The authors aim to design strategyproof mechanisms leveraging predictions for improved approximation guarantees in the Generalized Assignment Problem (GAP) within the private graph model.
Optimale Approximationsgarantien in Mechanismen mit Vorhersagen sind herausfordernd.
Side information, such as machine learning predictions, can be effectively integrated into multidimensional mechanism design to simultaneously achieve high welfare and revenue, mitigating the traditional trade-off between these objectives.
This research paper introduces the Harmonic mechanism, a novel approach for strategic facility location in general metric spaces that leverages predictions to improve upon traditional mechanisms, particularly when dealing with a large number of agents.