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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2501.18442 |
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| _version_ | 1866916590460600320 |
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| author | Ronen, Amit Hess, Jonah Evan Belfer, Yael Mauras, Simon Eden, Alon |
| author_facet | Ronen, Amit Hess, Jonah Evan Belfer, Yael Mauras, Simon Eden, Alon |
| contents | We consider the stable matching problem (e.g. between doctors and hospitals) in a one-to-one matching setting, where preferences are drawn uniformly at random. It is known that when doctors propose and the number of doctors equals the number of hospitals, then the expected rank of doctors for their match is $Θ(\log n)$, while the expected rank of the hospitals for their match is $Θ(n/\log n)$, where $n$ is the size of each side of the market. However, when adding even a single doctor, [Ashlagi, Kanoria and Leshno, 2017] show that the tables have turned: doctors have expected rank of $Θ(n/\log n)$ while hospitals have expected rank of $Θ(\log n)$. That is, (slight) competition has a much more dramatically harmful effect than the benefit of being on the proposing side. Motivated by settings where agents inflate their value for an item if it is already allocated to them (termed endowment effect), we study the case where hospitals exhibit ``loyalty".
We model loyalty as a parameter $k$, where a hospital currently matched to their $\ell$th most preferred doctor accepts proposals from their $\ell-k-1$th most preferred doctors. Hospital loyalty should help doctors mitigate the harmful effect of competition, as many more outcomes are now stable. However, we show that the effect of competition is so dramatic that, even in settings with extremely high loyalty, in unbalanced markets, the expected rank of doctors already becomes $\tildeΘ(\sqrt{n})$ for loyalty $k=n-\sqrt{n}\log n=n(1-o(1))$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18442 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stable Marriage: Loyalty vs. Competition Ronen, Amit Hess, Jonah Evan Belfer, Yael Mauras, Simon Eden, Alon Computer Science and Game Theory We consider the stable matching problem (e.g. between doctors and hospitals) in a one-to-one matching setting, where preferences are drawn uniformly at random. It is known that when doctors propose and the number of doctors equals the number of hospitals, then the expected rank of doctors for their match is $Θ(\log n)$, while the expected rank of the hospitals for their match is $Θ(n/\log n)$, where $n$ is the size of each side of the market. However, when adding even a single doctor, [Ashlagi, Kanoria and Leshno, 2017] show that the tables have turned: doctors have expected rank of $Θ(n/\log n)$ while hospitals have expected rank of $Θ(\log n)$. That is, (slight) competition has a much more dramatically harmful effect than the benefit of being on the proposing side. Motivated by settings where agents inflate their value for an item if it is already allocated to them (termed endowment effect), we study the case where hospitals exhibit ``loyalty". We model loyalty as a parameter $k$, where a hospital currently matched to their $\ell$th most preferred doctor accepts proposals from their $\ell-k-1$th most preferred doctors. Hospital loyalty should help doctors mitigate the harmful effect of competition, as many more outcomes are now stable. However, we show that the effect of competition is so dramatic that, even in settings with extremely high loyalty, in unbalanced markets, the expected rank of doctors already becomes $\tildeΘ(\sqrt{n})$ for loyalty $k=n-\sqrt{n}\log n=n(1-o(1))$. |
| title | Stable Marriage: Loyalty vs. Competition |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2501.18442 |