Affiliation:
1. Indian Institute of Science
Abstract
We consider the problem of fairly allocating indivisible goods, among agents, under cardinality constraints and additive valuations. In this setting, we are given a partition of the entire set of goods---i.e., the goods are categorized---and a limit is specified on the number of goods that can be allocated from each category to any agent. The objective here is to find a fair allocation in which the subset of goods assigned to any agent satisfies the given cardinality constraints. This problem naturally captures a number of resource-allocation applications, and is a generalization of the well-studied unconstrained fair division problem. The two central notions of fairness, in the context of fair division of indivisible goods, are envy freeness up to one good (EF1) and the (approximate) maximin share guarantee (MMS). We show that the existence and algorithmic guarantees established for these solution concepts in the unconstrained setting can essentially be achieved under cardinality constraints. Furthermore, focusing on the case wherein all the agents have the same additive valuation, we establish that EF1 allocations exist even under matroid constraints.
Publisher
International Joint Conferences on Artificial Intelligence Organization
Cited by
13 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. The budgeted maximin share allocation problem;Optimization Letters;2024-08-22
2. Unified Fair Allocation of Goods and Chores via Copies;ACM Transactions on Economics and Computation;2023-12-19
3. Egalitarian Price of Fairness for Indivisible Goods;PRICAI 2023: Trends in Artificial Intelligence;2023-11-10
4. Guaranteeing Envy-Freeness under Generalized Assignment Constraints;Proceedings of the 24th ACM Conference on Economics and Computation;2023-07-07
5. Towards Fair Allocation in Social Commerce Platforms;Proceedings of the ACM Web Conference 2023;2023-04-30