Fine-Grained View on Bribery for Group Identification

Authors: Niclas Boehmer, Robert Bredereck, Dušan Knop, Junjie Luo

IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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.