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Meta Dynamic Pricing: Transfer Learning Across Experiments

Author

Listed:
  • Hamsa Bastani

    (Operations, Information and Decisions, Wharton School, Philadelphia, Pennsylvania 19104)

  • David Simchi-Levi

    (Institute for Data, Systems, and Society, Department of Civil and Environmental Engineering, and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Ruihao Zhu

    (Supply Chain and Operations Management, Purdue Krannert School of Management, West Lafayette, Indiana 47907)

Abstract

We study the problem of learning shared structure across a sequence of dynamic pricing experiments for related products. We consider a practical formulation in which the unknown demand parameters for each product come from an unknown distribution (prior) that is shared across products. We then propose a meta dynamic pricing algorithm that learns this prior online while solving a sequence of Thompson sampling pricing experiments (each with horizon T ) for N different products. Our algorithm addresses two challenges: (i) balancing the need to learn the prior ( meta-exploration ) with the need to leverage the estimated prior to achieve good performance ( meta-exploitation ) and (ii) accounting for uncertainty in the estimated prior by appropriately “widening” the estimated prior as a function of its estimation error. We introduce a novel prior alignment technique to analyze the regret of Thompson sampling with a misspecified prior, which may be of independent interest. Unlike prior-independent approaches, our algorithm’s meta regret grows sublinearly in N , demonstrating that the price of an unknown prior in Thompson sampling can be negligible in experiment-rich environments (large N ). Numerical experiments on synthetic and real auto loan data demonstrate that our algorithm significantly speeds up learning compared with prior-independent algorithms.

Suggested Citation

  • Hamsa Bastani & David Simchi-Levi & Ruihao Zhu, 2022. "Meta Dynamic Pricing: Transfer Learning Across Experiments," Management Science, INFORMS, vol. 68(3), pages 1865-1881, March.
  • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:3:p:1865-1881
    DOI: 10.1287/mnsc.2021.4071
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    References listed on IDEAS

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    1. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.

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