MACBETH
Updated 2026-08-10
INTRODUCTION
English translation pending.
CORE DEFINITION
Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) elicits judgments in words rather than numbers. The decision maker compares alternatives on each criterion and selects a semantic category describing the difference in attractiveness, from no difference through very weak, weak, moderate, strong, very strong, to extreme. A linear program then finds numeric scores and criterion weights that satisfy all the stated comparisons simultaneously, which makes the scale internally consistent. The approach lowers the burden of producing precise numbers while preserving the ordinal information the judgments contain. Its assumption is that the semantic categories are used consistently.
SCAFFOLDING EFFECT
Reduce cognitive load
- Use verbal comparison: express preference differences as weak, moderate, or strong instead of guessing numbers. - Use consistency solve: let the model derive scores that satisfy all comparisons rather than fixing them by hand. - Use category definition: agree on what each verbal level means before starting comparisons.
Anchor fast decisions
Asking people for precise numeric weights produces numbers that are arbitrary, and asking only for rankings discards too much information. Semantic categories sit between the two, capturing ordinal strength without demanding cardinal precision. Because the categories are ordinal, a system of inequalities can be solved to find a numeric scale consistent with all of them, which converts qualitative judgments into quantitative scores without inventing data. Consistency is the constraint that makes the result meaningful, and the method reports conflicting judgments so they can be revisited.
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/Multiple-criteria_decision_analysisverified
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