人工智能代理在电子商务中的购买行为：评估、影响与新兴问题 (What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce)

Online marketplaces will be transformed by autonomous AI agents acting on behalf of consumers. Rather than humans browsing and clicking, AI agents can parse webpages or interact through APIs to evaluate products, and transact. This raises a fundamental question: what do AI agents buy-and why? We develop ACES, a sandbox environment that pairs a platform-agnostic agent with a fully programmable mock marketplace to study this. We first explore aggregate choices, revealing that modal choices can differ across models, with AI agents sometimes concentrating on a few products, raising competition questions. We then analyze the drivers of choices through rationality checks and randomized experiments on product positions and listing attributes. Models show sizeable and heterogeneous position effects: all favor the top row, yet different models prefer different columns, undermining the assumption of a universal ``top'' rank. They penalize sponsored tags, reward endorsements, and sensitivities to price, ratings, and reviews are directionally as expected, but vary sharply across models. Finally, we find that a seller-side agent that makes minor tweaks to product descriptions can deliver substantial market-share gains by targeting AI buyer preferences. Our findings reveal how AI agents behave in e-commerce, and surface concrete seller strategy, platform design, and regulatory questions.

翻译：在线市场将由代表消费者行事的自主人工智能代理所变革。与人类浏览和点击不同，人工智能代理能够解析网页或通过API交互来评估产品并进行交易。这引发了一个根本性问题：人工智能代理购买什么——以及为何购买？我们开发了ACES，这是一个沙盒环境，它将一个与平台无关的代理与一个完全可编程的模拟市场配对，以研究此问题。我们首先探索了聚合选择，揭示了不同模型间的模态选择可能存在差异，人工智能代理有时会集中于少数产品，这引发了竞争问题。随后，我们通过理性检验以及对产品位置和列表属性的随机实验，分析了选择背后的驱动因素。模型显示出显著且异质的位置效应：所有模型都偏好顶行，但不同模型偏好不同的列，这削弱了存在普遍“顶级”排名的假设。它们会惩罚带有赞助标签的产品，奖励带有认可标识的产品，并且对价格、评分和评论的敏感度在方向上符合预期，但在不同模型间差异显著。最后，我们发现，一个对产品描述进行微小调整的卖方代理，通过针对人工智能买家的偏好，能够带来可观的市场份额增长。我们的研究结果揭示了人工智能代理在电子商务中的行为模式，并提出了具体的卖方策略、平台设计和监管问题。