Incentives in landing slot problems

Schummer, James; Abizada, Azar

doi:10.1016/j.jet.2017.04.003

Cited by 32 publications

(18 citation statements)

References 36 publications

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“…By considering a real-world resource allocation problem, we contribute to the study of formal properties of specific allocation processes in the field and the study of alternative mechanisms. Studies in this vein include those on entry-level labor markets (Roth, 1984;Roth and Peranson, 1999), school choice (Balinski and Sönmez, 1999;Abdulkadiroglu and Sönmez, 2003;Sönmez, 2008, 2013a), spectrum auctions (Milgrom, 2000), kidney exchange (Roth, Sönmez andÜnver, 2004, 2005, internet auctions (Edelman, Ostrovsky and Schwarz, 2007;Varian, 2007), course allocation (Sönmez andÜnver, 2010;Budish, 2011), cadet-branch matching (Sönmez and Switzer, 2013;Sönmez, 2013), assignment of airport arrival slots (Schummer and Vohra, 2013;Schummer and Abizada, 2017), and refugee resettlement (Jones and Teytelboym, 2017;Delacrétaz, Kominers and Teytelboym, 2016;Andersson, 2017).…”

Section: Related Theoretical Literaturementioning

confidence: 99%

Leaving No Ethical Value Behind: Triage Protocol Design for Pandemic Rationing

Pathak¹,

Sönmez²,

Ünver³

et al. 2020

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We are grateful for input from several medical professionals. Nikhil Agarwal, Alex Rees-Jones, Robert Truog, Iván Werning, and Doug White provided helpful comments. Feedback from Eric Budish, David Delacrétaz, and Govind Persad was particularly valuable. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Section: Related Theoretical Literaturementioning

confidence: 99%

Leaving No Ethical Value Behind: Triage Protocol Design for Pandemic Rationing

Pathak¹,

Sönmez²,

Ünver³

et al. 2020

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Section: Related Theoretical Literaturementioning

confidence: 99%

Triage Protocol Design for Ventilator Rationing in a Pandemic: Integrating Multiple Ethical Values through Reserves

et al. 2020

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“…saving nearly 230-300 additional liver patients in South Korea alone (see the last line of Table IV in Section 3.2 and Table VI in Appendix B.2. Increasingly, economists are taking advantage of advances in technology to design new or improved allocation mechanisms in applications as diverse as entry-level labor markets (Roth and Peranson (1999)), spectrum auctions (Milgrom (2000)), internet auctions (Edelman, Ostrovsky, and Schwarz (2007), Varian (2007)), school choice (Abdulkadiroglu and Sönmez (2003)), kidney exchange (Roth, Sönmez, and Ünver (2004, 2007), course allocation (Sönmez and Ünver (2010), Budish and Cantillon (2012)), affirmative action (Kojima (2012), Hafalir, Yenmez, and Yildirim (2013), Echenique and Yenmez (2015)), cadet-branch matching (Sönmez and Switzer (2013), Sönmez (2013)), refugee matching (Moraga and Rapoport (2014), Jones and Teytelboym (2016)), and assignment of arrival slots (Schummer and Vohra (2013), Schummer and Abizada (2017)). Our paper contributes to the emerging field of market design by introducing a new application in dual-donor organ exchange, and also to transplantation literature by introducing three novel transplantation modalities.…”

Section: Introductionmentioning

confidence: 99%

Dual-Donor Organ Exchange

Ergin¹,

Sönmez²,

Ünver³

2017

Econometrica

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APPENDIX B: DYNAMIC SIMULATIONSIN THE DYNAMIC SIMULATIONS, PATIENTS AND THEIR DONORS ARRIVE over time and remain in the population until they are matched through exchange. We run statically optimal exchange algorithms once in each period. 25 In each simulation, we generate S = 500 such populations and report the averages and sample standard errors of the simulation statistics. B.1. Dynamic Simulations for Lung ExchangeIn the dynamic lung-exchange simulations, we consider 200 triples arriving over 20 periods at a uniform rate of 10 triples per period. This time horizon roughly corresponds to more than 1 year of Japanese patient arrival, when exchanges are run once about every 3 weeks. We only consider the 2-way and 2&3-way exchange regimes.Based on the 2&3-way exchange simulation results reported in Table V, we can increase the number of living-donor transplants by 190%, thus nearly tripling them. This increase corresponds to 24% of all triples in the population. Even the logistically simpler 2-way exchange technology has a potential to increase the number of living-donor transplants by 125%. B.2. Dynamic Simulations for Dual-Graft Liver ExchangeFor dual-graft liver exchange, we consider 500 triples arriving over 20 periods at a uniform rate of 25 triples per period. In each period, we follow the same four-step transplantation scenario we used for the static simulations. The unmatched triples remain in the patient population waiting for the next period. 26Haluk Ergin: hie@berkeley.edu Tayfun Sönmez: sonmezt@bc.edu M. Utku Ünver: unver@bc.edu 25 Moreover, among the optimal matchings, we choose a random one rather than using a priority rule to choose whom to match now and whom to leave to future runs. The number of patients who can be matched dynamically can be further improved using dynamic optimization. For example, see Ünver (2010) for such an approach for kidney exchanges. 26 Roughly, a dynamic simulation corresponds to 3 5 months in real time and the exchange is run once every 5-6 days. This is a very crude mapping that relies on our specific patient and paired donor generation

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Incentives in landing slot problems

Cited by 32 publications

References 36 publications

Leaving No Ethical Value Behind: Triage Protocol Design for Pandemic Rationing

Leaving No Ethical Value Behind: Triage Protocol Design for Pandemic Rationing

Triage Protocol Design for Ventilator Rationing in a Pandemic: Integrating Multiple Ethical Values through Reserves

Dual-Donor Organ Exchange

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