Online Resource Sharing: Better Robust Guarantees via Randomized Strategies

2025-05-20

AI Summary

This paper studies online fair resource allocation among multiple agents under a moneyless mechanism, aiming to improve the robustly guaranteed fraction of ideal utility each agent receives in dynamic, non-cooperative environments. Addressing the limitation of existing mechanisms—guaranteeing only a 1/2 utility lower bound—the authors propose a uniform random bidding strategy grounded in repeated first-price auctions and an artificial currency framework; bids are determined by quantiles of each agent’s private value distribution. This strategy raises the robust utility guarantee from 1/2 to $2 - sqrt{2} approx 0.59$, approaching the theoretical hardness bound $1 - 1/e approx 0.63$, and is proven near-optimal within the class of static strategies. Moreover, the paper establishes that no static strategy can exceed a 0.6 utility guarantee, thereby refuting the possibility of such improvement.

Technology Category

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

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 study the problem of fair online resource allocation via non-monetary mechanisms, where multiple agents repeatedly share a resource without monetary transfers. Previous work has shown that every agent can guarantee $1/2$ of their ideal utility (the highest achievable utility given their fair share of resources) robustly, i.e., under arbitrary behavior by the other agents. While this $1/2$-robustness guarantee has now been established under very different mechanisms, including pseudo-markets and dynamic max-min allocation, improving on it has appeared difficult. In this work, we obtain the first significant improvement on the robustness of online resource sharing. In more detail, we consider the widely-studied repeated first-price auction with artificial currencies. Our main contribution is to show that a simple randomized bidding strategy can guarantee each agent a $2 - sqrt 2 approx 0.59$ fraction of her ideal utility, irrespective of others' bids. Specifically, our strategy requires each agent with fair share $alpha$ to use a uniformly distributed bid whenever her value is in the top $alpha$-quantile of her value distribution. Our work almost closes the gap to the known $1 - 1/e approx 0.63$ hardness for robust resource sharing; we also show that any static (i.e., budget independent) bidding policy cannot guarantee more than a $0.6$-fraction of the ideal utility, showing our technique is almost tight.

Problem

• Improving robustness in online resource allocation without money
• Enhancing fair utility guarantees via randomized bidding strategies
• Closing gap between achievable and hardness bounds in resource sharing

Innovation

• Randomized bidding strategy for fair allocation
• Top α-quantile uniform bid for utility
• Nearly closes gap to 1 - 1/e hardness