同样受到警告，AI 智能体避开人少的路，而人类却选择它

中文摘要 AI 辅助

基于少数共享模型构建的 AI 智能体越来越多地为许多人采取行动。关于他人的共享预测可以协调它们的选择，并改变稀缺资源的分配方式。我们在一个双路径拥堵博弈中测试了这种反馈。添加一句警告，即其他人可能会遵循路由提示，使得 50 个 GPT 智能体群体拥挤在一条路上，而避开几乎空置的替代路线。平均出行时间从 64 分钟上升到 95 分钟，尽管任何拥挤道路上的智能体单独切换都可以节省 69 分钟。该警告抑制了它所预测的行动。这种模式持续了 100 轮。另外两个模型家族也发生了同样的转变，但没有锁定在一条路上。12 个全人类群体（240 名参与者）在数值报告或提示和警告下保持接近平衡。在 24 个混合群体中，另有 240 名参与者，在注册分析中，不平衡随着智能体份额的增加而加剧，而人类越来越多地选择智能体避开的道路。集体成本低于全智能体参考，但在 15 个智能体和 5 个人类的情况下，智能体座位的平均时间为 80 分钟，而人类座位为 44 分钟。因此，共享预测可以在相似的智能体之间维持集体低效。更好的群体平均值也可能隐藏不平等的负担。对共享资源的 AI 智能体的评估应测试群体，将消息视为干预措施，并报告谁承担成本。

英文摘要

AI agents built on a few shared models increasingly act for many people. A shared forecast about others can align their choices and change how scarce capacity is allocated. We tested this feedback in a two-road congestion game. Adding one sentence warning that others might follow a routing tip made populations of 50 GPT agents crowd one road while avoiding the nearly empty alternative. Average travel time rose from 64 to 95 min, although any crowded-road agent could have saved 69 min by switching alone. The warning discouraged the very move it predicted. The pattern persisted for 100 rounds. Two other model families shifted the same way without locking onto one road. Twelve all-human groups (240 participants) stayed near balance under numerical reports or the tip and warning. In 24 mixed groups with a further 240 participants, imbalance grew with the share of agents in the registered analysis, while people increasingly took the road the agents avoided. Collective costs stayed below the allagent reference, but with 15 agents and 5 humans, agent seats averaged 80 min, compared with 44 min for human seats. Shared forecasts can thus sustain collective inefficiency among similar agents. A better group average can also hide an unequal burden. Evaluations of AI agents that share resources should test populations, treat messages as interventions and report who bears the costs.

发表机构

• Research Center for Advanced Science and Technology, The University of Tokyo（东京大学先进科学技术研究中心）
• Department of Aeronautics and Astronautics, School of Engineering, The University of Tokyo（东京大学工学部航空航天工程系）

机构由 AI 辅助整理，请以论文原文为准。