HALE couples LLM group-level mobility decisions with large-scale activity-based ABM networks and better matches Salt Lake County COVID-19 peak timing and size than ABM-only runs.
Generative agent-based modeling: Unveiling social sys- tem dynamics through coupling mechanistic models with generative artificial intelligence
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abstract
We discuss the emerging new opportunity for building feedback-rich computational models of social systems using generative artificial intelligence. Referred to as Generative Agent-Based Models (GABMs), such individual-level models utilize large language models such as ChatGPT to represent human decision-making in social settings. We provide a GABM case in which human behavior can be incorporated in simulation models by coupling a mechanistic model of human interactions with a pre-trained large language model. This is achieved by introducing a simple GABM of social norm diffusion in an organization. For educational purposes, the model is intentionally kept simple. We examine a wide range of scenarios and the sensitivity of the results to several changes in the prompt. We hope the article and the model serve as a guide for building useful diffusion models that include realistic human reasoning and decision-making.
representative citing papers
The paper surveys LLM-based multi-agent systems, covering simulated domains, agent profiling and communication, mechanisms for capacity growth, and common benchmarks.
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LLM-powered reasoning in agent-based modeling
HALE couples LLM group-level mobility decisions with large-scale activity-based ABM networks and better matches Salt Lake County COVID-19 peak timing and size than ABM-only runs.
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Large Language Model based Multi-Agents: A Survey of Progress and Challenges
The paper surveys LLM-based multi-agent systems, covering simulated domains, agent profiling and communication, mechanisms for capacity growth, and common benchmarks.