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Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-Myopic Policies

Citations

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Cited by:

  1. Yinchu Zhu & Ilya O. Ryzhov, 2023. "Semidiscrete optimal transport with unknown costs," Papers 2310.00786, arXiv.org, revised Feb 2025.
  2. Xi Chen & Jianjun Gao & Dongdong Ge & Zizhuo Wang, 2022. "Bayesian dynamic learning and pricing with strategic customers," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3125-3142, August.
  3. Sentao Miao & Xi Chen & Xiuli Chao & Jiaxi Liu & Yidong Zhang, 2022. "Context‐based dynamic pricing with online clustering," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3559-3575, September.
  4. Yiwei Chen & Cong Shi, 2023. "Network revenue management with online inverse batch gradient descent method," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2123-2137, July.
  5. Jianqing Fan & Yongyi Guo & Mengxin Yu, 2024. "Policy Optimization Using Semiparametric Models for Dynamic Pricing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(545), pages 552-564, January.
  6. Adel Javanmard & Jingwei Ji & Renyuan Xu, 2024. "Multi-Task Dynamic Pricing in Credit Market with Contextual Information," Papers 2410.14839, arXiv.org, revised May 2025.
  7. Bing Wang & Wenjie Bi & Haiying Liu, 2023. "Dynamic Pricing with Parametric Demand Learning and Reference-Price Effects," Mathematics, MDPI, vol. 11(10), pages 1-14, May.
  8. Virag Shah & Jose Blanchet & Ramesh Johari, 2019. "Semi-parametric dynamic contextual pricing," Papers 1901.02045, arXiv.org, revised Aug 2019.
  9. den Boer, Arnoud V., 2015. "Tracking the market: Dynamic pricing and learning in a changing environment," European Journal of Operational Research, Elsevier, vol. 247(3), pages 914-927.
  10. Xiao, Baichun & Yang, Wei, 2021. "A Bayesian learning model for estimating unknown demand parameter in revenue management," European Journal of Operational Research, Elsevier, vol. 293(1), pages 248-262.
  11. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
  12. Xinqi Chen & Xingyu Bai & Zeyu Zheng & Nian Si, 2025. "Bias Analysis of Experiments for Multi-Item Multi-Period Inventory Control Policies," Papers 2501.11996, arXiv.org.
  13. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Eric Bergerson & Megan Kurka & Ludek Kopacek, 2020. "Learning Demand Curves in B2B Pricing: A New Framework and Case Study," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1287-1306, May.
  14. L. Jeff Hong & Chenghuai Li & Jun Luo, 2020. "Technical note: Finite‐time regret analysis of Kiefer‐Wolfowitz stochastic approximation algorithm and nonparametric multi‐product dynamic pricing with unknown demand," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(5), pages 368-379, August.
  15. Ningyuan Chen & Guillermo Gallego, 2018. "A Primal-dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint," Papers 1812.09234, arXiv.org, revised Oct 2021.
  16. Renato Matta & Timothy J. Lowe, 2023. "Product price alignment with seller service rating and consumer satisfaction," Annals of Operations Research, Springer, vol. 320(2), pages 695-725, January.
  17. Yuqing Zhang & Neil Walton, 2019. "Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches," Papers 1907.05381, arXiv.org.
  18. Ruben Geer & Arnoud V. Boer & Christopher Bayliss & Christine S. M. Currie & Andria Ellina & Malte Esders & Alwin Haensel & Xiao Lei & Kyle D. S. Maclean & Antonio Martinez-Sykora & Asbjørn Nilsen Ris, 2019. "Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(3), pages 185-203, June.
  19. Thomas Loots & Arnoud V. den Boer, 2023. "Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1169-1186, April.
