Mechanism Design with Predictions
Authors: Chenyang Xu, Pinyan Lu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |