| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 376791 | Artificial Intelligence | 2016 | 16 Pages |
Abstract
We present the first model of optimal voting under adversarial noise. From this viewpoint, voting rules are seen as error-correcting codes: their goal is to correct errors in the input rankings and recover a ranking that is close to the ground truth. We derive worst-case bounds on the relation between the average accuracy of the input votes, and the accuracy of the output ranking. Empirical results from real data show that our approach produces significantly more accurate rankings than alternative approaches.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Ariel D. Procaccia, Nisarg Shah, Yair Zick,