Credit Fairness: Online Fairness In Shared Resource Pools

2026-01-25

AI Summary

This work addresses the limitation of traditional max-min fairness mechanisms in ensuring temporal fairness over long-term shared resource allocation, where significant inequities can arise even among agents with identical average demands. To overcome this, the authors propose a credit-based fairness mechanism that introduces a cumulative and redeemable credit system, enabling agents who relinquish resources early to gain priority in reclaiming them in subsequent rounds, thereby achieving fairness across time. The mechanism preserves Pareto efficiency and strategyproofness while aligning with sharing incentive principles, and it dynamically optimizes allocation policies within an online resource allocation framework for discrete-time settings. Experimental results demonstrate that the approach substantially enhances long-term fairness in computational resource sharing scenarios, surpassing the fairness constraints inherent in conventional mechanisms.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Fair Division
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

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
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We consider a setting in which a group of agents share resources that must be allocated among them in each discrete time period. Agents have time-varying demands and derive constant marginal utility from each unit of resource received up to their demand, with zero utility for any additional resources. In this setting, it is known that independently maximizing the minimum utility in each round satisfies sharing incentives (agents weakly prefer participating in the mechanism to not participating), strategyproofness (agents have no incentive to misreport their demands), and Pareto efficiency (Freeman et al. 2018). However, recent work (Vuppalapati et al. 2023) has shown that this max-min mechanism can lead to large disparities in the total resources received by agents, even when they have the same average demand. In this paper, we introduce credit fairness, a strengthening of sharing incentives that ensures agents who lend resources in early rounds are able to recoup them in later rounds. Credit fairness can be achieved in conjunction with either Pareto efficiency or strategyproofness, but not both. We propose a mechanism that is credit fair and Pareto efficient, and we evaluate its performance in a computational resource-sharing setting.

Problem

• credit fairness
• resource allocation
• sharing incentives
• online fairness
• Pareto efficiency

Innovation

• credit fairness
• online resource allocation
• Pareto efficiency
• sharing incentives
• mechanism design