Extension card · Plithogenic
Plithogenic COPRAS
This is the form of COPRAS for situations where criterion scores are given as a truth-indeterminacy-falsity triple and criteria carry a degree of contradiction relative to one another. Its output is again a percentage utility degree relative to the best alternative.
Base method
COPRAS →
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 COPRAS every cell is a single number. Here every cell is three numbers: truth (T), indeterminacy (I), falsity (F). The sub-option idea from the plithogenic data-type card is carried, in this family, to the criteria themselves: one of the criteria is taken as dominant, with a contradiction degree of zero. How far each other criterion opposes this dominant criterion is given by a contradiction degree. This degree belongs to the criterion, not to the alternative; it does not vary from alternative to alternative.
Scale equalisation and scoring. Unlike the TOPSIS, VIKOR and MARCOS extensions, no complement is taken for a cost criterion here; COPRAS already manages the direction difference by summing benefit and cost separately, and this family follows the same route. Every cell is first adjusted by its own criterion's contradiction degree, then reduced to a single score (truth plus, twice indeterminacy and falsity minus, divided by two). This score is shifted by one to make it positive and, as in crisp COPRAS, divided by the column sum to turn it into a share; it is then multiplied by the weight.
Benefit and cost sums. As in crisp COPRAS, the weighted shares in the benefit criteria are summed separately, and the weighted shares in the cost criteria are summed separately. The only difference here is that the shares derive from a score built out of T-I-F; the summation and the relative-significance calculation are otherwise identical to crisp COPRAS.
Result and defuzzification. The relative significance values are divided by the highest one and converted to a percentage; the best alternative is again 100. Indeterminacy (I) and falsity (F) are not defuzzified in a separate step; they dissolve inside the score function, and the remaining steps proceed with crisp numbers.
DecisionMind holds the contradiction adjustment, the score function and the benefit/cost split fixed in this family. Criterion weights and contradiction degrees are taken from outside, separately.
How to Read the Output
The output is a utility degree, as in crisp COPRAS, and it is read the same way: the best alternative is always 100, this is not an absolute success percentage but a share relative to the best in this set, and it cannot be compared with a different analysis.
The difference lies here: this share is now the benefit-cost balance of a score derived from T-I-F, and this score has itself already been adjusted according to the criterion's contradiction degree. In the illustrative example below, the gap between A3 (95.39) and A2 (100.00) looks small; once the weights shift towards C2, this gap closes and A3 can even overtake A2.
Thus instead of writing:
"Plithogenic COPRAS found this alternative 95 per cent successful"
the report should read:
"With these weights and these contradiction degrees, this is the alternative with the highest benefit-cost balance; the second alternative reaches 95.39 per cent of this alternative's utility degree, and this share is sensitive to the weight distribution"
When to Prefer This over the Base Method
This extension is appropriate when your criterion scores are given as truth-indeterminacy-falsity triples and you want to report the result as "a percentage relative to the best." If some criteria are a more independent, or more contradictory, source of information than others, this strengthens the case for it. Where the T-I-F triple alone suffices and no such dominance-contradiction relationship exists between criteria, neutrosophic COPRAS (n-copras) is already sufficient; the plithogenic form adds one further assumption, and if that assumption is supplied without justification it creates a spurious distinction.
Turning a measured criterion into a T-I-F triple is producing uncertainty, not modelling it. Because the score function never produces a negative value, crisp COPRAS's restriction on negative values does not directly apply here. But this is not a relaxation: the T-I-F triple itself must still be valid within [0,1].
Mistakes Specific to This Extension
Violating the value space. Every cell must be a valid T, I, F triple, and every criterion's contradiction degree must be declared.
Assigning the contradiction degree without justification. When the weights are shifted from C1 to C2 (0.40→0.10, 0.35→0.65), A3 overtakes A2; the same kind of shift can also be produced through the contradiction degree. The degree must rest on a justification for the criterion's independence relative to the dominant criterion; it must not be chosen to pull the ranking in a desired direction.
Including a cost criterion in the benefit sum. In this family, as in crisp COPRAS, benefit and cost are summed separately; mistakenly writing a cost criterion into the benefit side rewards the highest-cost alternative.
Reading the utility degree as an absolute quality percentage. This mistake in crisp COPRAS applies here too; a value of 100 only means that alternative is the best within this set.
The governing principle is this:
The plithogenic COPRAS result is a summary of the T-I-F scores' benefit-cost balance relative to the best in this set; this summary is sensitive both to the weights and to the assumed contradiction degree between criteria.
Cases
The first case is DecisionMind's validation example. Smarandache's founding 2018 source defines the plithogenic operations but contains no decision-table example; DecisionMind has therefore built a small, hand-traceable table with the same formulas. The second case is an illustrative construction.
