Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Mechanism Design with Predictions
Authors: Chenyang Xu, Pinyan Lu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper initiates the systematic study of mechanism design in this model. In a number of well-studied mechanism design settings, we make use of imperfect predictions to design mechanisms that perform much better than traditional mechanisms if the predictions are accurate (consistency), while always retaining worst-case guarantees even with very imprecise predictions (robustness)." (Abstract) and "We study four very different and well-studied mechanism design problems and observe that predictions are indeed helpful." (Section 1.2). The paper proceeds with theorems, lemmas, and pseudocode, but no experimental results. |
| Researcher Affiliation | Collaboration | Chenyang Xu1 , Pinyan Lu2,3, 1College of Computer Science, Zhejiang University 2ITCS, Shanghai University of Finance and Economics 3Huawei TCS Lab |
| Pseudocode | Yes | Algorithm 1 Single-Item Auction with Predictions", "Algorithm 2 Frugal Path Auction with Predictions", "Algorithm 3 Truthful Job Scheduling with Predictions", "Algorithm 4 Two-Facility Game with Predictions |
| Open Source Code | No | The paper does not contain any statements about making source code publicly available, nor does it provide any links to code repositories. |
| Open Datasets | No | The paper presents theoretical work on mechanism design and does not describe experiments that would involve training on a dataset. No dataset information or access details are provided. |
| Dataset Splits | No | The paper is theoretical and focuses on algorithm design and proofs, not empirical evaluation. Therefore, it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | This is a theoretical paper focusing on mechanism design and proofs. It does not describe any computational experiments or hardware specifications. |
| Software Dependencies | No | The paper describes theoretical algorithms and their analysis. It does not mention any software dependencies or specific version numbers for implementation or experimentation. |
| Experiment Setup | No | The paper focuses on theoretical contributions in mechanism design and does not report on empirical experiments or their setup, including hyperparameters or system-level training settings. |