Incomplete Preferences in Single-Peaked Electorates
Authors: Martin Lackner
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that for incomplete profiles the problem of determining single-peakedness is NP-complete. Despite this computational hardness result, we find four polynomial-time algorithms for reasonably restricted settings. |
| Researcher Affiliation | Academia | Martin Lackner Vienna University of Technology, Austria lackner@dbai.tuwien.ac.at |
| Pseudocode | Yes | Algorithm 1: The Guided Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets; thus, no information regarding dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets; thus, no information regarding dataset splits for validation is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on proofs and algorithms; it does not mention any specific hardware used for computations. |
| Software Dependencies | No | The paper describes theoretical algorithms and complexity results but does not specify any software dependencies with version numbers required for implementation or reproduction. |
| Experiment Setup | No | The paper presents theoretical results and algorithms, and therefore does not include details on experimental setup or hyperparameters. |