Meta Dynamic Pricing: Transfer Learning Across Experiments
Name
SSRN-id3334629.pdf
Description
Submitted version
Size
1.56 MB
Format
Adobe PDF
Checksum (MD5)
0a89f529717a54e70bdd214293ff4a0f
Author(s)
Bastani, Hamsa
Simchi-Levi, David
Zhu, Ruihao
Date Issued
2022
Journal
Management Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Bastani, Hamsa, Simchi-Levi, David and Zhu, Ruihao. 2022. "Meta Dynamic Pricing: Transfer Learning Across Experiments." Management Science, 68 (3).
Version
Original manuscript
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. This paper was accepted by George J. Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/MNSC.2021.4071
