Credit Fairness: Online Fairness In Shared Resource Pools

TL;DR

This paper introduces credit fairness in shared resource pools, ensuring early lenders can recoup resources later, balancing fairness with efficiency or strategyproofness in dynamic agent settings.

Contribution

It proposes a new credit fairness mechanism that enhances sharing incentives by allowing resource lenders to recover their contributions over time.

Findings

The proposed mechanism achieves credit fairness and Pareto efficiency.

It balances fairness and efficiency in resource sharing.

Experimental results demonstrate improved fairness in computational settings.

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.