Decision Science (MCDA)

WASPAS

Combines WSM and WPM approaches for robust ranking

Rubric Type

weighted-sum

Complexity

medium

Extractor

technical

Required Inputs

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

  • ✓criteria
  • ✓weights
  • ✓performance matrix
  • ✓lambda coefficient

Best For

Engineers

How WASPAS Works in SolveRight

When you run a decision through SolveRight, WASPAS 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 weighted-sum 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 WASPASdisagrees with other methodologies.

WASPAS — Frequently Asked Questions

What is WASPAS?+
Combines WSM and WPM approaches for robust ranking. In SolveRight, WASPAS uses a weighted-sum rubric to compute a normalized 0–100 score for each option.
When should I use WASPAS?+
WASPAS 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 WASPAS?+
SolveRight runs WASPAS alongside up to 154 other frameworks simultaneously. The AI extractor identifies 4 key data points from your decision description, then the weighted-sum rubric computes deterministic scores. If WASPAS 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