Fuzzy concept


A fuzzy concept is a concept of which the boundaries of application can vary considerably according to context or conditions, instead of being fixed once and for all. This means the concept is vague in some way, lacking a fixed, precise meaning, without however being unclear or meaningless altogether. It has a definite meaning, which can be made more precise only through further elaboration and specification - including a closer definition of the context in which the concept is used. The study of the characteristics of fuzzy concepts and fuzzy language is called fuzzy semantics. The inverse of a "fuzzy concept" is a "crisp concept".
A fuzzy concept is understood by scientists as a concept which is "to an extent applicable" in a situation. That means the concept has gradations of significance or unsharp boundaries of application. A fuzzy statement is a statement which is true "to some extent", and that extent can often be represented by a scaled value. The best known example of a fuzzy concept around the world is an amber traffic light, and indeed fuzzy concepts are widely used in traffic control systems. The term is also used these days in a more general, popular sense - in contrast to its technical meaning - to refer to a concept which is "rather vague" for any kind of reason.
In the past, the very idea of reasoning with fuzzy concepts faced considerable resistance from academic elites. They did not want to endorse the use of imprecise concepts in research or argumentation. Yet although people might not be aware of it, the use of fuzzy concepts has risen gigantically in all walks of life from the 1970s onward. That is mainly due to advances in electronic engineering, fuzzy mathematics and digital computer programming. The new technology allows very complex inferences about "variations on a theme" to be anticipated and fixed in a program.
The new neuro-fuzzy computational methods make it possible, to identify, to measure and respond to fine gradations of significance, with great precision. It means that practically useful concepts can be coded and applied to all kinds of tasks, even if, ordinarily, these concepts are never precisely defined. Nowadays engineers, statisticians and programmers often represent fuzzy concepts mathematically, using fuzzy logic, fuzzy values, fuzzy variables and fuzzy sets.

Origins

Problems of vagueness and fuzziness have probably always existed in human experience. From ancient history, philosophers and scientists have reflected about those kinds of problems.

Sorites paradox

The ancient Sorites paradox first raised the logical problem of how we could exactly define the threshold at which a change in quantitative gradation turns into a qualitative or categorical difference. With some physical processes this threshold is relatively easy to identify. For example, water turns into steam at 100 °C or 212 °F.
With many other processes and gradations, however, the point of change is much more difficult to locate, and remains somewhat vague. Thus, the boundaries between qualitatively different things may be unsharp: we know that there are boundaries, but we cannot define them exactly.
According to the modern idea of the continuum fallacy, the fact that a statement is to an extent vague, does not automatically mean that it is invalid. The problem then becomes one of how we could ascertain the kind of validity that the statement does have.

Loki's wager

The Nordic myth of Loki's wager suggested that concepts that lack precise meanings or precise boundaries of application cannot be usefully discussed at all. However, the 20th-century idea of "fuzzy concepts" proposes that "somewhat vague terms" can be operated with, since we can explicate and define the variability of their application, by assigning numbers to gradations of applicability. This idea sounds simple enough, but it had large implications.

Precursors

The intellectual origins of the species of fuzzy concepts as a logical category have been traced back to a diversity of famous and less well-known thinkers, including Eubulides, Plato, Cicero, Georg Wilhelm Friedrich Hegel, Karl Marx and Friedrich Engels, Friedrich Nietzsche, Hugh MacColl, Charles S. Peirce, Max Black, Jan Łukasiewicz, Emil Leon Post, Alfred Tarski, Georg Cantor, Nicolai A. Vasiliev, Kurt Gödel, Stanisław Jaśkowski and Donald Knuth.
Across at least two and a half millennia, all of them had something to say about graded concepts with unsharp boundaries. This suggests at least that the awareness of the existence of concepts with "fuzzy" characteristics, in one form or another, has a very long history in human thought. Quite a few logicians and philosophers have also tried to analyze the characteristics of fuzzy concepts as a recognized species, sometimes with the aid of some kind of many-valued logic or substructural logic.
An early attempt in the post-WW2 era to create a theory of sets where set membership is a matter of degree was made by Abraham Kaplan and Hermann Schott in 1951. They intended to apply the idea to empirical research. Kaplan and Schott measured the degree of membership of empirical classes using real numbers between 0 and 1, and they defined corresponding notions of intersection, union, complementation and subset. However, at the time, their idea "fell on stony ground". J. Barkley Rosser Sr. published a treatise on many-valued logics in 1952, anticipating "many-valued sets". Another treatise was published in 1963 by Aleksandr A. Zinov'ev and others
In 1964, the American philosopher William Alston introduced the term "degree vagueness" to describe vagueness in an idea that results from the absence of a definite cut-off point along an implied scale.
The German mathematician published a German-language paper on fuzzy sets in 1965, but he used a different terminology.
Two popular introductions to many-valued logic in the late 1960s were by Robert J. Ackermann and Nicholas Rescher respectively. Rescher's book includes a bibliography on fuzzy theory up to 1965, which was extended by Robert Wolf for 1966-1974. Haack provides references to significant works after 1974. Bergmann provides a more recent introduction to fuzzy reasoning.

Lotfi Zadeh

Usually the Iranian-born American computer scientist Lotfi A. Zadeh is credited with inventing the specific idea of a "fuzzy concept" in his seminal 1965 paper on fuzzy sets, because he gave a formal mathematical presentation of the phenomenon that was widely accepted by scholars. It was also Zadeh who played a decisive role in developing the field of fuzzy logic, fuzzy sets and fuzzy systems, with a large number of scholarly papers. Unlike most philosophical theories of vagueness, Zadeh's engineering approach had the advantage that it could be directly applied to computer programming.
Zadeh's seminal 1965 paper is acknowledged to be one of the most-cited scholarly articles in the 20th century. In 2014, it was placed 46th in the list of the world's 100 most-cited research papers of all time. Since the mid-1960s, many scholars have contributed to elaborating the theory of reasoning with graded concepts, and the research field continues to expand.

Definition

The ordinary scholarly definition of a concept as "fuzzy" has been in use from the 1970s onward.

Criteria

Radim Bělohlávek explains:
Hence, a concept is generally regarded as "fuzzy" in a logical sense if:
The fact that a concept is fuzzy does not prevent its use in logical reasoning; it merely affects the type of reasoning which can be applied. If the concept has gradations of meaningful significance, it is necessary to specify and formalize what those gradations are, if they can make an important difference. Not all fuzzy concepts have the same logical structure, but they can often be formally described or reconstructed using fuzzy logic or other substructural logics. The advantage of this approach is, that numerical notation enables a potentially infinite number of truth-values between complete truth and complete falsehood, and thus it enables - in theory, at least - the greatest precision in stating the degree of applicability of a logical rule.

