Altruism Design in Networked Public Goods Games
Authors: Sixie Yu, David Kempe, Yevgeniy Vorobeychik
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our central algorithmic question then revolves around the computational complexity of modifying the altruism network to achieve desired public goods game investment profiles. We first show that the problem can be solved using linear programming when a principal can fractionally modify the altruism network. While the problem becomes in general intractable if the principal s actions are all-or-nothing, we exhibit several tractable special cases. |
| Researcher Affiliation | Academia | 1Washington University in St. Louis 2University of Southern California {sixie.yu,yvorobeychik}@wustl.edu, david.m.kempe@gmail.com |
| Pseudocode | No | The paper describes algorithmic approaches but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions "An extended version of the paper with complete proofs is available at: https://arxiv.org/abs/2105.00505.", which links to the paper itself, not source code. There is no other mention of source code being released or made available. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |