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Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments

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Cited by:

  1. Kshitija Taywade & Brent Harrison & Judy Goldsmith, 2022. "Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand," Papers 2201.00486, arXiv.org.
  2. Preil, Deniz & Krapp, Michael, 2022. "Bandit-based inventory optimisation: Reinforcement learning in multi-echelon supply chains," International Journal of Production Economics, Elsevier, vol. 252(C).
  3. Xiao Liu, 2023. "Dynamic Coupon Targeting Using Batch Deep Reinforcement Learning: An Application to Livestream Shopping," Marketing Science, INFORMS, vol. 42(4), pages 637-658, July.
  4. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
  5. Jingwen Zhang & Yifang Chen & Amandeep Singh, 2022. "Causal Bandits: Online Decision-Making in Endogenous Settings," Papers 2211.08649, arXiv.org, revised Feb 2023.
  6. Diego Aparicio & Zachary Metzman & Roberto Rigobon, 2024. "The pricing strategies of online grocery retailers," Quantitative Marketing and Economics (QME), Springer, vol. 22(1), pages 1-21, March.
  7. Keller, Alisa & Vogelsang, Mila & Totzek, Dirk, 2022. "How displaying price discounts can mitigate negative customer reactions to dynamic pricing," Journal of Business Research, Elsevier, vol. 148(C), pages 277-291.
  8. Davide Proserpio & John R. Hauser & Xiao Liu & Tomomichi Amano & Alex Burnap & Tong Guo & Dokyun (DK) Lee & Randall Lewis & Kanishka Misra & Eric Schwarz & Artem Timoshenko & Lilei Xu & Hema Yoganaras, 2020. "Soul and machine (learning)," Marketing Letters, Springer, vol. 31(4), pages 393-404, December.
  9. Jason Rhuggenaath & Alp Akcay & Yingqian Zhang & Uzay Kaymak, 2022. "Setting Reserve Prices in Second-Price Auctions with Unobserved Bids," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2950-2967, November.
  10. Samuel Cohen & Tanut Treetanthiploet, 2021. "Generalised correlated batched bandits via the ARC algorithm with application to dynamic pricing," Papers 2102.04263, arXiv.org, revised Oct 2022.
  11. Ratchford, Brian & Soysal, Gonca & Zentner, Alejandro & Gauri, Dinesh K., 2022. "Online and offline retailing: What we know and directions for future research," Journal of Retailing, Elsevier, vol. 98(1), pages 152-177.
  12. Marcial López-Pastor & Jesús García-Madariaga & Joaquín Sánchez & Jose Figueiredo, 2020. "Demand Impact for Prices Ending with “9” and “0” in Online and Offline Consumer Goods Retail Trade Channels," International Review of Management and Marketing, Econjournals, vol. 10(6), pages 58-78.
  13. Joon Suk Huh & Ellen Vitercik & Kirthevasan Kandasamy, 2024. "Bandit Profit-maximization for Targeted Marketing," Papers 2403.01361, arXiv.org.
  14. Miao, Xiaoyu & Niu, Ben & Yang, Congcong & Feng, Yuanyue, 2023. "Examining the gamified effect of the blindbox design: The moderating role of price," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
  15. Vinay Singh & Brijesh Nanavati & Arpan Kumar Kar & Agam Gupta, 2023. "How to Maximize Clicks for Display Advertisement in Digital Marketing? A Reinforcement Learning Approach," Information Systems Frontiers, Springer, vol. 25(4), pages 1621-1638, August.
  16. 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.
  17. Alina Ferecatu & Arnaud De Bruyn, 2022. "Understanding Managers’ Trade-Offs Between Exploration and Exploitation," Marketing Science, INFORMS, vol. 41(1), pages 139-165, January.
  18. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
  19. Pai, Mallesh & Hansen, Karsten, 2020. "Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms," CEPR Discussion Papers 14372, C.E.P.R. Discussion Papers.
  20. Huang, Ming-Hui & Rust, Roland T., 2022. "A Framework for Collaborative Artificial Intelligence in Marketing," Journal of Retailing, Elsevier, vol. 98(2), pages 209-223.
  21. Isabel P. Riquelme & Sergio Román, 2023. "Personal antecedents of perceived deceptive pricing in online retailing: the moderating role of price inequality," Electronic Commerce Research, Springer, vol. 23(2), pages 739-783, June.
  22. Kshitija Taywade & Brent Harrison & Adib Bagh, 2022. "Modelling Cournot Games as Multi-agent Multi-armed Bandits," Papers 2201.01182, arXiv.org.
  23. Leif Nelson & Duncan Simester & K. Sudhir, 2020. "Introduction to the Special Issue on Marketing Science and Field Experiments," Marketing Science, INFORMS, vol. 39(6), pages 1033-1038, November.
  24. Elea McDonnell Feit & Ron Berman, 2019. "Test & Roll: Profit-Maximizing A/B Tests," Marketing Science, INFORMS, vol. 38(6), pages 1038-1058, November.
  25. Karsten T. Hansen & Kanishka Misra & Mallesh M. Pai, 2021. "Frontiers: Algorithmic Collusion: Supra-competitive Prices via," Marketing Science, INFORMS, vol. 40(1), pages 1-12, January.
  26. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
  27. Po-Yi Liu & Chi-Hua Wang & Henghsiu Tsai, 2022. "Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing," Papers 2208.09372, arXiv.org, revised Sep 2022.
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