The family of Groves mechanisms, which includes the well-known VCG mechanism (also known as the Clarke mechanism), is a family of efficient and strategy-proof mechanisms. Unfortunately, the Groves mechanisms are generally not budget balanced. That is, under such mechanisms, payments may flow into or out of the system of the agents, resulting in deficits or reduced utilities for the agents. We consider the following problem: within the family of Groves mechanisms, we want to identify mechanisms that give the agents the highest utilities, under the constraint that these mechanisms must never incur deficits.
We adopt a prior-free approach. We introduce two general measures for comparing mechanisms in prior-free settings. We say that a non-deficit Groves mechanism $M$ {\em individually dominates} another non-deficit Groves mechanism $M'$ if for every type profile, every agent's utility under $M$ is no less than that under $M'$, and this holds with strict inequality for at least one type profile and one agent. We say that a non-deficit Groves mechanism $M$ {\em collectively dominates} another non-deficit Groves mechanism $M'$ if for every type profile, the agents' total utility under $M$ is no less than that under $M'$, and this holds with strict inequality for at least one type profile. The above definitions induce two partial orders on non-deficit Groves mechanisms. We study the maximal elements corresponding to these two partial orders, which we call the {\em individually undominated} mechanisms and the {\em collectively undominated} mechanisms, respectively.
Online Learning for Dynamic Vickrey-Clarke-Groves Mechanism in Unknown Environments
Department of Industrial \& Enterprise Systems Engineering；Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA；Department of Industrial \& Enterprise Systems Engineering, Department of Electrical \& Computer Engineering
Truthful and Trustworthy IoT AI Agents via Immediate-Penalty Enforcement under Approximate VCG Mechanisms
Toyohashi University of Technology（丰桥技术科学大学）；University of Electro-Communications（电波大学）；Tohoku University（东北大学）；Muroran Institute of Technology（室兰工业大学）
Operations Research Center, MIT（麻省理工学院运筹学中心）；Operations Research and Information Engineering, Cornell University（康奈尔大学运筹信息与工程）；Civil and Environmental Engineering, UC Berkeley（加州大学伯克利分校土木与环境工程）
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach
Yale University（耶鲁大学）；The Hong Kong University of Science and Technology（香港科技大学）；University of Chicago（芝加哥大学）；Purdue University（普渡大学）；Northwestern University（西北大学）；University of California, Berkeley（加州大学伯克利分校）
Faithful Edge Federated Learning: Scalability and Privacy
Zhejiang University/University of Illinois at Urbana–Champaign Institute（浙江大学伊利诺伊大学厄巴纳-香槟校区联合学院）；Zhejiang University（浙江大学）；Northwestern University（西北大学）
