MAUT: Multi-Attribute Utility Theory

Overview

Additive multi-attribute utility function. Output typically utility (higher value = preferred).

Strengths

• •Method-specific: Additive multi-attribute utility function

Limitations

• •Assumes: Criteria preferences are independent (no synergistic interactions)
• •Assumes: Compensation is acceptable: high score on one criterion can offset low on another
• •Assumes: Decision matrix is complete (no missing values)

Assumptions to verify

• •Criteria preferences are independent (no synergistic interactions)
• •Compensation is acceptable: high score on one criterion can offset low on another
• •Decision matrix is complete (no missing values)

When not to use

• •Criteria strongly correlated → consider DEMATEL/ANP for interdependence
• •Non-compensatory preferences → consider outranking (ELECTRE/PROMETHEE)

Edge cases

• •ties u_ij = u_j(x_ij) for each alternative.

Common pitfalls

• •Hatalı: 'MAUT bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Criteria preferences are independent (no synergistic interactions)
• •Hatalı: 'MAUT bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Compensation is acceptable: high score on one criterion can offset low on another
• •Hatalı: 'MAUT bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix is complete (no missing values)
• •Hatalı: MAUT'yi 'Criteria strongly correlated → consider DEMATEL/ANP for interdependence' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: MAUT'yi 'Non-compensatory preferences → consider outranking (ELECTRE/PROMETHEE)' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.

Worked example

1. 1.Adım 1 (F1): Step 1: Elicit single-attribute utility functions u_j(x). Formül: u_{j}(x_{ij}):\ \mathbb{R}\to[0,1],\ u_{j}(x^{\min}_{j})=0,\ u_{j}(x^{\max}_{j})=1 Anchor: Keeney-Raiffa 1976, Ch.3 Eq.(3.1)
2. 2.Adım 2 (F2): Step 2: Evaluate utilities u_ij = u_j(x_ij) for each alternative. Formül: u_{ij} = u_{j}(x_{ij}) Anchor: Keeney-Raiffa 1976, Ch.3 Eq.(3.4)
3. 3.Adım 3 (F3): Step 3: Multi-attribute utility via additive form U_i = Σ w_j u_ij (assuming preferential independence). Formül: U_{i} = \sum_{j=1}^{n} w_{j}\,u_{ij} Anchor: Keeney-Raiffa 1976, Ch.6 Eq.(6.1)
4. 4.Adım 4 (F4): Step 4: Descending ranking by U_i. Formül: \text{rank}(A_{i})\propto -U_{i} Anchor: Keeney-Raiffa 1976, Ch.6

Commonly paired with

• •AHP + MAUT (high)
• •BWM + MAUT (high)
• •ENTROPY + MAUT (high)
• •CRITIC + MAUT (high)
• •SWARA + MAUT (high)

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