1,307 research outputs found
Operation Frames and Clubs in Kidney Exchange
A kidney exchange is a centrally-administered barter market where patients
swap their willing yet incompatible donors. Modern kidney exchanges use
2-cycles, 3-cycles, and chains initiated by non-directed donors (altruists who
are willing to give a kidney to anyone) as the means for swapping.
We propose significant generalizations to kidney exchange. We allow more than
one donor to donate in exchange for their desired patient receiving a kidney.
We also allow for the possibility of a donor willing to donate if any of a
number of patients receive kidneys. Furthermore, we combine these notions and
generalize them. The generalization is to exchange among organ clubs, where a
club is willing to donate organs outside the club if and only if the club
receives organs from outside the club according to given specifications. We
prove that unlike in the standard model, the uncapped clearing problem is
NP-complete.
We also present the notion of operation frames that can be used to sequence
the operations across batches, and present integer programming formulations for
the market clearing problems for these new types of organ exchanges.
Experiments show that in the single-donation setting, operation frames
improve planning by 34%--51%. Allowing up to two donors to donate in exchange
for one kidney donated to their designated patient yields a further increase in
social welfare.Comment: Published at IJCAI-1
Randomized Parameterized Algorithms for the Kidney Exchange Problem
In order to increase the potential kidney transplants between patients and their incompatible donors, kidney exchange programs have been created in many countries. In the programs, designing algorithms for the kidney exchange problem plays a critical role. The graph theory model of the kidney exchange problem is to find a maximum weight packing of vertex-disjoint cycles and chains for a given weighted digraph. In general, the length of cycles is not more than a given constant L (typically 2 L 5), and the objective function corresponds to maximizing the number of possible kidney transplants. In this paper, we study the parameterized complexity and randomized algorithms for the kidney exchange problem without chains from theory. We construct two different parameterized models of the kidney exchange problem for two cases L = 3 and L 3, and propose two randomized parameterized algorithms based on the random partitioning technique and the randomized algebraic technique, respectively
Ignorance is Almost Bliss: Near-Optimal Stochastic Matching With Few Queries
The stochastic matching problem deals with finding a maximum matching in a
graph whose edges are unknown but can be accessed via queries. This is a
special case of stochastic -set packing, where the problem is to find a
maximum packing of sets, each of which exists with some probability. In this
paper, we provide edge and set query algorithms for these two problems,
respectively, that provably achieve some fraction of the omniscient optimal
solution.
Our main theoretical result for the stochastic matching (i.e., -set
packing) problem is the design of an \emph{adaptive} algorithm that queries
only a constant number of edges per vertex and achieves a
fraction of the omniscient optimal solution, for an arbitrarily small
. Moreover, this adaptive algorithm performs the queries in only a
constant number of rounds. We complement this result with a \emph{non-adaptive}
(i.e., one round of queries) algorithm that achieves a
fraction of the omniscient optimum. We also extend both our results to
stochastic -set packing by designing an adaptive algorithm that achieves a
fraction of the omniscient optimal solution, again
with only queries per element. This guarantee is close to the best known
polynomial-time approximation ratio of for the
\emph{deterministic} -set packing problem [Furer and Yu, 2013]
We empirically explore the application of (adaptations of) these algorithms
to the kidney exchange problem, where patients with end-stage renal failure
swap willing but incompatible donors. We show on both generated data and on
real data from the first 169 match runs of the UNOS nationwide kidney exchange
that even a very small number of non-adaptive edge queries per vertex results
in large gains in expected successful matches
07431 Abstracts Collection -- Computational Issues in Social Choice
From the 21st to the 26th of October 2007, the Dagstuhl Seminar 07431
on ``Computational Issues in Social Choice\u27\u27 was held
at the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their recent
research, and ongoing work and open problems were discussed.
The abstracts of the talks given during the seminar are collected in this paper.
The first section summarises the seminar topics and goals in general.
Links to full papers are provided where available
Finding long chains in kidney exchange using the traveling salesman problem
As of May 2014 there were more than 100,000 patients on the waiting list for a kidney transplant from a deceased donor. Although the preferred treatment is a kidney transplant, every year there are fewer donors than new patients, so the wait for a transplant continues to grow. To address this shortage, kidney paired donation (KPD) programs allow patients with living but biologically incompatible donors to exchange donors through cycles or chains initiated by altruistic (nondirected) donors, thereby increasing the supply of kidneys in the system. In many KPD programs a centralized algorithm determines which exchanges will take place to maximize the total number of transplants performed. This optimization problem has proven challenging both in theory, because it is NP-hard, and in practice, because the algorithms previously used were unable to optimally search over all long chains. We give two new algorithms that use integer programming to optimally solve this problem, one of which is inspired by the techniques used to solve the traveling salesman problem. These algorithms provide the tools needed to find optimal solutions in practice
Adapting a Kidney Exchange Algorithm to Align with Human Values
The efficient and fair allocation of limited resources is a classical problem
in economics and computer science. In kidney exchanges, a central market maker
allocates living kidney donors to patients in need of an organ. Patients and
donors in kidney exchanges are prioritized using ad-hoc weights decided on by
committee and then fed into an allocation algorithm that determines who gets
what--and who does not. In this paper, we provide an end-to-end methodology for
estimating weights of individual participant profiles in a kidney exchange. We
first elicit from human subjects a list of patient attributes they consider
acceptable for the purpose of prioritizing patients (e.g., medical
characteristics, lifestyle choices, and so on). Then, we ask subjects
comparison queries between patient profiles and estimate weights in a
principled way from their responses. We show how to use these weights in kidney
exchange market clearing algorithms. We then evaluate the impact of the weights
in simulations and find that the precise numerical values of the weights we
computed matter little, other than the ordering of profiles that they imply.
However, compared to not prioritizing patients at all, there is a significant
effect, with certain classes of patients being (de)prioritized based on the
human-elicited value judgments
A Microfoundation of Monetary Economics
In this lecture, I explain what the microfoundations of money are about and why they are necessary for monetary economics. Then, I review recent developments of a particular microfoundation of money, commonly known as the search theory of money. Finally, I outline some unresolved issues.Money; Search; Microfoundation.
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