{"type":"ScholarlyArticle","title":"Online Resource Sharing: Better Robust Guarantees via Randomized Strategies","authors":[{"name":"David X. Lin","slug":"david-x-lin","given":"David X.","family":"Lin","affiliation":""},{"name":"Daniel Hall","slug":"daniel-hall","given":"Daniel","family":"Hall","affiliation":""},{"name":"Giannis Fikioris","slug":"giannis-fikioris","given":"Giannis","family":"Fikioris","affiliation":""},{"name":"Siddhartha Banerjee","slug":"siddhartha-banerjee","given":"Siddhartha","family":"Banerjee","affiliation":""},{"name":"Éva Tardos","slug":"eva-tardos","given":"Éva","family":"Tardos","affiliation":""}],"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.\r\n\r\nIn 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 - √2 ≈ 0.59 fraction of her ideal utility, irrespective of others' bids. Specifically, our strategy requires each agent with fair share α to use a uniformly distributed bid whenever her value is in the top α-quantile of her value distribution. Our work almost closes the gap to the known 1 - 1/e ≈ 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.","doi":"10.24963/ijcai.2025/21","url":"https://ijcai.org/proceedings/2025/21","pdf":"https://ijcai.org/proceedings/2025/0021.pdf","markdown":"https://ijcai.org/proceedings/2025/21.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/21","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":21,"pages":{"first":179,"last":186},"keywords":["Agent-based and Multi-agent Systems: MAS: Resource allocation","Game Theory and Economic Paradigms: GTEP: Auctions and market-based systems","Game Theory and Economic Paradigms: GTEP: Fair division","Game Theory and Economic Paradigms: GTEP: Noncooperative games"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}