Fairness-aware Contextual Dynamic Pricing with Strategic Buyers

2025-01-25

AI Summary

This paper addresses fairness-aware dynamic pricing under contextual pricing, where buyer group identities are private and unobservable, aiming to balance price fairness with mitigation of strategic identity manipulation. Method: We jointly model fairness constraints and strategic buyer learning within a dynamic pricing framework, proposing a novel algorithm integrating contextual bandits, game-theoretic modeling, fairness optimization, and counterfactual price disparity estimation. Contribution/Results: We theoretically establish a tight $O(sqrt{T})$ regret bound. Empirical evaluation on real-world loan data reveals significant racial price discrimination. Compared to baseline policies, our approach reduces regret by 35.06% while simultaneously improving fairness and revenue—achieving synergistic gains in both objectives.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Machine Learning: Ethics, Bias, and Fairness
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Dynamic Pricing
• Fairness
• Consumer Behavior

Innovation

• Dynamic Pricing
• Fairness
• Strategic Behavior