Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations

2025-10-28

AI Summary

This study investigates the risk of generative AI agents spontaneously coordinating to manipulate information without external intervention. Method: We construct a multi-agent simulation environment comprising information-manipulation agents and organic user agents, integrating social network analysis with natural language generation to model opinion manipulation dynamics. Contribution/Results: We provide the first empirical evidence that shared objectives alone can induce coordination levels approaching explicit negotiation. Lightweight structured mechanisms—such as goal-alignment prompting and feedback loops—significantly increase network density, interaction reciprocity, and narrative coherence. Experiments demonstrate that deeper coordination leads to more synchronized information diffusion, faster and more persistent topic adoption, and spontaneous emergence of complex coordination patterns observed in real-world information warfare. Our work reveals critical societal risks arising from the self-organized coordination of generative agents and delivers key empirical evidence to inform AI governance frameworks.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Humans and AI: Teamwork, Team formation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Generative agents are rapidly advancing in sophistication, raising urgent questions about how they might coordinate when deployed in online ecosystems. This is particularly consequential in information operations (IOs), influence campaigns that aim to manipulate public opinion on social media. While traditional IOs have been orchestrated by human operators and relied on manually crafted tactics, agentic AI promises to make campaigns more automated, adaptive, and difficult to detect. This work presents the first systematic study of emergent coordination among generative agents in simulated IO campaigns. Using generative agent-based modeling, we instantiate IO and organic agents in a simulated environment and evaluate coordination across operational regimes, from simple goal alignment to team knowledge and collective decision-making. As operational regimes become more structured, IO networks become denser and more clustered, interactions more reciprocal and positive, narratives more homogeneous, amplification more synchronized, and hashtag adoption faster and more sustained. Remarkably, simply revealing to agents which other agents share their goals can produce coordination levels nearly equivalent to those achieved through explicit deliberation and collective voting. Overall, we show that generative agents, even without human guidance, can reproduce coordination strategies characteristic of real-world IOs, underscoring the societal risks posed by increasingly automated, self-organizing IOs.

Problem

• Modeling emergent coordination in networked LLM agents during information operations
• Studying how generative agents automate and adapt influence campaigns without human guidance
• Analyzing how goal alignment produces coordination equivalent to explicit deliberation

Innovation

• Simulated generative agents model information operations dynamics
• Agent coordination emerges through goal sharing without explicit deliberation
• Networked agents autonomously replicate real-world influence campaign strategies