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..
Fine-Grained View on Bribery for Group Identification
Authors: Niclas Boehmer, Robert Bredereck, Dušan Knop, Junjie Luo
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Complementing previous results showing polynomial-time solvability or NP-hardness of bribery for various social rules in the constructive (aiming at making specific individuals socially qualified) or destructive (aiming at making specific individuals socially disqualified) setting, we provide a comprehensive picture of the parameterized computational complexity landscape. |
| Researcher Affiliation | Academia | Niclas Boehmer1 , Robert Bredereck1 , Duˇsan Knop2 and Junjie Luo1 1Technische Universit at Berlin, Chair of Algorithmics and Computational Complexity 2Czech Technical University in Prague, Prague, Czech Republic |
| Pseudocode | Yes | Algorithm 1 Calc B(A,ϕ, A+, A ,p) Input: Agents A, qualification profile ϕ, subset of agents A+ and A , maximal depth of recursion p Output: Set of agents A to bribe |
| Open Source Code | No | Not found. The paper does not provide any concrete access information (e.g., repository links, explicit statements of code release) for open-source code related to the methodology described. |
| Open Datasets | No | Not found. The paper focuses on theoretical computational complexity analysis and does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | Not found. The paper is theoretical and does not describe experimental setups involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | Not found. The paper is theoretical and does not describe any experiments that would require specific hardware, therefore, no hardware specifications are provided. |
| Software Dependencies | No | Not found. The paper is theoretical and does not list any specific software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | Not found. The paper is theoretical and does not describe an empirical experimental setup with hyperparameters or training configurations. |