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].
Iterative Delegations in Liquid Democracy with Restricted Preferences
Authors: Bruno Escoffier, Hugo Gilbert, Adèle Pass-Lanneau1926-1933
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we investigate the stability of the delegation process in liquid democracy when voters have restricted types of preference on the agent representing them (e.g., single-peaked preferences). We show that various natural structures of preference guarantee the existence of an equilibrium and we obtain both tractability and hardness results for the problem of computing several equilibria with some desirable properties. |
| Researcher Affiliation | Collaboration | Bruno Escoffier Sorbonne Universit e, CNRS, LIP6 75005 Paris, France and Institut Universitaire de France bruno.escoffier@lip6.fr Hugo Gilbert Gran Sasso Science Institute L Aquila, 67100, Italy EMAIL Ad ele Pass-Lanneau Sorbonne Universit e, CNRS, LIP6 75005 Paris, France and EDF R&D 91120 Palaiseau, France EMAIL |
| Pseudocode | No | The paper describes algorithmic procedures (e.g., 'We give an O(n2) procedure that builds an equilibrium for any DB profile'), but these descriptions are embedded in the text and not presented as clearly labeled, structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and uses abstract examples for illustrative purposes (e.g., 'Example 1', 'Example 2') rather than empirical evaluation on publicly available datasets. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not involve empirical experiments with datasets; therefore, no information on training, validation, or test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments; thus, no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical and algorithmic properties; it does not describe implementation details or require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include empirical experiments, thus no details regarding experimental setup, hyperparameters, or training configurations are provided. |