Liquid Democracy: An Algorithmic Perspective
Authors: Anson Kahng, Simon Mackenzie, Ariel D. Procaccia
JAIR 2021 | Venue 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... Even though we assume that voters can only delegate their votes to better-informed voters, we show that local delegation mechanisms, which only take the local neighborhood of each voter as input (and, arguably, capture the spirit of liquid democracy), cannot provide the foregoing guarantee. By contrast, we design a non-local delegation mechanism that does provably outperform direct voting under mild assumptions about voters. |
| Researcher Affiliation | Academia | Anson Kahng EMAIL Computer Science Department Carnegie Mellon University Simon Mackenzie EMAIL University of New South Wales Ariel D. Procaccia EMAIL School of Engineering and Applied Sciences Harvard University |
| Pseudocode | Yes | Algorithm 1: Greedy Cap input: labeled graph G with n vertices, cap C : N N 2: while V = do 3: let i argmaxj V |A 1 G (j) V | 4: J A 1 G (i) V 5: if |J| C(n) 1 then 8: let J J such that |J | = C(n) 1 10: vertices in J delegate to i 11: V V \ ({i} {J }) 12: end while |
| Open Source Code | No | No explicit statement about code release or repository links found in the paper. The paper focuses on theoretical analysis and algorithm design. |
| Open Datasets | No | The paper defines a theoretical model of voters, competence levels, and social networks for algorithmic analysis, rather than using external datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits. |
| Hardware Specification | No | The paper presents theoretical analysis and algorithms, and does not describe any experimental evaluations on specific hardware. |
| Software Dependencies | No | The paper focuses on theoretical analysis and algorithm design, and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include experimental details such as hyperparameters or specific training configurations. |