Contextual Dynamic Pricing with Strategic Buyers
Pangpang Liu, Zhuoran Yang, Zhaoran Wang, Will Wei Sun

TL;DR
This paper develops a strategic dynamic pricing policy that accounts for buyers' manipulation of features, achieving sublinear regret and outperforming non-strategic policies in personalized pricing scenarios.
Contribution
It introduces a novel online learning pricing policy that incorporates strategic buyer behavior and handles unknown manipulation costs, with proven regret bounds.
Findings
Existing non-strategic policies have linear regret.
Proposed policy achieves $O(\sqrt{T})$ regret.
Experiments show superior performance over unaware policies.
Abstract
Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this paper, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the buyer's true feature, but a manipulated feature according to buyers' strategic behavior. In addition, the seller does not observe the buyers' valuation of the product, but only a binary response indicating whether a sale happens or not. Recognizing these challenges, we propose a strategic dynamic pricing policy that incorporates the buyers' strategic behavior into the online learning to maximize the…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Auction Theory and Applications · Smart Grid Energy Management
