用大模型模拟社会资本理论，揭示集体行动的微观机制
From Blueprint to Reality: Modeling and Applying Putnam's Social Capital Theory with LLM-based Multi-agent Simulations
- 构建基于大模型的多智能体系统，融合社交网络与信任演化
- 仿真重现普特南理论的宏观模式，群体层面与人类行为高度一致
- 可追踪因果路径，适合社会学与智能养老研究者使用
普特南的社会资本理论是理解集体行动与社区繁荣的基础框架。然而，传统实证方法在控制力和可复现性上存在局限；而现有的大模型社会模拟多以行为驱动，缺乏与该理论对齐的建模环境。为此，我们提出SocaSim——一个基于大模型的多智能体仿真框架，旨在从理论蓝图走向仿真现实。具体地，构建集成社交网络演化、信任动态与规范传播的环境，让智能体进行重复的集体行动实验，并将三个维度应用于智慧养老中的适应性挑战分析。仿真结果再现了普特南理论的宏观模式，在群体层面表现出强于人类的行为一致性。相较于传统方法，SocaSim通过逐轮仿真与反事实干预，追踪社会网络、信任与规范的微观因果路径，实现过程级可解释性。综合来看，该框架建立了一种利用大模型智能体连接社会科学与计算机科学的新范式。
原文摘要 · Abstract (English)
Putnam's Social Capital Theory is a foundational framework for collective action and community prosperity. However, traditional empirical methods face practical limits on control and replication. Meanwhile, LLM-based social simulations are typically behavior-driven and lack theory-aligned environments for modeling Putnam's core propositions. To address these gaps, we introduce SocaSim, an LLM-based multi-agent simulation framework to study Putnam's Social Capital Theory from theoretical blueprint to simulated reality. Specifically, we build an environment integrating social network evolution, trust dynamics, and norm propagation, where agents engage in repeated collective-action experiments, and then apply the three dimensions to analyze adaptation challenges in smart elderly care. Our simulations reproduce Putnam's macro-level patterns and exhibit strong human-agent alignment at the group level. Unlike traditional methods, SocaSim traces micro-level causal pathways of social network, trust, and norms via round-by-round simulations and counterfactual interventions, enabling process-level interpretability. Taken together, these capabilities establish a research paradigm that leverages LLM agents to bridge social science and computer science.
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