论文标题
通过演员批评定向定价的非平稳动态定价
Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing
论文作者
论文摘要
本文介绍了一种新型的非平稳动态定价算法设计，在该设计中，定价代理将面临不完整的需求信息和市场环境的变化。代理商进行了价格实验，以了解每种产品的需求曲线和最大化价格，同时意识到市场环境的变化，以避免提供高优势价格的高机会成本。拟议的酸P扩展了来自统计机器学习的信息指导的采样（IDS）算法，以包括微观经济选择理论，并采用新颖的定价策略审核程序，以避免在市场环境转移后避免亚地区定价。拟议的酸P在一系列市场环境变化中优于包括上置信度结合（UCB）和汤普森采样（TS）在内的竞争匪徒算法。
This paper presents a novel non-stationary dynamic pricing algorithm design, where pricing agents face incomplete demand information and market environment shifts. The agents run price experiments to learn about each product's demand curve and the profit-maximizing price, while being aware of market environment shifts to avoid high opportunity costs from offering sub-optimal prices. The proposed ACIDP extends information-directed sampling (IDS) algorithms from statistical machine learning to include microeconomic choice theory, with a novel pricing strategy auditing procedure to escape sub-optimal pricing after market environment shift. The proposed ACIDP outperforms competing bandit algorithms including Upper Confidence Bound (UCB) and Thompson sampling (TS) in a series of market environment shifts.
