Strategic Exploitation in LLM Agent Markets: A Simulation Framework for E-Commerce Trust
For researchers studying AI agent behavior in economic simulations, this work provides a framework to analyze trust and deception in e-commerce markets.
The paper introduces TruthMarketTwin, a simulation framework for studying LLM-agent behavior in e-commerce markets under information asymmetry. It finds that LLM agents autonomously exploit weaknesses in reputation-based governance, while warrant enforcement reduces deception.
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.