Fuzzy concept

A fuzzy concept is a kind of concept of which the boundaries of application can vary considerably according to context or conditions, instead of being fixed once and for all.<ref>Susan Haack, Deviant logic, fuzzy logic: beyond the formalism. Chicago: University of Chicago Press, 1996.</ref> This means the concept is vague in some way, lacking a fixed, precise meaning, without however being unclear or meaningless altogether.<ref>Richard Dietz & Sebastiano Moruzzi (eds.), Cuts and clouds. Vagueness, Its Nature, and Its Logic. Oxford University Press, 2009; Delia Graff & Timothy Williamson (eds.), Vagueness. London: Routledge, 2002.</ref> 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.<ref>Timothy Williamson, Vagueness. London: Routledge, 1994, p. 124f; Lotfi A. Zadeh, "Quantitative fuzzy semantics". Information Sciences, Vol. 3, No. 2, April 1971, pp. 159-176.</ref> The inverse of a "fuzzy concept" is a "crisp concept" (i.e. a precise 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 (variable) 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 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.<ref>Bart Kosko, Fuzzy Thinking: The New Science of Fuzzy Logic. New York: Hyperion, 1993; Bart Kosko, Heaven in a chip: fuzzy visions of society and science in the digital age. New York: Three Rivers Press, 1999; Daniel McNeill & Paul Freiberger, Fuzzy Logic: The Revolutionary Computer Technology that Is Changing Our World. New York: Simon & Schuster, 1994. Charles Elkan, "The paradoxical success of fuzzy logic." IEEE Expert, August 1994.[1] A useful overview of the field is provided in: Radim Bělohlávek, Joseph W. Dauben & George J. Klir, Fuzzy Logic and Mathematics: A Historical Perspective. Oxford University Press, 2017.</ref>

New neuro-fuzzy computational methods make it possible to identify, measure and respond to fine gradations of significance with great precision.<ref>A useful overview is provided in: Enrique Ruspini et al. Handbook of fuzzy computation. Bristol & Philadelphia: Institute of Physics Publishing, 1998.</ref> 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.<ref>Radim Behlohlavek & George J. Klir (eds.), Concepts and fuzzy logic. Cambridge, Mass.: MIT Press, 2011.</ref>

Origins

Problems of vagueness and fuzziness have probably always existed in human experience.<ref>Rudolf Seising et al., On fuzziness: homage to Lotfi A. Zadeh, Vol. 2. Heidelberg: Springer, 2013, p. 656; Ellen Christiaanse, "1.5 million years of information systems; from hunters-gatherers to the domestication of the networked computer". In: David Avison et al., The past and future of information systems: 1976-2006 and beyond. New York: IFIP/Springer, 2006, pp. 165-176.</ref> 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.<ref>Rosanna Keefe & Peter Smith, Vagueness: a reader. Cambridge, Mass.: MIT Press, 1996.</ref> With some physical processes this threshold is relatively easy to identify. For example, water turns into steam at 100 °C or 212 °F (the boiling point depends partly on atmospheric pressure, which decreases at higher altitudes).

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.<ref>Massimo Pigliucci & Maarten Boudry (eds.), Philosophy of Pseudoscience: Reconsidering the Demarcation Problem. University of Chicago Press, 2013, p. 95.</ref> 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,<ref>Nicholas Rescher, Many-Valued Logic. New York: McGraw-Hill, 1969.</ref> including (among many others) Eubulides, Plato, Cicero, Georg Wilhelm Friedrich Hegel,<ref>Angelica Nuzzo, "Vagueness and Meaning Variance in Hegel's Logic". In: Angelica Nuzzo, Hegel and the analytical tradition. New York: Continuum International Publishing Group, 2010, pp. 61-82. </ref> Karl Marx and Friedrich Engels,<ref>Robert L. Carneiro, "The transition from quantity to quality; a neglected causal mechanism in accounting for social evolution". Proceedings of the National Academy of Sciences of the United States of America (PNAS), Vol. 97 No. 23, 7 November 2000, pp. 12926-12931.[2]</ref> Friedrich Nietzsche, Hugh MacColl,<ref>S. Rahman & J. Redmond, "Hugh MacColl and the Birth of Logical Pluralism". In: Handbook of History of Logic, Vol. 4. Elsevier, 2008.</ref> Charles S. Peirce,<ref>Mihai Nadin, "The logic of vagueness", in: Eugene Freeman (ed.), The Relevance of Charles Peirce. La Salle, Ill.: Open Court, 1983, pp. 154-166.</ref> Max Black,<ref>Max Black, "Vagueness: An exercise in logical analysis". Philosophy of Science, Vol. 4, 1937, pp. 427–455. Max Black, "Reasoning with Loose Concepts". In: Canadian Philosophical Review, Volume 2, Issue 1, June 1963, pp. 1-12.</ref> Jan Łukasiewicz,<ref>Jan Łukasiewicz, "On three-valued logic". In: Jan Łukasiewicz, Selected Works. Amsterdam: North Holland Publishing Company, 1970, pp. 87-88.</ref> Emil Leon Post,<ref>Emil Leon Post, "Introduction to a general theory of elementary propositions". American Journal of Mathematics, Vol. 43, No. 3, July 1921, p. 163-185.</ref> Alfred Tarski,<ref>Alfred Tarski, Logic, semantics, metamathematics. Oxford: Oxford University Press, 1956.</ref> Georg Cantor, Nicolai A. Vasiliev,<ref>Valentine Bazhanov, "The fate of one forgotten idea: N. A. Vasiliev and his imaginary logic." Studies in Soviet Thought, Vol.39 No. 3, 1990, pp.333-341.[3] Archived 2006-07-19 at the Wayback Machine</ref> Kurt Gödel, Stanisław Jaśkowski<ref>Susan Haack notes that Stanisław Jaśkowski provided axiomatizations of many-valued logics in: Jaśkowski, "On the rules of supposition in formal logic". Studia Logica No. 1, 1934.[4] See Susan Haack, Philosophy of Logics. Cambridge University Press, 1978, p. 205.</ref> and Donald Knuth.<ref>Priyanka Kaushal, Neeraj Mohan and Parvinder S. Sandhu, "Relevancy of Fuzzy Concept in Mathematics". International Journal of Innovation, Management and Technology, Vol. 1, No. 3, August 2010.[5]</ref>

