Extension card · Neutrosophic
Neutrosophic WISP (Stanujkić et al., 2022)
Neutrosophic WISP is the form of WISP used when the values in the decision table are given as degrees of truth, indeterminacy and falsity. It computes the four comparison logics by combining benefit and cost criteria in separate groups through neutrosophic summation and multiplication operations.
Base method
WISP →
Philosophy, mechanics, strengths and weaknesses are on the base method card; this card describes only the difference.
Data type (family)
Neutrosophic →
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?
Three things change. The four comparison logics and the decision logic stay the same.
Cells. In crisp WISP every cell is a single number. Here every cell consists of a degree of truth (T), indeterminacy (I) and falsity (F); all three lie between 0 and 1, and their sum cannot exceed 3. The founding source (Stanujkić et al., 2022) proposes deriving these triples from a nine-term linguistic scale (from "very poor" to "very good"). DecisionMind does not perform this scale mapping itself; the user enters the T, I, F degrees directly, the same input form as the rest of the family (such as Neutrosophic WPM). Criterion weights are taken from outside as crisp numbers.
Keeping the benefit and cost groups separate. Crisp WISP normalises cost cells the same way as benefit cells, dividing the column by its own largest value; it handles direction only at the fourth step, by keeping the benefit and cost sums separate, and never reverses any single cell on its own. Neutrosophic WISP preserves the same logic: a cost criterion's T, I, F values are not complemented, and stay as they are, the truth-indeterminacy-falsity degree of the judgement "this alternative has a high value on this criterion" for the criterion it belongs to. This is where it departs from Neutrosophic WPM within the same family, which complements the cost cell immediately (writing F, I, T instead of T, I, F). Because crisp WPM reverses the cost column in a single step, the WPM extension also complements the cell; because crisp WISP performs no such reversal, the WISP extension does not complement it either. Instead, DecisionMind gathers benefit criteria into one group and cost criteria into a separate group; each group's weights are renormalised to sum to 1 within that group.
Aggregation and defuzzification. For every alternative, the benefit group is reduced to two separate triples: one by a neutrosophic weighted ARITHMETIC mean (SVNWA, the neutrosophic counterpart of additive logic), and one by a neutrosophic weighted GEOMETRIC mean (SVNWG, the counterpart of multiplicative logic). The same operation is carried out for the cost group. This yields four neutrosophic triples: S^max, S^min (additive) and P^max, P^min (multiplicative). These four are reduced to single numbers by Stanujkić's own formula (s = (2 + T − I − F) / 3). This score function differs from the s = (1 + T − 2I − F) / 2 function seen elsewhere in the family (such as Neutrosophic WPM and Neutrosophic ARAS): it penalises indeterminacy once rather than twice, and its constant is 2/3 rather than 1/2. Four crisp numbers result (s_max, s_min, p_max, p_min). From these, crisp WISP's four measures are built: the additive difference (s_max − s_min), the multiplicative difference (p_max − p_min), the additive ratio (s_max / s_min), and the multiplicative ratio (p_max / p_min). All four are scaled against their own best alternative and then averaged.
For this extension, DecisionMind fixes Stanujkić's s = (2 + T − I − F) / 3 function as the defuzzification rule, along with the equally weighted average of the four measures. Weights are taken from outside; the method does not generate weights and does not support group decision-making.
How to Read the Output
The output is a score, as in crisp WISP. A value of 1 does not mean "perfect" but "the alternative all four logics find best in this set." The score is not a percentage and cannot be compared with the score of a different analysis.
The difference is this: each logic (additive, multiplicative) here passes first through the neutrosophic combination of the benefit and cost groups on their own, and then through the defuzzification of this combined triple. When two alternatives' scores are very close, this closeness may stem from genuine similarity in performance, or from the indeterminacy degrees (I) largely cancelling one another out within the score function. This distinction cannot be made without inspecting the T, I and F components separately.
