标题： 评估大型语言模型在社会科学研究中作为合成社会代理人的使用 显示英文标题

标题： Evaluating the Use of Large Language Models as Synthetic Social Agents in Social Science Research

摘要： 大型语言模型（LLMs）在社会科学中被越来越多地用作合成代理，应用范围从增强调查回答到推动多代理模拟。 由于强大的预测加上条件提示、标记对数概率和重复抽样可以模仿贝叶斯工作流程，它们的输出可能被误认为是来自一致模型的后验类似证据。 然而，预测并不等同于概率主义，准确的点并不意味着校准的不确定性。 本文概述了在解释LLM输出时应采取的注意事项，并提出了一个实用的重新框架，用于社会科学中LLM的使用，即在明确的范围条件下将LLM作为高容量模式匹配器进行准预测性插值，而不是作为概率推理的替代品。 引入了实际的保障措施，如独立抽样、预先注册的人类基线、依赖可靠性的验证和子组校准，以便研究人员可以在避免类别错误的同时进行有用的原型设计和预测。 显示英文摘要

摘要： Large Language Models (LLMs) are being increasingly used as synthetic agents in social science, in applications ranging from augmenting survey responses to powering multi-agent simulations. Because strong prediction plus conditioning prompts, token log-probs, and repeated sampling mimic Bayesian workflows, their outputs can be misinterpreted as posterior-like evidence from a coherent model. However, prediction does not equate to probabilism, and accurate points do not imply calibrated uncertainty. This paper outlines cautions that should be taken when interpreting LLM outputs and proposes a pragmatic reframing for the social sciences in which LLMs are used as high-capacity pattern matchers for quasi-predictive interpolation under explicit scope conditions and not as substitutes for probabilistic inference. Practical guardrails such as independent draws, preregistered human baselines, reliability-aware validation, and subgroup calibration, are introduced so that researchers may engage in useful prototyping and forecasting while avoiding category errors.