Name
Abstract | ||
|---|---|---|
One important issue of aggregating preference rankings is to determine the weights of different ranking places. This paper proposes the use of ordered weighted averaging (OWA) operator weights to aggregate preference rankings, which allows the weights associated with different ranking places to be determined in terms of a decision maker (DM)'s optimism level characterized by an orness degree. By adjusting the DM's optimism level, ties can be avoided and winner can be selected. Two numerical examples are examined using OWA operator weights to show their applications, simplicity and flexibility in aggregating preference rankings. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.ins.2007.01.008 | Inf. Sci. |
Keywords | Field | DocType |
important issue,operator weight,aggregating preference ranking,aggregate preference ranking,owa operator weight,different ranking place,orness degree,numerical example,optimism level,decision maker,preference aggregation | Ranked voting system,Aggregation problem,Ranking,Artificial intelligence,Operator (computer programming),Machine learning,Decision maker,Mathematics | Journal |
Volume | Issue | ISSN |
177 | 16 | 0020-0255 |
Citations | PageRank | References |
44 | 1.85 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying-Ming Wang | 1 | 3256 | 166.96 |
| Ying Luo | 2 | 467 | 20.48 |
| Zhongsheng Hua | 3 | 740 | 55.13 |