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.<ref>Abraham Kaplan and Hermann F. Schott, "A calculus for empirical classes", Methodos, Vol. 3, 1951, pp. 165–188.</ref> However, at the time, their idea "fell on stony ground".<ref>Timothy Williamson, Vagueness. London: Routledge, 1996, p. 120.</ref> J. Barkley Rosser Sr. published a treatise on many-valued logics in 1952, anticipating "many-valued sets".<ref>J. Barkley Rosser Sr. and Atwell R. Turquette, Many-valued logics. Amsterdam: North-Holland Publishing Company, 1952, p. 109.</ref> Another treatise was published in 1963 by Aleksandr A. Zinov'ev and others<ref>Aleksandr A. Zinov'ev, David Dinsmore Comey and Guido Küng, Philosophical problems of many-valued logic. Dordrecht: D. Reidel, 1963.</ref>

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 (in contrast to "combinatory vagueness" caused by a term that has a number of logically independent conditions of application).<ref>William P. Alston, Philosophy of Language. Englewood Cliffs, N.J.: Prentice Hall, 1964, p. 87; William P. Alston, "Vagueness," in Paul Edwards (ed.), Encyclopedia of Philosophy, vol. 8. New York: Macmillan, first edition 1967, pp. 218–221; William P. Alston, A Realist Conception of Truth. Ithaca: Cornell University Press, 1996 p. 62.</ref>

The German mathematician Dieter Klaua published a German-language paper on fuzzy sets in 1965,<ref>Dieter Klaua. "Über einen Ansatz zur mehrwertigen Mengenlehre". Monatsberichte der Deutschen Akademie der Wissenschaften (Berlin), Vol. 7, pp. 859–867, 1965. Siegfried Gottwald, "An early approach toward graded identity and graded membership in set theory". Fuzzy Sets and Systems. Vol. 161 Issue 18, September 2010, pp. 2369–2379.</ref> but he used a different terminology (he referred to "many-valued sets", not "fuzzy sets").<ref>Siegfried Gottwald, "Shaping the logic of fuzzy set theory". In: Cintula, Petr et al. (eds.), Witnessed years. Essays in honour of Petr Hájek. London: College Publications, 2009, pp. 193–208. [6] Archived 2012-10-01 at the Wayback Machine</ref>

Two popular introductions to many-valued logic in the late 1960s were by Robert J. Ackermann and Nicholas Rescher respectively.<ref>Robert John Ackermann, An introduction to many-valued logics. London, Routledge & Kegan Paul, 1967; Nicholas Rescher, Many-Valued Logic. New York: McGraw-Hill, 1969.</ref> Rescher's book includes a bibliography on fuzzy theory up to 1965, which was extended by Robert Wolf for 1966–1974.<ref>Robert G. Wolf, "A survey of many-valued logic (1966–1974)", in: J. Michael Dunn and George Epstein (eds.), Modern Uses of Multiple-Valued Logic. Dordrecht: D. Reidel, 1977, 167–323.</ref> Haack provides references to significant works after 1974.<ref>Susan Haack, Deviant logic, fuzzy logic: beyond the formalism. Chicago: University of Chicago Press, 1996.</ref> Bergmann provides a more recent (2008) introduction to fuzzy reasoning.<ref>Merrie Bergmann, An Introduction to Many-Valued and Fuzzy Logic: Semantics, Algebras, and Derivation Systems. Cambridge University Press, 2008.</ref>

Lotfi Zadeh

The Iranian-born American computer scientist Lotfi A. Zadeh (1921-2017) is usually 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.<ref>Lotfi A. Zadeh (June 1965). "Fuzzy sets" (PDF). Information and Control. 8 (3): 338–353. doi:10.1016/S0019-9958(65)90241-X. Archived from the original (PDF) on 2007-11-27. Retrieved 2007-11-06. See also A. Dumitras, & G. Moschytz, "Understanding Fuzzy Logic: An Interview with Lotfi Zadeh". IEEE signal processing magazine, May 2007, pp. 102-105.</ref> 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.<ref>Radim Bělohlávek, Joseph W. Dauben & George J. Klir, Fuzzy Logic and Mathematics: A Historical Perspective. Oxford University Press, 2017. Lotfi A Zadeh with George J. Klir and Bo Yua, Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers. Singapore and River Edge (N.J.): World Scientific Publishing Company, 1996. This last title includes a bibliography of Zadeh's writings up to 1996.</ref> Unlike most philosophical theories of vagueness, Zadeh's engineering approach had the advantage that it could be directly applied to computer programming.<ref>Rudolf Seising, "Cybernetics, system(s) theory, information theory and Fuzzy Sets and Systems in the 1950s and 1960s". Information Sciences No. 180, 2010, pp. 4459-4476.</ref> Zadeh's seminal 1965 paper is acknowledged to be one of the most-cited scholarly articles in the 20th century.<ref>IFSA Newsletter (International Fuzzy Systems Association), Vol. 10, No. 1, March 2013 [7]</ref> In 2014, it was placed 46th in the list of the world's 100 most-cited research papers of all time.<ref>Richard Van Noorden, Brendan Maher & Regina Nuzzo, "The top 100 papers". Nature, 29 October 2014.[8]</ref> Since the mid-1960s, many scholars have contributed to elaborating the theory of reasoning with graded concepts, and the research field continues to expand.<ref>Radim Bělohlávek, Joseph W. Dauben & George J. Klir, Fuzzy Logic and Mathematics: A Historical Perspective. Oxford University Press, 2017.</ref>

Definition

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

Criteria

Radim Bělohlávek explains:

"There exists strong evidence, established in the 1970s in the psychology of concepts... that human concepts have a graded structure in that whether or not a concept applies to a given object is a matter of degree, rather than a yes-or-no question, and that people are capable of working with the degrees in a consistent way. This finding is intuitively quite appealing, because people say "this product is more or less good" or "to a certain degree, he is a good athlete", implying the graded structure of concepts. In his classic paper, Zadeh called the concepts with a graded structure fuzzy concepts and argued that these concepts are a rule rather than an exception when it comes to how people communicate knowledge. Moreover, he argued that to model such concepts mathematically is important for the tasks of control, decision making, pattern recognition, and the like. Zadeh proposed the notion of a fuzzy set that gave birth to the field of fuzzy logic..."<ref>Radim Bělohlávek, "What is a fuzzy concept lattice? II", in: Sergei O. Kuznetsov et al. (eds.), Rough sets, fuzzy sets, data mining and granular computing. Berlin: Springer Verlag, 2011, pp. 19–20.[9]</ref>

Hence, a concept is generally regarded as "fuzzy" in a logical sense if:

• defining characteristics of the concept apply to it "to a certain degree or extent" (or, more unusually, "with a certain magnitude of likelihood").<ref>The vast majority of scientific or scholarly users of the idea of fuzzy concepts refer to scaled (graded) characteristics, and not to the variations in the likelihoods of their applicability. A probabilistic interpretation of concepts is discussed in Edward E. Smith & Douglas L. Medin, Categories and concepts. Cambridge: Harvard University Press, 1981.</ref>
• or, the boundaries of applicability (the truth-value) of a concept can vary in degrees, according to different conditions.
• or, the fuzzy concept itself straightforwardly consists of a fuzzy set, or a combination of such sets.

