arXiv:2605.11404v2 Announce Type: replace Abstract: Large language models (LLMs) can simulate human-like reasoning and decision-making in individual agents. LLM-powered multi-agent systems (MAS) combine such agents to simulate population-scale social phenomena such as polarization, information cascades, and market panics. Such studies require attributing macro emergence to individual agents, but existing axiomatic methods scale combinatorially in $N$ and have been confined to $N \lesssim 10^3$, while the phenomena they explain occur at $N \geq 10^6$. We address this gap by adapting Aumann--Sha
Advances in large language models enable the creation of million-agent simulations, pushing the boundaries of studying complex social phenomena with unprecedented scale.
Understanding how macro-level emergent behaviors arise from individual agent interactions is crucial for predicting and managing the societal impacts of AI systems and for informing policy.
The ability to attribute emergence in massive multi-agent systems will transition the study of complex adaptive systems from theoretical to experimentally verifiable at population scale.
- · AI researchers
- · Social scientists
- · AI ethics and safety organizations
- · Simulator developers
- · Traditional qualitative sociology
- · Small-scale simulation methodologies
Researchers can now more effectively identify the root causes of emergent phenomena in large AI simulations.
This capability leads to better design principles for robust multi-agent systems and more accurate predictions of their real-world societal effects.
Improved understanding of multi-agent system emergence could inform governance frameworks for AI, potentially influencing regulatory approaches to AI deployment and interaction.
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