Thus instead of writing:
"Neutrosophic WISP found the best alternative decisively"
the report should read:
"With these triple inputs, the average of the four comparison logics favours A1; the gap to A3 is very small, and the order between these two alternatives could change with a small shift in a criterion weight or a single cell"
When to Prefer This over the Base Method
Consider this extension when criteria come not from a measurement but from a judgement whose truth is contested, incomplete or contradictory. Which situations can be carried into the neutrosophic structure is covered on the Neutrosophic data-type card. Crisp WISP's exit condition applies here too: this extension is unsuitable when no compromise is acceptable on one criterion.
Forcing the sum of T + I + F to equal 1, or deriving I as 1 − T − F, zeroes out this extension's contribution; the general warning on the neutrosophic data-type card applies here too. Crisp WISP's sensitivity to a near-zero cell also carries over: if a group's aggregated T is very close to zero, the multiplicative measures (P^max, P^min) give an unreliable result.
Mistakes Specific to This Extension
Wrongly complementing the cost cell. In this extension, a cost criterion's (T, I, F) triple is not reversed into (F, I, T), unlike some other members of the family (such as Neutrosophic WPM). It enters the cost group as it is; the penalty happens in the difference/ratio operation at the fourth step. Computing it with the cell complemented rewards cost in the wrong direction.
Confusing the score function. This extension's defuzzification rule is s = (2 + T − I − F) / 3. The s = (1 + T − 2I − F) / 2 function used elsewhere in the family (Neutrosophic WPM, Neutrosophic ARAS) does not apply here. The two formulas carry different constants and a different weight on indeterminacy (once versus twice); confusing one with the other can produce a different ranking.
Forcing T + I + F to sum to 1. The neutrosophic components are independent; forcing their sum to equal 1 removes the structure's one contribution, carrying contradictory or incomplete evidence separately.
Never checking the four measures separately. Reporting only the final average, without checking whether the additive difference, multiplicative difference, additive ratio and multiplicative ratio favour the same alternative; when they disagree, the average conceals this disagreement.
The governing principle is this:
In Neutrosophic WISP, cost criteria are not complemented; they are gathered into a separate group and subtracted from, or set in ratio to, the benefit group. This extension's defuzzification rule differs from the rest of the family and must not be confused with it.
Cases
The first case is DecisionMind's validation example. The manifest record states that the founding paper's (Stanujkić et al., 2022) own numerical example was not carried into the engine pool; instead, a closed-form synthetic example based on F.steps was constructed. The second case is an illustrative construction.
1. Illustrative example (DecisionMind's validation example): Comparing three suppliers with Neutrosophic WISP
A firm is assessing three suppliers (A1, A2, A3) on three criteria. The first two criteria are "higher is better", the third is "lower is better"; the third criterion's cells are the truth-indeterminacy-falsity degree of the judgement "this supplier's cost is high."
| Supplier | Criterion 1 (higher is better) | Criterion 2 (higher is better) | Criterion 3 (cost level, lower is better) |
|---|---|---|---|
| A1 | (0.70; 0.20; 0.10) | (0.60; 0.30; 0.20) | (0.50; 0.40; 0.30) |
| A2 | (0.50; 0.30; 0.40) | (0.70; 0.20; 0.20) | (0.60; 0.30; 0.30) |
| A3 | (0.80; 0.10; 0.20) | (0.50; 0.40; 0.30) | (0.70; 0.30; 0.20) |
| Weight | 0.40 | 0.30 | 0.30 |
The method gathers Criterion 1 and 2 into the benefit group, and Criterion 3 into the cost group. It reduces each group to a triple with SVNWA (additive) and SVNWG (multiplicative), defuzzifies to four crisp values (s_max, s_min, p_max, p_min), and computes and averages the four measures (additive and multiplicative difference, additive and multiplicative ratio).
| Supplier | Neutrosophic WISP score | Rank |
|---|---|---|
| A1 | 1.0000 | 1 |
| A3 | 0.8790 | 2 |
| A2 | 0.8787 | 3 |
The result reads as follows: although A1 is not the highest on the first criterion (A3 is), it has the lowest truth of "high cost" on the cost criterion (0.50), and this puts it ahead of the other two on all four measures.