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 (see fuzzy logic). 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.<ref>Nikolaos Galatos, Peter Jipsen, Tomasz Kowalski & Hiroakira Ono, Residuated lattices: an algebraic glimpse at substructural logics. Elsevier Science, 2007.</ref> 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

Petr Hájek, writing about the foundations of fuzzy logic, sharply distinguished between "fuzziness" and "uncertainty":

"The sentence "The patient is young" is true to some degree – the lower the age of the patient (measured e.g. in years), the more the sentence is true. Truth of a fuzzy proposition is a matter of degree. I recommend to everybody interested in fuzzy logic that they sharply distinguish fuzziness from uncertainty as a degree of belief (e.g. probability). Compare the last proposition with the proposition "The patient will survive next week". This may well be considered as a crisp proposition which is either (absolutely) true or (absolutely) false; but we do not know which is the case. We may have some probability (chance, degree of belief) that the sentence is true; but probability is not a degree of truth.<ref>Petr Hájek, Metamathematics of fuzzy logic. Dordrecht: Springer, 1998, p. 2.</ref>

In metrology (the science of measurement), 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.<ref>Lotfi A. Zadeh, "The Concept of a Linguistic Variable and Its Application to Approximate Reasoning–1", Information Sciences, Vol. 8, pp. 199–249, 1975. Jerry M. Mendel and Robert I. Bob John, "Type-2 Fuzzy Sets Made Simple." IEEE transactions on fuzzy systems, Vol. 10, No. 2, April 2002, pp. 117-127. Jerry M. Mendel, "Advances in type-2 fuzzy sets and systems". In: Information Sciences 177, 2007, pp. 84–110.[10]</ref> Simply put, in the former case, each fuzzy number is linked to a non-fuzzy (natural) 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 conditions under which they truly make sense).<ref>Delia Graff Fara, "Shifting Sands: An Interest Relative Theory of Vagueness". Philosophical Topics, Vol. 28 No. 1, 2000, pp. 45-81.</ref> 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. The collaborative written works of French philosopher Gilles Deleuze and French psychoanalyst Félix Guattari refer occasionally to fuzzy sets in conjunction with their idea of multiplicities. In A Thousand Plateaus, they note that "a set is fuzzy if its elements belong to it only by virtue of specific operations of consistency and consolidation, which themselves follow a special logic",<ref>Deleuze and Guattari, A Thousand Plateaus (1988, 551).</ref> and in What Is Philosophy?, a work dealing with the functions of concepts, they write that concepts as a whole are "vague or fuzzy sets, simple aggregates of perceptions and affections, which form within the lived as immanent to a subject".<ref>Deleuze and Guattari, What Is Philosophy? (1994, 141).</ref>

Sciences

In mathematics and statistics, a fuzzy variable (such as "the temperature", "hot" or "cold") 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 (such as "high", "medium" or "low") using some kind of scale or conceptual hierarchy.

Fuzzy logic

Main article: 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,<ref>Roy T. Cook, A dictionary of philosophical logic. Edinburgh University Press, 2009, p. 84.</ref> but the more usual term is fuzzy logic or many-valued logic.<ref>Nicholas Rescher, Many-valued logic. New York: McGraw-Hill, 1969.</ref> The novelty of fuzzy logic is, that it "breaks with the traditional principle that formalisation should correct and avoid, but not compromise with, vagueness".<ref>Susan Haack, Philosophy of Logics. Cambridge University Press, 1978, p. xii.</ref> 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:

"Whereas in classical set theory an object either is or is not a member of a given set, in fuzzy set theory membership is a matter of degree; the degree of membership of an object in a fuzzy set is represented by some real number between 0 and 1, with 0 denoting no membership and 1 full membership."<ref>Susan Haack, Philosophy of Logics. Cambridge University Press, 1978, p. 165.</ref>

"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.<ref>Petr Hájek, "Ten questions and one problem on fuzzy logic". Annals of Pure and Applied Logic, Vol. 96, Issues 1-3, March 1999, 157-165, at p. 162.</ref>

Machinery and 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 (i.e., reasoning with graded concepts) turns out to have many practical uses.<ref>Kazuo Tanaka, An Introduction to Fuzzy Logic for Practical Applications. Springer, 1996; Constantin Zopounidis, Panos M. Pardalos & George Baourakis, Fuzzy Sets in Management, Economics and Marketing. Singapore; World Scientific Publishing Co. 2001. Humberto Bustince et al. (eds.), Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Intelligent Systems from Decision Making to Data Mining, Web Intelligence and Computer Vision. Berlin: Springer, 2008.</ref> It is nowadays widely used in:

• The programming of vehicle and transport electronics, household appliances, video games, language filters, robotics, and driverless vehicles. Fuzzy logic washing machines are gaining popularity.<ref>Samsung support information page [11].</ref>
• All kinds of control systems that regulate access, traffic, movement, balance, conditions, temperature, pressure, routers etc.
• Electronic equipment used for pattern recognition, surveying and monitoring (including radars, satellites, alarm systems and surveillance systems).
• Cybernetics research, artificial intelligence,<ref>Lotfi Zadeh, "Coping with the imprecision of the real world" (interview). Communications of the ACM, Vol.27, No. 4, 1 April 1984, pp.304-311.</ref> virtual intelligence, machine learning, database design and soft computing research.<ref>Stosberg, Mark (16 December 1996). "The Role of Fuzziness in Artifical [sic] Intelligence". Minds and Machines. Archived from the original on 20 May 2013. Retrieved 19 April 2013.</ref>
• "Fuzzy risk scores" are used by project managers and portfolio managers to express financial risk assessments.<ref>Irem Dikmen, M. Talat Birgonal and Sedat Han, "Using fuzzy risk assessment to rate cost overrun risk in international construction projects." International Journal of Project Management, Vol. 25 No. 5, July 2007, pp. 494–505.</ref>
• Fuzzy logic has been applied to the problem of predicting cement strength.<ref>Fa-Liang Gao, "A new way of predicting cement strength — Fuzzy logic". Cement and Concrete Research, Volume 27, Issue 6, June 1997, Pages 883–888.</ref>

