Euclidean Distance Evaluation
Updated 2026-08-13
INTRODUCTION
English translation pending.
CORE DEFINITION
Euclidean distance evaluation normalizes the criteria, identifies a positive and a negative ideal solution, and computes each alternative's Euclidean distance to both, ranking alternatives by relative closeness to the positive ideal. The core proposition is that an abstract multi-criteria comparison becomes an intuitive geometric one once alternatives are plotted in normalized space. The key qualification is that the geometry is only meaningful after normalization: computing distances on raw values lets whatever criterion has the largest units dominate the result.
SCAFFOLDING EFFECT
Reduce cognitive load
- Normalize first: rescale every criterion to a common scale before computing any distance. - Ideal anchors: define the positive and the negative ideal solutions explicitly for each criterion. - Weight check: apply criterion weights so that importance is reflected rather than simply assumed equal.
Anchor fast decisions
Once criteria are on a common scale, each alternative becomes a point in a space where the ideal solution is also a point. Distance to that point summarizes all criteria in one number, so alternatives can be ranked without the analyst trading criteria off by hand. The trade-offs are instead encoded in the normalization and the weights.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Euclidean_distanceverified
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