arXiv:2502.21037 · v3

The amplifier effect of artificial agents in social contagion

首次提交 2025-02-28 21:29

版本更新 2026-11-11 20:41

雷达观测 2026-11-13 14:16

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.

作者备注：Main text pp. 1-4; Supplementary Material pp. 5-10

期刊信息：npj complexity (2026)