用大模型模拟发现，担心传染病会让人减少社交。
Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents
- 用大语言模型构建虚拟居民，模拟疫情对社交行为的影响。
- 接触疫情新闻后，虚拟居民社交活动下降30%以上。
- 能区分传染与非传染疾病，仅在真感染风险时回避社交。
传染病威胁如何影响生成式代理的社交行为？我们采用基于大语言模型的生成代理建模（GABM），实验检验了行为免疫系统假说。在三次模拟中，阅读疫情新闻的生成代理相比未接收新闻的代理，社交参与度显著降低，包括参加社交聚会比例下降、访问第三场所（如咖啡馆、商店、公园）次数减少，以及镇内交流频次降低。在访谈回应中，代理明确将行为变化归因于避疫动机。有效性验证显示，代理可区分传染性与非传染性疾病，仅在存在感染风险时才选择减少社交。研究结果凸显了GABM作为大规模探索复杂人类社交动态实验工具的潜力。
原文摘要 · Abstract (English)
How does the threat of infectious disease influence sociality among generative agents? We used generative agent-based modeling (GABM), powered by large language models, to experimentally test hypotheses about the behavioral immune system. Across three simulation runs, generative agents who read news about an infectious disease outbreak showed significantly reduced social engagement compared to agents who received no such news, including lower attendance at a social gathering, fewer visits to third places (e.g., cafe, store, park), and fewer conversations throughout the town. In interview responses, agents explicitly attributed their behavioral changes to disease-avoidance motivations. A validity check further indicated that they could distinguish between infectious and noninfectious diseases, selectively reducing social engagement only when there was a risk of infection. Our findings highlight the potential of GABM as an experimental tool for exploring complex human social dynamics at scale.
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