Incentive Networks
Authors: Yuezhou Lv, Thomas Moscibroda
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study an alternative approach Incentive Networks in which a participant s reward depends not only on his own contribution; but also in part on the contributions made by his social contacts or friends. We show that the key parameter effecting the efficiency of such an Incentive Network-based economic system depends on the participant s degree of directed altruism. Specifically, we characterize the condition under which an Incentive Network-based economy is more efficient than the basic pay-for-your-contribution economy. We quantify by how much incentive networks can reduce the total reward that needs to be paid to the participants in order to achieve a certain overall contribution. Finally, we study the impact of the network topology and various exogenous parameters on the efficiency of incentive networks. |
| Researcher Affiliation | Collaboration | Yuezhou Lv Tsinghua University Beijing, China lvyz11@tsinghua.edu.cn Thomas Moscibroda Microsoft Research & Tsinghua University Beijing, China moscitho@microsoft.com |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets, thus no dataset availability information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with datasets, thus no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training settings. |