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].
Negotiable Votes
Authors: Umberto Grandi, Davide Grossi, Paolo Turrini
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study voting games on binary issues, where voters hold an objective over the outcome of the collective decision and are allowed, before the vote takes place, to negotiate their ballots with the other participants. We analyse the voters rational behaviour in the resulting two-phase game when ballots are aggregated via non-manipulable rules and, more specifically, quota rules. We show under what conditions undesirable equilibria can be removed and desirable ones sustained as a consequence of the pre-vote phase. |
| Researcher Affiliation | Academia | Umberto Grandi EMAIL Institut de Recherche en Informatique de Toulouse (IRIT) University of Toulouse...Davide Grossi EMAIL Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence University of Groningen...Paolo Turrini EMAIL Department of Computer Science University of Warwick |
| Pseudocode | No | The paper describes theoretical models and provides mathematical proofs and definitions without presenting any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that the authors are releasing source code for the methodology described, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper presents a theoretical framework and does not involve empirical evaluation on datasets. Therefore, no information about open datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, so there is no mention of dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not describe any computational experiments or hardware specifications. |
| Software Dependencies | No | The paper describes a theoretical framework and does not mention any specific software or library dependencies with version numbers for implementation. |
| Experiment Setup | No | The paper outlines a theoretical model and mathematical proofs, not an empirical study, so there are no experimental setup details or hyperparameter values provided. |