Name
Abstract | ||
|---|---|---|
Single-peaked preferences are a natural way to avoid paradoxes and impossibility theorems in social choice and have recently been involved in the study of various computational aspects of social choice. Since strict single-peakedness is hard to achieve in practice, approximate single-peakedness appears more appropriate and is gaining popularity. In this paper, we study approximate single-peakedness of large, randomly-generated profiles. We focused on characterizing the asymptotically optimal social axis, which is asymptotically consistent with most agents' preferences generated from a statistical model. We characterize all asymptotically optimal social axes under the Mallows model for two case: the case where the dispersion parameter $\varphi$ is close to 0, and the case where $\varphi$ is close to 1. We also design an algorithm to help characterize all asymptotically optimal social axes for all $\varphi$ when the number of alternative is no more than 10. These results help us understand the structure of approximate single-peakedness in large elections. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICBK50248.2020.00068 | 2020 IEEE International Conference on Knowledge Graph (ICKG) |
Keywords | DocType | ISBN |
Social Choice,Large Election,Mallows Model,Single Peakedness | Conference | 978-1-7281-8157-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhihuai Chen | 1 | 0 | 0.68 |
| Qian Li | 2 | 0 | 0.34 |
| Xiaoming Sun | 3 | 280 | 41.19 |
| Lirong Xia | 4 | 1034 | 86.84 |
| Jialin Zhang | 5 | 0 | 0.68 |