High-Dimensional Dynamic Pricing Under Nonstationarity: Learning and Earning with Change-Point Detection

Published Online:https://doi.org/10.1287/mnsc.2023.00889

References

  • Araman VF, Caldentey R (2009) Dynamic pricing for nonperishable products with demand learning. Oper. Res. 57(5):1169–1188.LinkGoogle Scholar
  • Auer P, Cesa-Bianchi N, Freund Y, Schapire RE (2002) The nonstochastic multiarmed bandit problem. SIAM J. Comput. 32(1):48–77.CrossrefGoogle Scholar
  • Bai J, Perron P (2003) Computation and analysis of multiple structural change models. J. Appl. Econometrics 18(1):1–22.CrossrefGoogle Scholar
  • Ban GY, Keskin NB (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity. Management Sci. 67(9):5549–5568.LinkGoogle Scholar
  • Bastani H, Bayati M (2020) Online decision making with high-dimensional covariates. Oper. Res. 68(1):276–294.LinkGoogle Scholar
  • Bastani H, Simchi-Levi D, Zhu R (2022) Meta dynamic pricing: Transfer learning across experiments. Management Sci. 68(3):1865–1881.LinkGoogle Scholar
  • Bauer H, Burkacky O, Kenevan P, Mahindroo A, Patel M (2020) How the semiconductor industry can emerge stronger after the COVID-19 crisis. Technical report, McKinsey, New York.Google Scholar
  • Berge C (1957) Two theorems in graph theory. Proc. Natl. Acad. Sci. USA 43(9):842–844.CrossrefGoogle Scholar
  • Besbes O, Zeevi A (2009) Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Oper. Res. 57(6):1407–1420.LinkGoogle Scholar
  • Besbes O, Zeevi A (2011) On the minimax complexity of pricing in a changing environment. Oper. Res. 59(1):66–79.LinkGoogle Scholar
  • Besbes O, Gur Y, Zeevi A (2014) Stochastic multi-armed-bandit problem with non-stationary rewards. Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger K, eds. 28th Annual Conf. Neural Inform. Processing Systems, vol. 27, Advances in Neural Information Processing Systems (Curran Associates, Red Hook, NY), 199–207.Google Scholar
  • Besson L, Kaufmann E, Maillard OA, Seznec J (2022) Efficient change-point detection for tackling piecewise-stationary bandits. J. Machine Learn. Res. 23(77):1−40.Google Scholar
  • Birge JR, Chen HK, Keskin NB (2025) Markdown policies for demand learning with forward-looking customers. Oper. Res. 73(5):2550–2566.LinkGoogle Scholar
  • Birge JR, Feng Y, Keskin NB, Schultz A (2021) Dynamic learning and market making in spread betting markets with informed bettors. Oper. Res. 69(6):1746–1766.LinkGoogle Scholar
  • Broder J, Rusmevichientong P (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965–980.LinkGoogle Scholar
  • Brodsky E, Darkhovsky BS (1993) Nonparametric Methods in Change Point Problems (Kluwer Academic Publishers, Dordrecht).CrossrefGoogle Scholar
  • Bühlmann P, Van De Geer S (2011) Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer, Berlin, Heidelberg).CrossrefGoogle Scholar
  • Cao Y, Wen Z, Kveton B, Xie Y (2019) Nearly optimal adaptive procedure for piecewise-stationary bandit: A change-point detection approach. Chaudhuri K, Sugiyama M, eds. Proc. Twenty-Second Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research, vol. 89 (PMLR, Cambridge, MA), 418–427.Google Scholar
  • Chen H (2019) Sequential change-point detection based on nearest neighbors. Ann. Statist. 47(3):1381–1407.CrossrefGoogle Scholar
  • Chen N, Gallego G (2019) Welfare analysis of dynamic pricing. Management Sci. 65(1):139–151.LinkGoogle Scholar
  • Chen N, Gallego G (2021) Nonparametric pricing analytics with customer covariates. Oper. Res. 69(3):974–984.LinkGoogle Scholar
  • Chen J, Gupta AK (2012) Parametric Statistical Change Point Analysis (Birkhäuser, Boston).CrossrefGoogle Scholar
  • Chen X, Miao S, Wang Y (2022) Differential privacy in personalized pricing with nonparametric demand models. Oper. Res. 71(2):581–602.LinkGoogle Scholar
  • Chen Y, Wen Z, Xie Y (2026) Dynamic pricing in an evolving and unknown marketplace. Management Sci. Forthcoming.Google Scholar
  • Cheung WC, Simchi-Levi D, Wang H (2017) Dynamic pricing and demand learning with limited price experimentation. Oper. Res. 65(6):1722–1731.LinkGoogle Scholar
  • Cheung WC, Simchi-Levi D, Zhu R (2019) Learning to optimize under non-stationarity. Chaudhuri K, Sugiyama M, eds. Proc. Twenty-Second Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research, vol. 89 (PMLR, Cambridge, MA), 1079–1087.Google Scholar
