NeurIPS 2015poster25 citations
Is Approval Voting Optimal Given Approval Votes?
Ariel D Procaccia, Nisarg Shah
Abstract
Some crowdsourcing platforms ask workers to express their opinions by approving a set of k good alternatives. It seems that the only reasonable way to aggregate these k-approval votes is the approval voting rule, which simply counts the number of times each alternative was approved. We challenge this assertion by proposing a probabilistic framework of noisy voting, and asking whether approval voting yields an alternative that is most likely to be the best alternative, given k-approval votes. While the answer is generally positive, our theoretical and empirical results call attention to situations where approval voting is suboptimal.
BibTeX
@inproceedings{NIPS2015_a2137a2a,
author = {Procaccia, Ariel D and Shah, Nisarg},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Is Approval Voting Optimal Given Approval Votes?},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/a2137a2ae8e39b5002a3f8909ecb88fe-Paper.pdf},
volume = {28},
year = {2015}
}