Delphi Method: iterative expert consensus for criterion importance elicitation

Overview

Weight_Subjective (expert consensus, iterative Likert/ranking). Output typically weight (higher value = preferred).

Strengths

• •Method-specific: Weight_Subjective (expert consensus, iterative Likert/ranking)
• •Native group-decision support (multi-DM aggregation built into the pipeline)

Limitations

• •Assumes: Domain experts available
• •Assumes: Experts can express consistent comparisons

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

• •If CV_j < threshold for all j → stop; otherwise feed back statistics and collect round k+1.

Common pitfalls

• •Hatalı: 'DELPHI bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Domain experts available
• •Hatalı: 'DELPHI bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Experts can express consistent comparisons
• •Hatalı: DELPHI'yi 'No experts available → use objective weighting' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: DELPHI'yi 'High inconsistency → discard and re-elicit' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.

Worked example

1. 1.Adım 1 (F1): Round 1-k: collect importance ratings from each expert; compute mean μ_j and CV_j = σ_j/μ_j per criterion. If CV_j < threshold for all j → stop; otherwise feed back statistics and collect round k+1. Formül: \mu_j = \frac{1}{k}\sum_e r_{ej};\quad CV_j = \frac{\sigma_j}{\mu_j};\quad w_j = \frac{\mu_j}{\sum_l \mu_l} Anchor: Dalkey & Helmer 1963, p.460 (consensus procedure; pending PDF page verification)

Commonly paired with

• •DELPHI + TOPSIS (high)
• •DELPHI + VIKOR (high)
• •DELPHI + EDAS (high)
• •DELPHI + PROMETHEE (high)
• •DELPHI + ELECTRE-III (high)

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