Extension card · Plithogenic
Plithogenic SAW
This is the form of SAW for when criteria are given as degrees of truth, indeterminacy and falsity, and a criterion's contradiction to a dominant one is also taken into account. It carries out the weighted combination on these three degrees, and again ranks the result with a single score.
Base method
SAW →
Philosophy, mechanics, strengths and weaknesses are on the base method card; this card describes only the difference.
Data type (family)
Plithogenic →
What this data type is, when to use it, how to write it in a cell: the family's full account is here.
What Changes from the Base Method?
Four things change; the decision logic does not.
Cells. In crisp SAW every cell is a single number. Here every cell consists of three degrees: truth, indeterminacy, falsity. In addition, every criterion is given a contradiction degree relative to whichever criterion is taken as dominant within the criterion set. The dominant criterion's contradiction is zero; the other criteria take a number according to how far they oppose it. This applies the sub-alternative idea from the plithogenic data-type card at the criterion level: here the criteria themselves are treated as sub-alternatives of a single overall suitability question.
Scale equalisation. Crisp SAW divides every column by its own best value. Here equalisation works differently. For a cost criterion, the truth and falsity degrees are swapped first; this is the plithogenic counterpart of reversing the three numbers. Each criterion's contradiction degree is then worked into the cell: the cell is pulled, by an amount equal to its contradiction, towards the dominant criterion's reference value, that is, fully true, zero indeterminacy, zero false. Because the dominant criterion's contradiction is zero, this step never changes it; the most contradictory criterion's cell changes the most.
Combination. Crisp SAW multiplies the equalised columns by their weights and sums them directly. Here a plithogenic weighted average is used instead of a sum. Each criterion's adjusted three degrees combine with a power equal to the criterion's weight; on the truth dimension the weighted product of the probability of "not being true at all" is taken and subtracted from one, while on the indeterminacy and falsity dimensions the weighted product is taken directly. This is a multiplicative combination, unlike crisp SAW's additive average.
Result and defuzzification. A single score is drawn from the three combined degrees: truth plus, twice indeterminacy and falsity minus, divided by two. Crisp SAW has no defuzzification step because the calculation already starts from a single number; here defuzzification happens at the last step, through this score function that reduces the three degrees to a single score.
DecisionMind fixes, for classical Plithogenic SAW, the way the contradiction degree pulls towards the dominant value, the weighted combination, and this score function. Weights are taken from outside and must sum to 1; the method does not generate weights.
How to Read the Output
The score is read like crisp SAW's total score: it produces a ranking only within this alternative set, and it is not a percentage or a probability. The difference is here. Beneath the score now lies a three-degree structure and an assumption about contradiction between criteria; if that assumption changes, so does the score. Which criterion is dominant, and how the contradiction degrees are assigned, determine the result as much as the weights do.
Thus instead of writing:
"Plithogenic SAW has found the best alternative"
the report should read:
"With these weights, these truth-indeterminacy-falsity degrees and this contradiction assumption, the alternative with the highest score is this one; the ranking may change if the dominant criterion or the contradiction degrees change"
When to Prefer This over the Base Method
If criteria are measured, crisp SAW is enough. Plithogenic SAW is used when criteria are given by expert judgement as degrees of truth, indeterminacy and falsity, one criterion is clearly dominant over the others, and the contradiction between them rests on a measurable basis. If the contradiction degree does not come from a measurable basis, that is, if it has been given by feel, this structure should not be built, as the plithogenic data-type card warns; the result would then rest on an invented number. The matrix must be of a single kind: if some criteria are crisp, these too are written as a cell in which all three components are equal, fully true, zero indeterminacy, zero false. SAW's exit condition applies here too: if no compromise is acceptable on one criterion, this extension is also compensatory and will not screen out anything below a threshold.
Mistakes Specific to This Extension
Confusing the contradiction degree with the criterion's importance. As the plithogenic data-type card also warns, the contradiction degree measures how far a criterion opposes the dominant criterion, not how important it is. Importance is already given by the weight; raising the contradiction as if it were importance too counts the same information twice.
Assigning the contradiction degree without justification. The calculation uses this number to decide how far to pull each criterion's cell towards the dominant criterion's reference value. If the number is given by feel, the defuzzification is also done by feel.
Changing the dominant criterion after the analysis is finished. Which criterion is taken as dominant, and the others' contradiction relative to it, are declared before the analysis. Changing the dominant criterion afterwards invalidates every contradiction degree and, with it, the result.
Inventing a contradiction degree for a criterion that has no sub-alternative. If a criterion genuinely has no contradiction positioned relative to a dominant reference, a measured price, say, this structure becomes indistinguishable from a neutrosophic one. Adding a contradiction degree then only adds apparent complexity.
The governing principle is this:
The contradiction degree is a number that must come from a measurable basis measuring how far a criterion opposes the dominant reference; if it is confused with importance or invented, Plithogenic SAW's one distinctive contribution, this distinction, is lost.
Cases
The first case is DecisionMind's validation example. Smarandache's founding 2018 source defines the plithogenic operations but contains no decision-matrix example; DecisionMind has therefore built a small, hand-traceable table to demonstrate the correctness of the formulas. The second case is an illustrative construction.
