研究大模型在拍卖中串通行为，发现沟通与压力会加剧合谋
Evaluating LLM Agent Collusion in Double Auctions
- 通过模拟连续双重拍卖市场，测试大模型卖家的串通行为
- 有直接通信时，卖家合谋倾向显著上升，不同模型差异明显
- 监管压力和紧迫感可抑制串通，对部署有重要警示意义
大型语言模型（LLMs）在多个领域展现出作为自主代理的惊人能力。随着这些代理越来越多地参与社会经济互动，识别其潜在的不良行为变得至关重要。本文研究了大模型代理在选择性合谋（即秘密合作损害他人利益）场景下的行为。我们通过一系列受控实验，考察了大模型代理在模拟连续双重拍卖市场中作为卖方的行为。实验分析了通信能力、模型选择及环境压力等因素对卖家合谋稳定性与出现的影响。结果表明，直接通信会增加合谋倾向，不同模型的合谋意愿存在差异，而来自权威人物的监督和紧迫感等环境压力会影响合谋行为。研究揭示了部署基于大模型的市场代理时重要的经济与伦理考量。
原文摘要 · Abstract (English)
Large language models (LLMs) have demonstrated impressive capabilities as autonomous agents with rapidly expanding applications in various domains. As these agents increasingly engage in socioeconomic interactions, identifying their potential for undesirable behavior becomes essential. In this work, we examine scenarios where they can choose to collude, defined as secretive cooperation that harms another party. To systematically study this, we investigate the behavior of LLM agents acting as sellers in simulated continuous double auction markets. Through a series of controlled experiments, we analyze how parameters such as the ability to communicate, choice of model, and presence of environmental pressures affect the stability and emergence of seller collusion. We find that direct seller communication increases collusive tendencies, the propensity to collude varies across models, and environmental pressures, such as oversight and urgency from authority figures, influence collusive behavior. Our findings highlight important economic and ethical considerations for the deployment of LLM-based market agents.
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