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].
Aggregation over Metric Spaces: Proposing and Voting in Elections, Budgeting, and Legislation
Authors: Laurent Bulteau, Gal Shahaf, Ehud Shapiro, Nimrod Talmon
JAIR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | While here we concentrate on the theoretical foundations of our framework, coping with the NP-hardness of the remaining scenarios is left for future study. Throughout, we studied the worst-case complexity of our aggregation methods for several applications with their corresponding metric spaces, and, indeed, showed that for some applications, the combinatorial problem corresponding to identifying the winners is computationally intractable in the sense that it is an NP-hard problem. |
| Researcher Affiliation | Academia | Laurent Bulteau EMAIL LIGM, CNRS, Univ Gustave Ei๏ฌel 5 Bd Descartes, 77454 Marne la Vall ee, France Gal Shahaf EMAIL Ehud Shapiro EMAIL Weizmann Institute of Science Herzl St 234, Rehovot, Israel Nimrod Talmon EMAIL Ben-Gurion University Ben-Gurion Boulevard, Be er Sheva, Israel |
| Pseudocode | No | The paper describes algorithms and methods in prose and mathematical notation (e.g., "Condorcet aggregation and the L1 aggregation are the median of V", "L2(V) = 1/n P i vi (the average of V)", "efficiently computed via gradient descent methods") but does not present any dedicated pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide links to any code repositories in the main text or supplementary sections. |
| Open Datasets | No | The paper is theoretical and uses illustrative examples rather than empirical evaluation on datasets. It does not mention specific datasets, nor does it provide access information for any. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper presents theoretical foundations and complexity analysis, and does not report on experimental results that would require specific hardware. |
| Software Dependencies | No | The paper focuses on theoretical foundations and does not include an experimental section that would list specific software dependencies with version numbers. |
| Experiment Setup | No | As the paper focuses on theoretical concepts and complexity analysis rather than empirical experiments, it does not provide details on experimental setup, hyperparameters, or training configurations. |