让大模型模拟社会需模拟思维，而非仅模仿言行。
Simulating Society Requires Simulating Thought
- 引入认知科学构建可修正、可追溯的信念结构
- 提出RECAP基准评估推理真实性和干预一致性
- 适合研究社会行为建模与可信决策模拟的学者
用大语言模型模拟社会，不仅需要生成看似合理的个体与群体行为，更需具备有结构、可修订、可追溯的认知推理能力。当前基于提示和微调的代理虽广泛用于行为模拟，但仍局限于‘人口统计输入，行为输出’的行为主义范式，侧重表面合理性，缺乏内在一致性、因果推理与信念可追溯性，难以真实反映人类的思考、权衡与应对干预过程。为此，我们提出概念性建模范式‘生成心智（GenMinds）’，借鉴认知科学支持生成代理中的结构化信念表示；并设计评估框架RECAP（REconstructing CAusal Paths），通过因果可追溯性、人口统计基础和干预一致性来检验推理真实性。这些工作推动了从表层模仿到真正模拟思维的范式转变，使生成代理能有效模拟社会推理过程。
原文摘要 · Abstract (English)
Simulating society with large language models (LLMs), we argue, requires more than generating plausible behavior; it demands cognitively grounded reasoning that is structured, revisable, and traceable. LLM-based agents are increasingly used to emulate individual and group behavior, primarily through prompting and supervised fine-tuning. Yet current simulations remain grounded in a behaviorist "demographics in, behavior out" paradigm, focusing on surface-level plausibility. As a result, they often lack internal coherence, causal reasoning, and belief traceability, making them unreliable for modeling how people reason, deliberate, and respond to interventions. To address this, we present a conceptual modeling paradigm, Generative Minds (GenMinds), which draws from cognitive science to support structured belief representations in generative agents. To evaluate such agents, we introduce the RECAP (REconstructing CAusal Paths) framework, a benchmark designed to assess reasoning fidelity via causal traceability, demographic grounding, and intervention consistency. These contributions advance a broader shift: from surface-level mimicry to generative agents that simulate thought, not just language, for social simulations.
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