Online Allocation with Multi-Class Arrivals: Group Fairness vs Individual Welfare

2025-01-27

AI Summary

This paper addresses the multi-group online resource allocation problem, aiming to jointly optimize individual utility efficiency and inter-group fairness under dynamically arriving heterogeneous user streams and a fixed total resource budget. We first formalize three distinct group-level fairness criteria and characterize the fundamental trade-off between fairness and social welfare. To address this, we propose a multi-threshold online algorithm with resource reservation, which integrates threshold-driven decision-making, resource partitioning, and utility modeling to achieve Pareto-optimal fairness–efficiency trade-offs. Theoretically, we prove that our algorithm is optimal with respect to two fairness criteria and achieves near-optimality for the third. Empirical evaluation on real-world network caching datasets demonstrates significant improvements over state-of-the-art baseline methods in both fairness and utility metrics.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Machine Learning: Online Learning & Bandits
• Search and Optimization: Algorithm Configuration

Application Category

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

Abstract

We introduce and study a multi-class online resource allocation problem with group fairness guarantees. The problem involves allocating a fixed amount of resources to a sequence of agents, each belonging to a specific group. The primary objective is to ensure fairness across different groups in an online setting. We focus on three fairness notions: one based on quantity and two based on utility. To achieve fair allocations, we develop two threshold-based online algorithms, proving their optimality under two fairness notions and near-optimality for the more challenging one. Additionally, we demonstrate a fundamental trade-off between group fairness and individual welfare using a novel representative function-based approach. To address this trade-off, we propose a set-aside multi-threshold algorithm that reserves a portion of the resource to ensure fairness across groups while utilizing the remaining resource to optimize efficiency under utility-based fairness notions. This algorithm is proven to achieve the Pareto-optimal trade-off. We also demonstrate that our problem can model a wide range of real-world applications, including network caching and cloud computing, and empirically evaluate our proposed algorithms in the network caching problem using real datasets.

Problem

• Online Resource Allocation
• Fairness Optimization
• Efficiency-Fairness Trade-off

Innovation

• Fairness Metrics
• Online Algorithms
• Efficiency-Fairness Balance