Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Truthfulness of a Proportional Sharing Mechanism in Resource Exchange
Authors: Yukun Cheng, Xiaotie Deng, Qi Qi, Xiang Yan
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main result is a proof that an agent manipulating the proportional sharing mechanism by misreporting its resource amount will not benefit its own utility eventually. This result establishes a strategic stability property of the resource exchange protocol. We further illustrate and confirm the result via network examples. In this paper, we solve this open problem and prove the truthfulness of the proportional sharing mechanism. |
| Researcher Affiliation | Academia | 1Zhejiang University of Finance and Economics, Hangzhou China EMAIL 2Shanghai Jiao Tong University, Shanghai China EMAIL, EMAIL 3The Hongkong University of Science and Technology, Hong Kong EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not use datasets for empirical training. The numerical example is illustrative, not based on a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers needed to replicate an experiment. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameters or training configurations. |