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.<ref>""2017 Golden Goose Awardee: Fuzzy Logic, Clear Impact"". Archived from the original on 2019-12-13. Retrieved 2018-03-12.</ref> 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 (see also Fuzzy control system).<ref>Michio Sugeno (ed.), Industrial applications of fuzzy control. Amsterdam: North Holland, 1992; Andrew Pollack, "Technology; Fuzzy Logic For Computers". New York Times, 11 October 1984; Andrew Pollack, "Fuzzy Computer Theory: How to Mimic the Mind?" New York Times, 2 April 1989.</ref> The idea became so popular in Japan, that the English word entered Japanese language (ファジィ概念). "Fuzzy theory" (ファジー理論) 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.

• The main international body is the International Fuzzy Systems Association (IFSA).<ref>The IFSA URL is: http://isdlab.ie.ntnu.edu.tw/ntust/ifsa/</ref>
• The Computational Intelligence Society of the Institute of Electrical and Electronics Engineers, Inc. (IEEE) has an international membership and deals with fuzzy logic, neural networks and evolutionary computing. It publishes the journal IEEE Transactions on Fuzzy Systems and holds international conferences.<ref>IEEE CIS website [12] Archived 2018-04-02 at the Wayback Machine.</ref>
• The conference on Fuzzy Systems and Data Mining (FSDM) chose Bangkok for its 4th international conference in November 2018.<ref>"FSDM website". Archived from the original on 2018-04-11. Retrieved 2018-04-10.</ref>
• The interdisciplinary Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) traces its origin back to 1972 and publishes two journals.<ref>See the SOFT website [13].</ref>
• The original Korea Fuzzy System Society founded in 1991 is now known as the Korean Institute of Intelligent Systems (KIIS) to make it more inclusive.<ref>KIIS website [14].</ref>
• In mainland China, there is the Fuzzy Mathematics and Fuzzy systems Association of China,<ref>Yingming Liu, Guoqing Chen and Mingshen Ying (eds.), Fuzzy logic, soft computing and computational intelligence. Eleventh International Fuzzy Systems Association World Congress July 28–31, 2005, Beijing, China. Volume III. Beijing: Tsinghua University Press/Springer Verlag, 2005, p. viii.</ref> and there exists also an important Taiwan Fuzzy Systems Association.<ref>The TFSA publishes the International Journal of Fuzzy Systems</ref>
• The North American Fuzzy Information Processing Society (NAFIPS) was founded in 1981.<ref>The NAFIPS website URL is http://nafips.ece.ualberta.ca/</ref>
• In Europe, there is a European Society for Fuzzy Logic and Technology (EUSFLAT) which includes the Working Group on Mathematical Fuzzy Logic.<ref>The EUSFLAT URL is: http://www.eusflat.org/. Mathfuzzlog url is: http://www.mathfuzzlog.org/index.php/Main_Page</ref>
• In 2002, the Iran Fuzzy Systems Society was approved as an affiliate of the Statistics Association of Iran, and in 2005 registered as a non-commercial scientific institute.<ref>IFSS website [15].</ref> When Lotfi A. Zadeh received an honorary doctorate from the University of Teheran on 9 March 2017, a member of Iran's parliament stated that Iran now ranks third in the world with regard to the output of scientific research about fuzzy systems.<ref>"Iran ranks 3rd in producing fuzzy systems related science: Official". The Iran Project, 9 March 2017.[16]</ref>
• In 2005, Russia's Association for Fuzzy Systems (founded in January 1990) became the Russian Association for Fuzzy Systems and Soft Computing (RAFSSoftCom).<ref>(RAFSSoftCom)</ref> Zadeh's seminal paper on fuzzy sets was translated into Russian in 1974, and from that time Russian fuzzy research began to take off - increasingly overcoming official skepticism.<ref>Ildar Batyrshin, "A retrospective glance from Russia at wonderland of fuzziness." In: Rudolf Seising et al. (eds.), On Fuzziness: A Homage to Lotfi A. Zadeh, Volume 1. Berlin: Springer, 2013, pp. 33-38.</ref>
• In 2009, the Brazilian Applied Mathematical Society (SBMAC) created the Thematic Committee on Fuzzy Systems which inspired the First Brazilian Congress on Fuzzy Systems (CBSF I) in 2010.<ref>CBSF website [17].</ref> CBSF IV was held in Campinas in 2016.<ref>IV CBSF website</ref>
• In India, the Center for Soft Computing Research at the Indian Statistical Institute (Kolkata) organizes and publishes research on fuzzy sets, rough sets, and applications of fuzzy logic.<ref>CSCR website</ref>
• The Sri Lanka Association for Artificial Intelligence is a non-profit scientific association devoted to understanding the mechanisms underlying thoughts and intelligent behaviour, and their emulation in machines.<ref>SLAAI website [18].</ref>
• The Asia Pacific Neural Network Society, founded in 1993, has board members from 13 countries: Australia, China, Hong Kong, India, Japan, Malaysia, New Zealand, Singapore, South Korea, Qatar, Taiwan, Thailand, and Turkey.<ref>See the APNNS website [19].</ref>

Achievements

Lotfi A. Zadeh 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.<ref>Lotfi A. Zadeh, "Factual Information about the Impact of Fuzzy Logic". Berkeley Initiative in Soft Computing, at Electrical Engineering and Computer Sciences Department, University of Berkeley, California, circa 2014.[20] For more details about the global fuzzy logic community, see [21].</ref> 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.<ref>Cade Metz, "Lotfi Zadeh, Father of Mathematical 'Fuzzy Logic,' Dies at 96." New York Times, 11 September 2017.</ref>

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,<ref>Andrei Popescu, "A general approach to fuzzy concepts". Mathematical Logic Quarterly Vol. 50, No. 3, 2005, pp. 265–280.</ref> a concept can be operationally defined to consist of:

• an intent, which is a description or specification stated in a language,
• an extent, which is the collection of all the objects to which the description refers,
• a context, which is stated by: (i) the universe of all possible objects within the scope of the concept, (ii) the universe of all possible attributes of objects, and (iii) the logical definition of the relation whereby an object possesses an attribute.

