We develop an LLM-powered virtual population model that simulates demand for pricing decisions, in settings where products are described by rich unstructured information, such as text descriptions and images, and where decision makers need not only mean-demand predictions but also uncertainty estimates for counterfactual prices. Our model represents exposed customers as draws from a finite mixture of customer personas. For each persona, product, and candidate price, an LLM elicits a persona-level purchase probability using both structured persona information and unstructured product information. These probabilities are aggregated through calibrated mixture weights to form a predictive distribution of aggregate demand. The resulting simulator can evaluate counterfactual prices under various pricing objectives, including expected revenue and risk-aware criteria such as conditional value at risk. We test the framework on an online H&M fashion dataset with product descriptions and images. The calibrated LLM-based simulator achieves the best overall predictive performance among the models considered, and supports sample-efficient pricing decisions. Our framework provides a practical way to use LLMs as demand simulators for products with limited historical demand data but rich product information. By producing a full predictive demand distribution rather than only a point forecast, it enables managers to compare candidate prices, quantify demand uncertainty, and choose prices that target either average-case revenue or risk-aware objectives.

翻译：我们提出了一种基于大语言模型（LLM）的虚拟群体模型，用于模拟定价决策中的需求。该模型适用于以下场景：产品具有丰富的非结构化信息（如文本描述和图像），且决策者不仅需要平均需求预测，还需获得反事实价格下的不确定性估计。我们的模型将曝光客户表示为有限混合客户原型的随机抽样。针对每个原型、产品和候选价格，LLM通过结构化原型信息和非结构化产品信息，生成原型层面的购买概率。这些概率经校准后的混合权重聚合，形成总需求预测分布。由此构建的模拟器能评估不同定价目标下的反事实价格，包括期望收益及风险敏感指标（如条件风险价值）。我们在包含产品描述和图像的H&M在线时尚数据集上测试了该框架。经校准的LLM模拟器在所有对比模型中取得了最佳综合预测性能，并支持样本高效的定价决策。该框架为历史需求数据有限但产品信息丰富的场景提供了使用LLM作为需求模拟器的实用方案。通过生成完整的需求预测分布而非单点预测，该方法使管理者能够比较候选价格、量化需求不确定性，并选择针对平均收益或风险敏感目标的最优价格。