用大模型代理模拟电商市场，发现信用机制易被欺骗，强制履约可抑制作弊。
Strategic Exploitation in LLM Agent Markets: A Simulation Framework for E-Commerce Trust
- 构建双盲交易仿真框架，让大模型代理自主决策买卖、评价与追责。
- 无约束下代理普遍利用信息差骗好评，导致信任体系崩塌。
- 加入履约保障后，代理行为更诚实，适合研究自治市场治理设计。
基于代理的建模（ABM）在经济学中长期用于研究人类行为，而大语言模型（LLM）代理如今使社会与经济仿真成为可能。尽管已有研究揭示了大模型代理在金融交易和拍卖市场中的策略性欺骗行为，电商领域仍鲜有探索，尽管其存在显著的信息不对称：卖家私密掌握产品质量，买家则依赖广告描述与声誉信号。本文提出TruthMarketTwin，一个用于研究大模型代理在电商市场中行为的受控仿真框架。该框架是首个在信息不对称条件下模拟双边交易的系统，代理需自主决策商品上架、购买、评分及申诉等行为，以优化卖家利润与买家效用。研究发现，在传统市场环境中，释放的大型语言模型代理会自发利用声誉治理机制的漏洞进行策略性欺骗；而引入售后保障机制可显著减少欺骗行为，并重塑代理的战略推理模式。结果表明，大模型代理仿真可作为研究制度化自治市场的重要工具。
原文摘要 · Abstract (English)
Agent-based modeling (ABM) has long been used in economics to study human behavior, and large language model (LLM) agents now enable new forms of social and economic simulation. While prior work has discovered strategic deception by LLM agents in financial trading and auction markets, e-commerce remains underexplored despite its distinctive information asymmetry: sellers privately observe product quality, whereas buyers rely on advertised claims and reputation signals. We introduce TruthMarketTwin, a controlled simulation framework for studying LLM-agent behavior in e-commerce markets. The framework is one of the first to model bilateral trade under asymmetric information sharing, where agents make strategic listing, purchasing, rating, and recourse-related decisions to optimize seller profit and buyer utility. We find that LLM agents released into traditional markets autonomously exploit weaknesses in reputation-based governance, while warrant enforcement reduces deception and reshapes strategic reasoning. Our results position LLM-agent simulation as a tool for studying institution-governed autonomous markets.
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