Probabilistic Linguistic extension of MARCOS
PL-MARCOS (Probabilistic Linguistic extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, J., Wei, G., Wei, C., Wei, Y. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Key highlights
- 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.
Intuition
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How it works
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When to use it
pl-marcos extends MARCOS to handle Probabilistic Linguistic uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Probabilistic Linguistic Term Set (PLTS: {L_k|p_k}) algebra. The final scores are defuzzified via expected linguistic value E = Σ p_k · index(L_k) before ranking.
Strengths & limitations
- 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.
- May exhibit rank reversal when alternatives are added to or removed from the set.
- Assumes full compensation — a strong score on one criterion can offset a weak score on another.
Common pitfalls
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Sources
- 1.(). UNCONFIRMED — PL-MARCOS specific seminal not confirmed via systematic literature search. PENDING
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Cite this page
ScholarGate. (2026, June 2). PL-MARCOS. ScholarGate. https://scholargate.app/decision-making/pl-marcos