Decision Science (MCDA)

ARAS (Additive Ratio Assessment)

Ranks alternatives by comparison to an optimal/ideal alternative using additive ratios

Rubric Type

distance-based

Complexity

medium

Extractor

technical

Required Inputs

SolveRight's AI extractor automatically derives these data points from your decision description:

  • ✓criteria
  • ✓weights
  • ✓performance matrix
  • ✓optimal values

Best For

Engineers

How ARAS (Additive Ratio Assessment) Works in SolveRight

When you run a decision through SolveRight, ARAS (Additive Ratio Assessment) is one of up to 155 frameworks that analyze your options simultaneously. The AI extractor identifies 4 key data points from your decision description, then the distance-based rubric computes a normalized 0-100 score for each option. This score is combined with results from other frameworks to produce your overall ranking, with contradiction detection highlighting where ARAS (Additive Ratio Assessment)disagrees with other methodologies.

ARAS (Additive Ratio Assessment) — Frequently Asked Questions

What is ARAS (Additive Ratio Assessment)?+
Ranks alternatives by comparison to an optimal/ideal alternative using additive ratios. In SolveRight, ARAS (Additive Ratio Assessment) uses a distance-based rubric to compute a normalized 0–100 score for each option.
When should I use ARAS (Additive Ratio Assessment)?+
ARAS (Additive Ratio Assessment) is best suited for Decision Science (MCDA) decisions. It evaluates factors like criteria, weights, performance matrix, making it valuable when you need systematic multi-criteria analysis backed by decision theory.
How does SolveRight use ARAS (Additive Ratio Assessment)?+
SolveRight runs ARAS (Additive Ratio Assessment) alongside up to 154 other frameworks simultaneously. The AI extractor identifies 4 key data points from your decision description, then the distance-based rubric computes deterministic scores. If ARAS (Additive Ratio Assessment) disagrees with other frameworks, contradiction detection highlights the divergence.
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Related Decision Science (MCDA) Frameworks

Weighted Decision Matrix

Scores options against weighted criteria for systematic comparison

Analytic Hierarchy Process (AHP)

Derives priority weights from pairwise comparisons with consistency check

Multi-Criteria Decision Analysis (MCDA)

Formal multi-criteria evaluation combining multiple scoring methods

Regret Minimization Framework

Evaluates options through the lens of future regret minimization

ANP (Analytic Network Process)

Extends AHP to handle interdependencies and feedback between criteria and alternatives

TOPSIS

Ranks alternatives by closeness to ideal solution and distance from anti-ideal solution