Automated Dynamic Mechanism Design
Authors: Hanrui Zhang, Vincent Conitzer
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we present experimental results where our algorithms are applied to synthetic dynamic environments with different characteristics, which not only serve as a proof of concept for our algorithms, but also exhibit intriguing phenomena in dynamic mechanism design. and Besides using our algorithms directly for appropriate applications, the experimental results that they enable (including those that we presented in Appendix F) can guide new theory. |
| Researcher Affiliation | Academia | Hanrui Zhang Duke University hrzhang@cs.duke.edu Vincent Conitzer Duke University conitzer@cs.duke.edu |
| Pseudocode | No | The paper describes algorithms and an LP formulation, but does not include any clearly labeled 'Pseudocode' or 'Algorithm X' block. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | Moreover, we present experimental results where our algorithms are applied to synthetic dynamic environments with different characteristics - This indicates synthetic data, and no access information for a public dataset is provided. |
| Dataset Splits | No | The paper mentions experimental results but does not provide specific information about training, validation, or test dataset splits. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for its implementation or experiments. |
| Experiment Setup | No | The paper mentions experimental results but does not provide specific experimental setup details such as hyperparameters or training configurations. |