Data types
Picture Fuzzy
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
What Is It?
A picture fuzzy data structure expresses an alternative's assessment on a criterion through three degrees: support for the judgement (μ, yes), abstention (η, neither yes nor no), and rejection (ν, no). All three lie between 0 and 1, and their sum cannot exceed 1. The remainder is the "no opinion given" share: those who did not vote, left the question blank, or did not engage with it.
This structure is the natural counterpart of a vote or a survey. For a board member, "55 per cent yes, 20 per cent abstain, 25 per cent no" is directly the cell (0.55, 0.20, 0.25). Unlike the intuitionistic fuzzy structure, the abstention share is not left over from support and rejection; it is a separate degree. Unlike the neutrosophic structure, the three degrees are bound together: the sum cannot exceed 1, because the same person cannot say both a full yes and a full no at once.
When to Use It
Use it wherever the assessment is gathered as a vote, a survey, or a distribution of opinion. A board's votes on a proposal, a survey's support/opposition/undecided proportions, an expert panel's spread of opinion: these are the type's natural sources. It suits problems where the undecided share matters to the decision itself, where "wherever the undecided go, the result follows."
By contrast, where the assessment is a single expert's single numerical judgement, or the abstention share was never separately measured, there is no need for the picture fuzzy structure; inventing that share afterwards removes its whole contribution.
Can Classical Data Be Converted into Picture Fuzzy Data?
Yes, but each of the three degrees must come from separately counted shares of the same measurement. Two steps are required.
First, turn the criterion into a judgement: the proposition "the alternative meets this criterion." A price or a measurement is not itself true or false; but the judgement "this price is acceptable" can be counted as yes, abstain or no.
Second, count each of the three degrees separately from the same source: μ as the proportion saying yes, η as the proportion abstaining, ν as the proportion saying no. Those who gave no opinion are written into none of the three degrees; the remaining share is theirs.
What must not be done is deriving the other two degrees from a single support figure: "55 per cent support, so 45 per cent must be against" sets the abstention share to zero and reduces the structure to an ordinary proportion.
Abstaining Is Not the Same as Not Responding
The abstention degree (η) is the share of those who heard the question and said "neither yes nor no." The no-opinion share is the share of those who did not answer at all, measured by what is left over from the sum of the three degrees. Someone who ticks "undecided" is not the same as someone who leaves the survey blank.
For that reason:
"Everyone who is neither yes nor no is an abstainer"
is better replaced by:
"Abstention is counted separately; non-response is kept as the share left outside the three degrees"
If the abstention share and the non-response share are put in the same basket, the audience that could be won over with more information gets confused with the audience that cannot be reached at all.
Strengths
The chief advantage of the picture fuzzy structure is that it carries a distribution of opinion into the calculation without reducing it to a single proportion. Where two alternatives share the same support rate but one has many opponents and the other many undecided voters, this difference is preserved and shown in the ranking.
Furthermore, keeping abstention as a separate component shows how "settled" the decision is: the result for an alternative with a large abstention share can still shift with further information.
Limitations
Because the three degrees are counted from the same source, they are bound together; missing information and opposing opinion cannot be represented separately. "I am undecided" and "there is no information on this" fall into the same component. Where the unknown share must be carried independently of the opposing view, a neutrosophic structure is required.
For ranking, the three degrees must be reduced to one value, and how abstention is weighted in that reduction is itself a choice; different score functions can give different rankings. The structure is designed for a distribution of opinion; it does not suit a single expert's approximate judgement (fuzzy) or several plausible values (hesitant).
Common Mistakes
The most frequent mistake is generating a triple from a single proportion: writing (0.55, 0, 0.45) from "55 per cent support" ignores the abstention share, and erases the structure's one real contribution.
The second is filling in the abstention share afterwards by guesswork: if a survey offered no "undecided" option, η is simply unknown, not "presumably around 10 per cent." A third is forcing the triple's sum to equal 1, or writing one whose sum exceeds 1; the first erases the non-response share, the second is invalid for this type.
The governing principle is this:
Every degree in a picture fuzzy triple must be a share counted separately in the same measurement; no degree may be derived from the others, and their sum must not exceed 1.
Examples
Each example opens with a familiar, single exact figure and shows the conditions and steps under which that same figure moves into picture fuzzy form.
1. Public sector: A proposal accepted in committee by 14 votes
An exact figure. An investment proposal was accepted by a provincial coordination committee with 14 votes. "14 votes" is a counted value.
Step 1: Turn the criterion into a judgement. The choice is among three investment proposals, on the criterion "committee support." Judgement: "The committee supports this proposal."
Step 2: Count the three degrees separately from the same vote. In a 25-member committee, 14 voted yes, 5 abstained, 4 voted no; 2 members were absent.
