AVERAGE-RANKING: Average ranking: per-alternative mean rank

Overview

Order statistic: column-wise arithmetic mean. Output typically rank_position (lower value = preferred).

Strengths

• •Method-specific: Order statistic: column-wise arithmetic mean
• •Native group-decision support (multi-DM aggregation built into the pipeline)

Limitations

• •Assumes: Input is a rank matrix (1=best, m=worst per voter)
• •Assumes: Each voter ranks all alternatives

Assumptions to verify

• •Input is a rank matrix (1=best, m=worst per voter)
• •Each voter ranks all alternatives

When not to use

• •Cardinal preferences important → use a MAUT method

Edge cases

• •See F.steps and D.parameters for AVERAGE-RANKING-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.

Common pitfalls

• •Hatalı: 'AVERAGE-RANKING bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Input is a rank matrix (1=best, m=worst per voter)
• •Hatalı: 'AVERAGE-RANKING bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Each voter ranks all alternatives
• •Hatalı: AVERAGE-RANKING'yi 'Cardinal preferences important → use a MAUT method' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.

Worked example

1. 1.Adım 1 (F1): For each alternative, compute arithmetic mean of its ranks across rankings. Lower mean = better. Formül: mean_i = (1/n) Σ_j r_ij; rank ascending Anchor: Orakçı 2024 §1.8.9

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