Data types

This is the data structure in which every cell holds one number and that number is taken as beyond dispute; every other data type exists only because something is missing from this structure.

Example cell: 5

When a value is not exact, this is the data structure that keeps track, through membership grades, not of which probability but of which "plausibility" attaches to each possible value.

Example cell: 2, 5, 7

This is the data structure that lets more than one plausible value be kept together for the same assessment.

Example cell: 0.4, 0.6, 0.7

This is the data structure that, where a criterion splits into several sub-options (attribute values), keeps both the degrees given to each sub-option and how contradictory the sub-options are to one another.

Example cell: 0.7, 0.2, 0.1

This is the data structure that preserves the degrees of truth, indeterminacy and falsity in an assessment as three independent values.

Example cell: 0.7, 0.2, 0.1

This is the data structure that keeps how far you support a judgement and how far you reject it as two separate degrees, carrying the gap between them as a hesitation margin.

Example cell: 0.6, 0.3

This is the data structure that takes all three of a judgement's degrees of support, rejection and hesitancy directly from the expert, and bounds the sum of their squares at 1.

Example cell: 0.5, 0.4, 0.3

This is the data structure that lets the support and rejection degrees given to a judgement sum to more than 1, bounding only the sum of their squares.

Example cell: 0.8, 0.5

This is the data structure that regulates how large the support and rejection degrees given to a judgement may be together through an exponent (q) chosen to fit the data.

Example cell: 0.7, 0.4

This is the data structure that preserves the situation in which only a lower and an upper bound of a value are known, and no value between them is taken as more likely or more reasonable than another.

Example cell: 3, 5

This is the data structure that keeps the "yes," "abstain" and "no" degrees given to a judgement together, in such a way that their sum does not exceed 1.

Example cell: 0.5, 0.2, 0.2

This is the data structure that preserves a judgement's positive and negative direction of effect on separate axes, or grades the same judgement separately from more than one point of view.

Example cell: 0.6, −0.3

This is the data structure that keeps an assessment's value and how far that value can be trusted together as two separate components.

Example cell: A: 2, 5, 7 · B: 0.6, 0.8, 1

This is the data structure in which an assessment is made not with a number but with a term drawn from a predefined, ordered set of words, and in which the calculation runs on those terms without ever converting them into numbers.

Example cell: s₄, 0

This is the data structure that, when an assessment cannot be fixed precisely with the distinguishing information at hand, preserves both the bound of "certainly this" and the bound of "possibly this" together.

Example cell: 3, 5

This is the data structure that keeps a value as a probability distribution rather than a single figure, and returns the decision result as "which alternative is acceptable, and with what probability."

Example cell: Normal(ort., sapma)