| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 377406 | Artificial Intelligence | 2008 | 24 Pages |
Abstract
Ranking theory delivers an account of iterated contraction; each ranking function induces a specific iterated contraction behavior. The paper shows how to reconstruct a ranking function from its iterated contraction behavior uniquely up to multiplicative constant and thus how to measure ranks on a ratio scale. Thereby, it also shows how to completely axiomatize that behavior. The complete set of laws of iterated contraction it specifies amend the laws hitherto discussed in the literature.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
