Weighted Matching Markets with Budget Constraints
Abstract
We investigate markets with a set of students on one side and a set of colleges on the other. A student and college can be linked by a weighted contract that defines the student's wage, while a college's budget for hiring students is limited. Stability is a crucial requirement for matching mechanisms to be applied in the real world. A standard stability requirement is coalitional stability, i.e., no pair of a college and group of students has any incentive to deviate. We find that a coalitionally stable matching is not guaranteed to exist, verifying the coalitional stability for a given matching is coNP-complete, and the problem of finding whether a coalitionally stable matching exists in a given market, is SigmaP2-complete: NPNP-complete. Other negative results also hold when blocking coalitions contain at most two students and one college. Given these computational hardness results, we pursue a weaker stability requirement called pairwise stability, where no pair of a college and single student has an incentive to deviate. Unfortunately, a pairwise stable matching is not guaranteed to exist either. Thus, we consider a restricted market called a typed weighted market, in which students are partitioned into types that induce their possible wages. We then design a strategy-proof and Pareto efficient mechanism that works in polynomial-time for computing a pairwise stable matching in typed weighted markets.
Cite
Text
Ismaili et al. "Weighted Matching Markets with Budget Constraints." Journal of Artificial Intelligence Research, 2019. doi:10.1613/JAIR.1.11582Markdown
[Ismaili et al. "Weighted Matching Markets with Budget Constraints." Journal of Artificial Intelligence Research, 2019.](https://mlanthology.org/jair/2019/ismaili2019jair-weighted/) doi:10.1613/JAIR.1.11582BibTeX
@article{ismaili2019jair-weighted,
title = {{Weighted Matching Markets with Budget Constraints}},
author = {Ismaili, Anisse and Hamada, Naoto and Zhang, Yuzhe and Suzuki, Takamasa and Yokoo, Makoto},
journal = {Journal of Artificial Intelligence Research},
year = {2019},
pages = {393-421},
doi = {10.1613/JAIR.1.11582},
volume = {65},
url = {https://mlanthology.org/jair/2019/ismaili2019jair-weighted/}
}