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Ranking
Rank Reversal Analysis - Detection of ranking instability when alternatives are added/removed
Robustness diagnostic - rank reversal detection and quantification
Triantaphyllou, E.2000doi:10.1007/978-1-4757-3157-6_2 ↗
Overview
Rank Reversal Analysis - Detection of ranking instability when alternatives are added/removed
- Output
- robustness score, higher is better
- Data
- Crisp, complete numeric matrix
- Size
- 2+ alternatives, 3-12 criteria works best
- Used for
- Sensitivity analysis, uncertainty quantification
How it works
- 1
Validate inputs for rank-reversal.
\text{RR ratio}=\frac{\text{# reversals across perturbations}}{\text{# perturbations}\cdot\binom{m}{2}}Triantaphyllou 2000, (pending PDF page verification)
Look elsewhere when
- •Quick analysis needed. Defer robustness check.
Assumptions to verify
- A base ranking method is selected
- Computational budget for repeated runs
Edge cases and pitfalls
Applying RANK-REVERSAL without verifying this assumption.
Requirement: A base ranking method is selected
Applying RANK-REVERSAL without verifying this assumption.
Requirement: Computational budget for repeated runs
Using RANK-REVERSAL when: Quick analysis needed → defer robustness check.
An alternative method is recommended in this situation.
How to cite
Triantaphyllou, E. (2000). Multi-Criteria Decision Making Methods: A Comparative Study. Kluwer Academic Publishers, Dordrecht. https://doi.org/10.1007/978-1-4757-3157-6_2
System ID, as it appears in reports and the API
RANK-REVERSAL