The firm's hesitation lies in second and third place; the gap between A3 and A2 is only 0.0003, effectively a tie. This tie is fragile against criterion weight. If the weight given to the first criterion is lowered from 0.40 to below 0.30 (with the remaining weight distributed to the second and third criteria in their original proportion), A2 moves ahead and pushes A3 into second place; A3 stays second as long as the weight remains at 0.40 or above. Also, if A2's "high cost" truth on the cost criterion (0.60) improves by only 0.05, that is, falls to 0.55, A2 overtakes A3 again. Both tests have been verified independently in Python. A1's lead, by contrast, is far more robust; it stays first across the entire weight range tested.
In the report: "A1 is clearly first on the average of the four comparison logics (1.0000). The second-third order between A3 and A2, being nearly tied (0.8790 against 0.8787), could change with a small shift in a criterion weight or in the cost estimate; no firm priority between these two suppliers is claimed."
Source: DecisionMind's validation example for N-WISP. The founding source (Stanujkić et al., 2022) defines the method's SVNWA/SVNWG aggregation steps and defuzzification formula (Eq. 7), but the paper's own numerical example was not carried into the engine pool; a small table, faithful to F.steps and traceable by hand, was therefore constructed by DecisionMind. The scores and sensitivity tests for this card were independently recomputed in Python.
2. Agriculture: A dairy farm's choice of feed supplier
A dairy farm business will choose one of three supplier quotations (T1, T2, T3) to meet its annual forage requirement. Two criteria are "higher is better": the feed's nutritional value and the supplier's delivery reliability. The third is a cost criterion given by the truth-indeterminacy-falsity degree of the judgement "this supplier's price is high." Because the farm's management largely knows delivery reliability from past records, it assessed it with low indeterminacy; it assessed nutritional value, from a new supplier, with higher indeterminacy.
The method splits the three quotations into benefit and cost groups, combines each group with SVNWA and SVNWG, defuzzifies, and averages the four measures. Suppose the quotation with the highest nutritional value, also placing second on delivery reliability, still comes out ahead of a slightly higher-priced quotation.
The farm's hesitation: the high indeterminacy in the nutritional-value assessment, arising because the supplier is new, is a component the score function penalises. The farm should not finalise its decision without re-measuring this indeterminacy and updating the score after the first delivery batch.
In the report: "Weighted by nutritional value and delivery reliability, T1 leads. However, T1's nutritional-value assessment carries a high share of indeterminacy; once this indeterminacy falls after the first delivery, re-checking the result is recommended."
3. What Not to Do
The first error is complementing the illustrative example's cost criterion (Criterion 3) into (F, I, T) before computing; this unfairly rewards A2, the high-cost supplier. The second error is using another family member's score function (s = (1 + T − 2I − F) / 2) for defuzzification. This extension's own rule is s = (2 + T − I − F) / 3, and the two give different numbers. The third error is reporting the 0.0003 gap between A3 and A2 as a meaningful lead, and never testing how sensitive this tie is to a change in weight or data.
Sources
For the formulas behind each step, the intermediate tables and citation formats, see the DecisionMind method page: decisionmind.app/library/n-wisp
Stanujkić, D., Karabašević, D., Popović, G., Smarandache, F., Stanimirović, P. S., Saračević, M., & Katsikis, V. N. (2022). A single valued neutrosophic extension of the simple WISP method. Informatica, 33(3), 635–651. DOI: 10.15388/22-INFOR483
Stanujkić, D., Popović, G., Karabašević, D., Meidutė-Kavaliauskienė, I., & Ulutaş, A. (2023). An integrated simple weighted sum product method—WISP. IEEE Transactions on Engineering Management, 70(5), 1933–1944. DOI: 10.1109/TEM.2021.3075783
Smarandache, F. (1998). Neutrosophy: Neutrosophic Probability, Set, and Logic. American Research Press, Rehoboth. (no DOI)
Wang, H., Smarandache, F., Zhang, Y., & Sunderraman, R. (2010). Single valued neutrosophic sets. Multispace and Multistructure, 4, 410–413. (no DOI)