Liquid Democracy: An Algorithmic Perspective
Authors: Anson Kahng, Simon Mackenzie, Ariel Procaccia
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study liquid democracy, a collective decision making paradigm that allows voters to transitively delegate their votes, through an algorithmic lens. In our model, there are two alternatives, one correct and one incorrect, and we are interested in the probability that the majority opinion is correct. Our main question is whether there exist delegation mechanisms that are guaranteed to outperform direct voting, in the sense of being always at least as likely, and sometimes more likely, to make a correct decision. |
| Researcher Affiliation | Academia | Anson Kahng Computer Science Department Carnegie Mellon University akahng@cs.cmu.edu Simon Mackenzie Computer Science Department Carnegie Mellon University simonm@andrew.cmu.edu Ariel D. Procaccia Computer Science Department Carnegie Mellon University arielpro@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1: GREEDYCAP |
| Open Source Code | No | The paper does not provide any links to its own source code or explicitly state that the code for the described methodology is being released. |
| Open Datasets | No | This paper is theoretical and does not use or refer to any publicly available datasets for training, as it does not conduct empirical experiments. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical data splits (training, validation, test) for reproduction. |
| Hardware Specification | No | This paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | This paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe an empirical experimental setup with hyperparameters or training settings. |