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..
Parliamentary Voting Procedures: Agenda Control, Manipulation, and Uncertainty
Authors: Robert Bredereck, Jiehua Chen, Rolf Niedermeier, Toby Walsh
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our polynomial-time algorithms leave open how many alternatives can win through control (or manipulation). We therefore use the data from Preflib due to Mattei and Walsh [2013] to investigate empirically the likelihood of successful manipulation or agenda control. Since only one case of the possible and the necessary winner problems is polynomial-time solvable and since Preflib offers only a very restricted variant of incomplete preferences, we do not run experiments for these two problems. Our results are shown in Table 2. |
| Researcher Affiliation | Academia | 1Institut f ur Softwaretechnik und Theoretische Informatik, TU Berlin, Germany EMAIL 2NICTA and the University of New South Wales, Australia EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We therefore use the data from Preflib due to Mattei and Walsh [2013] to investigate empirically the likelihood of successful manipulation or agenda control. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |