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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Smart Voting
Authors: Rachael Colley, Umberto Grandi, Arianna Novaro
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a generalisation of liquid democracy in which a voter can either vote directly on the issues at stake, delegate her vote to another voter, or express complex delegations to a set of trusted voters. By requiring a ranking of desirable delegations and a backup vote from each voter, we are able to put forward and compare four algorithms to solve delegation cycles and obtain a ο¬nal collective decision. ... We investigate further algorithmic properties of our setting in Section 3, and we conclude with a study of ranked delegations to single voters and participation axioms (Section 4). |
| Researcher Affiliation | Academia | Rachael Colley , Umberto Grandi and Arianna Novaro IRIT, University of Toulouse EMAIL |
| Pseudocode | Yes | Algorithm 1 General unravelling procedure UNRAVEL; Algorithm 2 UPDATE(U); Algorithm 3 UPDATE(DU); Algorithm 4 UPDATE(RU); Algorithm 5 UPDATE(DRU) |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper uses an illustrative example (Example 2) with hypothetical data to explain the unravelling procedures but does not use or provide access to any public or real-world dataset for training, testing, or validation. |
| Dataset Splits | No | The paper does not describe specific training, validation, or test splits for any dataset, as it primarily focuses on theoretical and algorithmic analysis. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications (e.g., GPU/CPU models, memory details) used for running its algorithms or any theoretical experiments. The work focuses on algorithmic analysis. |
| Software Dependencies | No | The paper describes algorithms and their theoretical properties. It does not mention any specific software dependencies or version numbers required to implement or replicate the work. |
| Experiment Setup | No | The paper describes algorithms and their theoretical properties. It does not detail an experimental setup with hyperparameters, training configurations, or system-level settings, as it is not an empirical study. |