Probability

, writing about the foundations of fuzzy logic, sharply distinguished between "fuzziness" and "uncertainty":
In metrology, it is acknowledged that for any measure we care to make, there exists an amount of uncertainty about its accuracy, but this degree of uncertainty is conventionally expressed with a magnitude of likelihood, and not as a degree of truth. In 1975, Lotfi A. Zadeh introduced a distinction between "Type 1 fuzzy sets" without uncertainty and "Type 2 fuzzy sets" with uncertainty, which has been widely accepted. Simply put, in the former case, each fuzzy number is linked to a non-fuzzy number, while in the latter case, each fuzzy number is linked to another fuzzy number.

Applications

Philosophy

In philosophical logic and linguistics, fuzzy concepts are often regarded as vague concepts which in their application, or formally speaking, are neither completely true nor completely false, or which are partly true and partly false; they are ideas which require further elaboration, specification or qualification to understand their applicability. The "fuzzy area" can also refer simply to a residual number of cases which cannot be allocated to a known and identifiable group, class or set if strict criteria are used.

Sciences

In mathematics and statistics, a fuzzy variable is a value which could lie in a probable range defined by some quantitative limits or parameters, and which can be usefully described with imprecise categories using some kind of scale or conceptual hierarchy.

Fuzzy logic

In mathematics and computer science, the gradations of applicable meaning of a fuzzy concept are described in terms of quantitative relationships defined by logical operators. Such an approach is sometimes called "degree-theoretic semantics" by logicians and philosophers, but the more usual term is fuzzy logic or many-valued logic. The novelty of fuzzy logic is, that it "breaks with the traditional principle that formalisation should correct and avoid, but not compromise with, vagueness".
The basic idea of fuzzy logic is that a real number is assigned to each statement written in a language, within a range from 0 to 1, where 1 means that the statement is completely true, and 0 means that the statement is completely false, while values less than 1 but greater than 0 represent that the statements are "partly true", to a given, quantifiable extent. Susan Haack comments:
"Truth" in this mathematical context usually means simply that "something is the case", or that "something is applicable". This makes it possible to analyze a distribution of statements for their truth-content, identify data patterns, make inferences and predictions, and model how processes operate.
Petr Hájek claimed that "fuzzy logic is not just some "applied logic", but may bring "new light to classical logical problems", and therefore might be well classified as a distinct branch of "philosophical logic" similar to e.g. modal logics.

Machinery & analytics

Fuzzy logic offers computationally-oriented systems of concepts and methods, to formalize types of reasoning which are ordinarily approximate only, and not exact. In principle, this allows us to give a definite, precise answer to the question, "To what extent is something the case?", or, "To what extent is something applicable?". Via a series of switches, this kind of reasoning can be built into electronic devices. That was already happening before fuzzy logic was invented, but using fuzzy logic in modelling has become an important aid in design, which creates many new technical possibilities.
Fuzzy reasoning turns out to have many practical uses. It is nowadays widely used in:
It looks like fuzzy logic will eventually be applied in almost every aspect of life, even if people are not aware of it, and in that sense fuzzy logic is an astonishingly successful invention. The scientific and engineering literature on the subject is constantly increasing.

Community

Originally lot of research on fuzzy logic was done by Japanese pioneers inventing new machinery, electronic equipment and appliances. The idea became so popular in Japan, that the English word entered Japanese language. "Fuzzy theory" is a recognized field in Japanese scientific research.
Since that time, the movement has spread worldwide; nearly every country nowadays has its own fuzzy systems association, although some are larger and more developed than others. In some cases, the local body is a branch of an international one. In other cases, the fuzzy systems program falls under artificial intelligence or soft computing.
estimated around 2014 that there were more than 50,000 fuzzy logic–related, patented inventions. He listed 28 journals at that time dealing with fuzzy reasoning, and 21 journal titles on soft computing. His searches found close to 100,000 publications with the word "fuzzy" in their titles, but perhaps there are even 300,000. In March 2018, Google Scholar found 2,870,000 titles which included the word "fuzzy". When he died on 11 September 2017 at age 96, Professor Zadeh had received more than 50 engineering and academic awards, in recognition of his work.

Lattices and big data sets

The technique of fuzzy concept lattices is increasingly used in programming for the formatting, relating and analysis of fuzzy data sets.

Concept formalization

According to the computer scientist Andrei Popescu at Middlesex University London, a concept can be operationally defined to consist of:
Once the context is defined, we can specify relationships of sets of objects with sets of attributes which they do, or do not share.

Fuzzy concept lattice

Whether an object belongs to a concept, and whether an object does, or does not have an attribute, can often be a matter of degree. Thus, for example, "many attributes are fuzzy rather than crisp". To overcome this issue, a numerical value is assigned to each attribute along a scale, and the results are placed in a table which links each assigned object-value within the given range to a numerical value denoting a given degree of applicability.
This is the basic idea of a "fuzzy concept lattice", which can also be graphed; different fuzzy concept lattices can be connected to each other as well. Fuzzy concept lattices are a useful programming tool for the exploratory analysis of big data, for example in cases where sets of linked behavioural responses are broadly similar, but can nevertheless vary in important ways, within certain limits. It can help to find out what the structure and dimensions are, of a behaviour that occurs with an important but limited amount of variation in a large population.

Sandwich example

Big data

Coding with fuzzy lattices can be useful, for instance, in the psephological analysis of big data about voter behaviour, where researchers want to explore the characteristics and associations involved in "somewhat vague" opinions; gradations in voter attitudes; and variability in voter behaviour within a set of parameters. The basic programming techniques for this kind of fuzzy concept mapping and deep learning are by now well-established and big data analytics had a strong influence on the US elections of 2016. A US study concluded in 2015 that for 20% of undecided voters, Google's secret search algorithm had the power to change the way they voted.
Very large quantities of data can now be explored using computers with fuzzy logic programming and open-source architectures such as Apache Hadoop, Apache Spark, and MongoDB. One author claimed in 2016 that it is now possible to obtain, link and analyze "400 data points" for each voter in a population, using Oracle systems.
However, NBC News reported in 2016 that the Anglo-American firm Cambridge Analytica which profiled voters for Donald Trump did not have 400, but 4,000 data points for each of 230 million US adults. Cambridge Analytica's own website claimed that "up to 5,000 data points" were collected for each of 220 million Americans, a data set of more than 1 trillion bits of formatted data. The Guardian later claimed that Cambridge Analytica in fact had, according to its own company information, "up to 7,000 data points" on 240 million American voters.
Harvard University Professor Latanya Sweeney calculated, that if a U.S. company knows just your date of birth, your ZIP code and sex, the company has an 87% chance to identify you by name – simply by using linked data sets from various sources. With 4,000–7,000 data points instead of three, a very comprehensive personal profile becomes possible for almost every voter, and many behavioural patterns can be inferred by linking together different data sets. It also becomes possible to identify and measure gradations in personal characteristics which, in aggregate, have very large effects.

