Dynamic Learning and Pricing with Model Misspecification
Name
SSRN-id2859672.pdf
Description
Accepted version
Size
1.39 MB
Format
Adobe PDF
Checksum (MD5)
b3ed69cdf97a9d239a0a86346a6532c1
Author(s)
Nambiar, Mila
Wang, He
Simchi-Levi, David
Date Issued
November 2019
Journal
Management Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Nambiar, Mila et al. "Dynamic Learning and Pricing with Model Misspecification." Managment Science 65, 1 (August 2019): 4951-5448 © 2019 INFORMS
Version
Author's final manuscript
Abstract
We study a multiperiod dynamic pricing problem with contextual information, where the seller uses a misspecified demand model. The seller sequentially observes past demand, updates model parameters, and then chooses the price for the next period based on time-varying features. We show that model misspecification leads to a correlation between price and prediction error of demand per period, which, in turn, leads to inconsistent price elasticity estimates and hence suboptimal pricing decisions. We propose a “random price shock” (RPS) algorithm that dynamically generates randomized price shocks to estimate price elasticity, while maximizing revenue. We show that the RPS algorithm has strong theoretical performance guarantees, that it is robust to model misspecification, and that it can be adapted to a number of business settings, including (1) when the feasible price set is a price ladder and (2) when the contextual information is not IID. We also perform offline simulations to gauge the performance of RPS on a large fashion retail data set and find that is expected to earn 8%–20% more revenue on average than competing algorithms that do not account for price endogeneity.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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.2018.3194
