An Invitation to Market Design

Kominers, Scott Duke; Teytelboym, Alexander; Crawford, Vincent P.

doi:10.2139/ssrn.3032458

Cited by 5 publications

(4 citation statements)

References 268 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As in other market design applications, it is important to consider whether a proposed system is fair and ethically compelling (Roth 2007;Kominers et al 2017;Li 2017). It is important to clarify that a points system involves no use of monetary payments to donors.…”

Section: Ineciency Of Current Mechanismsmentioning

confidence: 99%

Market Failure in Kidney Exchange

Agarwal

Ashlagi

Azevedo

et al. 2019

American Economic Review

View full text Add to dashboard Cite

We show that kidney exchange markets suffer from market failures whose remedy could increase transplants by 30 to 63 percent. First, we document that the market is fragmented and inefficient; most transplants are arranged by hospitals instead of national platforms. Second, we propose a model to show two sources of inefficiency: hospitals only partly internalize their patients’ benefits from exchange, and current platforms suboptimally reward hospitals for submitting patients and donors. Third, we calibrate a production function and show that individual hospitals operate below efficient scale. Eliminating this inefficiency requires either a mandate or a combination of new mechanisms and reimbursement reforms. (JEL D24, D47, I11)

show abstract

Section: Ineciency Of Current Mechanismsmentioning

confidence: 99%

Market Failure in Kidney Exchange

Agarwal

Ashlagi

Azevedo

et al. 2019

American Economic Review

View full text Add to dashboard Cite

show abstract

“…We argue that techniques from market design as well as AI are most suited to design a UTM system that satisfies all important desiderata (like efficiency and fairness) while also being scalable in practice. Market design is a research area at the intersection of computer science, economics, and operations research, that focuses on the design of well-functioning marketplaces (Kominers, Teytelboym, and Crawford 2017). 3 In particular, using tools from game theory and mechanism design (Myerson 2008), it explicitly takes the strategic behavior of agents into account and aims to design rules that nonetheless lead to good outcomes.…”

Section: Introductionmentioning

confidence: 99%

Market Design for Drone Traffic Management

Seuken

Friedrich

Dierks

2022

AAAI

View full text Add to dashboard Cite

The rapid development of drone technology is leading to more and more use cases being proposed. In response, regulators are drawing up drone traffic management frameworks. However, to design solutions that are efficient, fair, simple, non-manipulable, and scalable, we need market design and AI expertise. To this end, we introduce the drone traffic management problem as a new research challenge to the market design and AI communities. We present five design desiderata that we have derived from our interviews with stakeholders from the regulatory side as well as from public and private enterprises. Finally, we provide an overview of the solution space to point out possible directions for future research.

show abstract

“…While there is a long history of mechanism design research on assignment problems, school allocation, organ allocation, refugee matching, etc. (Kominers, Teytelboym, and Crawford (2017) provide an excellent recent introduction to market design), and much recent interest in the AI and broader computer science community in mechanism design for social good 1 , there has been limited prior work on homelessness specifically. The most relevant study is that of Azizi et al (2018), who consider allocation policies specifically for homeless youth.…”

Section: Introductionmentioning

confidence: 99%

Allocating Interventions Based on Predicted Outcomes: A Case Study on Homelessness Services

Kube

Das

Fowler

2019

AAAI

View full text Add to dashboard Cite

Modern statistical and machine learning methods are increasingly capable of modeling individual or personalized treatment effects. These predictions could be used to allocate different interventions across populations based on individual characteristics. In many domains, like social services, the availability of different possible interventions can be severely resource limited. This paper considers possible improvements to the allocation of such services in the context of homelessness service provision in a major metropolitan area. Using data from the homeless system, we use a counterfactual approach to show potential for substantial benefits in terms of reducing the number of families who experience repeat episodes of homelessness by choosing optimal allocations (based on predicted outcomes) to a fixed number of beds in different types of homelessness service facilities. Such changes in the allocation mechanism would not be without tradeoffs, however; a significant fraction of households are predicted to have a higher probability of re-entry in the optimal allocation than in the original one. We discuss the efficiency, equity and fairness issues that arise and consider potential implications for policy.

show abstract

An Invitation to Market Design

Cited by 5 publications

References 268 publications

Market Failure in Kidney Exchange

Market Failure in Kidney Exchange

Market Design for Drone Traffic Management

Allocating Interventions Based on Predicted Outcomes: A Case Study on Homelessness Services

Contact Info

Product

Resources

About