Preserving Consistency in Multi-Issue Liquid Democracy
Authors: Rachael Colley, Umberto Grandi
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose instead to elicit and apply the agents priorities over the delegated issues, designing and analysing two algorithms that find consistent votes from the agents delegations in polynomial time. We first consider two rules that minimise either the number of ignored delegations in the voters ballots, as proposed in previous work by Brill and Talmon [2018] and Jain et al. [2021], or minimise the number of changes to the agents final votes once all delegations have been resolved. After showing that both rules are NP-complete, we propose two polynomial algorithms inspired by the two minimisation rules, that apply the agents priorities to output consistent final votes. Our results show that although these principled algorithms cannot be considered as approximations of the minimisation rules, one of our algorithms respects the agents priorities over their delegations more than its minimisation counterpart. |
| Researcher Affiliation | Academia | Rachael Colley , Umberto Grandi IRIT, University of Toulouse, France {rachael.colley, umberto.grandi}@irit.fr |
| Pseudocode | Yes | Algorithm 1 Priority delegation changing (PDC) and Algorithm 2 Priority vote changing (PVC) |
| Open Source Code | No | The paper does not provide any specific repository link, explicit code release statement, or mention of code in supplementary materials for the methodology described. |
| Open Datasets | No | The paper describes theoretical algorithms and their properties, illustrating them with small examples. It does not use or provide concrete access information (specific link, DOI, repository name, formal citation with authors/year) for any publicly available or open dataset for empirical evaluation. |
| Dataset Splits | No | The paper focuses on theoretical analysis and algorithm design rather than empirical evaluation. Therefore, it does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical algorithms and their complexity. It does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) as no empirical experiments requiring such hardware are detailed. |
| Software Dependencies | No | The paper focuses on theoretical algorithm design and analysis. It does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate an experimental setup. |
| Experiment Setup | No | The paper is theoretical in nature, focusing on algorithm design and analysis. It does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings, as it does not describe empirical experiments. |