Human judgement

Some researchers argue that this kind of big data analysis has severe limitations, and that the analytical results can only be regarded as indicative, and not as definitive. This was confirmed by Kellyanne Conway, Donald Trump’s campaign advisor and counselor, who emphasized the importance of human judgement and common sense in drawing conclusions from fuzzy data. Conway candidly admitted that much of her own research would "never see the light of day", because it was client confidential. Another Trump adviser criticized Conway, claiming that she "produces an analysis that buries every terrible number and highlights every positive number"

Propaganda machine

In a video interview published by The Guardian in March 2018, whistleblower Christopher Wylie called Cambridge Analytica a "full-service propaganda machine" rather than a bona fide data science company. Its own site revealed with "case studies" that it has been active in political campaigns in numerous different countries, influencing attitudes and opinions. Wylie explained, that "we spent a million dollars harvesting tens of millions of Facebook profiles, and those profiles were used as the basis of the algorithms that became the foundation of Cambridge Analytica itself. The company itself was founded on using Facebook data".

Audit

On 19 March 2018, Facebook announced it had hired the digital forensics firm Stroz Friedberg to conduct a "comprehensive audit" of Cambridge Analytica, while Facebook shares plummeted 7 percent overnight. Cambridge Analytica had not just used the profiles of Facebook users to compile data sets. According to Christopher Wylie's testimony, the company also harvested the data of each user's network of friends, leveraging the original data set. It then converted, combined and migrated its results into new data sets, which can in principle survive in some format, even if the original data sources are destroyed. It created and applied algorithms using data to which - critics argue - it could not have been entitled. This was denied by Cambridge Analytica, which stated on its website that it legitimately "uses data to change audience behavior" among customers and voters. If advertisers can do that, why not a data company? Where should the line be drawn? Legally, it remained a "fuzzy" area.

Legal issue

The tricky legal issue then became, what kind of data Cambridge Analytica is actually allowed to have and keep. Facebook itself became the subject of another U.S. Federal Trade Commission inquiry, to establish whether Facebook violated the terms of a 2011 consent decree governing its handing of user data. Wired journalist Jessi Hempel commented in a CBNC panel discussion that "Now there is this fuzziness from the top of the company that I have never seen in the fifteen years that I have covered it."

Data privacy

Interrogating Facebook's CEO Mark Zuckerberg before the U.S. House Energy and Commerce Committee in April 2018, New Mexico Congressman Rep. Ben Ray Luján put it to him that the Facebook corporation might well have "29,000 data points" on each Facebook user. Zuckerberg claimed that he "did not really know". Lujan's figure was based on ProPublica research, which in fact suggested that Facebook may even have 52,000 data points for many Facebook users. When Zuckerberg replied to his critics, he stated that because the revolutionary technology of Facebook had ventured into previously unknown territory, it was unavoidable that mistakes would be made, despite the best of intentions. He justified himself saying that:
In July 2018, Facebook and Instagram barred access from Crimson Hexagon, a company that advises corporations and governments using one trillion scraped social media posts, which it mined and processed with artificial intelligence and image analysis.

Integrity

It remained "fuzzy" what was more important to Zuckerberg: making money from user's information, or real corporate integrity in the use of personal information. Zuckerberg implied, that he believed that, on balance, Facebook had done more good than harm, and that, if he had believed that wasn't the case, he would never have persevered with the business. Thus, "the good" was itself a fuzzy concept, because it was a matter of degree. He had to sell stuff, to keep the business growing. If people did not like Facebook, then they simply should not join it, or opt out, they have the choice. Many critics however feel that people really are in no position to make an informed choice, because they have no idea of how exactly their information will or might be used by third parties contracting with Facebook; because the company legally owns the information that users provide online, they have no control over that either, except to restrict themselves in what they write online.
After the New York Times broke the news on 17 March 2018, that copies of the Facebook data set scraped by Cambridge Analytica could still be downloaded from the Internet, Facebook was severely criticized by government representatives. When questioned, Zuckerberg admitted that "In general we collect data on people who are not signed up for Facebook for security purposes" with the aim "to help prevent malicious actors from collecting public information from Facebook users, such as names". From 2018 onward, Facebook faced more and more lawsuits brought against the company, alleging data breaches, security breaches and misuse of personal information. There still exists no international regulatory framework for social network information, and it is often unclear what happens to the stored information, after a provider company closes down, or is taken over by another company.
On 2 May 2018, it was reported that the Cambridge Analytica company was shutting down and was starting bankruptcy proceedings, after losing clients and facing escalating legal costs. The reputational damage which the company had suffered or caused, had become too great.

Speed

A traditional objection to big data is, that it cannot cope with rapid change: events move faster that the statistics can keep up with. Yet the technology now exists for corporations like Amazon, Google and Microsoft to pump cloud-based data streams from app-users straight into big data analytics programmes, in real time. Provided that the right kinds of analytical concepts are used, it is now technically possible to draw definite and important conclusions about gradations of human and natural behaviour using very large fuzzy data sets and fuzzy programming – and increasingly it can be done very fast. Obviously this achievement has become highly topical in military technology, but military uses can also have spin-offs for medical applications.

Controversies

There have been many academic controversies about the meaning, relevance and utility of fuzzy concepts.

"Fuzzy" label

himself confessed that:
However, the impact of the invention of fuzzy reasoning went far beyond names and labels. When Zadeh gave his acceptance speech in Japan for the 1989 Honda Foundation prize, which he received for inventing fuzzy theory, he stated that "The concept of a fuzzy set has had an upsetting effect on the established order."

Do they exist

Some philosophers and scientists have claimed that in reality "fuzzy" concepts do not exist.

Frege

According to The Foundations of Arithmetic by the logician Gottlob Frege,

Kálmán

Similarly, Rudolf E. Kálmán stated in 1972 that "there is no such thing as a fuzzy concept... We do talk about fuzzy things but they are not scientific concepts".
The suggestion is that a concept, to qualify as a concept, must always be clear and precise, without any fuzziness. A vague notion would be at best a prologue to formulating a concept.

