LBWA - Level Based Weight Assessment

Overview

Provide every criterion level and influence value, the explicit best criterion, and r_0>r. Lower influence means greater importance within a level.

Output

Weight, higher is better

Data

Crisp, expert input required

Weights

Derived internally, no weight source needed

Size

2+ alternatives, 3-12 criteria works best

Used for

Expert-driven decision making, MAGDM

How it works

1. 1

   Most significant criterion c_1; assign integers I_j (lower = more important) within levels.

   $$c_1=\arg \underset{j}{\max }\text{importance};I_j\in \mathbb{N}\text{ at level }l$$

   Žižović-Pamucar 2019, p.3 Sec.3

2. 2

   Influence function A(I_j) = (r_0 - I_j)/r_0^{L_j} for chosen elasticity r_0.

   $$A(I_j)=\frac{r_0-I_j}{r_0^{L_j}}$$

   Žižović-Pamucar 2019, p.4 Eq.(1)

3. 3

   Most-important weight w_1 = 1/(1+Σ f_j); other w_j = f_j · w_1.

   $$w_1=\frac{1}{1+{\sum }_{j\ne 1}{f}_j},w_j=f_j\cdot w_1$$

   Žižović-Pamucar 2019, p.4 Eqs.(2)-(3)

Fits when / Look elsewhere when

Assumptions to verify

• Domain experts available
• Experts can express consistent comparisons

Edge cases and pitfalls

• Using (r_0-I)/r_0^level is not the seminal LBWA formula.

• LBWA does not define a native multi-expert aggregation rule; reach consensus before execution.

Works with

How to cite