Mechanism Design in Social Networks
Authors: Bin Li, Dong Hao, Dengji Zhao, Tao Zhou
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies an auction design problem for a seller to sell a commodity in a social network, where each individual (the seller or a buyer) can only communicate with her neighbors. The challenge to the seller is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors so that more buyers will participate in the auction and hence, the seller will be able to make a higher revenue. We propose a novel auction mechanism, called information diffusion mechanism (IDM), which incentivizes the buyers to not only truthfully report their valuations on the commodity to the seller, but also further propagate the auction information to all their neighbors. In comparison, the direct extension of the well-known Vickrey-Clarke-Groves (VCG) mechanism in social networks can also incentivize the information diffusion, but it will decrease the seller s revenue or even lead to a deficit sometimes. The formalization of the problem has not yet been addressed in the literature of mechanism design and our solution is very significant in the presence of large-scale online social networks. |
| Researcher Affiliation | Academia | Bin Li,a Dong Hao,a Dengji Zhao,b Tao Zhoua a Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China {libin@std.uestc., haodong@uestc., zhutou@ustc.}edu.cn b School of Information Science and Technology, Shanghai Tech University, Shanghai, China zhaodj@shanghaitech.edu.cn |
| Pseudocode | No | The paper defines mechanisms and functions mathematically, but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps formatted like code. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links, explicit statements of code release) for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments using datasets, thus there is no mention of publicly available or open datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments, so there is no mention of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments, therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not report on empirical experiments, therefore no specific experimental setup details such as hyperparameters or system-level training settings are provided. |