Allocation Made PracticaL

Projektinformationen

AMPLify

ID Finanzhilfevereinbarung: 670077

DOI

10.3030/670077

Das Projekt endete am 15 Februar 2020

EK-Unterschriftsdatum 1 September 2015

Startdatum 1 Juni 2016

Enddatum 31 Mai 2021

Finanziert unter

• EXCELLENT SCIENCE - European Research Council (ERC)

Gesamtkosten

€ 2 499 681,00

EU-Beitrag

€ 2 499 681,00

Koordiniert durch

TECHNISCHE UNIVERSITAT BERLIN
Germany

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

Berichtszeitraum: 2019-06-01 bis 2020-11-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

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.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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).

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

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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