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Activity Number: 405 - Winners: Business and Economic Statistics Student Paper Awards
Type: Topic-Contributed
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #317317
Title: Contextual Dynamic Pricing with Unknown Nonparametric Market Noise via Perturbed Linear Bandit
Author(s): Yiyun Luo* and Will Wei Sun and Yufeng Liu
Companies: University of North Carolina at Chapel Hill and Purdue University and University of North Carolina at Chapel Hill
Keywords: Bandit; Classification; Contextual Dynamic Pricing; Unknown Market Noise; Nonparametric Demand; Regret Analysis
Abstract:

We consider a contextual dynamic pricing problem in which the seller receives sequential binary purchasing decisions from the customers. A customer's decision depends on both the seller's offered price and the customer's valuation of the product, which involves a random market noise and a linear structure of the product features, marketing environment and customer characteristics. We design a new pricing policy that can tackle both an unknown linear structure and an unknown nonparametric market noise. Specifically, we formulate the linear structure estimation into a classification problem. Given such an estimation, the online pricing problem with unknown market noise and varying contextual information is formulated as a newly introduced perturbed linear bandit. An optimism-based algorithm is proposed for this perturbed linear bandit to balance between market noise learning and greedy pricing. We prove that our policy is able to achieve a T-period regret bound of O(T^(2/3)) against a clairvoyant policy. We also demonstrate the suprior performance of our policy on both synthetic datasets and a real-life Auto-Loan dataset when compared to the state-of-the-art pricing algorithms.


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