Pareto optimal matchings in many-to-many markets with ties

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Abstract

We consider Pareto-optimalmatchings (POMs) in amany-tomany market of applicants and courses where applicants have preferences, which may include ties, over individual courses and lexicographic preferences over sets of courses. Since this is the most general setting examined so far in the literature, our work unifies and generalizes several known results. Specifically, we characterize POMs and introduce the Generalized Serial Dictatorship Mechanism with Ties (GSDT) that effectively handles ties via properties of network flows.We show that GSDT can generate all POMs using different priority orderings over the applicants, but it satisfies truthfulness only for certain such orderings. This shortcoming is not specific to our mechanism; we show that any mechanism generating all POMs in our setting is prone to strategic manipulation. This is in contrast to the one-to-one case (with or without ties), for which truthful mechanisms generating all POMs do exist.

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Cechlárová, K., Eirinakis, P., Fleiner, T., Magos, D., Manlove, D. F., Mourtos, I., … Rastegari, B. (2015). Pareto optimal matchings in many-to-many markets with ties. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9347, pp. 27–39). Springer Verlag. https://doi.org/10.1007/978-3-662-48433-3_3

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