RetailSim: LLM Agent Framework Simulates End-to-End Seller-Buyer Retail Dynamics

A recent study published on arXiv presents RetailSim, a comprehensive retail simulation framework that encompasses the entire retail process, from seller persuasion to buyer-seller interactions and purchase decisions. This framework aims to overcome the shortcomings of current retail simulators, which typically only address limited facets of the process and overlook inter-stage dependencies. RetailSim incorporates various product categories, persona-based agents, and multi-turn dialogues to enhance simulation accuracy. The researchers assessed RetailSim through a two-pronged approach: human evaluations of behavioral accuracy and meta-evaluations against real-world economic patterns. Findings indicate that RetailSim effectively mirrors significant trends, including demographic buying habits, the price-demand correlation, and varying price elasticity. The paper can be found on arXiv under the identifier 2604.04468v2 and was noted as a replacement type. This research underscores the capabilities of LLM agents in modeling intricate economic interactions, providing a valuable resource for testing retail strategies prior to implementation.