resilience have attracted significant attention across various research domains, the precise definition of this concept within the field of cooperative artificial intelligence remains unclear. This paper addresses this gap by proposing a clear definition of `cooperative resilience' and outlining a methodology for its quantitative measurement. The methodology is validated in an environment with RL-based and LLM-augmented autonomous agents, subjected to environmental changes and the introduction of agents with unsustainable behaviors. These events are parameterized to create various scenarios for measuring cooperative resilience. The results highlight the crucial role of resilience metrics in analyzing how the collective system prepares for, resists, recovers from, sustains well-being, and transforms in the face of disruptions. These findings provide foundational insights into the definition, measurement, and preliminary analysis of cooperative resilience, offering significant implications for the broader field of AI. Moreover, the methodology and metrics developed here can be adapted to a wide range of AI applications, enhancing the reliability and effectiveness of AI in dynamic and unpredictable environments.