Our work revisits the design of mechanisms via the learning-augmented framework. In this model, the algorithm is enhanced with imperfect (machine-learned) information concerning the input, usually referred to as prediction. The goal is to design algorithms whose performance degrades gently as a function of the prediction error and, in particular, perform well if the prediction is accurate, but also provide a worst-case guarantee under any possible error. This framework has been successfully applied recently to various mechanism design settings, where in most cases the mechanism is provided with a prediction about the types of the players. We adopt a perspective in which the mechanism is provided with an output recommendation. We make no assumptions about the quality of the suggested outcome, and the goal is to use the recommendation to design mechanisms with low approximation guarantees whenever the recommended outcome is reasonable, but at the same time to provide worst-case guarantees whenever the recommendation significantly deviates from the optimal one. We propose a generic, universal measure, which we call quality of recommendation, to evaluate mechanisms across various information settings. We demonstrate how this new metric can provide refined analysis in existing results. This model introduces new challenges, as the mechanism receives limited information comparing to settings that use predictions about the types of the agents. We study, through this lens, several well-studied mechanism design paradigms, devising new mechanisms, but also providing refined analysis for existing ones, using as a metric the quality of recommendation. We complement our positive results, by exploring the limitations of known classes of strategyproof mechanisms that can be devised using output recommendation.

翻译：本研究通过学习增强框架重新审视机制设计问题。在此模型中，算法通过关于输入的不完美（机器学习）信息得到增强，这类信息通常被称为预测。目标是设计性能随预测误差平缓下降的算法，特别是在预测准确时表现良好，同时在任何可能的误差下提供最坏情况保证。该框架最近已成功应用于多种机制设计场景，其中大多数情况下机制会获得关于参与者类型的预测。我们采用一种新视角：为机制提供输出建议。我们对建议结果的质量不作任何假设，目标是利用建议设计在推荐结果合理时具有低近似保证的机制，同时在推荐结果显著偏离最优解时提供最坏情况保证。我们提出了一种通用的度量标准——建议质量，用于评估不同信息场景下的机制性能。我们展示了这一新度量标准如何为现有结果提供精细化分析。与使用智能体类型预测的设置相比，此模型因机制获得的信息有限而带来新的挑战。通过这一视角，我们研究了多个经典机制设计范式，不仅设计了新机制，还利用建议质量这一度量标准为现有机制提供了精细化分析。我们通过探索已知类别的策略证明机制在使用输出建议时的局限性，对正面结果进行了补充分析。