Multi-Unit Auction in Social Networks with Budgets
Authors: Mingyu Xiao, Yuchao Song, Bakh Khoussainov5228-5235
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design a mechanism encouraging buyers to report their valuations truthfully and spread the sale information. Our design uses the idea of the clinching mechanism to decide the transaction price and can be viewed as a network version of the mechanism. ... We prove key properties of our mechanism, such as no-positive-transfers, individual rationality, incentive compatibility, non-wastefulness and social welfare preservation. |
| Researcher Affiliation | Academia | University of Electronic Science and Technology of China myxiao@uestc.edu.cn, ycsongcs@gmail.com, bmk@uestc.edu.cn |
| Pseudocode | Yes | Algorithm 1: The Social Network Clinching Auction Mechanism (SNCA) |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper is theoretical and does not describe or use a publicly available dataset for training any model or conducting empirical studies. |
| Dataset Splits | No | The paper does not specify any training/test/validation dataset splits, as it does not report empirical experiments with data. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, as it presents theoretical work without empirical evaluations. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, as it describes a theoretical mechanism and its properties rather than an implemented system. |
| Experiment Setup | No | The paper does not provide details about an experimental setup, such as hyperparameters or system-level training settings, because it is a theoretical paper focusing on mechanism design and proofs rather than empirical evaluation. |