Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing

Autor:	Liu, Po-Yi, Wang, Chi-Hua, Tsai, Henghsiu
Rok vydání:	2022
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Economics - General Economics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 24 pages, 13 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.09372 Zobrazit plný text záznamu View this record from Arxiv