MENTAL MODEL · M6763

PIPRECIA

DecidemediumDecision Science

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Updated 2026-08-11

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INTRODUCTION

English translation pending.

CORE DEFINITION

PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) is a criterion weighting method. One criterion is designated as the reference, and every other criterion is compared with it to establish relative importance, so the number of comparisons grows linearly with the number of criteria rather than quadratically as in AHP. The reference criterion is assigned a base value, the others are scaled relative to it, and the results are normalized into weights. The method preserves the reasoning of pairwise comparison while reducing elicitation burden, but assumes the reference is representative.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- Use reference selection: choose a central, uncontroversial criterion as the comparison base. - Use linear comparison: compare each remaining criterion against the reference rather than against every other. - Use normalization: convert the relative comparisons into weights that sum to one.

anchor

Anchor fast decisions

Full pairwise comparison elicits the most information but its cost grows with the square of the number of criteria, which makes it impractical beyond a handful. Comparing everything against one reference keeps the comparative reasoning while cutting the number of judgments to one per criterion. The weights then follow from the reference's base value and the relative ratios, which stays interpretable. The weakness is concentrated in the reference choice, because an unrepresentative reference shifts the entire weight vector.

MINIMUM ACTION

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Practice this model in one real situation:

Select a reference criterion that is central to the decision. 2. Compare each remaining criterion with the reference to establish relative importance. 3. Assign the reference a base value and scale the others against it. 4. Normalize the values into weights that sum to one. 5. Apply the resulting weights in the multi-criteria decision model.

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Source support: Explicit

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doi.orghttps://doi.org/10.3390/sym10110586ZH · Explicit
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