Meta Dynamic Pricing: Transfer Learning Across Experiments

Hamsa Bastani (), David Simchi-Levi () and Ruihao Zhu ()
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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

Management Science, 2022, vol. 68, issue 3, 1865-1881

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.

Keywords: Thompson sampling; misspecified prior; transfer learning; meta learning; empirical Bayes (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:3:p:1865-1881

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