Allocation Made PracticaL

Informazioni relative al progetto

AMPLify

ID dell’accordo di sovvenzione: 670077

DOI

10.3030/670077

Progetto concluso il 15 Febbraio 2020

Data della firma CE 1 Settembre 2015

Data di avvio 1 Giugno 2016

Data di completamento 31 Maggio 2021

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale

€ 2 499 681,00

Contributo UE

€ 2 499 681,00

Coordinato da

TECHNISCHE UNIVERSITAT BERLIN
Germany

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

Periodo di rendicontazione: 2019-06-01 al 2020-11-30

Sintesi del contesto e degli obiettivi generali del progetto

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.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

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).

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

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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