Winner Determination in Huge Elections with MapReduce
Abstract
In computational social choice, we are concerned with the development of methods for joint decision making. A central problem in this field is the winner determination problem, which aims at identifying the most preferred alternative(s). With the rise of modern e-business platforms, processing of huge amounts of preference data has become an issue. In this work, we apply the MapReduce framework - which has been specifically designed for dealing with big data - to various versions of the winner determination problem. We obtain efficient and highly parallel algorithms and provide a theoretical analysis and experimental evaluation.
Cite
Text
Csar et al. "Winner Determination in Huge Elections with MapReduce." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10606Markdown
[Csar et al. "Winner Determination in Huge Elections with MapReduce." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/csar2017aaai-winner/) doi:10.1609/AAAI.V31I1.10606BibTeX
@inproceedings{csar2017aaai-winner,
title = {{Winner Determination in Huge Elections with MapReduce}},
author = {Csar, Theresa and Lackner, Martin and Pichler, Reinhard and Sallinger, Emanuel},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {451-458},
doi = {10.1609/AAAI.V31I1.10606},
url = {https://mlanthology.org/aaai/2017/csar2017aaai-winner/}
}