AI 中文总结
该研究对比四种大语言模型与人类实地数据，发现AI代理代人购物时搜索深度更高、从不弃权，列表位置对其查看影响弱且非单调，结果属性比位置更重要，最终所有模型均选相同非劣势列表。
AI 中文摘要
搜索排名之所以有价值，是因为人类的注意力稀缺且具有序列性，排名更高的选项更容易被找到，因此会被更多地查看和购买。如今消费者将搜索任务委托给能一次性读取整个结果页面的AI代理。我们在5000次AI代理会话中随机排列100个酒店列表的顺序，将四种大型语言模型与人类实地数据进行比较。AI代理的搜索深度超过人类，且从不弃权（不执行）购买。位置仍然能预测哪些列表会被查看，但影响较弱且非单调：结果页面中间的查看概率最低，而非底部。位置对部分模型会进入选择阶段，对其他模型则不会，这种异质性既不与提供商相关，也不与能力相关。不过所有模型最终都会选择相同的非劣势列表。对于代理式搜索而言，结果页面上显示的属性比其在页面中的位置更重要。
英文摘要
When shopping is delegated to AI agents, it is unclear whether the ranking advantage documented for humans persists. Across 7,000 sessions with five large language models (LLMs) and varying reasoning effort, we compare AI agents with human field data. AI agents search more extensively than human consumers. As with humans, lower-ranked listings are less likely to be inspected, although the effect is smaller for AI agents. Unlike humans, AI agents show a pattern consistent with the lost-in-the-middle effect, whereby middle listings have the lowest probability of inspection. At the choice stage, position effects are concentrated at lower reasoning effort, but higher effort reduces the middle penalty for every LLM that exhibits it. These findings suggest that, when search is delegated, displayed attributes may matter more than placement, and that exposure to position bias depends on how the AI agent is configured.