  • Cheung WC, Simchi-Levi D, Zhu R (2022) Hedging the drift: Learning to optimize under nonstationarity. Management Sci. 68(3):1696–1713.LinkGoogle Scholar
  • Davis RA, Lee TCM, Rodriguez-Yam GA (2006) Structural break estimation for nonstationary time series models. J. Amer. Statist. Assoc. 101(473):223–239.CrossrefGoogle Scholar
  • den Boer AV, Keskin NB (2022) Dynamic pricing with demand learning and reference effects. Management Sci. 68(10):7112–7130.LinkGoogle Scholar
  • den Boer AV, Zwart B (2015) Dynamic pricing and learning with finite inventories. Oper. Res. 63(4):965–978.LinkGoogle Scholar
  • Dubey P, Xu H, Yu Y (2021) Online network change point detection with missing values. Preprint, submitted October 13, https://arxiv.org/abs/2110.06450.Google Scholar
  • Farias VF, Van Roy B (2010) Dynamic pricing with a prior on market response. Oper. Res. 58(1):16–29.LinkGoogle Scholar
  • Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J. Statist. Software 33(1):1–22.CrossrefGoogle Scholar
  • Garivier A, Moulines E (2011) On upper-confidence bound policies for switching bandit problems. Kivinen J, Szepesvári C, Ukkonen E, Zeugmann T, eds. Algorithmic Learn. Theory: 22nd Internat. Conf. Proc., Lecture Notes in Computer Science, vol. 6925 (Springer, Berlin/Heidelberg), 174–188.CrossrefGoogle Scholar
  • Harrison JM, Keskin NB, Zeevi A (2012) Bayesian dynamic pricing policies: Learning and earning under a binary prior distribution. Management Sci. 58(3):570–586.LinkGoogle Scholar
  • He X, Xie Y, Wu SM, Lin FC (2018) Sequential graph scanning statistic for change-point detection. Matthews MB, ed. 2018 52nd Asilomar Conf. Signals, Systems, Comput. (ACSSC) (IEEE, Piscataway, NJ), 1317–1321.Google Scholar
  • Javanmard A, Nazerzadeh H (2019) Dynamic pricing in high-dimensions. J. Machine Learn. Res. 20(1):315–363.Google Scholar
  • Jia H, Shi C, Shen S (2022) Online learning and pricing for service systems with reusable resources. Oper. Res. 72(3):1203–1241.LinkGoogle Scholar
  • Kaul A, Jandhyala VK, Fotopoulos SB (2019) An efficient two step algorithm for high dimensional change point regression models without grid search. J. Machine Learn. Res. 20(111):1–40.Google Scholar
  • Keshavarz H, Michailidis G, Atchadé Y (2020) Sequential change-point detection in high-dimensional Gaussian graphical models. J. Machine Learn. Res. 21(1):3125–3181.Google Scholar
  • Keskin NB, Li M (2026) Selling quality-differentiated products in a Markovian market with unknown transition probabilities. Oper. Res. Forthcoming.Google Scholar
  • Keskin NB, Zeevi A (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Oper. Res. 62(5):1142–1167.LinkGoogle Scholar
  • Keskin NB, Zeevi A (2017) Chasing demand: Learning and earning in a changing environment. Math. Oper. Res. 42(2):277–307.LinkGoogle Scholar
  • Keskin NB, Li Y, Song JS (2022) Data-driven dynamic pricing and ordering with perishable inventory in a changing environment. Management Sci. 68(3):1938–1958.LinkGoogle Scholar
  • Keskin NB, Li Y, Sunar N (2020) Data-driven clustering and feature-based retail electricity pricing with smart meters. Preprint, submitted October 22, https://doi.org/10.2139/ssrn.3686518.Google Scholar
  • Kocsis L, Szepesvári C (2006) Bandit based Monte-Carlo planning. Fürnkranz J, Scheffer T, Spiliopoulou M, eds. Machine Learn. 17th Eur. Conf. Machine Learn. Proc., Lecture Notes in Computer Science, vol. 4212 (Springer, Berlin/Heidelberg), 282–293.Google Scholar
  • Lai TL (1995) Sequential changepoint detection in quality control and dynamical systems. J. Roy. Statist. Soc. Ser. B Statist. Methodology 57(4):613–644.CrossrefGoogle Scholar
  • Lai TL (1998) Information bounds and quick detection of parameter changes in stochastic systems. IEEE Trans. Inform. Theory 44(7):2917–2929.CrossrefGoogle Scholar
  • Lai TL, Xing H (2010) Sequential change-point detection when the pre-and post-change parameters are unknown. Sequential Anal. 29(2):162–175.CrossrefGoogle Scholar
  • Lee S, Seo MH, Shin Y (2016) The lasso for high dimensional regression with a possible change point. J. Roy. Statist. Soc. Ser. B Statist. Methodology 78(1):193–210.CrossrefGoogle Scholar
  • Li K, Yang Y, Narisetty NN (2021) Regret lower bound and optimal algorithm for high-dimensional contextual linear bandit. Electronic J. Statist. 15(2):5652–5695.CrossrefGoogle Scholar