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".<ref>Radim Bělohlávek and Vilem Vychodil, "What is a fuzzy concept lattice?" Department of Computer Science, Palacky University, Olomouc, 2005.[22]</ref> 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 (a score) 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 (for example, in "fuzzy conceptual clustering" techniques used to group data, originally invented by Enrique H. Ruspini). 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.<ref>"See further the COMPASSS site". Archived from the original on 2017-01-01. Retrieved 2016-12-31.</ref>

Sandwich example

Fuzzy definition of sandwiches
	Food item	Contains bread	Bread is separately baked	Bread contains the other ingredients during eating	Two separate bread layers	"Sandwich" is in the name (U.S.)	Made with slices from English sandwich bread loaf	Unweighted score	Classified as
Peanut butter and jelly sandwich	Yes	Yes	Yes	Yes	Yes	Yes	Yes	7	Sandwich
Bacon, lettuce, and tomato sandwich	Yes	Yes	Yes	Yes	Yes	Yes	Yes	7	Sandwich
Toast sandwich	Yes	Yes	Yes	Yes	Yes (despite inner 3rd bread slice)	Yes	Yes	7	Sandwich
Croque-monsieur	Yes	Yes	Yes (but re-cooked)	No (due to cheese on outside)	Yes	No	Yes	5	Sandwich
Banh mi	Yes	Yes	Yes	Yes	Maybe	Maybe (sometimes called "banh mi sandwich")	No (baguette)	5	Roll (UK/Australia) or sandwich (US)
Panini	Yes	Yes	Yes (but re-toasted)	Yes	Yes	No (only in Italian)	No	5	Pressed sandwich (e.g. with the Cuban sandwich)
Hamburger with bun	Yes	Yes	Yes	Yes	Yes	No	No (hamburger bun or bread roll)	5	Burger (UK/Australia), sometimes disputed as a sandwich vs. hamburger (US) due to tradition and the use of bun instead of bread.<ref>Is a hamburger a sandwich?</ref>
Hamburger without bun	Yes	No	No	No	No	No	No	1	Burger (patty) with toppings
Hot dog with bun	Yes	Yes	Yes	Yes	No	No	No (hot dog bun)	4	Disputed. Some classify as a sausage sandwich.<ref>Merriam-Webster: 10 Types of Sandwiches</ref><ref name="ny">New York State Department of Taxation and Finance: Tax Bulletin ST-835 - Sandwiches</ref> Others classify as a hot dog (a type of non-sandwich sausage dish due to tradition or the vertical orientation of the bread sides.<ref>It's Not a Sandwich</ref><ref>National Hot Dog and Sausage Council</ref><ref>Sandwich alignment chart</ref>
Submarine sandwich	Yes	Yes	Yes	Yes	Maybe	Yes	No (hoagie roll)	5.5	Roll (UK/Australia) or sandwich (US)
Pita pocket	Yes	Yes	Yes	Yes	No	No	No	4	Pocket sandwich
Gyro	Yes	Yes	Yes	Yes	No	No	No	4	Sandwich
Wraps and burritos	Yes	Yes	Yes	Yes	No	No	No	4	Disputed. Legal classification varies by jurisdiction.<ref>"What Burritos And Sandwiches Can Teach Us About Innovation". NPR. Archived from the original on 2023-07-05.</ref>
Tacos and quesadillas	Yes	Yes	Yes	Yes	No	No	No	4	Disputed, with some classifying as non-sandwich tortilla-based dishes, either due to separate culinary tradition (Spain vs. UK) or the vertical nature of bread sides in tacos.<ref>Is a Taco a Sandwich: Or How Do We Classify Foods?</ref><ref>Should Tacos Be Considered a Sandwich? The Verdict Is in</ref>
Calzone	Yes	Yes	No	Yes	No	No	No	3	Dumpling or folded pizza
Bread dumpling	Yes	Yes	No	Yes	No	No	No	3	Dumpling
Egg roll	Yes	Yes	No	Yes	No	No	No	3	Dumpling
Cha siu bao	Yes	Yes	No	Yes	No	No	No	3	Dumpling
Open-faced sandwich	Yes	Yes	Yes	No	No	Yes	Yes	5	Open-faced sandwich
Sandwich cake	Yes	Maybe (cake is bread-like)	No	No	Yes	Maybe ("layer cake" in US, "sandwich" in UK)	No	3	Cake (mostly named by analogy due to repeated layering)
Pizza	Yes	Yes	No	No	No	No	No	2	Savory pie
Salad with croutons	Yes	Yes	Yes	No	No	No	No	2	Salad
Ice cream cone with ice cream	Yes	No	No	No	No	No	No	1	Pastry
Ice cream sandwich	Yes	No	No	No	No	Yes	No	2	Sandwich cookie (named by analogy to bread sandwiches)
Aluminium foam sandwich	No	No	No	No	No	Yes	No	1	(named by analogy to bread sandwiches)

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 (or personal characteristics) within a set of parameters.<ref>Daniel Kreiss, Prototype Politics: Technology-Intensive Campaigning and the Data of Democracy. Oxford University Press, 2016.</ref> The basic programming techniques for this kind of fuzzy concept mapping and deep learning are by now well-established<ref>E.g. Mikael Collan, Mario Fedrizzi, Janusz Kacprzyk, Fuzzy Technology: Present Applications and Future Challenges. Heidelberg: Springer, 2016, p. 65f.; Daniel J. Lewis and Trevor P. Martin, "Managing Vagueness with Fuzzy in Hierarchical Big Data". Procedia Computer Science, Volume 53, 2015, pages 19–28.[23]</ref> and big data analytics had a strong influence on the US elections of 2016.<ref>Chris Preimesberger, "Big-Data Analytics Plays Big Role in 2016 Election Campaigns". eWeek, 24 September 2016. [24];Gregory Thomas, "The Big Data Advantage in the Race for the White House." Bemyapp Media, 2 September 2016.[25] Archived 2016-11-18 at the Wayback Machine; Alex Woodie, "Why Winning Politics Is Now Tied to Big Data Analytics". Datanami.com, 10 May 2016.[26]; Lisa Ragusa, "And the Winner of the 2016 Election Is… Big Data". Liaison, 4 November 2016.[27]; John Markman, "Big Data And The 2016 Election". Forbes Magazine, 8 August 2016.[28]; Taylor Armerding, "Big Data and elections: The candidates know you – better than you know them." CSOonline.com, 17 July 2016.[29]</ref> 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.<ref>Robert Epstein, "How Google Could Rig the 2016 Election". Politico.com, 19 August 2015 [30]; Marcel Rosenbach, "How Google and Facebook Can Reshape Elections", Der Spiegel online (English edition), 8 November 2016. [31]</ref>

