# Three AI-agents walk into a bar . . . . `Lord of the Flies' tribalism emerges among smart AI-Agents

arXiv: https://arxiv.org/abs/2602.23093 · HTML: https://mlsanity.com/paper/2602.23093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 13 of 13)

- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Playing repeated games with Large Language Models](https://mlsanity.com/paper/2305.16867/) (2023)
- [Open Problems in Cooperative AI](https://mlsanity.com/paper/2012.08630/) (2020)
- [On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents](https://mlsanity.com/paper/2408.00989/) (2024)
- [Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation](https://mlsanity.com/paper/2309.17234/) (2023)
- [From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium](https://mlsanity.com/paper/2506.08292/) (2025)
- [Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence](https://mlsanity.com/paper/2503.03800/) (2025)
- [FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory](https://mlsanity.com/paper/2504.14325/) (2025)
- [Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamics](https://mlsanity.com/paper/2512.07462/) (2025)
- [Emergent Social Dynamics of LLM Agents in the El Farol Bar Problem](https://mlsanity.com/paper/2509.04537/) (2025)
- [Game-Theoretic Lens on LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2601.15047/) (2026)
- [Social Game for Building Energy Efficiency: Utility Learning, Simulation, and Analysis](https://mlsanity.com/paper/1407.0727/) (2014)
- [Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results](https://mlsanity.com/paper/2212.01725/) (2022)

## Built on by (most cited first, 2 of 2)

- [Increasing intelligence in AI agents can worsen collective outcomes](https://mlsanity.com/paper/2603.12129/) (2026)
- [Evolve Together, Fall into Place: How Social Scaffolding Shapes Self-Evolving Multi-Agent Systems](https://mlsanity.com/paper/2610.19799/) (2026)

## Abstract (arXiv)

Near-future infrastructure systems may be controlled by autonomous AI agents that repeatedly request access to limited resources such as energy, bandwidth, or computing power. We study a simplified version of this setting using a framework where N AI-agents independently decide at each round whether to request one unit from a system with fixed capacity C. An AI version of "Lord of the Flies" arises in which controlling tribes emerge with their own collective character and identity. The LLM agents do not reduce overload or improve resource use, and often perform worse than if they were flipping coins to make decisions. Three main tribal types emerge: Aggressive (27.3%), Conservative (24.7%), and Opportunistic (48.1%). The more capable AI-agents actually increase the rate of systemic failure. Overall, our findings show that smarter AI-agents can behave dumber as a result of forming tribes.