Calculate priority weights and consistency ratios from pairwise comparison matrices.
npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill ahp-method
Please help me install this Agent Skill. Skill: ahp-method Source: https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000/tree/main/.github/skills/ahp-method Command: npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill ahp-method
The Analytic Hierarchy Process (AHP) provides a principled way to derive weights from subjective judgments by constructing a pairwise comparison matrix and extracting weights via the eigenvector method, enabling consistent decision making.
To begin, prepare a small pairwise comparison matrix and run the ahp_method to obtain weights and the CR. The function returns a weights vector, the computed CR, and a flag indicating whether the judgments are consistent.
High-intent search queries and answers about installing and using this skill.
The Analytic Hierarchy Process (AHP) solves multi-criteria decision analysis by deriving priority weights from subjective pairwise comparisons. It uses the eigenvector method to extract weights and calculates a consistency ratio to validate judgment reliability.
AHP consistency checks compute the Consistency Index and Consistency Ratio from your pairwise comparison matrix. The method returns a flag indicating whether your subjective judgments meet acceptable consistency thresholds for reliable multi-criteria decisions.
AHP is suitable for small-to-medium multi-criteria decision problems like vendor evaluation and feature prioritization. It handles subjective judgments by converting pairwise comparisons into objective priority weights for ranking alternatives.
AHP weights can be combined with objective weighting methods like entropy for blended multi-criteria analysis. The resulting weight vector integrates into downstream scoring workflows such as TOPSIS to rank alternatives using both subjective and objective data.
A high AHP consistency ratio indicates your pairwise comparison matrix lacks logical transitivity. The method returns a consistency flag to guide refinement of subjective judgments until the matrix achieves acceptable consistency for reliable weight extraction.
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