Very large quantities of data can now be explored using computers with fuzzy logic programming<ref>For example, Kyle C. Longest and Stephen Vaisey, "Fuzzy: A program for Performing Qualitative Comparative Analyses (QCA) in Stata." Stata Journal, Vol. 8 No. 1, 2008: pp. 79–104. Gregory Viot, "Fuzzy logic in C". Dr Dobb's journal, 1 February 1993.[32]</ref> 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 (a "data point" is a number linked to one or more categories, which represents a characteristic).<ref>Chris Preimesberger, "Big-Data Analytics Plays Big Role in 2016 Election Campaigns". eWeek, 24 September 2016.</ref>

However, NBC News reported in 2016 that the Anglo-American firm Cambridge Analytica which profiled voters for Donald Trump (Steve Bannon was a board member)<ref>Kenneth P. Vogel, "The heiress quietly shaping Trump's operation." Politico.com, 21 November 2016.[33]</ref> did not have 400, but 4,000 data points for each of 230 million US adults.<ref>Kate Brannely, "Trump Campaign Pays Millions to Overseas Big Data Firm." NBC News, 4 November 2016.[34]</ref> 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.<ref>"Cambridge Analytica – About Us". Cambridge Analytica website. Archived from the original on 2016-02-16.</ref> 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.<ref>Carole Cadwalladr, "UK regulator orders Cambridge Analytica to release data on US voter". The Guardian, 5 May 2018.[35]</ref>

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.<ref>Adam Tanner, "Nine Things You Don't Know About The Gathering Of Your Personal Data." Forbes Magazine, 4 November 2014.[36]</ref> 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.<ref>Steve Lohr and Natasha Singernov, "How Data Failed Us in Calling an Election." New York Times, 10 November 2016 [37]</ref> 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.<ref>"How Trump won the presidency". Interview of Gerald F. Seib with Kellyanne Conway, Wall Street Journal (WSJ CEO Council full interview video), 14 November 2016.[38] See also: Jonathan Vanian, "How Bad Polling Data Fooled Everyone Except Donald Trump". Fortune, 10 November 2016.[39]</ref> Conway candidly admitted that much of her own research would "never see the light of day", because it was client confidential.<ref>"How Trump won the presidency". Interview of Gerald F. Seib with Kellyanne Conway, Wall Street Journal (WSJ CEO Council full interview video), 14 November 2016.[40]</ref> Another Trump adviser criticized Conway, claiming that she "produces an analysis that buries every terrible number and highlights every positive number"<ref>Ryan Lizza, "Kellyanne Conway's political machinations", The New Yorker, 17 October 2016.[41](see also alternative facts)</ref>

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.<ref>Jina Moore, "Cambridge Analytica Had a Role in Kenya Election, Too". New York Times, 20 March 2018.[42]</ref> 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".<ref>Carole Cadwalladr & Emma Graham-Harrison, "Pressure mounts on Cambridge Analytica and Facebook over data scandal." The Guardian, 18 March 2018.[43]</ref>

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 (erasing roughly $40 billion in market capitalization).<ref>Jonathan Shieber, "Facebook hired a forensics firm to investigate Cambridge Analytica as stock falls 7%." TC Techcrunch.com, 19 March 2018.[44]</ref> 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 (who choose to view and provide information). 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 (or any similar company) is actually allowed to have and keep.<ref>Spencer Phade, "Who Should Profit From Selling Your Personal Data?". Futures Platform, 27 March 2018.[45]</ref> 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 (data which was allegedly transferred to Cambridge Analytica without Facebook's and user's knowledge).<ref>David McLaughlin, Ben Brody, and Billy House, "FTC Probing Facebook for Use of Personal Data, Source Says." Bloomberg, 20 March 2018; Tony Romm and Craig Timberg, "FTC opens investigation into Facebook after Cambridge Analytica scrapes millions of users' personal information." Washington Post, 20 March 2018.[46]</ref> Wired journalist Jessi Hempel commented in a CBNC panel discussion that "Now there is this fuzziness from the top of the company [i.e. Facebook] that I have never seen in the fifteen years that I have covered it."<ref>Sara Salinas, "Facebook hires firm to conduct a 'comprehensive audit' of Cambridge Analytica". CNBC news, 19 March 2018 and squawkbox panel video [47]</ref>

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.<ref>Christopher Carbone, "Facebook might have 29,000 data points on you, but Mark Zuckerberg doesn't really know." Fox News, 11 April 2018.[48] [49] Julia Angwin, Surya Mattu and Terry Parris Jr., "Facebook Doesn't Tell Users Everything It Really Knows About Them." ProPublica, 27 December 2016.[50]</ref> When Zuckerberg replied to his critics, he stated that because the revolutionary technology of Facebook (with 2.2 billion users worldwide) 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.<ref>Olivia Solon and Julia Carrie Wong, "Facebook suspends another analytics firm amid questions over surveillance." The Guardian, 20 July 2018.[51]</ref>

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.<ref>Richard Waters, "Is Facebook a victim of rapid growth or an abuser of user data?" Financial Times, 20 December 2018.</ref> 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 ("more good than bad"). 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 (the same applies to many other online services).

