Decision Science (MCDA)

MACBETH

Converts qualitative pairwise judgments of attractiveness differences into cardinal scores via linear programming

Rubric Type

comparative-pairwise

Complexity

medium

Extractor

technical

Required Inputs

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

  • ✓criteria
  • ✓qualitative judgments
  • ✓reference levels

Best For

Consultants

How MACBETH Works in SolveRight

When you run a decision through SolveRight, MACBETH is one of up to 155 frameworks that analyze your options simultaneously. The AI extractor identifies 3 key data points from your decision description, then the comparative-pairwise 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 MACBETHdisagrees with other methodologies.

MACBETH — Frequently Asked Questions

What is MACBETH?+
Converts qualitative pairwise judgments of attractiveness differences into cardinal scores via linear programming. In SolveRight, MACBETH uses a comparative-pairwise rubric to compute a normalized 0–100 score for each option.
When should I use MACBETH?+
MACBETH is best suited for Decision Science (MCDA) decisions. It evaluates factors like criteria, qualitative judgments, reference levels, making it valuable when you need systematic multi-criteria analysis backed by decision theory.
How does SolveRight use MACBETH?+
SolveRight runs MACBETH alongside up to 154 other frameworks simultaneously. The AI extractor identifies 3 key data points from your decision description, then the comparative-pairwise rubric computes deterministic scores. If MACBETH disagrees with other frameworks, contradiction detection highlights the divergence.
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Related Decision Science (MCDA) Frameworks

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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