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Home›Decision-making›LOgarithmic DEcomposition of Criteria Importance
MCDMWeight Objectivecrisp

LOgarithmic DEcomposition of Criteria Importance

LODECI (LOgarithmic DEcomposition of Criteria Importance) is a weight objective multi-criteria decision-making (MCDM) method introduced by Pala, O. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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LODECI
AHPSORTAPLOCOARASAROMANARTASICOBRACOCOSOCODAS

When to use it

Apply F.steps in order.

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
  • Assumes full compensation — a strong score on one criterion can offset a weak score on another.

Sources

  1. Pala, O. (2024). Assessment of the social progress on European Union by logarithmic decomposition of criteria importance. Expert Systems With Applications DOI: 10.1016/j.eswa.2023.121846 ↗

How to cite this page

ScholarGate. (2026, June 2). LOgarithmic DEcomposition of Criteria Importance. ScholarGate. https://scholargate.app/en/decision-making/lodeci

Related methods

AHPSORTAPLOCOARASAROMANARTASICOBRACOCOSOCODAS

Which method?

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Similar methods

LOPCOWAPLOCOLMAWWENSLOP-RAWECP-OCRALOGARITHMIC-NORMALIZATIONIDOCRIW

Related reference concepts

Decision MakingDecision Support SystemsParticipative Decision MakingWeighted ScoresDecision Making SkillsPolicy Analysis

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

ScholarGate — LODECI (LOgarithmic DEcomposition of Criteria Importance). Retrieved 2026-07-20 from https://scholargate.app/en/decision-making/lodeci · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Pala, O.
Subfamily
Weight Objective
Year
2024
Type
Objective weighting via logarithmic decomposition
Value Space
crisp
Uncertainty
None
Compensation
full
Rank Reversal
No
Related methods
AHPSORTAPLOCOARASAROMANARTASICOBRACOCOSOCODAS
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