RAM: Root Assessment Method

Overview

Power-function aggregation with benefit/cost separation. Output typically ranking.

Strengths

• •Method-specific: Power-function aggregation with benefit/cost separation

Edge cases

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

Common pitfalls

• •Bkz. RAM F.steps citation_anchor'lar ve P.verification_status.

Worked example

1. 1.Adım 1 (F1): Step 1: Sum normalization (direction-unaware: r_ij = x_ij / Σ_k x_kj). All columns treated uniformly; benefit/cost distinction applied in F3 by separating columns. Formül: r_{ij} = \frac{x_{ij}}{\sum_k x_{kj}} Anchor: Sotoudeh-Anvari 2023 §2
2. 2.Adım 2 (F2): Step 2: Weighted normalized matrix y_ij = w_j · r_ij. Formül: y_{ij} = w_j \cdot r_{ij} Anchor: Sotoudeh-Anvari 2023 §2
3. 3.Adım 3 (F3): Step 3: Separate benefit and cost sums: S+_i = sum of wnmatrix columns for benefit criteria; S-_i = sum of cost criteria columns. Formül: S^{+}_{i} = \sum_{j \in J^{+}} y_{ij};\quad S^{-}_{i} = \sum_{j \in J^{-}} y_{ij} Anchor: Sotoudeh-Anvari 2023 §2
4. 4.Adım 4 (F4): Step 4: RAM score r_i = (2 + S+_i)^(1/(2 + S-_i)). Rank descending (higher = better). Note: high S-_i (cost) decreases the exponent's denominator → lowers r_i; high S+_i (benefit) increases base → raises r_i. Formül: r_i = (2 + S^{+}_{i})^{\frac{1}{2 + S^{-}_{i}}},\quad \text{rank descending} Anchor: Sotoudeh-Anvari 2023 §2 Eq.(main)

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