DIN and ISO standards

There is no general agreement among philosophers and scientists about how the notion of a "concept", should be defined. A concept could be defined as a mental representation, as a cognitive capacity, as an abstract object, etc. Edward E. Smith & Douglas L. Medin stated that “there will likely be no crucial experiments or analyses that will establish one view of concepts as correct and rule out all others irrevocably.” Of course, scientists also quite often do use imprecise analogies in their models to help understanding an issue. A concept can be clear enough, but not precise.
Rather uniquely, terminology scientists at the German national standards institute provided an official standard definition of what a concept is. According to the official German definition, a concept is a unit of thought which is created through abstraction for a set of objects, and which identifies shared characteristics of those objects.
The subsequent ISO definition is very similar. Under the ISO 1087 terminology standard of the International Standards Organization, a concept is defined as a unit of thought or an idea constituted through abstraction on the basis of properties common to a set of objects. It is acknowledged that although a concept usually has one definition or one meaning, it may have multiple designations, terms of expression, symbolizations or representations. Thus, for example, the same concept can have different names in different languages. Both verbs and nouns can express concepts. A concept can also be thought of as "a way of looking at the world".

Corruption

Reasoning with fuzzy concepts is often viewed as a kind of "logical corruption" or scientific perversion because, it is claimed, fuzzy reasoning rarely reaches a definite "yes" or a definite "no". A clear, precise and logically rigorous conceptualization is no longer a necessary prerequisite, for carrying out a procedure, a project, or an inquiry, since "somewhat vague ideas" can always be accommodated, formalized and programmed with the aid of fuzzy expressions. The purist idea is, that either a rule applies, or it does not apply. When a rule is said to apply only "to some extent", then in truth the rule does not apply. Thus, a compromise with vagueness or indefiniteness is, on this view, effectively a compromise with error - an error of conceptualization, an error in the inferential system, or an error in physically carrying out a task.

Kahan

The computer scientist William Kahan argued in 1975 that "the danger of fuzzy theory is that it will encourage the sort of imprecise thinking that has brought us so much trouble." He said subsequently,
According to Kahan, statements of a degree of probability are usually verifiable. There are standard tests one can do. By contrast, there is no conclusive procedure which can decide the validity of assigning particular fuzzy truth values to a data set in the first instance. It is just assumed that a model or program will work, "if" particular fuzzy values are accepted and used, perhaps based on some statistical comparisons or try-outs.

Bad design

In programming, a problem can usually be solved in several different ways, not just one way, but an important issue is, which solution works best in the short term, and in the long term. Kahan implies, that fuzzy solutions may create more problems in the long term, than they solve in the short term. For example, if one starts off designing a procedure, not with well thought-out, precise concepts, but rather by using fuzzy or approximate expressions which conveniently patch up badly formulated ideas, the ultimate result could be a complicated, malformed mess, that does not achieve the intended goal.
Had the reasoning and conceptualization been much sharper at the start, then the design of the procedure might have been much simpler, more efficient and effective - and fuzzy expressions or approximations would not be necessary, or required much less. Thus, by allowing the use of fuzzy or approximate expressions, one might actually foreclose more rigorous thinking about design, and one might build something that ultimately does not meet expectations.
If an entity X turns out to belong for 65% to category Y, and for 35% to category Z, how should X be allocated? One could plausibly decide to allocate X to Y, making a rule that, if an entity belongs for 65% or more to Y, it is to be treated as an instance of category Y, and never as an instance of category Z. One could, however, alternatively decide to change the definitions of the categorization system, to ensure that all entities such as X fall 100% in one category only.
This kind of argument claims, that boundary problems can be resolved simply by using better categorization or conceptualization methods. If we treat X "as if" it belongs 100% to Y, while in truth it only belongs 65% to Y, then arguably we are really misrepresenting things. If we keep doing that with a lot of related variables, we can greatly distort the true situation, and make it look like something that it isn't.
In a "fuzzy permissive" environment, it might become far too easy, to formalize and use a concept which is itself badly defined, and which could have been defined much better. In that environment, there is always a quantitative way out, for concepts that do not quite fit, or which don't quite do the job for which they are intended. The cumulative adverse effect of the discrepancies might, in the end, be much larger than ever anticipated.

Counter-argument

A typical reply to Kahan's objections is, that fuzzy reasoning never "rules out" ordinary binary logic, but instead presupposes ordinary true-or-false logic. Lotfi Zadeh stated that "fuzzy logic is not fuzzy. In large measure, fuzzy logic is precise." It is a precise logic of imprecision. Fuzzy logic is not a replacement of, or substitute for ordinary logic, but an enhancement of it, with many practical uses. Fuzzy thinking does oblige action, but primarily in response to a change in quantitative gradation, not in response to a contradiction.
One could say, for example, that ultimately one is either "alive" or "dead", which is perfectly true. Meantime though one is "living", which is also a significant truth - yet "living" is a fuzzy concept. It is true that fuzzy logic by itself usually cannot eliminate inadequate conceptualization or bad design. Yet it can at least make explicit, what exactly the variations are in the applicability of a concept which has unsharp boundaries.
If one always had perfectly crisp concepts available, perhaps no fuzzy expressions would be necessary. In reality though, one often does not have all the crisp concepts to start off with. One might not have them yet for a long time, or ever - or, several successive "fuzzy" approximations might be needed, to get there.
At a deeper level, a "fuzzy permissive" environment may be desirable, precisely because it permits things to be actioned, that would never have been achieved, if there had been crystal clarity about all the consequences from the start, or if people insisted on absolute precision prior to doing anything. Scientists often try things out on the basis of "hunches", and processes like serendipity can play a role.
Learning something new, or trying to create something new, is rarely a completely formal-logical or linear process, there are not only "knowns" and "unknowns" involved, but also "partly known" phenomena, i.e. things which are known or unknown "to some degree". Even if, ideally, we would prefer to eliminate fuzzy ideas, we might need them initially to get there, further down the track. Any method of reasoning is a tool. If its application has bad results, it is not the tool itself that is to blame, but its inappropriate use. It would be better to educate people in the best use of the tool, if necessary with appropriate authorization, than to ban the tool pre-emptively, on the ground that it "could" or "might" be abused. Exceptions to this rule would include things like computer viruses and illegal weapons that can only cause great harm if they are used. There is no evidence though that fuzzy concepts as a species are intrinsically harmful, even if some bad concepts can cause harm if used in inappropriate contexts.

