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..
Incomplete Preferences in Single-Peaked Electorates
Authors: Martin Lackner
AAAI 2014 | Venue PDF | 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 EMAIL |
| 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. |