用大模型模拟社会角色，生成深度社会假设。
The Qualitative Laboratory: Theory Prototyping and Hypothesis Generation with Large Language Models
- 用大模型构建社会角色对话，模拟不同群体对信息的反应。
- 发现保守群体拒绝国家安全框架等反直觉假设。
- 适合社会学研究者探索复杂社会认知，辅助理论验证。
社会科学的核心挑战之一是生成关于不同社会群体如何解读新信息的丰富定性假设。本文提出并演示了一种新方法：利用大语言模型（LLMs）进行社会学人物模拟，将其视为‘定性实验室’。相比传统方法，该方法通过生成自然语言对话，克服了情景问卷调查缺乏话语深度的问题；同时，通过自然语言表达复杂世界观，规避了基于规则的代理模型（ABMs）的建模瓶颈。我们以气候接受度的社会学理论为基础，构建人物角色并模拟其对政策信息的反应，生成了如保守派拒绝国家安全框架等新颖且反直觉的假设，挑战了既有理论。结论指出，该方法作为‘模拟后验证’流程的一部分，能有效生成可深入检验的理论假设。
原文摘要 · Abstract (English)
A central challenge in social science is to generate rich qualitative hypotheses about how diverse social groups might interpret new information. This article introduces and illustrates a novel methodological approach for this purpose: sociological persona simulation using Large Language Models (LLMs), which we frame as a "qualitative laboratory". We argue that for this specific task, persona simulation offers a distinct advantage over established methods. By generating naturalistic discourse, it overcomes the lack of discursive depth common in vignette surveys, and by operationalizing complex worldviews through natural language, it bypasses the formalization bottleneck of rule-based agent-based models (ABMs). To demonstrate this potential, we present a protocol where personas derived from a sociological theory of climate reception react to policy messages. The simulation produced nuanced and counter-intuitive hypotheses - such as a conservative persona's rejection of a national security frame - that challenge theoretical assumptions. We conclude that this method, used as part of a "simulation then validation" workflow, represents a superior tool for generating deeply textured hypotheses for subsequent empirical testing.
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