Prob-ARAS - Stochastic extension of PROB-ARAS

Overview

prob-aras extends PROB-ARAS to handle Stochastic uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Stochastic element (distribution or scenario probabilities) algebra. The final scores are defuzzified via expected value E[x] = Σ p_k x_k before ranking.

Output

utility, higher is better

Data

Stochastic, uncertainty tuples complete

Weights

Needs a weight source

Size

2+ alternatives, 3-10 criteria works best

Used for

Probabilistic (Stochastic) MCDM, MAGDM under epistemic uncertainty, expert-driven evaluation with linguistic terms

Look elsewhere when

Assumptions to verify

• Decision matrix entries are valid Probabilistic (Stochastic) 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 Probabilities p_k ≥ 0, Σ p_k = 1 for each scenario before computation.

• Defuzzification method affects ranking: expected value E[x] = Σ p_k x_k is the canonical choice but alternatives exist.

Works with

How to cite