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

This academic paper investigates the implications of AI agents automating negotiations and transactions in consumer markets. The authors establish an experimental framework where different Large Language Models (LLMs) act as buyer and seller agents for real-world products, evaluating their performance and identifying potential risks. Key findings indicate significant disparities in negotiation capabilities among LLM agents, leading to an imbalanced game where users with less capable agents may face financial disadvantages. Furthermore, the study highlights critical behavioral anomalies in LLMs, such as constraint violations (overspending or selling below cost), excessive payments, negotiation deadlocks, and early settlements, demonstrating how these can translate into tangible economic losses for users.