  20. Yifan Wu & Ramesh Johari & Vasilis Syrgkanis & Gabriel Y. Weintraub, 2024. "Switchback Price Experiments with Forward-Looking Demand," Papers 2410.14904, arXiv.org.
  21. Felix Meyer & Göran Kauermann & Christopher Alder & Catherine Cleophas, 2025. "Modeling price-sensitive demand in turbulent times: an application to continuous pricing," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(2), pages 153-177, April.
  22. Doan, Xuan Vinh & Lei, Xiao & Shen, Siqian, 2020. "Pricing of reusable resources under ambiguous distributions of demand and service time with emerging applications," European Journal of Operational Research, Elsevier, vol. 282(1), pages 235-251.
  23. Jianyu Xu & Yu-Xiang Wang, 2021. "Logarithmic Regret in Feature-based Dynamic Pricing," Papers 2102.10221, arXiv.org, revised Oct 2021.
  24. Woonghee Tim Huh & Michael Jong Kim & Meichun Lin, 2022. "Bayesian dithering for learning: Asymptotically optimal policies in dynamic pricing," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3576-3593, September.
  25. Jue Wang, 2021. "Optimal Bayesian Demand Learning over Short Horizons," Production and Operations Management, Production and Operations Management Society, vol. 30(4), pages 1154-1177, April.
  26. Yi Zheng & Juxihong Julaiti & Guodong Pang, 2024. "Adaptive service rate control of an M/M/1 queue with server breakdowns," Queueing Systems: Theory and Applications, Springer, vol. 106(1), pages 159-191, February.
  27. Ying Zhong & L. Jeff Hong & Guangwu Liu, 2021. "Earning and Learning with Varying Cost," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2379-2394, August.
  28. David Muller & Yurii Nesterov & Vladimir Shikhman, 2021. "Dynamic pricing under nested logit demand," Papers 2101.04486, arXiv.org.
  29. Ariit Sengupta & Amit Kohar & Himanshu Rathore & Suresh K. Jakhar, 2025. "Decrease the price now, increase it later: a novel approach to demand learning and dynamic pricing of new experiential products through the lens of construal level theory," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(3), pages 266-284, June.
  30. Baris Ata & Alexandre Belloni & Ozan Candogan, 2018. "Latent Agents in Networks: Estimation and Targeting," Papers 1808.04878, arXiv.org, revised Jan 2022.
  31. Gur, Yonatan & Macnamara, Gregory & Saban, Daniela, 2020. "On the Disclosure of Promotion Value in Platforms with Learning Sellers," Research Papers 3865, Stanford University, Graduate School of Business.
  32. Baichun Xiao & Wei Yang, 2024. "A Bayesian learning and pricing model with multiple unknown demand parameters," Annals of Operations Research, Springer, vol. 343(1), pages 493-513, December.
  33. Yonatan Gur & Gregory Macnamara & Ilan Morgenstern & Daniela Saban, 2019. "Information Disclosure and Promotion Policy Design for Platforms," Papers 1911.09256, arXiv.org, revised Dec 2022.
  34. Ruben van de Geer & Arnoud V. den Boer & Christopher Bayliss & Christine Currie & Andria Ellina & Malte Esders & Alwin Haensel & Xiao Lei & Kyle D. S. Maclean & Antonio Martinez-Sykora & Asbj{o}rn Nil, 2018. "Dynamic Pricing and Learning with Competition: Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference," Papers 1804.03219, arXiv.org.
  35. Boxiao Chen, 2021. "Data‐Driven Inventory Control with Shifting Demand," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1365-1385, May.
  36. Yang, Xiangyu & Zhang, Jianghua & Hu, Jian-Qiang & Hu, Jiaqiao, 2024. "Nonparametric multi-product dynamic pricing with demand learning via simultaneous price perturbation," European Journal of Operational Research, Elsevier, vol. 319(1), pages 191-205.
  37. Joon Suk Huh & Ellen Vitercik & Kirthevasan Kandasamy, 2024. "Bandit Profit-maximization for Targeted Marketing," Papers 2403.01361, arXiv.org, revised Jul 2024.
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