Proportional Justified Representation
Authors: Luis Snchez-Fernndez, Edith Elkind, Martin Lackner, Norberto Fernndez, Jess Fisteus, Pablo Basanta Val, Piotr Skowron
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 luiss@it.uc3m.es Edith Elkind University of Oxford, United Kingdom elkind@cs.ox.ac.uk Martin Lackner University of Oxford, United Kingdom martin.lackner@cs.ox.ac.uk Norberto Fern andez Escuela Naval Militar (CUD), Spain norberto@cud.uvigo.es Jes us A. Fisteus Universidad Carlos III de Madrid, Spain jaf@it.uc3m.es Pablo Basanta Val Universidad Carlos III de Madrid, Spain pbasanta@it.uc3m.es Piotr Skowron University of Oxford, United Kingdom p.k.skowron@gmail.com |
| 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. |