ML Anthology

Several Stories About High-Multiplicity EFx Allocation (Student Abstract)

Nikita Morozov, Artur Ignatiev, Yuriy Dementiev
AAAI 2024 pp. 23587-23589
doi:10.1609/AAAI.V38I21.30484 /aaai/2024/morozov2024aaai-several/

Abstract

Fair division is a topic that has significant social and industrial value. In this work, we study allocations that simultaneously satisfy definitions of fairness and efficiency: EFx and PO. First, we prove that the problem of finding such allocations is NP-hard for two agents. Then, we propose a concept for an ILP-based solving algorithm, the running time of which depends on the number of EFx allocations. We generate input data and analyze algorithm's running time based on the results obtained.

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Cite

Text

Morozov et al. "Several Stories About High-Multiplicity EFx Allocation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30484

Markdown

[Morozov et al. "Several Stories About High-Multiplicity EFx Allocation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/morozov2024aaai-several/) doi:10.1609/AAAI.V38I21.30484

BibTeX

@inproceedings{morozov2024aaai-several,
  title     = {{Several Stories About High-Multiplicity EFx Allocation (Student Abstract)}},
  author    = {Morozov, Nikita and Ignatiev, Artur and Dementiev, Yuriy},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23587-23589},
  doi       = {10.1609/AAAI.V38I21.30484},
  url       = {https://mlanthology.org/aaai/2024/morozov2024aaai-several/}
}