We develop a versatile new methodology for multidimensional mechanism design that incorporates side information about agent types with the bicriteria goal of generating high social welfare and high revenue simultaneously. Side information can come from a variety of sources -- examples include advice from a domain expert, predictions from a machine-learning model trained on historical agent data, or even the mechanism designer's own gut instinct -- and in practice such sources are abundant. In this paper we adopt a prior-free perspective that makes no assumptions on the correctness, accuracy, or source of the side information. First, we design a meta-mechanism that integrates input side information with an improvement of the classical VCG mechanism. The welfare, revenue, and incentive properties of our meta-mechanism are characterized by a number of novel constructions we introduce based on the notion of a weakest competitor, which is an agent that has the smallest impact on welfare. We then show that our meta-mechanism -- when carefully instantiated -- simultaneously achieves strong welfare and revenue guarantees that are parameterized by errors in the side information. When the side information is highly informative and accurate, our mechanism achieves welfare and revenue competitive with the total social surplus, and its performance decays continuously and gradually as the quality of the side information decreases. Finally, we apply our meta-mechanism to a setting where each agent's type is determined by a constant number of parameters. Specifically, agent types lie on constant-dimensional subspaces (of the potentially high-dimensional ambient type space) that are known to the mechanism designer. We use our meta-mechanism to obtain the first known welfare and revenue guarantees in this setting.

翻译：我们开发了一种适用于多维机制设计的通用新方法，该方法融合了关于智能体类型的侧信息，同时追求产生高社会福利和高收入的双准则目标。侧信息可源自多种渠道——例如领域专家的建议、基于历史智能体数据训练的机器学习模型的预测，甚至机制设计者自身的直觉判断——而在实践中此类来源十分丰富。本文采用无先验视角，不对侧信息的正确性、准确性或来源作任何假设。首先，我们设计了一个元机制，该机制将输入侧信息与经典VCG机制的改进相结合。我们基于“最弱竞争对手”（即对福利影响最小的智能体）概念引入了一系列新颖构造，这些构造刻画了元机制的福利、收入及激励性质。随后我们证明，当元机制经过精心实例化时，能同时实现由侧信息误差参数化的强福利保障和收入保障。当侧信息信息量大且准确时，其福利与收入可媲美社会总剩余水平；随着侧信息质量下降，其性能呈连续渐次衰减。最后，我们将元机制应用于每个智能体类型由常数个参数决定的情景——具体而言，智能体类型位于机制设计者已知的常数维子空间（存在于潜在高维环境类型空间中）。借助该元机制，我们首次在此情景中获得了已知的福利与收入保障界限。