Emergent Relational Order in LLM Agent Societies: From Collective Affect to Authority Stratification
arXiv:2606.23764v1 Announce Type: cross Abstract: Fei Xiaotong's Differential Order Pattern characterizes rural society as egocentric and relationally graded, with cooperation attenuating over social distance. Although often treated as culturally specific, its mechanistic basis remains under-operationalized, and prior LLM-based simulations have mainly addressed short-term coordination rather than long-horizon social structure. We propose CAREB-MAS, a multi-agent framework grounded in Affect Control Theory, Social Identity Theory, and Durkheimian collective affect. Agents reason through an emot
The accelerating development of large language models is enabling more sophisticated multi-agent simulations to explore complex social dynamics and emergent behaviors.
This research provides a mechanistic basis for understanding human social structures like authority and hierarchy, informiing how AI agents might replicate or deviate from these patterns.
Our understanding of emergent social order in artificial intelligences evolves, moving beyond simple coordination to long-horizon, relationally graded societal structures.
- · AI ethicists
- · Social scientists
- · Multi-agent system developers
- · Organizations deploying AI teams
- · Simplistic AI coordination models
- · Traditional social science methodologies without AI integration
Advanced AI agents will exhibit complex social dynamics, including status hierarchies and collective affect, mirroring human societies.
Understanding these emergent social orders allows for the design of more robust, predictable, or ethically aligned AI agent societies.
AI societies could develop novel forms of governance or social organization that offer new models applicable to human societies, or challenge existing paradigms.
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Read at arXiv cs.AI