Stable Allocation Mechanism

The stable allocation problem is the generalization of the well-known and much studied stable (0,1)-matching problems to the allocation of real numbers (hours or quantities). There are two distinct sets of agents, a set I of "employees" or "buyers" and a set J of "employers" or "sellers", each agent with preferences over the opposite set and each with a given available time or quantity. In common with its specializations, and allocation problem may have exponentially many stable solutions (though in the "generic" case it has exactly one stable allocation). A mechanism is a function that selects exactly one stable allocation for any problem. The "employee-optimal" mechanism XI that always selects xI, the "employee-optimal" stable allocation, is characterized as the unique one that is, for employees, either "efficient", or "monotone", or "strategy-proof."Le problème d'allocations stables généralise les problèmes d'affectations stables (" one-to-one ", " one-to-many " ou " many-to-many ") à l'attribution de quantités réelles ou d'heures. Il existe deux ensembles d'agents distincts, un ensemble I " employés " et un ensemble J " employeurs " où chaque agent a un ordre de préférences sur les agents de l'ensemble opposé et chacun a un certain nombre d'heures. Comme dans les cas spécifiques, le problème d'allocations stables peut contenir un nombre exponentiel de stables (quoique dans le cas " générique " il admet exactement une allocation stable). Un mécanisme est une fonction qui sélectionne exactement une allocation stable pour n'importe quel problème. Le mécanisme " optimal-employés " qui sélectionne toujours l'allocation stable optimale pour les employés est caractérisé comme étant l ‘unique mécanisme " efficace " ou " monotone " ou " strategy-proof.

Preference Swaps for the Stable Matching Problem

An instance $I$ of the Stable Matching Problem (SMP) is given by a bipartite graph with a preference list of neighbors for every vertex. A swap in $I$ is the exchange of two consecutive vertices in a preference list. A swap can be viewed as a smallest perturbation of $I$. Boehmer et al. (2021) designed a polynomial-time algorithm to find the minimum number of swaps required to turn a given maximal matching into a stable matching. To generalize this result to the many-to-many version of SMP, we introduce a new representation of SMP as an extended bipartite graph and reduce the problem to submodular minimization. It is a natural problem to establish computational complexity of deciding whether at most $k$ swaps are enough to turn $I$ into an instance where one of the maximum matchings is stable. Using a hardness result of Gupta et al. (2020), we prove that it is NP-hard to decide whether at most $k$ swaps are enough to turn $I$ into an instance with a stable perfect matching. Moreover, this problem parameterized by $k$ is W[1]-hard. We also obtain a lower bound on the running time for solving the problem using the Exponential Time Hypothesis

Unique Stable Matchings

We provide necessary and sufficient conditions on the preferences of market participants for a unique stable matching in models of two-sided matching with non-transferable utility. We use the process of iterated deletion of unattractive alternatives (IDUA), a formalisation of the reduction procedure in Balinski and Ratier (1997), and we show that an instance of the matching problem possesses a unique stable matching if and only if IDUA collapses each participant preference list to a singleton. (This is in a sense the matching problem analog of a strategic game being dominance solvable.

Stable Matching Games

Gale and Shapley introduced a matching problem between two sets of agents where each agent on one side has an exogenous preference ordering over the agents on the other side. They defined a matching as stable if no unmatched pair can both improve their utility by forming a new pair. They proved, algorithmically, the existence of a stable matching. Shapley and Shubik, Demange and Gale, and many others extended the model by allowing monetary transfers. We offer a further extension by assuming that matched couples obtain their payoff endogenously as the outcome of a strategic game they have to play in a usual non-cooperative sense (without commitment) or in a semi-cooperative way (with commitment, as the outcome of a bilateral binding contract in which each player is responsible for his/her part of the contract). Depending on whether the players can commit or not, we define in each case a solution concept that combines Gale-Shapley pairwise stability with a (generalized) Nash equilibrium stability. In each case, we give the necessary and sufficient conditions for the set of stable allocations to be non-empty, we study its geometry (full/semi-lattice), and provide an algorithm that converges to its maximal element. Finally, we prove that our second model (with commitment) encompasses and refines most of the literature (matching with monetary transfers as well as matching with contracts)

