Incentive-Compatible Diffusion Auctions
Authors: Bin Li, Dong Hao, Dengji Zhao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we identify a sufficient and necessary condition for all dominant-strategy incentive-compatible (DSIC) diffusion auctions. We formulate the monotonic allocation policies in such multidimensional problems and show that any monotonic allocation policy can be implemented in a DSIC diffusion auction mechanism. Moreover, given any monotonic allocation policy, we obtain the optimal payment policy to maximize the seller s revenue. |
| Researcher Affiliation | Academia | Bin Li1 , Dong Hao1 , Dengji Zhao2 1School of Computer Science & Engineering, University of Electronic Science and Technology of China 2Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai Tech University {libin@std.uestc, haodong@uestc, zhaodj@shanghaitech}.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not involve experiments on datasets, thus no information about public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments or dataset splitting. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |