发表机构
School of Management, Xiamen University; The Hong Kong University of Science and Technology（厦门大学管理学院; 香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对带多模态收益的情境动态定价问题，提出经试点校正的分层决策划分策略，达到依赖极小极大平滑性的时域速率，填补了半参数情境动态定价的相关理论空白。
AI 中文摘要
我们研究具有任意协变量序列和有界、可能非二元购买量的情境动态定价问题。需求遵循半参数剩余指数模型，包含未知线性估值参数和未知Hölder平滑响应。我们对收益既不施加凹性也不施加强单峰性，允许最优价格不唯一。我们提出一种经试点校正的分层决策划分策略，结合定向试点估计、局部多项式学习、可预测数据分配和全局动作消除。试点校正消除了估值参数误差的一阶效应，永久标签使自适应采样下的浓度成为可能。该策略达到依赖极小极大平滑性的时域速率（仅差对数因子）；对于常情境二元需求子类，已存在匹配的下界。
英文摘要
We study contextual dynamic pricing with arbitrary covariate sequences and bounded, possibly nonbinary purchase quantities. Demand follows a semiparametric surplus-index model with an unknown linear valuation parameter and an unknown Hölder-smooth response. We impose neither concavity nor strong unimodality on revenue and allow nonunique optimal prices. We develop a pilot-corrected layered decision-partitioning policy that combines directional pilot estimation, local polynomial learning, predictable data assignment, and global action elimination. Pilot correction removes the first-order effect of valuation-parameter error, while permanent labels enable concentration under adaptive sampling. The policy attains the minimax smoothness-dependent horizon rate up to logarithmic factors; a matching lower bound already holds for a constant-context binary-demand subclass.
Comments53 pages
