Bicriteria Multidimensional Mechanism Design with Side Information
For mechanism designers, this provides a unified framework to incorporate various forms of side information to improve both welfare and revenue guarantees.
This paper develops a tunable mechanism for multidimensional mechanism design that leverages side information (e.g., expert advice, ML predictions) to achieve high welfare and revenue simultaneously. The mechanism achieves welfare and revenue competitive with the prior-free total social surplus when side information is accurate, with graceful degradation as quality decreases.
We develop a versatile methodology for multidimensional mechanism design that incorporates side information about agents to generate high welfare and high revenue simultaneously. Side information sources include advice from domain experts, predictions from machine learning models, and even the mechanism designer's gut instinct. We design a tunable mechanism that integrates side information with an improved VCG-like mechanism based on weakest types, which are agent types that generate the least welfare. We show that our mechanism, when its side information is of high quality, generates welfare and revenue competitive with the prior-free total social surplus, and its performance decays gracefully as the side information quality decreases. We consider a number of side information formats including distribution-free predictions, predictions that express uncertainty, agent types constrained to low-dimensional subspaces of the ambient type space, and the traditional setting with known priors over agent types. In each setting we design mechanisms based on weakest types and prove performance guarantees.