Two-Sided Matching Meets Fair Division
Two-Sided Matching Meets Fair Division
Rupert Freeman, Evi Micha, Nisarg Shah
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 203-209.
https://doi.org/10.24963/ijcai.2021/29
We introduce a new model for two-sided matching which allows us to borrow popular fairness notions from the fair division literature such as envy-freeness up to one good and maximin share guarantee. In our model, each agent is matched to multiple agents on the other side over whom she has additive preferences. We demand fairness for each side separately, giving rise to notions such as double envy-freeness up to one match (DEF1) and double maximin share guarantee (DMMS). We show that (a slight strengthening of) DEF1 cannot always be achieved, but in the special case where both sides have identical preferences, the round-robin algorithm with a carefully designed agent ordering achieves it. In contrast, DMMS cannot be achieved even when both sides have identical preferences.
Keywords:
Agent-based and Multi-agent Systems: Computational Social Choice
Agent-based and Multi-agent Systems: Resource Allocation
AI Ethics, Trust, Fairness: Fairness