AI代理在消费市场谈判中表现差异大，自动化有风险。
The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Markets
- 构建实验框架评估不同LLM代理的谈判能力
- 不同代理为用户争取的交易结果差异显著
- 模型行为异常可能导致双方经济损失
AI代理在消费者应用中日益普及，用于协助产品搜索、谈判与交易执行。本文探讨一个未来场景：消费者与商家均授权AI代理完全自动化谈判与交易。研究聚焦两个核心问题：(1) 不同大语言模型（LLM）代理在为用户争取有利交易方面能力是否不同？(2) 完全自动化交易会带来哪些风险？为此，我们构建了一个实验框架，在真实谈判与交易环境中评估多种LLM代理的表现。结果表明，由AI中介的交易本质上是一场不平衡的游戏——不同代理为用户带来的结果差异显著。此外，大模型的行为异常可能导致消费者超支或商家接受不合理条款，造成财务损失。这些发现表明，尽管自动化可提升效率，但同时也引入了重大风险。用户在委托商业决策给AI代理时应保持谨慎。
原文摘要 · Abstract (English)
AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we explore a future scenario where both consumers and merchants authorize AI agents to fully automate negotiations and transactions. We aim to answer two key questions: (1) Do different LLM agents vary in their ability to secure favorable deals for users? (2) What risks arise from fully automating deal-making with AI agents in consumer markets? To address these questions, we develop an experimental framework that evaluates the performance of various LLM agents in real-world negotiation and transaction settings. Our findings reveal that AI-mediated deal-making is an inherently imbalanced game -- different agents achieve significantly different outcomes for their users. Moreover, behavioral anomalies in LLMs can result in financial losses for both consumers and merchants, such as overspending or accepting unreasonable deals. These results underscore that while automation can improve efficiency, it also introduces substantial risks. Users should exercise caution when delegating business decisions to AI agents.
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