LLM-powered reasoning in agent-based modeling
Researchers introduced a scalable Hybrid Agent-based and Language-driven Epidemic (HALE) modeling framework that leverages large language models (LLMs) to predict human decision-making in agent-based modeling (ABM), with a proof-of-concept simulation of COVID-19 in Salt Lake County, UT.
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[Submitted on 7 Jul 2026]
Title:LLM-powered reasoning in agent-based modeling
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Abstract:Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making. However, ABMs have traditionally relied on static prior, which prevents the models from adapting to real-time changes. Our research provides a novel approach to addressing this information gap. Large language models (LLMs) offer new opportunities to predict human decision-making. Here, we introduce a scalable Hybrid Agent-based and Language-driven Epidemic (HALE) modeling framework that leverages LLMs to predict human decision-making in an ABM simulation. As a proof-of-concept, we use HALE to simulate COVID-19 and its effects in Salt Lake County, UT.
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2607.06757 [cs.AI]
(or arXiv:2607.06757v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.06757
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sifat Moon [view email] [v1] Tue, 7 Jul 2026 19:39:01 UTC (1,373 KB)
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