现有AI代理无法独立支撑社会模拟，需重新设计系统机制。
AI Agents Alone Are Not (Yet) Sufficient for Social Simulation
- 将代理行为建模为环境参与的马尔可夫博弈，明确暴露与调度机制
- 实验证明集体结果受交互协议和初始设定主导，非仅由代理对话决定
- 提出可审计的模拟框架，适合研究社会行为与系统设计的学者
大语言模型（LLMs）的进展推动了将集成LLM的智能体用于社会模拟的研究，常隐含假设：在联网多智能体环境中分配角色后，真实的人口动态将自然涌现。本文指出，仅靠基于LLM的智能体尚不足以实现有效社会模拟。这种过度乐观源于当前智能体流程优化与验证目标，与社会模拟作为科学所要求的能力之间存在系统性错配。具体而言，角色扮演的合理性不等于人类行为的真实性；集体结果往往由智能体-环境协同演化驱动，而非仅依赖智能体间通信；实验结果常被交互协议、调度策略和初始信息先验所主导。为此，我们提出一种统一的智能体社会模拟形式化框架——显式暴露与调度机制的环境参与型马尔可夫博弈，并据此推导出设计、评估与解释的具体行动建议。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have spurred growing interest in using LLM-integrated agents for social simulation, often under the implicit assumption that realistic population dynamics will emerge once role-specified agents are placed in a networked multi-agent setting. This position paper argues that LLM-based agents alone are not (yet) sufficient for social simulation. We attribute this over-optimism to a systematic mismatch between what current agent pipelines are typically optimized and validated to produce and what simulation-as-science requires. Concretely, role-playing plausibility does not imply faithful human behavioral validity; collective outcomes are frequently mediated by agent-environment co-dynamics rather than agent-agent messaging alone; and results can be dominated by interaction protocols, scheduling, and initial information priors. To make these underlying mechanisms explicit and auditable, we propose a unified formulation of AI agent-based social simulation as an environment-involved Markov game with explicit exposure and scheduling mechanisms, from which we derive concrete actions for design, evaluation, and interpretation.
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