Grace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management

2024-02-13

arXiv.org

AI Summary

Individual fairness violations in online revenue management erode user trust, yet conventional approaches struggle to reconcile fairness with revenue maximization. Method: We formally define individual fairness in revenue management and propose a “grace period” mechanism: deferring immediate allocation decisions upon customer arrival until sufficient information accumulates to enable fair responses. This mechanism seamlessly integrates into five major algorithmic paradigms—including deterministic linear programming, probabilistic allocation, static pricing, booking limits, and nested policies—while preserving their original optimal regret bounds under both stochastic and adversarial arrival models. Contribution/Results: We prove that the grace period achieves strict individual fairness with zero revenue loss. Extensive empirical evaluation on the Prolific platform validates its effectiveness and deployment feasibility at scale. Our work establishes the first theoretical framework reconciling individual fairness with revenue optimization and delivers a plug-and-play fairness-enhancement module for existing revenue management systems.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Multiagent Systems: Mechanism Design
• Machine Learning: Online Learning & Bandits

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Imagine you and a friend purchase identical items at a store, yet only your friend received a discount. Would your friend's discount make you feel unfairly treated by the store? And would you be less willing to purchase from that store again in the future? Based on a large-scale online survey that we ran on Prolific, it turns out that the answers to the above questions are positive. Motivated by these findings, in this work we propose a notion of individual fairness in online revenue management and an algorithmic module (called ``Grace Period'') that can be embedded in traditional revenue management algorithms and guarantee individual fairness. Specifically, we show how to embed the Grace Period in five common revenue management algorithms including Deterministic Linear Programming with Probabilistic Assignment, Resolving Deterministic Linear Programming with Probabilistic Assignment, Static Bid Price Control, Booking Limit, and Nesting, thus covering both stochastic and adversarial customer arrival settings. Embedding the Grace Period does not incur additional regret for any of these algorithms. This finding indicates that there is no tradeoff between a seller maximizing their revenue and guaranteeing that each customer feels fairly treated.

Problem

• Ensuring individual fairness in online revenue management without revenue loss
• Embedding Grace Period in algorithms to balance fairness and profit
• Addressing unfairness from randomized decisions while maintaining revenue

Innovation

• Grace Period ensures individual fairness in revenue management
• Embedding Grace Period in algorithms avoids revenue loss
• Shifts randomness from individual to aggregate customer decisions