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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Better Collective Decisions via Uncertainty Reduction
Authors: Shiri Alouf-Heffetz, Laurent Bulteau, Edith Elkind, Nimrod Talmon, Nicholas Teh
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish that these problems are NP-hard even in the onedimensional setting, but show that they are fixed-parameter tractable even in the general setting both with respect to the number of voters and the number of issues. Moreover, we consider a natural special case of the one-dimensional setting in which all three problems are polynomial-time solvable. We omit some proofs due to space constraints. |
| Researcher Affiliation | Collaboration | 1Ben-Gurion University, Israel 2LIGM, CNRS, Universit e Gustave Eiffel, France 3Department of Computer Science, University of Oxford, UK 4Two Five One Research |
| Pseudocode | No | The paper describes problem formulations and mathematical proofs, but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments with datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with data splitting for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments or specify hardware used. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the theoretical analysis or any computational work. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments or their setup, thus no specific hyperparameter values or training configurations are provided. |