Fairness-aware Contextual Dynamic Pricing with Strategic Buyers

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 strategic behaviors. Our policy achieves an upper bound of $O(\sqrt{T}+H(T))$ regret over $T$ time horizons, where the term $H(T)$ arises from buyers' assessment of the fairness of the pricing policy based on their learned price difference. When buyers are able to learn the fairness of the price policy, this upper bound reduces to $O(\sqrt{T})$. We also prove an $\Omega(\sqrt{T})$ regret lower bound of any pricing policy under our problem setting. We support our findings with extensive experimental evidence, showcasing our policy's effectiveness. In our real data analysis, we observe the existence of price discrimination against race in the loan application even after accounting for other contextual information. Our proposed pricing policy demonstrates a significant improvement, achieving 35.06% reduction in regret compared to the benchmark policy.