What Makes LLM Agent Simulations Useful for Policy? Insights From an Iterative Design Engagement in Emergency Preparedness

2025-09-26

AI Summary

Social simulation in policy-making suffers from limited credibility and trust across domains. Method: This study introduces a verifiable human-AI co-modeling paradigm grounded in large language model (LLM) agents, developed through a year-long iterative collaboration with a university emergency response team. The system comprises 13,000 LLM agents simulating crowd mobility and information diffusion dynamics during large-scale event emergencies. Contribution/Results: We propose three design principles: (1) initiating modeling with high-fidelity, verifiable scenarios to establish cross-disciplinary trust; (2) using preliminary simulations to elicit domain experts’ tacit knowledge; and (3) treating simulation development and policy refinement as a co-evolutionary process. The framework has directly informed operational improvements—including volunteer training optimization, evacuation protocol revision, and infrastructure layout adjustment—demonstrating end-to-end validation from simulation to policy implementation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

There is growing interest in using Large Language Models as agents (LLM agents) for social simulations to inform policy, yet real-world adoption remains limited. This paper addresses the question: How can LLM agent simulations be made genuinely useful for policy? We report on a year-long iterative design engagement with a university emergency preparedness team. Across multiple design iterations, we iteratively developed a system of 13,000 LLM agents that simulate crowd movement and communication during a large-scale gathering under various emergency scenarios. These simulations informed actual policy implementation, shaping volunteer training, evacuation protocols, and infrastructure planning. Analyzing this process, we identify three design implications: start with verifiable scenarios and build trust gradually, use preliminary simulations to elicit tacit knowledge, and treat simulation and policy development as evolving together. These implications highlight actionable pathways to making LLM agent simulations that are genuinely useful for policy.

Problem

• Developing trustworthy LLM agent simulations for policy decisions
• Using agent simulations to inform emergency preparedness planning
• Creating actionable pathways for LLM simulations in policy development

Innovation

• Simulated crowd movement with 13,000 LLM agents
• Used verifiable scenarios to build trust gradually
• Integrated simulations with evolving policy development