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.<ref>Matthew Rosenberg, Nicholas Confessore and Carole Cadwalladr, "How Trump Consultants Exploited the Facebook Data of Millions". New York Times, 17 March 2018.</ref> 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".<ref>Sarah Frier and Todd Shields, "Zuckerberg Says Facebook Collects Internet Data on Non-Users", Bloomberg, 11 April 2018.</ref> From 2018 onward, Facebook faced more and more lawsuits brought against the company, alleging data breaches, security breaches and misuse of personal information (see criticism of Facebook).<ref>Gabriel J.X. Dance, Michael LaForgia and Nicholas Confessore, "As Facebook Raised a Privacy Wall, It Carved an Opening for Tech Giants". New York Times, 18 December 2018.[52]</ref> 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.<ref>Rebecca Ballhaus and Jenny Gross, "Cambridge Analytica Closing Operations Following Facebook Data Controversy". Wall Street Journal, 2 May 2018; Munsif Vengattil, "Cambridge Analytica and parent SCL Elections shutting down." Reuters, 2 May 2018.[53]</ref> 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.<ref>Tableau.com, Big data: the top 8 trends for 2016.</ref> 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.<ref>Michael O'Hagan, "From military to medical and commercial applications of neural networks and fuzzy logic: a modern "swords-into-plowshares" play." Proceedings of IEEE WESCON '93 conference, San Francisco, 28-30 Sept. 1993. Republished in EEE Xplore, 6 August 2002.</ref>

Controversies

There have been many academic controversies about the meaning, relevance and utility of fuzzy concepts.<ref>Radim Bělohlávek, George J. Klir, Harold W. Lewis III, Eileen C. Way, "Concepts and fuzzy sets: Misunderstandings, misconceptions, and oversights". International Journal of Approximate Reasoning, Vol. 51, July 2009), pp. 23–34.[54] Angel Garrido & Piedad Yuste, "controversies about the introduction of non-classical logics". Brain, Vol. 5, No. 1-4, 2014.[55]</ref>

"Fuzzy" label

Lotfi A. Zadeh himself confessed that:

"I knew that just by choosing the label fuzzy I was going to find myself in the midst of a controversy... If it weren't called fuzzy logic, there probably wouldn't be articles on it on the front page of the New York Times. So let us say it has a certain publicity value. Of course, many people don't like that publicity value, and when they see it in the New York Times, it doesn't sit well with them."<ref>Daniel McNeill & Paul Freiberger, Fuzzy Logic: The Revolutionary Computer Technology that Is Changing Our World. New York: Simon & Schuster, 1994, p. 49.</ref>

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."<ref>Daniel McNeill & Paul Freiberger, Fuzzy Logic: The Revolutionary Computer Technology that Is Changing Our World. New York: Simon & Schuster, 1994, p. 50. The Honda Foundation judged that Zadeh had taken an "active role in making the future of information society a more humane civilization", with a broad range of contributions in applied logic.</ref>

Existence

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

Frege

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

"A definition of a concept... must be complete; it must unambiguously determine, as regards any object, whether or not it falls under the concept... the concept must have a sharp boundary... a concept that is not sharply defined is wrongly termed a concept. Such quasi-conceptual constructions cannot be recognized as concepts by logic. The law of the excluded middle is really just another form of the requirement that the concept should have a sharp boundary."<ref>P. Geach and M. Black (eds.), Translations from the Philosophical Writings of Gottlob Frege, 3rd edition. Blackwell, 1980, p. 159.</ref>

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".<ref>Lotfi A. Zadeh, "Is there a need for fuzzy logic?", Information Sciences, No. 178, 2008, p. 2753.</ref>

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.<ref>For the debate between Zadeh and Kálmán, see: Lotfi A. Zadeh, "The birth and evolution of fuzzy logic". International Journal of General Systems, Vol. 17, No. 2-3, 1990, pp. 95-105. See also: Yücel Yüksel, "On Zadeh's 'The Birth and Evolution of Fuzzy Logic'". In: Eyke Hüllermeier, Rudolf Kruse & Frank Hoffmann (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. Proceedings of the 13th International Conference, IPMU 2010, Dortmund, Germany, June 28–July 2, 2010 (Communications in Computer and Information Science, vol 81), Part II. Berlin: Springer, 2010, pp 350-355.</ref>

DIN and ISO standards

There is no general agreement among philosophers and scientists about how the notion of a "concept" (and in particular, a scientific concept), should be defined.<ref>Wolfgang G. Stock, "Concepts and Semantic Relations in Information Science". In: Journal of the American Society for Information Science and Technology Vol. 61 No. 10, October 2010, pp. 1951–1969.[56]; Eric Margolis & Stephen Laurence, "Concepts". In: Stanford Encyclopedia of Philosophy, 2011.[57]</ref> 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."<ref>Edward E. Smith & Douglas L. Medin, Categories and concepts. Cambridge: Harvard University Press, 1981, p. 182.</ref> Of course, scientists also quite often do use imprecise analogies in their models to help understanding an issue.<ref>Pawel Zeidler, Models and Metaphors as Research Tools in Science. Zurich: Lit Verlag, 2013; L. Magnani, N. J. Nersessian & P. Thagard (eds.), Model-based reasoning in scientific discovery. New York : Kluwer Academic/Plenum Publishers, 1999; Jonathan Lawry, Modelling and reasoning with vague concepts. New York: Springer, 2006.</ref> A concept can be clear enough, but not (or not sufficiently) precise.

Rather uniquely, terminology scientists at the German National Standards Institute (Deutsches Institut für Normung) provided an official standard definition of what a concept is (under the terminology standards DIN 2330 of 1957, completely revised in 1974 and last revised in 2013; and DIN 2342 of 1986, last revised in 2011).<ref>Go to Standards Library for info.</ref> 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 (or related) characteristics of those objects.

