arXiv:2607.06757v1 Announce Type: new 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 predic
The rapid advancement and accessibility of large language models (LLMs) enable their integration into complex modeling frameworks, addressing limitations of traditional static models.
This development allows for more dynamic and adaptable agent-based models, improving the accuracy and relevance of simulations for policy-making, especially in fast-evolving scenarios.
Agent-based models can now incorporate real-time, LLM-powered predictions of human decision-making, moving beyond static priors to dynamically adapt to evolving conditions.
- · AI researchers and developers
- · Policymakers and government agencies
- · Simulation and modeling software providers
- · Developers of traditional static ABMs
- · Organizations reliant on outdated modeling techniques
Increased accuracy and predictive power of agent-based models through LLM integration.
Improved policy responses and resource allocation in areas like public health and urban planning due to better simulations.
The development of 'digital twins' that can respond dynamically and intelligently to real-world stimuli, blurring the line between simulation and reality.
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Read at arXiv cs.AI