148 research outputs found
Popular matchings in the marriage and roommates problems
Popular matchings have recently been a subject of study in the context of the so-called House Allocation Problem, where the objective is to match applicants to houses over which the applicants have preferences. A matching M is called popular if there is no other matching MⲠwith the property that more applicants prefer their allocation in MⲠto their allocation in M. In this paper we study popular matchings in the context of the Roommates Problem, including its special (bipartite) case, the Marriage Problem. We investigate the relationship between popularity and stability, and describe efficient algorithms to test a matching for popularity in these settings. We also show that, when ties are permitted in the preferences, it is NP-hard to determine whether a popular matching exists in both the Roommates and Marriage cases
Computational complexity of -stable matchings
We study deviations by a group of agents in the three main types of matching
markets: the house allocation, the marriage, and the roommates models. For a
given instance, we call a matching -stable if no other matching exists that
is more beneficial to at least out of the agents. The concept
generalizes the recently studied majority stability. We prove that whereas the
verification of -stability for a given matching is polynomial-time solvable
in all three models, the complexity of deciding whether a -stable matching
exists depends on and is characteristic to each model.Comment: SAGT 202
Popular Matchings in Complete Graphs
Our input is a complete graph on vertices where each vertex
has a strict ranking of all other vertices in . Our goal is to construct a
matching in that is popular. A matching is popular if does not lose
a head-to-head election against any matching , where each vertex casts a
vote for the matching in where it gets assigned a better partner.
The popular matching problem is to decide whether a popular matching exists or
not. The popular matching problem in is easy to solve for odd .
Surprisingly, the problem becomes NP-hard for even , as we show here.Comment: Appeared at FSTTCS 201
Matchings and Copeland's Method
Given a graph where every vertex has a weak ranking over its
neighbors, we consider the problem of computing an optimal matching as per
agent preferences. The classical notion of optimality in this setting is
stability. However stable matchings, and more generally, popular matchings need
not exist when is non-bipartite. Unlike popular matchings, Copeland winners
always exist in any voting instance -- we study the complexity of computing a
matching that is a Copeland winner and show there is no polynomial-time
algorithm for this problem unless .
We introduce a relaxation of both popular matchings and Copeland winners
called weak Copeland winners. These are matchings with Copeland score at least
, where is the total number of matchings in ; the maximum
possible Copeland score is . We show a fully polynomial-time
randomized approximation scheme to compute a matching with Copeland score at
least for any
Matching under Preferences
Matching theory studies how agents and/or objects from different sets can be matched with each other while taking agents\u2019 preferences into account. The theory originated in 1962 with a celebrated paper by David Gale and Lloyd Shapley (1962), in which they proposed the Stable Marriage Algorithm as a solution to the problem of two-sided matching. Since then, this theory has been successfully applied to many real-world problems such as matching students to universities, doctors to hospitals, kidney transplant patients to donors, and tenants to houses. This chapter will focus on algorithmic as well as strategic issues of matching theory.
Many large-scale centralized allocation processes can be modelled by matching problems where agents have preferences over one another. For example, in China, over 10 million students apply for admission to higher education annually through a centralized process. The inputs to the matching scheme include the students\u2019 preferences over universities, and vice versa, and the capacities of each university. The task is to construct a matching that is in some sense optimal with respect to these inputs.
Economists have long understood the problems with decentralized matching markets, which can suffer from such undesirable properties as unravelling, congestion and exploding offers (see Roth and Xing, 1994, for details). For centralized markets, constructing allocations by hand for large problem instances is clearly infeasible. Thus centralized mechanisms are required for automating the allocation process.
Given the large number of agents typically involved, the computational efficiency of a mechanism's underlying algorithm is of paramount importance. Thus we seek polynomial-time algorithms for the underlying matching problems. Equally important are considerations of strategy: an agent (or a coalition of agents) may manipulate their input to the matching scheme (e.g., by misrepresenting their true preferences or underreporting their capacity) in order to try to improve their outcome. A desirable property of a mechanism is strategyproofness, which ensures that it is in the best interests of an agent to behave truthfully
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