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Home›Decision-making›Level Based Weight Assessment
MCDMWeight Subjectivecrisp

Level Based Weight Assessment

LBWA (Level Based Weight Assessment) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Žižović, M., Pamučar, D. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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LBWA
AHPSORTAPLOCOARASAROMANARTASICOBRACOCOSOCODAS

When to use it

LBWA groups criteria into levels (most important = Level 1) and assigns influence values I within each level. The elasticity coefficient r_0 controls how steeply weights decrease across levels. Criteria in Level 1 with I=0 always get the highest weight.

Strengths & limitations

Strengths
  • Follows a transparent, reproducible computational procedure that can be audited step by step.
  • Handles multiple criteria of differing scales and units within a single decision matrix.
Limitations
  • Results depend on the chosen normalisation, weights, and parameter settings.

Sources

  1. Žižović, M., Pamučar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering link ↗

How to cite this page

ScholarGate. (2026, June 2). Level Based Weight Assessment. ScholarGate. https://scholargate.app/en/decision-making/lbwa

Related methods

AHPSORTAPLOCOARASAROMANARTASICOBRACOCOSOCODAS

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LMAWSIWECDIBRF-LMAWFUZZY-SIWECMAIRCAMABACZF-LMAW

Related reference concepts

Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision MakingCriteria for Decision-Making under Risk and UncertaintyPolicy AnalysisBayesian Model AveragingCost-Benefit AnalysisBayesian Model Comparison and Selection

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — LBWA (Level Based Weight Assessment). Retrieved 2026-07-20 from https://scholargate.app/en/decision-making/lbwa · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Žižović, M., Pamučar, D.
Subfamily
Weight Subjective
Year
2019
Type
Level-partitioned criterion grouping with influence function weighting
Value Space
crisp
Uncertainty
None
Compensation
N/A
Rank Reversal
No
Related methods
AHPSORTAPLOCOARASAROMANARTASICOBRACOCOSOCODAS
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