Mechanism Design with Predictions
Talk Abstract
This talk presents recent advancements in the design of strategyproof mechanisms in the learning-augmented framework. To exhibit the potential benefits of this approach, I will mostly focus on the problem of facility location with strategic agents. In this problem, a set of agents report their locations in a metric space and the goal is to use these reports to open a new facility, minimizing an aggregate distance measure from the agents to the facility. However, agents are strategic and may misreport their locations to influence the facility’s placement in their favor. The aim is to design truthful mechanisms, ensuring agents cannot gain by misreporting. We study both the egalitarian and utilitarian social cost functions, and propose new strategyproof mechanisms that leverage predictions to guarantee an optimal trade-off between consistency and robustness guarantees.