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].
Proportional Justified Representation
Authors: Luis SĀnchez-FernĀndez, Edith Elkind, Martin Lackner, Norberto FernĀndez, JesĀs Fisteus, Pablo Basanta Val, Piotr Skowron
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our proof proceeds by constructing and solving a linear program that establishes bounds on RAV scores. We conclude the paper by discussing our results and indicating directions for future work. |
| Researcher Affiliation | Academia | Luis S anchez-Fern andez Universidad Carlos III de Madrid, Spain EMAIL Edith Elkind University of Oxford, United Kingdom EMAIL Martin Lackner University of Oxford, United Kingdom EMAIL Norberto Fern andez Escuela Naval Militar (CUD), Spain EMAIL Jes us A. Fisteus Universidad Carlos III de Madrid, Spain EMAIL Pablo Basanta Val Universidad Carlos III de Madrid, Spain EMAIL Piotr Skowron University of Oxford, United Kingdom EMAIL |
| Pseudocode | No | The paper describes voting rules in prose and formal definitions but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper constructs specific ballot profiles for theoretical proofs and counterexamples, but it does not use or provide access information for a publicly available or open dataset in the traditional sense of empirical research. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) needed to reproduce data partitioning for training, validation, or testing, as it focuses on theoretical proofs rather than empirical data analysis. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to conduct any computational analysis (e.g., solving the linear program). |
| Software Dependencies | No | The paper mentions solving a linear program (LPk) but does not specify the software or its version (e.g., a specific LP solver) used for this purpose. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values or training configurations, as it primarily focuses on theoretical analysis and proofs rather than empirical experimentation. |