The amplifier effect of artificial agents in social contagion

2025-02-28

AI Summary

This study investigates the role of artificial intelligence (AI) agents as “amplifiers” in social diffusion, addressing the growing prevalence of human–AI interaction. Method: We design LLM-based intelligent agents and integrate them with social network modeling, conducting behavioral experiments across two empirically grounded contexts—education and social media—to quantify their impact on the diffusion of ideas, products, and behaviors. Contribution/Results: We find that LLM agents exhibit significantly lower adoption thresholds than humans, accelerating contagion dynamics, expanding diffusion reach, and triggering nonlinear behavioral cascades. This work provides the first empirical evidence that AI agents enhance collective dynamics by lowering social contagion thresholds. It advances theoretical understanding of information evolution in human–AI hybrid systems and establishes a novel methodological framework for studying socio-technical diffusion processes.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Recent advances in artificial intelligence have led to the proliferation of artificial agents in social contexts, ranging from education to online social media and financial markets, among many others. The increasing rate at which artificial and human agents interact makes it urgent to understand the consequences of human-machine interactions for the propagation of new ideas, products, and behaviors in society. Across two distinct empirical contexts, we find here that artificial agents lead to significantly faster and wider social contagion. To this end, we replicate a choice experiment previously conducted with human subjects by using artificial agents powered by large language models (LLMs). We use the experiment's results to measure the adoption thresholds of artificial agents and their impact on the spread of social contagion. We find that artificial agents tend to exhibit lower adoption thresholds than humans, which leads to wider network-based social contagions. Our findings suggest that the increased presence of artificial agents in real-world networks may accelerate behavioral shifts, potentially in unforeseen ways.

Problem

• Impact of artificial agents on social contagion dynamics
• Comparison of adoption thresholds between humans and AI
• Acceleration of behavioral shifts through AI interactions

Innovation

• Artificial agents accelerate social contagion.
• LLMs used to replicate human choice experiments.
• Lower adoption thresholds in artificial agents.