As large language models (LLMs) transition from static tools to fully agentic systems, their potential for transforming social science research has become increasingly evident. This paper introduces a structured framework for understanding the diverse applications of LLM-based agents, ranging from simple data processors to complex, multi-agent systems capable of simulating emergent social dynamics. By mapping this developmental continuum across six levels, the paper clarifies the technical and methodological boundaries between different agentic architectures, providing a comprehensive overview of current capabilities and future potential. It highlights how lower-tier systems streamline conventional tasks like text classification and data annotation, while higher-tier systems enable novel forms of inquiry, including the study of group dynamics, norm formation, and large-scale social processes. However, these advancements also introduce significant challenges, including issues of reproducibility, ethical oversight, and the risk of emergent biases. The paper critically examines these concerns, emphasizing the need for robust validation protocols, interdisciplinary collaboration, and standardized evaluation metrics. It argues that while LLM-based agents hold transformative potential for the social sciences, realizing this promise will require careful, context-sensitive deployment and ongoing methodological refinement. The paper concludes with a call for future research that balances technical innovation with ethical responsibility, encouraging the development of agentic systems that not only replicate but also extend the frontiers of social science, offering new insights into the complexities of human behavior.

翻译：随着大语言模型（LLMs）从静态工具向完全自主的系统转变，其变革社会科学研究的潜力日益显著。本文构建了一个结构化框架，用以理解基于大语言模型的智能体在不同层面的多样化应用——从简单数据处理到能够模拟突发社会动态的复杂多智能体系统。通过将这一发展连续体映射为六个层级，本文阐明了不同智能体架构之间的技术和方法边界，全面概述了当前能力与未来潜力。文中指出，低层级系统可优化文本分类与数据标注等常规任务，而高层级系统则能实现群体动力学、规范形成及大规模社会过程等新型研究。然而，这些进展也带来了显著挑战，包括可重复性问题、伦理监督及突发偏见风险。本文批判性审视了这些关切，强调建立稳健验证协议、跨学科协作及标准化评估指标的必要性。研究认为，尽管基于大语言模型的智能体对社会学具有变革潜力，但实现这一愿景需审慎且情境敏感的部署，并持续完善方法论。最后，本文呼吁未来研究应平衡技术创新与伦理责任，鼓励开发不仅能复现、更能拓展社会科学前沿的智能体系统，为理解人类行为的复杂性提供新洞见。