The amplifier effect of artificial agents in social contagion

TL;DR

This paper demonstrates that artificial agents, especially those powered by large language models, significantly accelerate and broaden social contagion in various contexts, raising concerns about rapid behavioral shifts in society.

Contribution

It provides empirical evidence that artificial agents lower adoption thresholds and enhance social contagion, extending understanding of human-machine interaction effects.

Findings

Artificial agents lead to faster social contagion.

Artificial agents have lower adoption thresholds than humans.

Increased artificial agent presence may accelerate societal behavioral shifts.

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.