预测智能体社会中社会机制的规模极限
Predicting the scale limits of social mechanisms in agent societies
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中文总结 AI 辅助
本文针对智能体社会中社会机制的规模极限问题，提出一种审计方法，通过受控实验确定互惠等机制的规模存续条件，验证了预测方法的有效性，为跨群体规模解读社会机制提供了前瞻性手段。
中文摘要 AI 辅助
由语言模型构成的交互智能体社会为研究集体行为提供了可控且可重复的方式，其规模是用人类开展研究时难以实现的。然而，这类研究的科学价值取决于：在小群体中有效的社会机制，在成千上万个智能体交互时是否仍能发挥作用；若直接测试这一点，需要开展成本高昂的大规模运行。本文引入一种审计方法，可预测机制在群体规模增长时的命运，该方法需明确三个问题：机制的作用频率、智能体是否利用其提供的信息、测量本身是否会产生明显的规模效应。受控实验表明，单个结构项即可决定互惠、共识或惩罚机制能否在规模扩展中存续；对于流言机制，其失效时的群体规模由消息的传播范围和生命周期决定。在语言模型社会中，智能体不仅对社会信息做出反应，还对信息的表达方式做出反应：计数和百分比会导致不同的规模行为。执行前做出的预测在第三方代码和第二个模型家族上得到验证，而一次失败的预测则揭示了该发现的边界。该审计方法为判断哪些社会机制可跨群体规模进行解读提供了前瞻性手段。
英文摘要
Societies of interacting language-model agents offer a controllable and repeatable way to study collective behaviour at scales that would be difficult to test with people. Their scientific value, however, depends on whether a social mechanism that works in a small group still operates when thousands of agents interact, and testing this directly requires costly large-scale runs. Here we introduce an audit that predicts a mechanism's fate as a population grows. It asks how often the mechanism can act, whether agents use the information it supplies, and whether the measurement itself creates apparent scale effects. Controlled experiments show that a single structural term can decide whether reciprocity, consensus or punishment survives scaling. For gossip, the population at which the mechanism fails is set by the reach and lifetime of its messages. In language-model societies, agents respond not only to social information but to how it is expressed: counts and percentages led to different scale behaviour. Predictions made before execution held on third-party code and a second model family, while a failed prediction exposed the boundary of the finding. The audit provides a prospective way to decide which social mechanisms can be interpreted across population scales.
