Committee Scoring Rules: Axiomatic Classification and Hierarchy

Authors: Piotr Faliszewski, Piotr Skowron, Arkadii Slinko, Nimrod Talmon

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

Reproducibility Variable Result LLM Response
Research Type Theoretical We consider several natural classes of committee scoring rules, namely, weakly separable, representation-focused, top-k-counting, OWAbased, and decomposable rules. We study some of their axiomatic properties, especially properties of monotonicity, and concentrate on containment relations between them. We characterize SNTV, Bloc, and k-approval Chamberlin Courant, as the only rules in certain intersections of these classes. We introduce decomposable rules, describe some of their applications, and show that the class of decomposable rules strictly contains the class of OWA-based rules.
Researcher Affiliation Academia Piotr Faliszewski AGH University Krakow, Poland faliszew@agh.edu.pl Piotr Skowron University of Oxford Oxford, United Kingdom p.k.skowron@gmail.com Arkadii Slinko University of Auckland Auckland, New Zealand a.slinko@auckland.ac.nz Nimrod Talmon Weizmann Institute of Science Rehovot, Israel nimrodtalmon77@gmail.com
Pseudocode No The paper focuses on theoretical analysis, definitions, and proofs. It does not include any pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement about releasing source code or a link to a code repository for the described methodology.
Open Datasets No This is a theoretical paper focusing on axiomatic classification and properties of committee scoring rules. It does not involve empirical studies, datasets, or training.
Dataset Splits No This is a theoretical paper and does not involve empirical evaluation with datasets, thus no training/validation/test splits are discussed.
Hardware Specification No This is a theoretical paper. No hardware specifications for running experiments are mentioned.
Software Dependencies No This is a theoretical paper. No specific software dependencies with version numbers are mentioned in relation to experiments or implementation.
Experiment Setup No This is a theoretical paper and does not describe any experimental setup, hyperparameters, or system-level training settings.