Objectif "Market designers study how to set the ""rules of a marketplace"" such that the market works well. However, markets are getting increasingly complex such that designing good market mechanisms ""by hand"" is often infeasible, in particular when certain design desiderata (such as efficiency, strategyproofness, or fairness) are in conflict with each other. Moreover, human agents are boundedly-rational: already in small domains, they are best modeled as having incomplete preferences, because they may only know a ranking or the values of their top choices. In combinatorial domains, the number of choices grows exponentially, such that it quickly becomes impossible for an agent to report its full valuation, even if it had complete preferences. In this ERC grant proposal, we propose to combine techniques from ""machine learning"" with ""market design"" to address these challenges.First, we propose to develop a new, automated approach to design mechanisms with the help of machine learning (ML). In contrast to prior ML-based automated mechanism design work, we explicitly aim to train the ML algorithm to exploit regularities in the mechanism design space. Second, we propose to study the ""design of machine learning-based mechanisms."" These are mechanisms that use machine learning internally to achieve good efficiency and incentives even when agents have incomplete knowledge about their own preferences.In addition to pushing the scientific boundaries of market design research, this ERC project will also have an immediate impact on practical market design. We will apply our techniques in two different settings: (1) for the design of combinatorial spectrum auctions, a multi-billion dollar domain; and (2) for the design of school choice matching markets, which are used to match millions of students to high school every year." Champ scientifique natural sciencescomputer and information sciencesartificial intelligencemachine learning Mots‑clés market design economics and computation mechanism design auctions machine learning Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Thème(s) ERC-2018-STG - ERC Starting Grant Appel à propositions ERC-2018-STG Voir d’autres projets de cet appel Régime de financement ERC-STG - Starting Grant Institution d’accueil UNIVERSITAT ZURICH Contribution nette de l'UE € 1 375 000,00 Adresse RAMISTRASSE 71 8006 Zurich Suisse Voir sur la carte Région Schweiz/Suisse/Svizzera Zürich Zürich Type d’activité Higher or Secondary Education Establishments Liens Contacter l’organisation Opens in new window Site web Opens in new window Participation aux programmes de R&I de l'UE Opens in new window Réseau de collaboration HORIZON Opens in new window Coût total € 1 375 000,00 Bénéficiaires (1) Trier par ordre alphabétique Trier par contribution nette de l'UE Tout développer Tout réduire UNIVERSITAT ZURICH Suisse Contribution nette de l'UE € 1 375 000,00 Adresse RAMISTRASSE 71 8006 Zurich Voir sur la carte Région Schweiz/Suisse/Svizzera Zürich Zürich Type d’activité Higher or Secondary Education Establishments Liens Contacter l’organisation Opens in new window Site web Opens in new window Participation aux programmes de R&I de l'UE Opens in new window Réseau de collaboration HORIZON Opens in new window Coût total € 1 375 000,00
