用大模型驱动的智能体模拟社交动态，揭示群体行为如何从互动中涌现。
Modeling Social Dynamics with an LLM-Enabled Agent Based Network-Dynamic (LAND) Model
- 构建31万+异构智能体，通过大模型生成对话与网络结构
- 30天内生成超50万条消息，观察到协调与影响力的动态涌现
- 适合研究社交媒体演化、群体智能或社会仿真的人参考
社交动态描述了个体在网络交互与话语传播中汇聚为集体影响、叙事主导和协同行为的过程。本文采用基于大模型的混合型智能体网络动态（LAND）框架——GhostField，构建名为AuraSight的社交模拟场景。该场景包含314,244个异构网络智能体与真实人类参与者，在30天内围绕一场虚构的国际歌曲创作比赛交换了529,327条消息。我们从四个分析层面系统考察了涌现的社交动态：个体网络拓扑、语义网络演化、协同行为模式及影响力传播机制。结果表明，由生成式模型构建的社会仿真能够真实再现社交动态，并且协调性与影响力并非源自单个智能体，而是网络结构与叙事交流之间递归互动的结果。
原文摘要 · Abstract (English)
Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance and coordinate behavior. This paper uses the the GhostField architecture, a hybrid LLM-Enabled Agent Based Network-Dynamic (LAND) model as a social simulation framework to build the AuraSight scenario. In the AuraSight scenario, 314,244 heterogeneous cyber social agents and human actors exchange 529,327 messages over 30 days surrounding a fictional international song-writing contest. We methodologically examine emergent social dynamics across four analytical layers: ego-network topology, semantic network evolution, coordination dynamics and influence dynamics. Our results show how generated social simulations do also produce social dynamics, and how the dynamics of coordination and influence emerge not from individual agents but from the recursive interaction between network topology and narrative exchange.
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