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.

翻译：我们提出一种通用的多维机制设计方法论，该方法通过整合关于智能体的辅助信息，同时实现高社会福利与高收益。辅助信息来源包括领域专家建议、机器学习模型预测，甚至机制设计者的直觉判断。我们设计了一种可调谐机制，该机制将辅助信息与基于最弱类型（生成最低社会福利的智能体类型）的改进版VCG类机制相结合。研究表明，当辅助信息质量较高时，该机制产生的社会福利与收益可与无先验假设的总社会剩余相媲美，且其性能随辅助信息质量下降而优雅退化。我们考察了多种辅助信息格式，包括无分布假设的预测、表达不确定性的预测、受限于环境类型空间低维子空间的智能体类型，以及传统基于智能体类型先验知识的场景。针对每种场景，我们设计了基于最弱类型的机制，并证明了其性能保障。