CRITIC
Updated 2026-08-11
INTRODUCTION
English translation pending.
CORE DEFINITION
CRITIC, Criteria Importance Through Intercriteria Correlation, is an objective weighting method. It determines indicator weights by considering two factors together. The first is contrast intensity, the standard deviation of an indicator's values, where greater variation means more information. The second is conflict, the correlation between indicators, where lower correlation means more independent information. The information content Cj equals the standard deviation times the sum of one minus the correlation coefficients, and the weight wj is Cj divided by the sum of all Cj. Because the weights come entirely from the data itself, they are free of subjective preference.
SCAFFOLDING EFFECT
Reduce cognitive load
- Weight without subjective bias: derive every weight from the dataset itself rather than personal judgment. - Reward genuine variation: indicators whose values vary more sharply carry more information. - Penalize redundancy: indicators that correlate highly with others contribute far less independent weight.
Anchor fast decisions
Built on objective weighting. The weight of each indicator is decided by two data-driven properties, contrast intensity measured by standard deviation and conflict measured by low correlation with other indicators, so a varying and independent indicator earns more weight than a static or redundant one.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- doi.orghttps://doi.org/10.1016/0305-0548(94)00059-Hverified
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