In picture fuzzy form. On the criterion "committee support," the proposal is (0.56, 0.20, 0.16). The sum is 0.92; the remaining 0.08 is the share of the two absent members. The summary "accepted by 14 votes" hides the presence of five abstainers; a second proposal could have the same summary with 14 yes, 0 abstain, 9 no, yet the picture inside the committee would differ.
Same figure, different case. Had the committee voted only "yes / no," there would be no abstain option; the result would be 14 yes, 9 no, 2 absent → (0.56, 0, 0.36). This triple is valid, but η = 0 is a measured zero, not an invented one. An abstain option that was never offered in a vote is not added to it afterwards.
2. Business: 62 per cent customer satisfaction
An exact figure. A product's customer survey shows a satisfaction rate of 62 per cent. The survey's summary result is a single proportion.
Step 1: Turn the criterion into a judgement. The choice is among three product lines, on the criterion "customer acceptance." Judgement: "Customers accept this product."
Step 2: Count the three degrees separately from the same survey. The full distribution across 500 customers: 310 satisfied, 90 undecided, 75 not satisfied, 25 left the question blank.
In picture fuzzy form. On the criterion "customer acceptance": (0.62, 0.18, 0.15). The sum is 0.95; the remaining 0.05 is the share of non-respondents.
Same figure, different case. Let the second product line's distribution be 62 per cent satisfied, 30 per cent undecided, 5 per cent not satisfied, 3 per cent non-response: (0.62, 0.30, 0.05). The satisfaction rate is the same, but this product has few dissatisfied customers and many undecided ones; it could be won over with a small improvement. A single proportion would show the two as equal; the picture fuzzy structure tells them apart.
3. Education: 70 per cent "agree" in a course evaluation
An exact figure. In an end-of-term course evaluation, 70 per cent agreed with the statement "the course objectives were clear."
Step 1: Turn the criterion into a judgement. The choice is among three course designs, on the criterion "student approval." Judgement: "Students approve of this design."
Step 2: Count the three degrees separately from the same survey. Of 40 students: 28 agree, 6 undecided, 4 disagree; 2 students left the item blank.
In picture fuzzy form. On the criterion "student approval": (0.70, 0.15, 0.10). The sum is 0.95; the remaining 0.05 is the share of those who left it blank.
Same figure, different case. The same course is evaluated again the following term, and most of the undecided move to "agree": (0.82, 0.03, 0.10). It is the same course; what has changed is that the abstention share has dissolved with further experience. This shows why keeping the abstention degree separate matters for the decision.
4. Medicine: 6 physicians on a board said "start treatment"
An exact figure. A board discussed whether to start drug treatment for a patient; 6 physicians said "it should be started."
Step 1. The choice is among three treatment options, on the criterion "necessity of starting drug treatment." Judgement: "Drug treatment should be started for this patient."
Step 2. Of 10 physicians, 6 said "should be started," 2 said "lifestyle change should be tried first, I am undecided," 1 said "should not be started," and 1 gave no opinion.
In picture fuzzy form. (0.60, 0.20, 0.10). The remaining 0.10 is the share of the physician who gave no opinion. If, after further tests, the two undecided physicians move to "should be started," it becomes (0.80, 0, 0.10); the triple shows where the evidence flows as it accumulates.
5. What Not to Do
Generating a triple from a single proportion: 62 per cent → (0.62, 0.15, 0.20), reasoning "there is probably some undecided, some dissatisfied." Where the abstention and rejection degrees have not been counted, they are not written in. A single measured proportion stays crisp; a picture fuzzy triple arises only from a vote or a survey in which all three shares have been separately counted.
The numbers in the examples are fictional; they are not real data.
Short decision rule
A single, reliable value → Crisp
Support and rejection degrees, abstention left over from these → Intuitionistic fuzzy
Yes / abstain / no each separately counted, sum at most 1 → Picture fuzzy
Yes / abstain / no degrees independent of one another, sum may exceed 1 → Neutrosophic
Sum of the squares of the three degrees does not exceed 1, wider freedom → Spherical fuzzy
A survey's margin of error → Not picture fuzzy, but stochastic
Key sources
Cường, B. C. (2014). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4), 409–420. DOI: 10.15625/1813-9663/30/4/5032
Cuong, B. C., & Kreinovich, V. (2013). Picture fuzzy sets – A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013), 1–6. DOI: 10.1109/WICT.2013.7113099
Garg, H. (2017). Some picture fuzzy aggregation operators and their applications to multicriteria decision-making. Arabian Journal for Science and Engineering, 42(12), 5275–5290. DOI: 10.1007/s13369-017-2625-9
Son, L. H. (2016). Generalized picture distance measure and applications to picture fuzzy clustering. Applied Soft Computing, 46, 284–295. DOI: 10.1016/j.asoc.2016.05.009
Jana, C., Senapati, T., Pal, M., & Yager, R. R. (2019). Picture fuzzy Dombi aggregation operators: Application to MADM process. Applied Soft Computing, 74, 99–109. DOI: 10.1016/j.asoc.2018.10.021