Multiwinner Elections With Diversity Constraints
Authors: Robert Bredereck, Piotr Faliszewski, Ayumi Igarashi, Martin Lackner, Piotr Skowron
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analyze the computational complexity of computing winning committees in this model, obtaining polynomial-time algorithms (exact and approximate) and NP-hardness results. We focus on several natural classes of voting rules and diversity constraints. Our main results are presented in Table 1. |
| Researcher Affiliation | Academia | Robert Bredereck University of Oxford, Oxford, UK; TU Berlin, Berlin, Germany robert.bredereck@tu-berlin.de Piotr Faliszewski AGH University, Krakow, Poland faliszew@agh.edu.pl Ayumi Igarashi University of Oxford, Oxford, UK ayumi.igarashi@cs.ox.ac.uk Martin Lackner TU Wien, Vienna, Austria lackner@dbai.tuwien.ac.at Piotr Skowron TU Berlin, Berlin, Germany p.k.skowron@gmail.com |
| Pseudocode | Yes | Algorithm 1: Greedy Algorithm 1; Algorithm 2: Greedy Algorithm for BCWD |
| Open Source Code | No | The paper is theoretical and does not mention or provide access to any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or describe any datasets for training. Therefore, it does not provide information on dataset availability. |
| Dataset Splits | No | The paper is theoretical and does not use or describe any datasets, thus no validation splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and focuses on computational complexity and algorithms. It does not describe any empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementations or provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, therefore no experimental setup details like hyperparameters or training configurations are provided. |