1. Illustrative example: Comparing three software licence quotations on three criteria (DecisionMind validation example)
An organisation will choose one of three software licence quotations. There are three criteria: technical fit, service quality, licence cost. Cost is "less is better", the other two "more is better". The organisation takes technical fit as the dominant criterion, with a contradiction of zero; service quality is moderately contradictory to it (1/3), and cost is the most contradictory (2/3). The weights are 0.40 for technical fit, 0.35 for service quality, 0.25 for cost.
| Quotation | Technical fit | Service quality | Licence cost |
|---|---|---|---|
| A1 | (0.70; 0.20; 0.10) | (0.50; 0.30; 0.20) | (0.60; 0.30; 0.20) |
| A2 | (0.80; 0.10; 0.10) | (0.60; 0.20; 0.20) | (0.40; 0.20; 0.30) |
| A3 | (0.60; 0.20; 0.20) | (0.70; 0.20; 0.10) | (0.50; 0.30; 0.20) |
| Direction | more is better | more is better | less is better |
| Contradiction | 0 | 1/3 | 2/3 |
| Weight | 0.40 | 0.35 | 0.25 |
The method first swaps the truth and falsity degrees of the cost criterion. It then pulls each criterion's cell towards the dominant reference by an amount equal to its contradiction degree; technical fit does not change at all, cost changes the most. Finally it combines the three criteria in a weighted fashion and reduces the result to a single score.
| Quotation | Score | Rank |
|---|---|---|
| A2 | 0.726 | 1 |
| A3 | 0.647 | 2 |
| A1 | 0.615 | 3 |
The result reads as follows. A2 has the highest truth (0.80) on technical fit, the dominant criterion, and because this criterion's contradiction is zero, it enters the score entirely unsoftened. A3 is lower on technical fit but is the best on service quality; because this criterion's weight is close to that of technical fit, this carries A3 into second place. A1 is best on no criterion and therefore comes last.
The organisation's hesitation: had A1's truth on technical fit risen from 0.70 to 0.90, that is, a clear improvement on the heaviest criterion, A1's score would rise to 0.669, overtaking A3 (0.647) and moving into second place; A2 remains first regardless. Even if the weights were swapped, so that technical fit carries 0.35 and service quality 0.40, A2 keeps first place with 0.722, and only the gap between A1 and A3 narrows. This shows that A2's first place is robust, while the ranking between A1 and A3 is sensitive to a small improvement on the dominant criterion and to the distribution of weights.
In the report: "A2 is first with a score of 0.726 because it has the highest truth on technical fit, the dominant criterion, whose contradiction is zero; the second place between A1 and A3 can change with a small increase in the truth on technical fit."
Source: DecisionMind's Plithogenic SAW validation example. It is a hand-traceable, synthetic 3×3 table built on Smarandache's (2018) plithogenic intersection and defuzzification operations; it is presented as an illustrative example because the founding source contains no decision-matrix example. The scores and sensitivity scenarios were independently computed in Python by this card's author, and matched DecisionMind's own validation record exactly (A1 = 0.6149; A2 = 0.7257; A3 = 0.6472).
2. Museum curation: Choosing among three collection-loan offers for a temporary exhibition
A museum will build its next season's temporary exhibition around one of three collection-loan offers from different institutions. There are three criteria: the artworks' suitability for the exhibition, the lending institution's transport and insurance assurance, and the loan fee. Fee is "less is better", the other two "more is better". The curatorial board takes exhibition suitability as the dominant criterion, treats assurance as moderately contradictory to it, and fee as the most contradictory. Each offer is scored on the three criteria with degrees of truth, indeterminacy and falsity.
The method reverses the fee criterion, pulls every cell towards the dominant reference according to the contradiction degrees, and combines the criteria in a weighted fashion. Suppose the result places first the offer with the highest truth on exhibition suitability but high indeterminacy on assurance, and second the offer with strong assurance but works less suited to the exhibition.
The board's hesitation: if the indeterminacy on the first offer's assurance criterion were to fall, that is, if the insurance and transport conditions were clarified, its score would rise further. As it stands, this indeterminacy should be stated separately in the report, because the strength on exhibition suitability does not by itself remove the question mark over assurance.
In the report: "The offer with the highest truth on exhibition suitability ranks first; the indeterminacy degree on this offer's assurance criterion is still high, and clarifying it before the contract is recommended."
3. What Not to Do
Raising the contradiction degree of the service-quality criterion in the illustrative example because that criterion is seen as less important: the contradiction degree measures opposition to the dominant criterion, not importance, and confusing the two counts the same information twice. The second error is running the licence-cost criterion without reversing it, that is, without swapping truth and falsity; the most expensive quotation would then also be treated as advantaged on the cost criterion. The third error is switching the dominant criterion from technical fit to service quality once the analysis is finished; this invalidates all three contradiction degrees and requires the result to be recalculated.
Sources
For the formulas behind each step, the intermediate tables and citation formats, see the DecisionMind method page: decisionmind.app/library/p-saw
Mohamed, M., Ayman, S., & Sleem, A. (2024). Valuation of Internet of Energy (IoE) Platforms in Smart Cities: A Hybrid Multi-Criteria Decision Making Approach. Plithogenic Logic and Computation, 1, 96–107. DOI: 10.61356/j.plc.2024.1253
Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research, 15(3), 537–542. DOI: 10.1287/opre.15.3.537
Smarandache, F. (2017). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing House, Brussels. (no DOI)
Smarandache, F. (2018). Plithogenic Set, an Extension of Crisp, Fuzzy, Intuitionistic Fuzzy, and Neutrosophic Sets – Revisited. Neutrosophic Sets and Systems, 21, 153–166. DOI: 10.5281/zenodo.1408740