AGENTIC-SYSTEMSJuly 9, 20261 min read1 views

LLM-powered reasoning in agent-based modeling

Researchers have introduced HALE, a hybrid framework combining agent-based modeling (ABM) with large language models (LLMs) to better predict human decision-making. This innovative approach could significantly improve policy-making and epidemic simulation.

Computer Science > Artificial Intelligence

Title:LLM-powered reasoning in agent-based modeling

View PDF HTML (experimental)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.

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