Better Collective Decisions via Uncertainty Reduction
Authors: Shiri Alouf-Heffetz, Laurent Bulteau, Edith Elkind, Nimrod Talmon, Nicholas Teh
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |