Unpaired Kidney Exchange: Overcoming Double Coincidence of Wants without Money

Akbarpour, Mohammad; Combe, Julien; He, Yinghua; Hiller, Victor; Shimer, Robert; Tercieux, Olivier

doi:10.3386/w27765

Cited by 7 publications

(6 citation statements)

References 1 publication

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“…53 In 2014, 155, 244, and 243 pairs and 5, 4, and 51 NDDs enrolled in the APKD, UNOS, and NKR, respectively. The NKR and APKD now offer NDDs a "voucher" intended to be used in the future by a family member, who would receive a chain-ending kidney (see, e.g., Wall et al 2017, Akbarpour et al 2020d). cember 2013, and France had 78 pairs between 2013(Ferrari et al 2015, Biró et al 2019c).…”

Section: Endnotesmentioning

confidence: 99%

Kidney Exchange: An Operations Perspective

Ashlagi

Roth

2021

Management Science

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Many patients in need of a kidney transplant have a willing but incompatible (or poorly matched) living donor. Kidney exchange programs arrange exchanges among such patient-donor pairs, in cycles and chains of exchange, so that each patient receives a compatible kidney. Kidney exchange has become a standard form of transplantation in the United States and a few other countries, in large part because of continued attention to the operational details that arose as obstacles were overcome and new obstacles became relevant. We review some of the key operational issues in the design of successful kidney exchange programs. Kidney exchange has yet to reach its full potential, and the paper further describes some open questions that we hope will continue to attract attention from researchers interested in the operational aspects of dynamic exchange. This paper was accepted by David Simchi Levi, Special Section of Management Science: 65th Anniversary.

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Section: Endnotesmentioning

confidence: 99%

Kidney Exchange: An Operations Perspective

Ashlagi

Roth

2021

Management Science

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“…As a generalization of a paired kidney exchange market, the dynamic matching problem was extended to finding disjoint 3-way circles and chains [19,3,6,9,34]. Anderson et al [5] and Ashlagi et al [8] analyzed the expected waiting time in the market.…”

Section: Stochastic Matching Marketmentioning

confidence: 99%

Dynamic Bipartite Matching Market with Arrivals and Departures

Kakimura¹,

Zhu²

2021

Preprint

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In this paper, we study a matching market model on a bipartite network where agents on each side arrive and depart stochastically by a Poisson process. For such a dynamic model, we design a mechanism that decides not only which agents to match, but also when to match them, to minimize the expected number of unmatched agents. The main contribution of this paper is to achieve theoretical bounds on the performance of local mechanisms with different timing properties. We show that an algorithm that waits to thicken the market, called the Patient algorithm, is exponentially better than the Greedy algorithm, i.e., an algorithm that matches agents greedily. This means that waiting has substantial benefits on maximizing a matching over a bipartite network. We remark that the Patient algorithm requires the planner to identify agents who are about to leave the market, and, under the requirement, the Patient algorithm is shown to be an optimal algorithm. We also show that, without the requirement, the Greedy algorithm is almost optimal. In addition, we consider the 1-sided algorithms where only an agent on one side can attempt to match. This models a practical matching market such as a freight exchange market and a labor market where only agents on one side can make a decision. For this setting, we prove that the Greedy and Patient algorithms admit the same performance, that is, waiting to thicken the market is not valuable. This conclusion is in contrast to the case where agents on both sides can make a decision and the non-bipartite case by [Akbarpour et al., Journal of Political Economy, 2020].

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“…Baccara et al 2020 studied optimal dynamic matching and thickness in a two-sided model. Moreover, online matching models have been applied to multiple domains, including kidney exchange [ Ünver 2010, Ashlagi et al 2013, Anderson et al 2015, Ashlagi et al 2019, Akbarpour et al 2020b], housing markets [Leshno 2019, Bloch and Houy 2012, Arnosti and Shi 2019, and ride-sharing [ Özkan and Ward 2020, Liu et al 2019, Castillo 2020.…”

Section: Related Workmentioning

confidence: 99%

The Value of Excess Supply in Spatial Matching Markets

Akbarpour¹,

Alimohammadi²,

Li³

et al. 2021

Preprint

Self Cite

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We study dynamic matching in a spatial setting. Drivers are distributed at random on some interval. Riders arrive in some (possibly adversarial) order at randomly drawn points. The platform observes the location of the drivers, and can match newly arrived riders immediately, or can wait for more riders to arrive. Unmatched riders incur a waiting cost c per period. The platform can match riders and drivers, irrevocably. The cost of matching a driver to a rider is equal to the distance between them. We quantify the value of slightly increasing supply. We prove that when there are (1 + ) drivers per rider (for any > 0), the cost of matching returned by a simple greedy algorithm which pairs each arriving rider to the closest available driver is O(log 3 (n)), where n is the number of riders. On the other hand, with equal number of drivers and riders, even the ex post optimal matching does not have a cost less than Θ( √ n). Our results shed light on the important role of (small) excess supply in spatial matching markets.

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Unpaired Kidney Exchange: Overcoming Double Coincidence of Wants without Money

Cited by 7 publications

References 1 publication

Kidney Exchange: An Operations Perspective

Kidney Exchange: An Operations Perspective

Dynamic Bipartite Matching Market with Arrivals and Departures

The Value of Excess Supply in Spatial Matching Markets

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