Rough-MARCOS - Rough extension of MARCOS

Overview

rough-marcos extends MARCOS to handle Rough uncertainty. All arithmetic operations (normalisation, weighting, row summation, utility ratio computation) are performed using Rough number (lower approximation L, upper approximation U) algebra. Utility degrees Y+/Y- are computed via IRN division before defuzzification. The final composite score f(Y_i) is obtained via MARCOS utility functions and ranked in descending order.

Output

utility, higher is better

Data

Rough Number, uncertainty tuples complete

Weights

Needs a weight source

Size

2+ alternatives, 3-10 criteria works best

Used for

Rough MCDM, MAGDM under epistemic uncertainty, expert-driven evaluation with linguistic terms

Look elsewhere when

Assumptions to verify

• Decision matrix entries are valid Rough numbers/tuples
• Underlying crisp method's compensation assumption holds in uncertain space
• All decision-maker(s) and experts use the same linguistic/uncertainty scale

Edge cases and pitfalls

• Value-space violation: ensure all entries satisfy Rough: L ≤ U; approximations defined by equivalence classes before computation.

• Defuzzification method affects ranking: midpoint (L+U)/2 is the canonical choice but alternatives exist.

Works with

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