Reducibility

once claimed that a many-valued logic requires neither intermediate terms between true and false, nor a rejection of bivalence. Her suggestion was, that the intermediate terms can always be restated as conditional if-then statements, and by implication, that fuzzy logic is fully reducible to binary true-or-false logic.
This interpretation is disputed, but even if it was correct, assigning a number to the applicability of a statement is often enormously more efficient than a long string of if-then statements that would have the same intended meaning. That point is obviously of great importance to computer programmers, educators and administrators seeking to code a process, activity, message or operation as simply as possible, according to logically consistent rules.

Quantification

It may be wonderful to have access to an unlimited number of distinctions to define what one means, but not all scholars would agree that any concept is equal to, or reducible to, a mathematical set. Some phenomena are difficult or impossible to quantify and count, in particular if they lack discrete boundaries.

Formalization

Qualities may not be fully reducible to quantities – if there are no qualities, it may become impossible to say what the numbers are numbers of, or what they refer to, except that they refer to other numbers or numerical expressions such as algebraic equations. A measure requires a counting unit defined by a category, but the definition of that category is essentially qualitative; a language which is used to communicate data is difficult to operate, without any qualitative distinctions and categories. We may, for example, transmit a text in binary code, but the binary code does not tell us directly what the text intends. It has to be translated, decoded or converted first, before it becomes comprehensible.
In creating a formalization or formal specification of a concept, for example for the purpose of measurement, administrative procedure or programming, part of the meaning of the concept may be changed or lost. For example, if we deliberately program an event according to a concept, it might kill off the spontaneity, spirit, authenticity and motivational pattern which is ordinarily associated with that type of event.
Quantification is not an unproblematic process. To quantify a phenomenon, we may have to introduce special assumptions and definitions which disregard part of the phenomenon in its totality.
Programmers, statisticians or logicians are concerned in their work with the main operational or technical significance of a concept which is specifiable in objective, quantifiable terms. They are not primarily concerned with all kinds of imaginative frameworks associated with the concept, or with those aspects of the concept which seem to have no particular functional purpose – however entertaining they might be. However, some of the qualitative characteristics of the concept may not be quantifiable or measurable at all, at least not directly. The temptation exists to ignore them, or try to infer them from data results.
If, for example, we want to count the number of trees in a forest area with any precision, we have to define what counts as one tree, and perhaps distinguish them from saplings, split trees, dead trees, fallen trees etc. Soon enough it becomes apparent that the quantification of trees involves a degree of abstraction – we decide to disregard some timber, dead or alive, from the population of trees, in order to count those trees that conform to our chosen concept of a tree. We operate in fact with an abstract concept of what a tree is, which diverges to some extent from the true diversity of trees there are.
Even so, there may be some trees, of which it is not very clear, whether they should be counted as a tree, or not; a certain amount of "fuzziness" in the concept of a tree may therefore remain. The implication is, that the seemingly "exact" number offered for the total quantity of trees in the forest may be much less exact than one might think - it is probably more an estimate or indication of magnitude, rather than an exact description. Yet - and this is the point - the imprecise measure can be very useful and sufficient for all intended purposes.
It is tempting to think, that if something can be measured, it must exist, and that if we cannot measure it, it does not exist. Neither might be true. Researchers try to measure such things as intelligence or gross domestic product, without much scientific agreement about what these things actually are, how they exist, and what the correct measures might be.
When one wants to count and quantify distinct objects using numbers, one needs to be able to distinguish between those separate objects, but if this is difficult or impossible, then, although this may not invalidate a quantitative procedure as such, quantification is not really possible in practice; at best, we may be able to assume or infer indirectly a certain distribution of quantities that must be there. In this sense, scientists often use proxy variables to substitute as measures for variables which are known to be there, but which themselves cannot be observed or measured directly.

Vague or fuzzy

The exact relationship between vagueness and fuzziness is disputed.

Philosophy

Philosophers often regard fuzziness as a particular kind of vagueness, and consider that "no specific assignment of semantic values to vague predicates, not even a fuzzy one, can fully satisfy our conception of what the extensions of vague predicates are like". Surveying recent literature on how to characterize vagueness, Matti Eklund states that appeal to lack of sharp boundaries, borderline cases and “sorites-susceptible" predicates are the three informal characterizations of vagueness which are most common in the literature.

Zadeh's argument

However, Lotfi A. Zadeh claimed that "vagueness connotes insufficient specificity, whereas fuzziness connotes unsharpness of class boundaries". Thus, he argued, a sentence like "I will be back in a few minutes" is fuzzy but not vague, whereas a sentence such as "I will be back sometime", is fuzzy and vague. His suggestion was that fuzziness and vagueness are logically quite different qualities, rather than fuzziness being a type or subcategory of vagueness. Zadeh claimed that "inappropriate use of the term 'vague' is still a common practice in the literature of philosophy".

Ethics

In the scholarly inquiry about ethics and meta-ethics, vague or fuzzy concepts and borderline cases are standard topics of controversy. Central to ethics are theories of "value", what is "good" or "bad" for people and why that is, and the idea of "rule following" as a condition for moral integrity, consistency and non-arbitrary behaviour.
Yet, if human valuations or moral rules are only vague or fuzzy, then they may not be able to orient or guide behaviour. It may become impossible to operationalize rules. Evaluations may not permit definite moral judgements, in that case. Hence, clarifying fuzzy moral notions is usually considered to be critical for the ethical endeavour as a whole.

Excessive precision

Nevertheless, Scott Soames has made the case that vagueness or fuzziness can be valuable to rule-makers, because "their use of it is valuable to the people to whom rules are addressed". It may be more practical and effective to allow for some leeway in the interpretation of how a rule should be applied - bearing in mind the overall purpose which the rule intends to achieve.
If a rule or procedure is stipulated too exactly, it can sometimes have a result which is contrary to the aim which it was intended to help achieve. For example, "The Children and Young Persons Act could have specified a precise age below which a child may not be left unsupervised. But doing so would have incurred quite substantial forms of arbitrariness ".

Rule conflict

A related sort of problem is, that if the application of a legal concept is pursued too exactly and rigorously, it may have consequences that cause a serious conflict with another legal concept. This is not necessarily a matter of bad law-making. When a law is made, it may not be possible to anticipate all the cases and events to which it will apply later. The longer a law is in force, the more likely it is, that people will run into problems with it, that were not foreseen when the law was made.
So, the further implications of one rule may conflict with another rule. "Common sense" might not be able to resolve things. In that scenario, too much precision can get in the way of justice. Very likely a special court ruling wil have to set a norm. The general problem for jurists is, whether "the arbitrariness resulting from precision is worse than the arbitrariness resulting from the application of a vague standard".

