AI Agents as Policymakers in Simulated Epidemics

2026-01-06

arXiv.org

AI Summary

This study investigates the decision-making capabilities of generative AI agents acting as policymakers in complex social systems, with a focus on recurrent policy choices in epidemic control. The AI agent assumes the role of a mayor within an SEIR epidemic simulation environment, adjusting business restriction policies weekly based on evolving outbreak dynamics. A dynamic memory mechanism with time-decay weighting integrates historical information to inform decisions. Crucially, a theory-guided prompting strategy—incorporating concise epidemiological principles—is introduced to enhance policy reasoning. Experimental results demonstrate that this approach enables the AI agent to exhibit human-like responsiveness and significantly improves both the quality and stability of its policy decisions. The effectiveness of theory-guided prompting in shaping coherent policy behavior is validated across both single-agent and multi-agent evaluation settings.

Technology Category

Multiagent Systems: Agent-Based Simulation and Emergent BehaviorHumans and AI: Human-Aware Planning and Behavior PredictionReasoning under Uncertainty: Sequential Decision Making

Application Category

Search and Retrieval-Augmented AI: Agentic searchSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSocial Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

AI agents are increasingly deployed as quasi-autonomous systems for specialized tasks, yet their potential as computational models of decision-making remains underexplored. We develop a generative AI agent to study repetitive policy decisions during an epidemic, embedding the agent, prompted to act as a city mayor, within a simulated SEIR environment. Each week, the agent receives updated epidemiological information, evaluates the evolving situation, and sets business restriction levels. The agent is equipped with a dynamic memory that weights past events by recency and is evaluated in both single- and ensemble-agent settings across environments of varying complexity. Across scenarios, the agent exhibits human-like reactive behavior, tightening restrictions in response to rising cases and relaxing them as risk declines. Crucially, providing the agent with brief systems-level knowledge of epidemic dynamics, highlighting feedbacks between disease spread and behavioral responses, substantially improves decision quality and stability. The results illustrate how theory-informed prompting can shape emergent policy behavior in AI agents. These findings demonstrate that generative AI agents, when situated in structured environments and guided by minimal domain theory, can serve as powerful computational models for studying decision-making and policy design in complex social systems.

Problem

• AI agents
• policy decisions
• epidemic simulation
• computational modeling
• decision-making

Innovation

• generative AI agent
• epidemic simulation
• dynamic memory
• theory-informed prompting
• policy decision-making