Weight_Subjective
Swing Weighting: importance weights derived from worst-to-best swing utility gains
von Winterfeldt, D., Edwards, W. · 1986
Overview
Weight_Subjective (swing from worst to best, relative gain assessment). Output typically weight (higher value = preferred).
Strengths
- •Method-specific: Weight_Subjective (swing from worst to best, relative gain assessment)
- •Native group-decision support (multi-DM aggregation built into the pipeline)
Limitations
- •Assumes: Domain experts available
- •Assumes: Experts can express consistent comparisons
Method assistant
Grounded explanations: it explains the method, it does not compute.
Assumptions to verify
- •Domain experts available
- •Experts can express consistent comparisons
When not to use
- •No experts available → use objective weighting
- •High inconsistency → discard and re-elicit
Edge cases
- •See F.steps and D.parameters for SWING-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.
Common pitfalls
- •Hatalı: 'SWING bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Domain experts available
- •Hatalı: 'SWING bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Experts can express consistent comparisons
- •Hatalı: SWING'yi 'No experts available → use objective weighting' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
- •Hatalı: SWING'yi 'High inconsistency → discard and re-elicit' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
Worked example
- 1.Adım 1 (F1): Normalise swing scores: w_j = s_j / Σ_k s_k. Formül: w_j = s_j / \sum_{k=1}^{n} s_k Anchor: von Winterfeldt & Edwards 1986, Ch.8 p.272 (swing weighting; pending PDF page verification)
Commonly paired with
- •SWING + TOPSIS (high)
- •SWING + VIKOR (high)
- •SWING + EDAS (high)
- •SWING + PROMETHEE (high)
- •SWING + ELECTRE-III (high)
How to cite
von Winterfeldt, D.; Edwards, W. (1986). Decision Analysis and Behavioral Research. Cambridge University Press.