Mathematics

The definitional disputes about fuzziness remain unresolved so far, mainly because, as anthropologists and psychologists have documented, different languages that have been created by people to signal meanings suggest different ontologies. Put simply: it is not merely that describing "what is there" involves symbolic representations of some kind. How distinctions are drawn, influences perceptions of "what is there", and vice versa, perceptions of "what is there" influence how distinctions are drawn. This is an important reason why, as Alfred Korzybski noted, people frequently confuse the symbolic representation of reality, conveyed by languages and signs, with reality itself.
Fuzziness implies, that there exists a potentially infinite number of truth values between complete truth and complete falsehood. If that is the case, it creates the foundational issue of what, in the case, can justify or prove the existence of the categorical absolutes which are assumed by logical or quantitative inference. If there is an infinite number of shades of grey, how do we know what is totally black and white, and how could we identify that?

Tegmark

To illustrate the ontological issues, cosmologist Max Tegmark argues boldly that the universe consists of math: "If you accept the idea that both space itself, and all the stuff in space, have no properties at all except mathematical properties," then the idea that everything is mathematical "starts to sound a little bit less insane."
Tegmark moves from the epistemic claim that mathematics is the only known symbol system which can in principle express absolutely everything, to the methodological claim that everything is reducible to mathematical relationships, and then to the ontological claim, that ultimately everything that exists is mathematical. The argument is then reversed, so that because everything is mathematical in reality, mathematics is necessarily the ultimate universal symbol system.
The main criticisms of Tegmark's approach are that the steps in this argument do not necessarily follow, no conclusive proof or test is possible for the claim that such an exhaustive mathematical expression or reduction is feasible, and it may be that a complete reduction to mathematics cannot be accomplished, without at least partly altering, negating or deleting a non-mathematical significance of phenomena, experienced perhaps as qualia.

Zalta

In his meta-mathematical metaphysics, Edward N. Zalta has claimed that for every set of properties of a concrete object, there always exists exactly one abstract object that encodes exactly that set of properties and no others - a foundational assumption or axiom for his ontology of abstract objects By implication, for every fuzzy object there exists always at least one defuzzified concept which encodes it exactly. It is a modern interpretation of Plato's metaphysics of knowledge, which expresses confidence in the ability of science to conceptualize the world exactly.

Platonism

The Platonic-style interpretation was critiqued by Hartry H. Field. Mark Balaguer argues that we do not really know whether mind-independent abstract objects exist or not; so far, we cannot prove whether Platonic realism is definitely true or false. Defending a cognitive realism, Scott Soames argues that the reason why this unsolvable conundrum has persisted, is because the ultimate constitution of the meaning of concepts and propositions was misconceived.
Traditionally, it was thought that concepts can be truly representational, because ultimately they are related to intrinsically representational Platonic complexes of universals and particulars. However, once concepts and propositions are regarded as cognitive-event types, it is possible to claim that they are able to be representational, because they are constitutively related to intrinsically representational cognitive acts in the real world. As another philosopher put it,
Along these lines, it could be argued that reality, and the human cognition of reality, will inevitably contain some fuzzy characteristics, which can be represented only by concepts which are themselves fuzzy to some or other extent.

Social science and the media

The idea of fuzzy concepts has also been applied in the philosophical, sociological and linguistic analysis of human behaviour.

Sociology and linguistics

In a 1973 paper, George Lakoff analyzed hedges in the interpretation of the meaning of categories. Charles Ragin and others have applied the idea to sociological analysis. For example, fuzzy set qualitative comparative analysis has been used by German researchers to study problems posed by ethnic diversity in Latin America. In New Zealand, Taiwan, Iran, Malaysia, the European Union and Croatia, economists have used fuzzy concepts to model and measure the underground economy of their country. Kofi Kissi Dompere applied methods of fuzzy decision, approximate reasoning, negotiation games and fuzzy mathematics to analyze the role of money, information and resources in a "political economy of rent-seeking", viewed as a game played between powerful corporations and the government.
A concept may be deliberately created by sociologists as an ideal type to understand something imaginatively, without any strong claim that it is a "true and complete description" or a "true and complete reflection" of whatever is being conceptualized.
In a more general sociological or journalistic sense, a "fuzzy concept" has come to mean a concept which is meaningful but inexact, implying that it does not exhaustively or completely define the meaning of the phenomenon to which it refers – often because it is too abstract. In this context, it is said that fuzzy concepts "lack clarity and are difficult to test or operationalize". To specify the relevant meaning more precisely, additional distinctions, conditions and/or qualifiers would be required.
A few examples can illustrate this kind of usage:
The main reason why the term "fuzzy concept" is now often used in describing human behaviour, is that human interaction has many characteristics which are difficult to quantify and measure precisely, among other things because they are interactive and reflexive. Those human characteristics can be usefully expressed only in an approximate way.
Newspaper stories frequently contain fuzzy concepts, which are readily understood and used, even although they are far from exact. Thus, many of the meanings which people ordinarily use to negotiate their way through life in reality turn out to be "fuzzy concepts". While people often do need to be exact about some things, many areas of their lives involve expressions which are far from exact.
Sometimes the term is also used in a pejorative sense. For example, a New York Times journalist wrote that Prince Sihanouk "seems unable to differentiate between friends and enemies, a disturbing trait since it suggests that he stands for nothing beyond the fuzzy concept of peace and prosperity in Cambodia".

Applied social science

The use of fuzzy logic in the social sciences and humanities has remained limited until recently. Lotfi A. Zadeh said in a 1994 interview that:
Two decades later, after a digital information explosion due to the growing use of the internet and mobile phones worldwide, fuzzy concepts and fuzzy logic are being widely applied in big data analysis of social, commercial and psychological phenomena. Many sociometric and psychometric indicators are based partly on fuzzy concepts and fuzzy variables.
Jaakko Hintikka once claimed that "the logic of natural language we are in effect already using can serve as a "fuzzy logic" better than its trade name variant without any additional assumptions or constructions." That might help to explain why fuzzy logic has not been used much to formalize concepts in the "soft" social sciences.
Lotfi A. Zadeh rejected such an interpretation, on the ground that in many human endeavours as well as technologies it is highly important to define more exactly "to what extent" something is applicable or true, when it is known that its applicability can vary to some important extent among large populations. Reasoning which accepts and uses fuzzy concepts can be shown to be perfectly valid with the aid of fuzzy logic, because the degrees of applicability of a concept can be more precisely and efficiently defined with the aid of numerical notation.
Another possible explanation for the traditional lack of use of fuzzy logic by social scientists is simply that, beyond basic statistical analysis the mathematical knowledge of social scientists is often rather limited; they may not know how to formalize and code a fuzzy concept using the conventions of fuzzy logic. The standard software packages used provide only a limited capacity to analyze fuzzy data sets, if at all, and considerable skills are required.
Yet Jaakko Hintikka may be correct, in the sense that it can be much more efficient to use natural language to denote a complex idea, than to formalize it in logical terms. The quest for formalization might introduce much more complexity, which is not wanted, and which detracts from communicating the relevant issue. Some concepts used in social science may be impossible to formalize exactly, even though they are quite useful and people understand their appropriate application quite well.

