让下一篇重要论文，不再错过。

原文摘要

Individuals and organizations increasingly consider delegating decision-making to AI agents, which may significantly alter social contagion dynamics. We ask whether artificial agents exhibit different adoption thresholds from those of human subjects when making decisions and, if so, how such differences may affect the spread of social contagions. To answer these questions, we conduct two choice experiments - recently administered to human participants - with artificial agents powered by large language models (LLMs). Across two domains and five LLMs, artificial agents exhibit systematically lower adoption thresholds than human subjects. Simulations of diffusion calibrated with the experimental findings show that, holding network structure fixed, even a modest share of artificial agents leads to higher total adoption. Our findings suggest that the presence of artificial agents in social networks can alter diffusion.