Conference proceeding
Achieving Proportionality up to the Maximin Item with Indivisible Goods
THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, v 35(6), pp 5143-5150
18 May 2021
Abstract
We study the problem of fairly allocating indivisible goods and focus on the classic fairness notion of proportionality. The indivisibility of the goods is long known to pose highly non-trivial obstacles to achieving fairness, and a very vibrant line of research has aimed to circumvent them using appropriate notions of approximate fairness. Recent work has established that even approximate versions of proportionality (PROPx) may be impossible to achieve even for small instances, while the best known achievable approximations (PROP1) are much weaker. We introduce the notion of proportionality up to the maximin item (PROPm) and show how to reach an allocation satisfying this notion for any instance involving up to five agents with additive valuations. PROPm provides a well-motivated middle-ground between PROP1 and PROPx, while also capturing some elements of the well-studied maximin share (MMS) benchmark: another relaxation of proportionality that has attracted a lot of attention.
Metrics
Details
- Title
- Achieving Proportionality up to the Maximin Item with Indivisible Goods
- Creators
- Artem Baklanov - National Research University Higher School of EconomicsPranav Garimidi - Conestoga CollegeVasilis Gkatzelis - Drexel UniversityDaniel Schoepflin - Drexel UniversityAssoc Advancement Artificial Intelligence
- Publication Details
- THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, v 35(6), pp 5143-5150
- Series
- AAAI Conference on Artificial Intelligence
- Publisher
- Assoc Advancement Artificial Intelligence
- Number of pages
- 8
- Grant note
- CCF1755955; CCF-2008280 / NSF; National Science Foundation (NSF) HSE University Basic Research Program
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000680423505028
- Scopus ID
- 2-s2.0-85125027560
- Other Identifier
- 991021868725404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Education, Scientific Disciplines