Fairness-aware Contextual Dynamic Pricing with Strategic Buyers
Pangpang Liu, Will Wei Sun

TL;DR
This paper develops a fairness-aware dynamic pricing strategy that accounts for strategic buyer behavior and group fairness constraints, achieving low regret and reducing discrimination in real-world data.
Contribution
It introduces a novel pricing policy that balances fairness and profit, with provable regret bounds and effectiveness demonstrated through experiments and real data analysis.
Findings
Achieves $O(\sqrt{T}+H(T))$ regret, reducing to $O(\sqrt{T})$ when buyers learn fairness.
Demonstrates 35.06% regret reduction over benchmark in experiments.
Identifies racial price discrimination in loan data even after controlling for context.
Abstract
Contextual pricing strategies are prevalent in online retailing, where the seller adjusts prices based on products' attributes and buyers' characteristics. Although such strategies can enhance seller's profits, they raise concerns about fairness when significant price disparities emerge among specific groups, such as gender or race. These disparities can lead to adverse perceptions of fairness among buyers and may even violate the law and regulation. In contrast, price differences can incentivize disadvantaged buyers to strategically manipulate their group identity to obtain a lower price. In this paper, we investigate contextual dynamic pricing with fairness constraints, taking into account buyers' strategic behaviors when their group status is private and unobservable from the seller. We propose a dynamic pricing policy that simultaneously achieves price fairness and discourages…
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Taxonomy
TopicsConsumer Market Behavior and Pricing · Digital Platforms and Economics · Sharing Economy and Platforms
