自動化卻充滿風險的博弈：消費者市場中代理間談判與交易的建模

摘要

AI代理在面向消費者的應用中日益普及，用於協助產品搜索、談判和交易執行等任務。本文探討了一個未來情境，其中消費者和商家都授權AI代理完全自動化談判和交易。我們旨在回答兩個關鍵問題：(1) 不同的LLM代理在為用戶爭取有利交易的能力上是否存在差異？(2) 在消費市場中完全自動化AI代理進行交易會帶來哪些風險？為解答這些問題，我們開發了一個實驗框架，評估各種LLM代理在現實世界談判和交易場景中的表現。研究結果顯示，AI中介的交易本質上是一場不平衡的博弈——不同代理為其用戶達成的結果存在顯著差異。此外，LLM中的行為異常可能導致消費者和商家遭受財務損失，例如過度支出或接受不合理交易。這些結果強調，雖然自動化可以提高效率，但也引入了重大風險。用戶在將商業決策委託給AI代理時應謹慎行事。

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.

自動化卻充滿風險的博弈：消費者市場中代理間談判與交易的建模

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets

摘要

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