  • Liu F, Lee J, Shroff N (2018) A change-detection based framework for piecewise-stationary multi-armed bandit problem. McIlraith SA, Weinberger KQ, eds. Proc. Thirty-Second AAAI Conf. Artificial Intelligence (AAAI-18) (AAAI Press, Palo Alto, CA), 3651–3658.Google Scholar
  • Luo Y, Sun WW, Liu Y (2021) Distribution-free contextual dynamic pricing. Preprint, submitted September 15, https://arxiv.org/abs/2109.07340.Google Scholar
  • Maillard OA (2019) Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds. Garivier A, Kale S, eds. Proc. 30th Internat. Conf. Algorithmic Learn. Theory, Proceedings of Machine Learning Research, vol. 98 (PMLR, Cambridge, MA), 610–632.Google Scholar
  • Nambiar M, Simchi-Levi D, Wang H (2019) Dynamic learning and pricing with model misspecification. Management Sci. 65(11):4980–5000.LinkGoogle Scholar
  • Negahban SN, Ravikumar P, Wainwright MJ, Yu B (2012) A unified framework for high-dimensional analysis of m-estimators with decomposable regularizers. Statist. Sci. 27(4):538–557.CrossrefGoogle Scholar
  • Oh M, Iyengar G, Zeevi A (2021) Sparsity-agnostic lasso bandit. Meila M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 139 (PMLR, Cambridge, MA), 8271–8280.Google Scholar
  • Phillips R, Şimşek AS, Van Ryzin G (2015) The effectiveness of field price discretion: Empirical evidence from auto lending. Management Sci. 61(8):1741–1759.LinkGoogle Scholar
  • Ren Z, Zhou Z (2023) Dynamic batch learning in high-dimensional sparse linear contextual bandits. Management Sci. 70(2):1315–1342.LinkGoogle Scholar
  • Rinaldo A, Wang D, Wen Q, Willett R, Yu Y (2021) Localizing changes in high-dimensional regression models. Banerjee A, Fukumizu K, eds. Proc. 24th Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research, vol. 130 (PMLR, Cambridge, MA), 2089–2097.Google Scholar
  • Safikhani A, Shojaie A (2022) Joint structural break detection and parameter estimation in high-dimensional nonstationary var models. J. Amer. Statist. Assoc. 117(537):251–264.CrossrefGoogle Scholar
  • Seznec J, Menard P, Lazaric A, Valko M (2020) A single algorithm for both restless and rested rotting bandits. Chiappa S, Calandra R, eds. Proc. Twenty Third Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research, vol. 108 (PMLR, Cambridge, MA), 3784–3794.Google Scholar
  • Siegmund D (1985) Sequential Analysis: Tests and Confidence Intervals (Springer, New York).CrossrefGoogle Scholar
  • Tarlton A (2020) 24 things that have been selling out online during the coronavirus pandemic. USA Today (April 8), https://www.usatoday.com/story/tech/reviewedcom/2020/04/08/24-things-have-been-selling-out-online-during-coronavirus-pandemic/2962369001/.Google Scholar
  • Tartakovsky A, Nikiforov I, Basseville M (2014) Sequential Analysis: Hypothesis Testing and Change Point Detection (CRC Press, Boca Raton, FL).CrossrefGoogle Scholar
  • Tibshirani R (1996) Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B Statist. Methodology 58(1):267–288.CrossrefGoogle Scholar
  • Tsybakov A (2009) Introduction to Nonparametric Estimation (Chapman and Hall/CRC, New York).CrossrefGoogle Scholar
  • Wang Y, Chen B, Simchi-Levi D (2021a) Multimodal dynamic pricing. Management Sci. 67(10):6136–6152.LinkGoogle Scholar
  • Wang D, Yu Y, Willett R (2020) Detecting abrupt changes in high-dimensional self-exciting Poisson processes. Preprint, submitted June 5, https://arxiv.org/abs/2006.03572.Google Scholar
  • Wang D, Zhao Z, Lin KZ, Willett R (2021b) Statistically and computationally efficient change point localization in regression settings. J. Machine Learn. Res. 22(1):11255–11300.Google Scholar
  • Whitten S (2020) What People Are Buying as They Heed Coronavirus Stay-at-Home Orders. CNBC (March 23), https://www.cnbc.com/2020/03/23/what-people-are-buying-as-they-heed-coronavirus-stay-at-home-orders.html.Google Scholar
  • Yu Y (2020) A review on minimax rates in change point detection and localisation. Preprint, submitted November 3, https://arxiv.org/abs/2011.01857.Google Scholar
  • Yu Y, Padilla OHM, Wang D, Rinaldo A (2020) A note on online change point detection. Preprint, submitted June 5, https://arxiv.org/abs/2006.03283.Google Scholar
  • Zhao Y, Huo X (2023) A survey of numerical algorithms that can solve the lasso problems. Wiley Interdisciplinary Rev. Comput. Statist. 15(4):e1602.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.