Combining Fairness and Optimality when Selecting and Allocating Projects

被引：0
|
作者：
Belahcene, Khaled [1 ]
Mousseau, Vincent [2 ]
Wilczynski, Anaelle [2 ]
机构：
[1] Univ Technol Compiegne, CNRS, Heudiasyc, Compiegne, France
[2] Univ Paris Saclay, MICS, Cent Supelec, Paris, France
来源：
PROCEEDINGS OF THE THIRTIETH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2021 | 2021年
关键词：
ENVY-FREENESS; EFFICIENCY;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We consider the problem of the conjoint selection and allocation of projects to a population of agents, e.g. students are assigned papers and shall present them to their peers. The selection can be constrained either by quotas over subcategories of projects, or by the preferences of the agents themselves. We explore fairness and optimality issues and refine the analysis of the rank-maximality and popularity optimality concepts. We show that they are compatible with reasonable fairness requirements related to rank-based envy-freeness and can be adapted to select globally good projects according to the preferences of the agents.
引用
收藏
页码：38 / 44
页数：7
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