On Data-Driven Multi-Product Pricing

To handle optimization with only historical data, we present a novel learning framework combining parametric estimation and pricing optimization in the multi-product pricing problem. Motivated by the existence of errors, we first introduce the task-based learning with decision bias for handling estimation errors, which can lead to better decision making under demand parameter uncertainty. Then, we follow the idea of model-free learning, which can design better revenue estimators without knowing the parameter structure to handle model misspecification. Furthermore, to design a more robust estimator, we incorporate the boosting idea to combine a number of estimators for more robust pricing. We validate the superior performance of this framework with numerical studies.