Dynamic capacities and priorities in stable matching

Cette thèse aborde les facettes dynamiques des principes fondamentaux du problème de l'appariement stable plusieurs-à-un. Nous menons notre étude dans le contexte du choix de l'école et de l'appariement entre les hôpitaux et les résidents. Dans la première étude, en utilisant le modèle résident-hôpital, nous étudions la complexité de calcul de l'optimisation des variations de capacité des hôpitaux afin de maximiser les résultats pour les résidents, tout en respectant les contraintes de stabilité et de budget. Nos résultats révèlent que le problème de décision est NP-complet et que le problème d'optimisation est inapproximable, même dans le cas de préférences strictes et d'allocations de capacités disjointes. Ces résultats posent des défis importants aux décideurs qui cherchent des solutions efficaces aux problèmes urgents du monde réel. Dans la seconde étude, en utilisant le modèle du choix de l'école, nous explorons l'optimisation conjointe de l'augmentation des capacités scolaires et de la réalisation d'appariements stables optimaux pour les étudiants au sein d'un marché élargi. Nous concevons une formulation innovante de programmation mathématique qui modélise la stabilité et l'expansion des capacités, et nous développons une méthode efficace de plan de coupe pour la résoudre. Des données réelles issues du système chilien de choix d'école valident l'impact potentiel de la planification de la capacité dans des conditions de stabilité. Dans la troisième étude, nous nous penchons sur la stabilité de l'appariement dans le cadre de priorités dynamiques, en nous concentrant principalement sur le choix de l'école. Nous introduisons un modèle qui tient compte des priorités des frères et sœurs, ce qui nécessite de nouveaux concepts de stabilité. Notre recherche identifie des scénarios où des appariements stables existent, accompagnés de mécanismes en temps polynomial pour leur découverte. Cependant, dans certains cas, nous prouvons également que la recherche d'un appariement stable de cardinalité maximale est NP-difficile sous des priorités dynamiques, ce qui met en lumière les défis liés à ces problèmes d'appariement. Collectivement, cette recherche contribue à une meilleure compréhension des capacités et des priorités dynamiques dans les scénarios d'appariement stable et ouvre de nouvelles questions et de nouvelles voies pour relever les défis d'allocation complexes dans le monde réel.This research addresses the dynamic facets in the fundamentals of the many-to-one stable matching problem. We conduct our study in the context of school choice and hospital-resident matching. In the first study, using the resident-hospital model, we investigate the computational complexity of optimizing hospital capacity variations to maximize resident outcomes, while respecting stability and budget constraints. Our findings reveal the NP-completeness of the decision problem and the inapproximability of the optimization problem, even under strict preferences and disjoint capacity allocations. These results pose significant challenges for policymakers seeking efficient solutions to pressing real-world issues. In the second study, using the school choice model, we explore the joint optimization of increasing school capacities and achieving student-optimal stable matchings within an expanded market. We devise an innovative mathematical programming formulation that models stability and capacity expansion, and we develop an effective cutting-plane method to solve it. Real-world data from the Chilean school choice system validates the potential impact of capacity planning under stability conditions. In the third study, we delve into stable matching under dynamic priorities, primarily focusing on school choice. We introduce a model that accounts for sibling priorities, necessitating novel stability concepts. Our research identifies scenarios where stable matchings exist, accompanied by polynomial-time mechanisms for their discovery. However, in some cases, we also prove the NP-hardness of finding a maximum cardinality stable matching under dynamic priorities, shedding light on challenges related to these matching problems. Collectively, this research contributes to a deeper understanding of dynamic capacities and priorities within stable matching scenarios and opens new questions and new avenues for tackling complex allocation challenges in real-world settings