Uncertainty

Fuzzy concepts can generate uncertainty because they are imprecise. In that case, they do not provide a clear orientation for action or decision-making ; reducing fuzziness, perhaps by applying fuzzy logic, might generate more certainty.

Relevance

However, this is not necessarily always so. A concept, even although it is not fuzzy at all, and even though it is very exact, could equally well fail to capture the meaning of something adequately. That is, a concept can be very precise and exact, but not – or insufficiently – applicable or relevant in the situation to which it refers. In this sense, a definition can be "very precise", but "miss the point" altogether.

Security

A fuzzy concept may indeed provide more security, because it provides a meaning for something when an exact concept is unavailable – which is better than not being able to denote it at all. A concept such as God, although not easily definable, for instance can provide security to the believer.

Observer effect

In physics, the observer effect and Heisenberg's uncertainty principle indicate that there is a physical limit to the amount of precision that is knowable, with regard to the movements of subatomic particles and waves. That is, features of physical reality exist, where we can know that they vary in magnitude, but of which we can never know or predict exactly how big or small the variations are. This insight suggests that, in some areas of our experience of the physical world, fuzziness is inevitable and can never be totally removed. Since the physical universe itself is incredibly large and diverse, it is not easy to imagine it, grasp it or describe it without using fuzzy concepts.

Language

Ordinary language, which uses symbolic conventions and associations which are often not logical, inherently contains many fuzzy concepts – "knowing what you mean" in this case depends partly on knowing the context.
This can be easily verified for instance by consulting a dictionary, a thesaurus or an encyclopedia which show the multiple meanings of words, or by observing the behaviours involved in ordinary relationships which rely on mutually understood meanings. Bertrand Russell regarded ordinary language as intrinsically vague.

Implicature

To communicate, receive or convey a message, an individual somehow has to bridge his own intended meaning and the meanings which are understood by others, i.e., the message has to be conveyed in a way that it will be socially understood, preferably in the intended manner. Thus, people might state: "you have to say it in a way that I understand". Even if the message is clear and precise, it may nevertheless not be received in the way it was intended.
Bridging meanings may be done instinctively, habitually or unconsciously, but it usually involves a choice of terms, assumptions or symbols whose meanings are not completely fixed, but which depend among other things on how the receivers of the message respond to it, or the context. In this sense, meaning is often "negotiated" or "interactive". This gives rise to many fuzzy concepts.
The semantic challenge of conveying meanings to an audience was explored in detail, and analyzed logically, by the British philosopher Paul Grice - using, among other things, the concept of implicature. Implicature refers to what is suggested by a message to the recipient, without being either explicitly expressed or logically entailed by its content. The suggestion could be very clear to the recipient, but it could also be vague or fuzzy.

Paradoxes

Even using ordinary set theory and binary logic to reason something out, logicians have discovered that it is possible to generate statements which are logically speaking not completely true or imply a paradox, even although in other respects they conform to logical rules. David Hilbert concluded that the existence of such logical paradoxes tells us "that we must develop a meta-mathematical analysis of the notions of proof and of the axiomatic method; their importance is methodological as well as epistemological".

Psychology

Various different aspects of human experience commonly generate concepts with fuzzy characteristics.

Human vs. Computer

The formation of fuzzy concepts is partly due to the fact that the human brain does not operate like a computer.
According to fuzzy-trace theory, partly inspired by Gestalt psychology, human intuition is a non-arbitrary, reasonable and rational process of cognition; it literally "makes sense".

Learning

In part, fuzzy concepts arise also because learning or the growth of understanding involves a transition from a vague awareness, which cannot orient behaviour greatly, to clearer insight, which can orient behaviour. At the first encounter with an idea, the sense of the idea may be rather hazy. When more experience with the idea has occurred, a clearer and more precise grasp of the idea results, as well as a better understanding of how and when to use the idea.
In his study of implicit learning, Arthur S. Reber affirms that there does not exist a very sharp boundary between the conscious and the unconscious, and "there are always going to be lots of fuzzy borderline cases of material that is marginally conscious and lots of elusive instances of functions and processes that seem to slip in and out of personal awareness".
Thus, an inevitable component of fuzziness exists and persists in human consciousness, because of continual variation of gradations in awareness, along a continuum from the conscious, the preconscious, and the subconscious to the unconscious. The hypnotherapist Milton H. Erickson noted likewise that the conscious mind and the unconscious normally interact.

Limits

Some psychologists and logicians argue that fuzzy concepts are a necessary consequence of the reality that any kind of distinction we might like to draw has limits of application. At a certain level of generality, a distinction works fine. But if we pursued its application in a very exact and rigorous manner, or overextend its application, it appears that the distinction simply does not apply in some areas or contexts, or that we cannot fully specify how it should be drawn. An analogy might be, that zooming a telescope, camera, or microscope in and out, reveals that a pattern which is sharply focused at a certain distance becomes blurry at another distance, or disappears altogether.

Complexity

Faced with any large, complex and continually changing phenomenon, any short statement made about that phenomenon is likely to be "fuzzy", i.e., it is meaningful, but – strictly speaking – incorrect and imprecise. It will not really do full justice to the reality of what is happening with the phenomenon. A correct, precise statement would require a lot of elaborations and qualifiers. Nevertheless, the "fuzzy" description turns out to be a useful shorthand that saves a lot of time in communicating what is going on.

Cognition

In psychophysics, it was discovered that the perceptual distinctions we draw in the mind are often more definite than they are in the real world. Thus, the brain actually tends to "sharpen up" or "enhance" our perceptions of differences in the external world.
If there are more gradations and transitions in reality, than our conceptual or perceptual distinctions can capture, then it could be argued that how those distinctions will actually apply, must necessarily become vaguer at some point.

Novelty

In interacting with the external world, the human mind may often encounter new, or partly new phenomena or relationships which cannot be sharply defined given the background knowledge available, and by known distinctions, associations or generalizations.

