The Algorithmic Statistician: Can Large Language Models Transform Policy Analysis?

• Daniel José Boza Muñoz

  Universidad Católica Andrés Bello, Venezuela.

  https://orcid.org/0009-0008-4528-156X

Large Language Models (LLMs) are emerging as transformative tools for governance, but their empirical value in public policy analysis remains under-examined. Through a systematic review of 141 publications (Google Scholar, arXiv), this paper analyzes the capacity of LLMs to simulate heterogeneous agents, extract legal and political text, and support decision-making. By synthesizing advances in multi-agent modeling, neurosymbolic extraction, and cooperative AI platforms, the article demonstrates that: a) the heterogeneity of LLMs approximates demographic diversity more faithfully than representative models; b) hybrid systems achieve state-of-the-art accuracy in document analysis; and c) open frameworks, while fostering transparency and participation, entail risks of bias and opacity. An agenda is proposed to improve multimodal inference and ethical governance. In conclusion, LLMs are powerful—not self-sufficient—complements to expert judgment, capable of improving policy design and evaluation if integrated into responsible sociotechnical ecosystems.

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