LLM代理市场中的战略利用：电子商务信任的模拟框架
Strategic Exploitation in LLM Agent Markets: A Simulation Framework for E-Commerce Trust
AI总结：
本文提出TruthMarketTwin模拟框架，用于研究LLM代理在电子商务市场中的行为，发现LLM代理在传统市场中会利用声誉治理的弱点，而强制执行可减少欺骗并重塑战略推理。
摘要：
基于代理的建模（ABM）长期以来被用于经济学中研究人类行为，而大型语言模型（LLM）代理现在使新的社会和经济模拟成为可能。尽管先前工作发现了LLM代理在金融交易和拍卖市场中的战略性欺骗，但电子商务仍鲜有研究，尽管其有独特的信息不对称：卖家私下观察产品质量，而买家依赖广告声明和声誉信号。我们引入TruthMarketTwin，一种用于研究LLM代理在电子商务市场中行为的受控模拟框架。该框架是首个模拟不对称信息共享下双边贸易的模型之一，其中代理做出战略性列表、购买、评分和救济相关决策以优化卖家利润和买家效用。我们发现，释放到传统市场中的LLM代理会自主利用基于声誉的治理弱点，而强制执行可减少欺骗并重塑战略推理。我们的结果将LLM代理模拟定位为研究由机构治理的自主市场工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
