Robust randomized matchings

The following game is played on a weighted graph: Alice selects a matching $M$ and Bob selects a number $k$. Alice's payoff is the ratio of the weight of the $k$ heaviest edges of $M$ to the maximum weight of a matching of size at most $k$. If $M$ guarantees a payoff of at least $\alpha$ then it is called $\alpha$-robust. In 2002, Hassin and Rubinstein gave an algorithm that returns a $1/\sqrt{2}$-robust matching, which is best possible. We show that Alice can improve her payoff to $1/\ln(4)$ by playing a randomized strategy. This result extends to a very general class of independence systems that includes matroid intersection, b-matchings, and strong 2-exchange systems. It also implies an improved approximation factor for a stochastic optimization variant known as the maximum priority matching problem and translates to an asymptotic robustness guarantee for deterministic matchings, in which Bob can only select numbers larger than a given constant. Moreover, we give a new LP-based proof of Hassin and Rubinstein's bound

Von Neumann-Morgenstern farsightedly stable sets in two-sided matching

We adopt the notion of von Neumann-Morgenstern (vNM) farsightedly stable sets to determine which matchings are possibly stable when agents are farsighted in one-to-one matching problems. We provide the characterization of vNM farsightedly stable sets: a set of matchings is a vNM farsightedly stable set if and only if it is a singleton subset of the core. Thus, contrary to the vNM (myopically) stable sets [Ehlers, J. of Econ. Theory 134 (2007), 537-547], vNM farsightedly stable sets cannot include matchings that are not in the core. Moreover, we show that our main result is robust to many-to-one matching problems with substitutable preferences: a set of matchings is a vNM farsightedly stable set if and only if it is a singleton set and its element is in the strong core.Matching problem, von Neumann-Morgenstern stable sets, farsighted stability

Von Neumann-Morgenstern farsightedly stable sets in two-sided matching.

We adopt the notion of von Neumann-Morgenstern farsightedly stable sets to predict which matchings are possibly stable when agents are farsighted in one-to-one matching problems. We provide the characterization of von Neumann-Morgenstern farsightedly stable sets: a set of matchings is a von Neumann-Morgenstern farsightedly stable set if and only if it is a singleton set and its element is a corewise stable matching. Thus, contrary to the von Neumann-Morgenstern (myopically) stable sets, von Neumann-Morgenstern farsightedly stable sets cannot include matchings that are not corewise stable ones. Moreover, we show that our main result is robust to many-to-one matching problems with responsive preferences.matching problem, von Neumann-Morgenstern stable sets, farsighted stability

Von Neuman-Morgenstern farsightedly stable sets in two-sided matching

We adopt the notion of von Neumann-Morgenstern farsightedly stable sets to predict with matchings are possibly stable when agents are farsighted in one-to-one matching problems. We provide the characterization of von Neumann-Morgenstern farsightedly stable sets : a set of matchings is a von Neumann-Morgenstern farsightedly stable set if and only if it is a singleton set and its element is a corewise stable matching. Thus, contrary to the von Neumann-Morgenstern (myopically) stable sets, von Neumann-Morgenstern farsightedly stable sets cannot include matchings thar are not corewise stable ones. Moreover, we show that our main result is robust to many-to-one matching problems with responsive preferences.matching problem, von Neumann-Morgenstern stable sets, farsightedly stability

Popular and Dominant Matchings with Uncertain, Multilayer and Aggregated Preferences

We study the Popular Matching problem in multiple models, where the preferences of the agents in the instance may change or may be unknown/uncertain. In particular, we study an Uncertainty model, where each agent has a possible set of preferences, a Multilayer model, where there are layers of preference profiles, a Robust model, where any agent may move some other agents up or down some places in his preference list and an Aggregated Preference model, where votes are summed over multiple instances with different preferences. We study both one-sided and two-sided preferences in bipartite graphs. In the one-sided model, we show that all our problems can be solved in polynomial time by utilizing the structure of popular matchings. We also obtain nice structural results. With two-sided preferences, we show that all four above models lead to NP-hard questions for popular matchings. By utilizing the connection between dominant matchings and stable matchings, we show that in the robust and uncertainty model, a certainly dominant matching in all possible prefernce profiles can be found in polynomial-time, whereas in the multilayer and aggregated models, the problem remains NP-hard for dominant matchings too. We also answer an open question about $d$-robust stable matchings

A one-shot deviation principle for stability in matching problems

This paper considers marriage problems, roommate problems with nonempty core, and college admissions problems with responsive preferences. All stochastically stable matchings are shown to be contained in the set of matchings which are most robust to one-shot deviation

A one-shot deviation principle for stability in matching problems

This paper considers marriage problems, roommate problems with nonempty core, and college admissions problems with responsive preferences. All stochastically stable matchings are shown to be contained in the set of matchings which are most robust to one-shot deviation

A one-shot deviation principle for stability in matching problems

This paper considers marriage problems, roommate problems with nonempty core, and college admissions problems with responsive preferences. All stochastically stable matchings are shown to be contained in the set of matchings which are most robust to one-shot deviation