The amplifier effect of artificial agents in social contagion
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916647784153088 |
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| author | Hitz, Eric Feng, Mingmin Tanase, Radu Algesheimer, René Mariani, Manuel S. |
| author_facet | Hitz, Eric Feng, Mingmin Tanase, Radu Algesheimer, René Mariani, Manuel S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21037 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The amplifier effect of artificial agents in social contagion Hitz, Eric Feng, Mingmin Tanase, Radu Algesheimer, René Mariani, Manuel S. Social and Information Networks General Economics Economics Physics and Society 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. |
| title | The amplifier effect of artificial agents in social contagion |
| topic | Social and Information Networks General Economics Economics Physics and Society |
| url | https://arxiv.org/abs/2502.21037 |