Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements in large language models (LLMs) highlight their potential to simulate human behavior, enabling the replication of individual responses and facilitating studies on many interdisciplinary studies. In this paper, we conduct a comprehensive survey of this field, illustrating the recent progress in simulation driven by LLM-empowered agents. We categorize the simulations into three types: (1) Individual Simulation, which mimics specific individuals or demographic groups; (2) Scenario Simulation, where multiple agents collaborate to achieve goals within specific contexts; and (3) Society Simulation, which models interactions within agent societies to reflect the complexity and variety of real-world dynamics. These simulations follow a progression, ranging from detailed individual modeling to large-scale societal phenomena. We provide a detailed discussion of each simulation type, including the architecture or key components of the simulation, the classification of objectives or scenarios and the evaluation method. Afterward, we summarize commonly used datasets and benchmarks. Finally, we discuss the trends across these three types of simulation. A repository for the related sources is at {\url{https://github.com/FudanDISC/SocialAgent}}.

翻译：传统社会学研究通常依赖人类参与，这种方法虽然有效，但成本高昂、难以扩展，且存在伦理问题。大语言模型（LLMs）的最新进展凸显了其模拟人类行为的潜力，使得复制个体反应成为可能，并促进了众多跨学科研究的发展。本文对该领域进行了全面综述，阐述了由LLM赋能的智能体所驱动仿真的最新进展。我们将仿真分为三类：（1）个体仿真，模拟特定个体或人口群体；（2）场景仿真，多个智能体在特定情境下协作以实现目标；（3）社会仿真，对智能体社会内部的互动进行建模，以反映现实世界动态的复杂性与多样性。这些仿真遵循从精细的个体建模到大规模社会现象的递进关系。我们对每种仿真类型进行了详细讨论，包括仿真的架构或关键组件、目标或场景的分类以及评估方法。随后，我们总结了常用的数据集和基准测试。最后，我们讨论了这三类仿真的发展趋势。相关资源的存储库位于 {\url{https://github.com/FudanDISC/SocialAgent}}。