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Simulating Public Opinion: Comparing Distributional and Individual-Level Predictions from LLMs and Random Forests

2025-07-07

Abstract excerpt

Understanding and modeling the flow of information in human societies is essential for capturing phenomena such as polarization, opinion formation, and misinformation diffusion. Traditional agent-based models often rely on simplified behavioral rules that fail to capture the nuanced and context-sensitive nature of human decision-making. In this study, we explore the potential of Large Language Models (LLMs) as dat...

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Literature Corpus work
88725243-29e5-5a29-95db-911d1e6b4e8c
DOI
10.20944/preprints202507.0531.v1
Open publication

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Simulating Public Opinion: Comparing Distributional and Individual-Level Predictions from LLMs and Random ForestsDOI 10.20944/preprints202507.0531.v1
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