Bicriteria Multidimensional Mechanism Design with Side Information

• 成果类型: Article; Early Access
• 署名作者: Balcan, Maria-Florina; Prasad, Siddharth; Sandholm, Tuomas
• 署名单位: Carnegie Mellon University; Toyota Technological Institute; Toyota Technological Institute - Chicago
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X; 1526-5471
• DOI: 10.1287/moor.2024.0729
• 发表日期: 2026-05-12
• 关键词: Mechanism design; revenue maximization; Welfare maximization; weakest types; algorithms with predictions; Optimal auction; Combinatorial; revenue

摘要

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 Vickrey-Clarke-- Groves-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.