The Qualitative Laboratory: Theory Prototyping and Hypothesis Generation with Large Language Models
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908744548352000 |
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| author | Draelants, Hugues |
| author_facet | Draelants, Hugues |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_00797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Qualitative Laboratory: Theory Prototyping and Hypothesis Generation with Large Language Models Draelants, Hugues Computation and Language Artificial Intelligence Computers and Society Multiagent Systems J.4; I.6.8; I.2.7 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. |
| title | The Qualitative Laboratory: Theory Prototyping and Hypothesis Generation with Large Language Models |
| topic | Computation and Language Artificial Intelligence Computers and Society Multiagent Systems J.4; I.6.8; I.2.7 |
| url | https://arxiv.org/abs/2601.00797 |