An LLM-Driven Multi-Agent Simulation Framework for Coupled Epidemic–Economic Dynamics

Traditional Agent-based Models (ABMs) often struggle to capture the nuance of adaptive human decision-making during complex crises due to their reliance on static, predefined rules. Large Language Models (LLMs) offer a transformative solution by acting as cognitive engines that empower agents with human-like common-sense reasoning. In this paper, we introduce an LLM-driven Multi-Agent Simulation framework to investigate coupled epidemic–economic dynamics, incorporating a Perception-Deliberation-Action (PDA) loop. Agents, acting as heterogeneous cognitive entities, utilize Chain-of-Thought processes to autonomously balance health risks against economic necessities. This approach endogenously generates adaptive behaviors without explicit scripting. Extensive experiment results across diverse LLM backends confirm the framework’s robustness, revealing divergent socio-economic trajectories under distinct macroscopic conditions and effectively quantifying the trade-offs between public health and economic stability. This approach establishes a high-fidelity computational laboratory for investigating complex scenarios under distinct macroscopic conditions, effectively bridging the gap between micro-level cognition and macro-level societal outcomes.