Meta Dynamic Pricing: Transfer Learning Across Experiments

• Main Authors: Bastani, H. (Author), Simchi-Levi, D. (Author), Zhu, R. (Author)
• Format: Article
• Language: English
• Published: INFORMS Inst.for Operations Res.and the Management Sciences 2022
• Subjects: Costs; Dynamic pricing; empirical Bayes; Empirical Bayes; Learn+; Learning algorithms; meta learning; Metalearning; misspecified prior; Misspecified prior; Pricing experiments; Related products; Shared structures; Thompson sampling; Thompson samplings; transfer learning; Transfer learning; Uncertainty analysis
• Online Access: View Fulltext in Publisher

 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 (pri- or) 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. Copyright: © 2021 INFORMS