基于大语言模型的智能体网络动态（LAND）模型对社会动力学的建模

中文摘要 AI 辅助

社会动力学描述个体的网络与话语互动聚合为集体影响力、叙事主导性及协调行为的过程。本文采用GhostField架构，一种混合大语言模型驱动的智能体网络动态（LAND）模型，作为社会模拟框架构建AuraSight场景。该场景中，314244个异质性网络社会智能体与人类行为主体围绕虚构国际歌曲创作竞赛，在30天内交换529327条消息。我们从自我网络拓扑、语义网络演化、协调动力学、影响力动力学四个分析层，系统性考察涌现的社会动力学。结果显示，生成的社会模拟确能产生社会动力学，且协调与影响力的动力学并非源于单个智能体，而是网络拓扑与叙事交换间的递归互动。

英文摘要

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.

发表机构

• Carnegie Mellon University（卡内基梅隆大学）

机构由 AI 辅助整理，请以论文原文为准。