Chaos

It also can be argued that fuzzy concepts are generated by a certain sort of lifestyle or way of working which evades definite distinctions, makes them impossible or inoperable, or which is in some way chaotic. To obtain concepts which are not fuzzy, it must be possible to test out their application in some way. But in the absence of any relevant clear distinctions, lacking an orderly environment, or when everything is "in a state of flux" or in transition, it may not be possible to do so, so that the amount of fuzziness increases.

Everyday occurrence

Fuzzy concepts often play a role in the creative process of forming new concepts to understand something. In the most primitive sense, this can be observed in infants who, through practical experience, learn to identify, distinguish and generalise the correct application of a concept, and relate it to other concepts.
However, fuzzy concepts may also occur in scientific, journalistic, programming and philosophical activity, when a thinker is in the process of clarifying and defining a newly emerging concept which is based on distinctions which, for one reason or another, cannot be more exactly specified or validated. Fuzzy concepts are often used to denote complex phenomena, or to describe something which is developing and changing, which might involve shedding some old meanings and acquiring new ones.

Areas

It could be argued that many concepts used fairly universally in daily life are inherently or intrinsically fuzzy concepts, to the extent that their meaning can never be completely and exactly specified with logical operators or objective terms, and can have multiple interpretations, which are at least in part purely subjective. Yet despite this limitation, such concepts are not meaningless. People keep using the concepts, even if they are difficult to define precisely.

Multiple meanings

It may also be possible to specify one personal meaning for the concept, without however placing restrictions on a different use of the concept in other contexts. In ordinary speech, concepts may sometimes also be uttered purely randomly; for example a child may repeat the same idea in completely unrelated contexts, or an expletive term may be uttered arbitrarily. A feeling or sense is conveyed, without it being fully clear what it is about.
Happiness may be an example of a word with variable meanings depending on context or timing.

Ambiguities

Fuzzy concepts can be used deliberately to create ambiguity and vagueness, as an evasive tactic, or to bridge what would otherwise be immediately recognized as a contradiction of terms. They might be used to indicate that there is definitely a connection between two things, without giving a complete specification of what the connection is, for some or other reason. This could be due to a failure or refusal to be more precise. But it could also be a prologue to a more exact formulation of a concept, or to a better understanding of it.

Efficiency

Fuzzy concepts can be used as a practical method to describe something of which a complete description would be an unmanageably large undertaking, or very time-consuming; thus, a simplified indication of what is at issue is regarded as sufficient, although it is not exact.

Popper

There is also such a thing as an "economy of distinctions", meaning that it is not helpful or efficient to use more detailed definitions than are really necessary for a given purpose. In this sense, Karl Popper rejected pedantry and commented that:
The provision of "too many details" could be disorienting and confusing, instead of being enlightening, while a fuzzy term might be sufficient to provide an orientation. The reason for using fuzzy concepts can therefore be purely pragmatic, if it is not feasible or desirable to provide "all the details" about the meaning of a shared symbol or sign. Thus people might say "I realize this is not exact, but you know what I mean" – they assume practically that stating all the details is not required for the purpose of the communication.

Fuzzy logic gambit

picked up this point, and drew attention to a "major misunderstanding" about applying fuzzy logic. It is true that the basic aim of fuzzy logic is to make what is imprecise more precise. Yet in many cases, fuzzy logic is used paradoxically to "imprecisiate what is precise", meaning that there is a deliberate tolerance for imprecision for the sake of simplicity of procedure and economy of expression.
In such uses, there is a tolerance for imprecision, because making ideas more precise would be unnecessary and costly, while "imprecisiation reduces cost and enhances tractability". Zadeh calls this approach the "Fuzzy Logic Gambit".
In the Fuzzy Logic Gambit, "what is sacrificed is precision in value, but not precision in meaning", and more concretely, "imprecisiation in value is followed by precisiation in meaning". Zadeh cited as example Takeshi Yamakawa's programming for an inverted pendulum, where differential equations are replaced by fuzzy if-then rules in which words are used in place of numbers.

Fuzzy vs. Boolean

Common use of this sort of approach, has led some logicians to regard fuzzy logic merely as an extension of Boolean logic.
However, Boolean concepts have a logical structure which differs from fuzzy concepts. An important feature in Boolean logic is, that an element of a set can also belong to any number of other sets; even so, the element either does, or does not belong to a set. By contrast, whether an element belongs to a fuzzy set is a matter of degree, and not always a definite yes-or-no question.
All the same, the Greek mathematician Costas Drossos suggests in various papers that, using a "non-standard" mathematical approach, we could also construct fuzzy sets with Boolean characteristics and Boolean sets with fuzzy characteristics. This would imply, that in practice the boundary between fuzzy sets and Boolean sets is itself fuzzy, rather than absolute. For a simplified example, we might be able to state, that a concept X is definitely applicable to a finite set of phenomena, and definitely not applicable to all other phenomena. Yet, within the finite set of relevant items, X might be fully applicable to one subset of the included phenomena, while it is applicable only “to some varying extent or degree” to another subset of phenomena which are also included in the set. Following ordinary set theory, this generates logical problems, if e.g. overlapping subsets within sets are related to other overlapping subsets within other sets.

Clarifying methods

In mathematical logic, computer programming, philosophy and linguistics fuzzy concepts can be analyzed and defined more accurately or comprehensively, by describing or modelling the concepts using the terms of fuzzy logic or other substructural logics. More generally, clarification techniques can be used such as:
diagram, one method of clarifying fuzzy concepts.
In this way, we can obtain a more exact understanding of the meaning and use of a fuzzy concept, and possibly decrease the amount of fuzziness. It may not be possible to specify all the possible meanings or applications of a concept completely and exhaustively, but if it is possible to capture the majority of them, statistically or otherwise, this may be useful enough for practical purposes.

Defuzzification

A process of defuzzification is said to occur, when fuzzy concepts can be logically described in terms of fuzzy sets, or the relationships between fuzzy sets, which makes it possible to define variations in the meaning or applicability of concepts as quantities. Effectively, qualitative differences are in that case described more precisely as quantitative variations, or quantitative variability. Assigning a numerical value then denotes the magnitude of variation along a scale from zero to one.
The difficulty that can occur in judging the fuzziness of a concept can be illustrated with the question "Is this one of those?". If it is not possible to clearly answer this question, that could be because "this" is itself fuzzy and evades definition, or because "one of those" is fuzzy and inadequately defined.
Thus, the source of fuzziness may be in the nature of the reality being dealt with, the concepts used to interpret it, or the way in which the two are being related by a person. It may be that the personal meanings which people attach to something are quite clear to the persons themselves, but that it is not possible to communicate those meanings to others except as fuzzy concepts.