Complexity of Shift Bribery in Committee Elections

Authors: Robert Bredereck, Piotr Faliszewski, Rolf Niedermeier, Nimrod Talmon

AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We study the (parameterized) complexity of SHIFT BRIBERY for multiwinner voting rules. We focus on the SNTV, Bloc, k-Borda, and Chamberlin Courant rules, as well as on approximate variants of the Chamberlin Courant rule, since the original rule is NP-hard to compute. We show that SHIFT BRIBERY tends to be significantly harder in the multiwinner setting than in the single-winner one by showing settings where SHIFT BRIBERY is easy in the single-winner cases, but is hard (and hard to approximate) in the multiwinner ones. We show that the non-monotonicity of those rules which are based on approximation algorithms for the Chamberlin Courant rule sometimes affects the complexity of SHIFT BRIBERY.
Researcher Affiliation Academia 1Institut f ur Softwaretechnik und Theoretische Informatik, TU Berlin, Berlin, Germany {robert.bredereck, rolf.niedermeier}@tu-berlin.de, nimrodtalmon77@gmail.com 2AGH University of Science and Technology, Krakow, Poland faliszew@agh.edu.pl
Pseudocode No The paper does not contain any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any concrete access information for open-source code related to the described methodology.
Open Datasets No The paper is theoretical and does not involve empirical studies with datasets, therefore no training dataset information is provided.
Dataset Splits No The paper is theoretical and does not involve empirical studies or dataset splits.
Hardware Specification No The paper is theoretical and does not describe any hardware used for experiments.
Software Dependencies No The paper is theoretical and does not list any specific software dependencies with version numbers for experimental reproducibility.
Experiment Setup No The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations.