收稿日期: 2025-10-16
修回日期: 2025-12-06
网络出版日期: 2026-02-11
LLM-driven Multi-agent Social Network Simulation: Interdisciplinary Integration and Cutting-edge Development
Received date: 2025-10-16
Revised date: 2025-12-06
Online published: 2026-02-11
大语言模型技术的突破性发展为社交网络研究提供了强大工具，推动社交网络多智能体仿真进入全新发展阶段。梳理了人工智能、心理学、传播学、社会学等多学科融合视域下，大模型驱动的社交网络多智能体仿真研究的最新进展，总结出该领域逐渐形成的涵盖微观个体行为、中观互动关系与宏观系统涌现的三层研究体系。微观层面的研究聚焦人类个体仿真，许多研究工作通过融合心理学理论与大模型来构建具有复杂认知-情感架构的“人性化”智能体；中观层面关注人类社交交互行为仿真，主要研究智能体间的动态互动与关系演化；宏观层面致力于通过仿真手段揭示信息传播、群体极化等复杂社会现象的涌现机制。归纳了各层次研究中的关键理论、技术路径及跨学科融合案例，分析了当前研究在智能体行为效度验证、生成不确定性和计算成本等方面面临的挑战。展望了虚实联动验证、标准化评估基准构建、记忆与计算效率优化等未来研究方向。
李济廷 , 孙毅 , 王一戎 , 蔺义芹 , 贾珺 , 丁纲松 . 大模型驱动的社交网络多智能体仿真综述[J]. 系统仿真学报, 2026 , 38(2) : 235 -260 . DOI: 10.16182/j.issn1004731x.joss.25-0997
The breakthrough of LLMs has provided powerful tools for social network research, advancing multi-agent social network simulation into a new era. This review systematically examined recent progress in LLM-driven multi-agent social network simulation research through a integrated perspective of multi-disciplines such as artificial intelligence, psychology, communication studies, and sociology. A three-tiered research system, which has gradually formed in this field and encompassed micro-level individual behaviors, meso-level interactive relations, and macro-level system emergence, was summarized. At the micro-level, research focuses on individual human behavior simulation, and numerous studies are dedicated to developing human-like agents with complex cognitive and affective architectures that integrate psychology theories with LLMs. At the meso-level, research focus shifts to human social interaction behavior simulation, mainly investigating the dynamic interactions and relation evolution among agents. At the macro-level, research aims to uncover the emergent dynamics underlying complex social phenomena such as information dissemination and group polarization by means of simulation. Key theories, technical routes, and interdisciplinary integration case studies of research at various levels were synthesized. The challenges faced by current research in aspects such as agent behavior validity verification, generative uncertainty, and computational costs were analyzed. Future research directions were discussed, such as virtual-real linkage validation, the establishment of standardized benchmarks, and optimization of memory and computational efficiency.
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