1. Illustrative example: Three alternatives, three criteria (DecisionMind validation example)
Three alternatives are evaluated on three criteria. The first two criteria are "more is better," the third is "less is better." C1 is taken as the dominant criterion with a contradiction degree of zero; C2 and C3's contradiction degrees relative to C1 are 0.33 and 0.67 respectively.
| Alternative | C1 (more is better) | C2 (more is better) | C3 (less is better) |
|---|---|---|---|
| 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) |
| Contradiction degree | 0.00 | 0.33 | 0.67 |
| Weight | 0.40 | 0.35 | 0.25 |
The method adjusts every cell by its own criterion's contradiction degree, reduces it to a single score, divides it by the column sum to convert it to a share, multiplies by the weight; it sums the shares in C1 and C2 (benefit) into a benefit sum, keeps the share in C3 (cost) separate as a cost sum, and combines the two into a percentage.
| Alternative | Utility degree | Rank |
|---|---|---|
| A2 | 100.00 | 1 |
| A3 | 95.39 | 2 |
| A1 | 94.15 | 3 |
The result reads as follows. A2 has the highest score on C1, the most heavily weighted criterion, and the lowest cost share on C3; these two criteria carry a combined weight of 0.65 and carry A2 to the highest utility degree. A3 has the highest score on C2, but this criterion's weight (0.35) and contradiction degree (0.33) are moderate, so it falls slightly behind A2.
The board's hesitation: if the weights are pulled from C1's 0.40 to 0.10 and from C2's 0.35 to 0.65 (C3 fixed at 0.25), the utility degrees become A1 = 93.99, A2 = 98.95, A3 = 100.00, and A3 moves into first place. This shows that once C2's weight is raised enough, A3's superiority on C2 can outweigh A2's superiority on C1 and C3.
In the report: "With the weights given (C1 = 0.40, C2 = 0.35, C3 = 0.25), A2 has the highest utility degree (100.00); the gap with A3 (95.39) is small, and A3 moves ahead once C2's weight is raised well above C1's (C1 = 0.10, C2 = 0.65)."
Source: DecisionMind's P-COPRAS validation manifest, verification example. The plithogenic operations rest on the formulas defined by Smarandache (2018); since the founding source gives no decision-table example, the table was constructed by DecisionMind faithfully to the formulas. The utility degrees and the weight scenario were independently recomputed by this card's author using the same algorithm.
2. Fisheries: A cooperative's choice of new fishing-vessel supplier
A fishing cooperative must decide between three shipyard quotations for a new vessel joining the fleet. Three criteria are used: the vessel's fuel efficiency, cold-storage capacity, and purchase price ("less is better"). The cooperative treats fuel efficiency as the dominant criterion; it judges that storage capacity carries information independent of efficiency, and that purchase price is more independent still than either, and sets the contradiction degrees accordingly. Each quotation is scored on each criterion with a T-I-F triple; the indeterminacy comes from verbal commitments made by the shipyards outside the technical specification.
The method finds the three quotations' benefit and cost sums separately and combines the two into a percentage. Suppose the quotation with the highest fuel efficiency is also the most expensive, and it still comes out first, because the weight on fuel efficiency exceeds that on price.
The cooperative's hesitation: if purchase price's contradiction degree has been kept low, this quotation's true financing burden may not carry enough weight in the ranking. The cooperative should not decide on the utility degree alone without applying a separate ceiling for price.
In the report: "With the high weight given to fuel efficiency, the most efficient quotation reaches the highest utility degree; since purchase price's contradiction degree has been kept low, a separate ceiling is recommended for financing burden."
3. What Not to Do
Had C3 (cost) been mistakenly included in the benefit sum in the illustrative example, the highest-cost alternative would be rewarded and the advantage A2 gains from low cost would be reversed. The second error is raising C2's contradiction degree without justification "to push A3 ahead." The third error is reporting A2's degree of 100.00 as "perfect"; this value only means it is the best among these three alternatives.
Sources
For the formulas behind each step, the intermediate tables and citation formats, see the DecisionMind method page: decisionmind.app/library/p-copras
Zavadskas, E. K., & Kaklauskas, A. (1996). Determination of an efficient contractor by using the new method of multicriteria assessment. In International Symposium for the Organization and Management of Construction: Shaping Theory and Practice, Vol. 2, 94–104. (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
Smarandache, F. (2017). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing House, Brussels. (no DOI)
Abdel-Basset, M., & Mohamed, R. (2020). A novel plithogenic TOPSIS-CRITIC model for sustainable supply chain risk management. Journal of Cleaner Production, 247, 119586. DOI: 10.1016/j.jclepro.2019.119586
Abdel-Basset, M., El-hoseny, M., Gamal, A., & Smarandache, F. (2019). A novel model for evaluation Hospital medical care systems based on plithogenic sets. Artificial Intelligence in Medicine, 100, 101710. DOI: 10.1016/j.artmed.2019.101710