BRAUERS-DOMINANCE: Brauers & Zavadskas (2014) Dominance Theory: used in MultiMOORA aggregation

Overview

Pareto-dominance based aggregation (absolute/general/transitive/equal). Output typically rank_position (lower value = preferred).

Strengths

• •Method-specific: Pareto-dominance based aggregation (absolute/general/transitive/equal)
• •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 BRAUERS-DOMINANCE-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.

Common pitfalls

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

Worked example

1. 1.Adım 1 (F1): Pairwise Pareto dominance check across rankings. Score = times this alt strictly dominates − times strictly dominated. Formül: i dominates k iff r_ij ≤ r_kj ∀j AND r_ij < r_kj ∃j; score = #dominates − #dominated_by Anchor: Orakçı 2024 §1.8.10

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