IJCAI 2023poster1 citations

First-Choice Maximality Meets Ex-ante and Ex-post Fairness

Xiaoxi Guo, Sujoy Sikdar, Lirong Xia, Yongzhi Cao, Hanpin Wang

Abstract

For the assignment problem where multiple indivisible items are allocated to a group of agents given their ordinal preferences, we design randomized mechanisms that satisfy first-choice maximality (FCM), i.e., maximizing the number of agents assigned their first choices, together with Pareto efficiency (PE). Our mechanisms also provide guarantees of ex-ante and ex-post fairness. The generalized eager Boston mechanism is ex-ante envy-free, and ex-post envy-free up to one item (EF1). The generalized probabilistic Boston mechanism is also ex-post EF1, and satisfies ex-ante efficiency instead of fairness. We also show that no strategyproof mechanism satisfies ex-post PE, EF1, and FCM simultaneously. In doing so, we expand the frontiers of simultaneously providing efficiency and both ex-ante and ex-post fairness guarantees for the assignment problem.

Game Theory and Economic Paradigms: GTEP: Computational social choiceAgent-based and Multi-agent Systems: MAS: Resource allocationGame Theory and Economic Paradigms: GTEP: Fair division
BibTeX
@inproceedings{ijcai2023p303,
  title     = {First-Choice Maximality Meets Ex-ante and Ex-post Fairness},
  author    = {Guo, Xiaoxi and Sikdar, Sujoy and Xia, Lirong and Cao, Yongzhi and Wang, Hanpin},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {2719--2727},
  year      = {2023},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2023/303},
  url       = {https://doi.org/10.24963/ijcai.2023/303},
}
First-Choice Maximality Meets Ex-ante and Ex-post Fairness · IJCAI 2023