标题： 基于大语言模型的社会模拟需要一个边界 显示英文标题

标题： LLM-Based Social Simulations Require a Boundary

摘要： 本文论述了基于大型语言模型（LLM）的社会模拟应建立明确的边界，以有意义地促进社会科学的研究。 尽管与传统的基于代理的建模相比，LLM在建模类人代理方面表现出令人期待的能力，但它们面临基本的限制，这些限制限制了其在社会模式发现中的可靠性。 核心问题在于LLM倾向于形成“平均人格”，缺乏足够的行为异质性，而这是模拟复杂社会动态的关键要求。 我们考察了三个关键的边界问题：对齐性（模拟行为与现实世界模式相匹配）、一致性（在时间上保持代理行为的一致性）和鲁棒性（在不同条件下具有可重复性）。 我们提出了启发式的边界，以确定何时基于LLM的模拟可以可靠地推进社会科学的理解。 我们认为，当专注于（1）集体模式而非个体轨迹，（2）代理行为与真实人口平均水平一致，尽管方差有限，以及（3）有适当的验证方法用于测试模拟鲁棒性时，这些模拟更具价值。 我们提供了一个实用的检查清单，以指导研究人员确定基于LLM的社会模拟的适当范围和主张。 显示英文摘要

摘要： This position paper argues that large language model (LLM)-based social simulations should establish clear boundaries to meaningfully contribute to social science research. While LLMs offer promising capabilities for modeling human-like agents compared to traditional agent-based modeling, they face fundamental limitations that constrain their reliability for social pattern discovery. The core issue lies in LLMs' tendency towards an ``average persona'' that lacks sufficient behavioral heterogeneity, a critical requirement for simulating complex social dynamics. We examine three key boundary problems: alignment (simulated behaviors matching real-world patterns), consistency (maintaining coherent agent behavior over time), and robustness (reproducibility under varying conditions). We propose heuristic boundaries for determining when LLM-based simulations can reliably advance social science understanding. We believe that these simulations are more valuable when focusing on (1) collective patterns rather than individual trajectories, (2) agent behaviors aligning with real population averages despite limited variance, and (3) proper validation methods available for testing simulation robustness. We provide a practical checklist to guide researchers in determining the appropriate scope and claims for LLM-based social simulations.