Allocation Made PracticaL

Información del proyecto

AMPLify

Identificador del acuerdo de subvención: 670077

DOI

10.3030/670077

Proyecto finalizado el 15 Febrero 2020

Fecha de la firma de la CE 1 Septiembre 2015

Fecha de inicio 1 Junio 2016

Fecha de finalización 31 Mayo 2021

Financiado con arreglo a

• EXCELLENT SCIENCE - European Research Council (ERC)

Coste total

€ 2 499 681,00

Aportación de la UE

€ 2 499 681,00

Coordinado por

TECHNISCHE UNIVERSITAT BERLIN
Germany

Periodic Reporting for period 3 - AMPLify (Allocation Made PracticaL)

Período documentado: 2019-06-01 hasta 2020-11-30

Resumen del contexto y de los objetivos generales del proyecto

The AMPLify project tackles a pressing problem facing society today: the efficient and fair allocation of resources and costs. Whilst the foundations of the field of resource and cost allocation have been laid with simple, abstract models, it is now timely to consider richer models that capture features like lack of synchronicity which reflect the practice of resource and cost allocation problems in the real world.The models we are developing are applicable to many new markets being developed in our highly connected and online world. The main goal of the project is to design new mechanisms for resource and cost allocation that exploit the features of these rich new models. The project lies at an exciting interface between three fields: social choice, game theory and optimisation. The emphasis of the project is on computational issues. We are using computation to tackle the complexity of such models and mechanisms, and to increase both efficiency and fairness of allocations. This is of benefit to society as we are able to do more with fewer resources and greater fairness.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

The work performed so far has focused on three specific models of real world resource allocation. All three models share an important (and till now neglected) common feature in that they are dynamic online models in which agents and or resources being allocated are not always present but arrive (and sometimes depart) over time.

The first model is derived from a real world application found around the world where a food bank needs to allocate food as it is donated to the different charities feeding the poor. In this case, the food, the resource being allocated, arrives over time. The second model is derived from the problem of organ matching. When kidneys are donated for transplant, they must be allocated to patients immediately. In this case, both the patients receiving organs, and the donated organs arrive and depart. The third model now under development is derived from the problem of electric car charging. In this case, the cars being allocated charging slots arrive over time.

Progress has been made on the axiomatic level (understanding fundamental axiomatic properties of these three matching markets), on the algorithmic level (designing and studying online mechanisms for these markets), and on the computational level (identifying the complexity of finding optimal and approximate allocations).

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

The rest of the project will be devoted to studying other more realistic allocation settings, and to extracting general lessons that can be applied to other novel and as yet unseen settings.

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