# Mechanism design augmented with output advice Source: [Mechanism design augmented with output advice]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:53:47.810Z Authors: George Christodoulou, Alkmini Sgouritsa, Ioannis Vlachos Published: 2024-06-20T10:10:21.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Auctions](): Auctions; Auctions; Consistency: 100. - [House Allocation on House Allocation unit-range valuations](): House Allocation; House Allocation unit-range valuations; Consistency: 1. - [House Allocation on House Allocation unit-sum valuations](): House Allocation; House Allocation unit-sum valuations; Consistency: 1. - [Facility Location on Facility Location egalitarian](): Facility Location; Facility Location egalitarian; Consistency: not recorded. ## Original sources and implementation [Original paper]() [PDF]()