The subsequent ISO definition is very similar. Under the ISO 1087 terminology standard of the International Standards Organization (first published in October 2000, and reviewed in 2005), 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.<ref>See e.g. [58] [59] [60]</ref> 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."<ref>Lotfi A. Zadeh, "The birth and evolution of fuzzy logic". International Journal of General Systems, Vol. 17, No. 2-3, 1990, pp. 95-105, at p. 98.</ref> He said subsequently,

"With traditional logic there is no guaranteed way to find that something is contradictory, but once it is found, you'd be obliged to do something. But with fuzzy sets, the existence of contradictory sets can't cause things to malfunction. Contradictory information doesn't lead to a clash. You just keep computing. (...) Life affords many instances of getting the right answer for the wrong reasons... It is in the nature of logic to confirm or deny. The fuzzy calculus blurs that. (...) Logic isn't following the rules of Aristotle blindly. It takes the kind of pain known to the runner. He knows he is doing something. When you are thinking about something hard, you'll feel a similar sort of pain. Fuzzy logic is marvellous. It insulates you from pain. It's the cocaine of science."<ref>Daniel McNeill & Paul Freiberger, Fuzzy Logic: The Revolutionary Computer Technology that Is Changing Our World. New York: Simon & Schuster, 1994, pp. 47-48.</ref>

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 (or compensate for) 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 (say) 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 (or vastly reduced) 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."<ref>A. Dumitras, & G. Moschytz, "Understanding Fuzzy Logic: An Interview with Lotfi Zadeh". IEEE signal processing magazine, May 2007, pp. 102-105, at p. 103.</ref> 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

Susan Haack once claimed that a many-valued logic requires neither intermediate terms between true and false, nor a rejection of bivalence.<ref>Susan Haack, Philosophy of Logics. Cambridge University Press, 1978, p. 213.</ref> Her suggestion was, that the intermediate terms (i.e. the gradations of truth) 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 (it assumes that the knowledge already exists to fit the intermediate terms to a logical sequence), 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.<ref>Susan L. Epstein, "Memory and concepts in reactive learning". Proceedings of the Canadian Workshop on Machine Learning 1992[61].</ref> Some phenomena are difficult or impossible to quantify and count, in particular if they lack discrete boundaries (for example, clouds).

Formalization

Qualities may not be fully reducible to quantities<ref>Stephen Mumford, "Quantities and Qualities", University of Nottingham blog post, September 30, 2012.[62]</ref> – 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.<ref>Robert M. Wachter, "How Measurement Fails Doctors and Teachers". New York Times, 16 January 2016.[63]</ref> 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.<ref>"A well-known quotation usually attributed to Einstein is "Not everything that can be counted counts, and not everything that counts can be counted." I'd amend it to a less eloquent, more prosaic statement: unless we know how things are counted, we don't know if it's wise to count on the numbers. The problem isn't with statistical tests themselves but with what we do before and after we run them. First, we count if we can, but counting depends a great deal on previous assumptions about categorization. (...) Second, after we've gathered some numbers relating to a phenomenon, we must reasonably aggregate them into some sort of recommendation or ranking. This is not easy. By appropriate choices of criteria, measurement protocols and weights, almost any desired outcome can be reached." - John Allen Paulos, "Metric Mania", in New York Times, 10 May 2010.[64] Whether Einstein really did originate the quotation which Paulos mentions, is in dispute. The quote is also credited to William Bruce Cameron, Informal Sociology, a casual introduction to sociological thinking. New York: Random House, 1963, p. 13.[65]</ref> To quantify a phenomenon, we may have to introduce special assumptions and definitions which disregard part of the phenomenon in its totality.

• The economist John Maynard Keynes concluded that formalization "runs the risk of leaving behind the subjectmatter we are interested in" and "also runs the risk of increasing rather than decreasing the muddle."<ref>J. Coates, "Keynes, vague concepts and fuzzy logic." In: G.C. Harcourt & P.A. Riach (ed.), A second edition of the General Theory, Volume 2. London: Routledge, 1997, pp. 244-259, at p. 256.</ref>
• Friedrich Hayek stated that "it is certainly not scientific to insist on measurement where you don't know what your measurements mean. There are cases where measurements are not relevant."<ref>F.A. Hayek, "Coping With Ignorance". Imprimis, Volume 7, Number 7, July 1978.[66]</ref>
• The Hayekian big data guru Viktor Mayer-Schönberger states that "A system based on money and price solved a problem of too much information and not enough processing power, but in the process of distilling information down to price, many details get lost."<ref>Viktor Mayer-Schönberger and Thomas Ramge, Reinventing capitalism in the age of big data. London: John Murray, 2018, p. 52.</ref>
• Michael Polanyi stated that "the process of formalizing all knowledge to the exclusion of any tacit knowing is self-defeating", since to mathematize a concept we need to be able to identify it in the first instance without mathematization.<ref>Michael Polanyi, The tacit dimension [1966]. Chicago: University of Chicago Press, 2009, pp. 20-21.</ref>

Measurement

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.<ref>What statisticians then often try to do, is to create a model which can predict the magnitude of the difference between the true (accurate and exact) number and the computed number obtained, in this case the true number of trees. Such a model however still relies on imperfect or fallible definitions. Even if fuzzy values are used instead, it is likely that a definite and exact number can never be reached. At most one can say that the number is correct, if the definitions are accepted.</ref> 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 (or thought) 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,<ref>Susan Haack, Deviant logic, fuzzy logic - beyond the formalism. Chicago: University of Chicago Press, 1996.</ref> 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".<ref>Matti Eklund, "Vagueness and Second-Level Indeterminacy", in: Richard Dietz & Sebastiano Moruzzi (eds.), Cuts and clouds. Vagueness, Its Nature, and Its Logic. Oxford University Press, 2009, p. 65.</ref> 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.<ref>Matti Eklund, "Characterizing Vagueness". Philosophy Compass, 2, 2007, pp. 896-909.</ref>

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".<ref>Lotfi A. Zadeh, "What is fuzzy logic?". IFSA Newsletter (International Fuzzy Systems Association), Vol. 10, No. 1, March 2013, pp. 5–6.</ref>

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.<ref>Tom Dougherty, "Vague value", in: Philosophy and phenomenological research, Vol. 89, No. 2, September 2014, pp. 352–372 [67]; Tom Dougherty, "Vagueness and Indeterminacy in Ethics". In: Tristram McPherson & David Plunkett, The Routledge Handbook of Metaethics. Oxford: Routledge, 2017.</ref>

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".<ref>Scott Soames, "The Value of Vagueness." Chapter 2 in: Andrei Marmor & Scott Soames, Philosophical Foundations of Language in the Law. Oxford: Oxford University Press, 2013, pp. 26-43, at p. 26.</ref> It may be more practical and effective to allow for some leeway (and personal responsibility) 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 (for various reasons, and particularly because of the different capacities of children of the same age)".<ref>Scott Soames, "The Value of Vagueness." Chapter 2 in: Andrei Marmor & Scott Soames, Philosophical Foundations of Language in the Law. Oxford: Oxford University Press, 2013, pp. 26-43, at p. 33.</ref>

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 (even if 95% of possible cases are predictable). 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".<ref>Scott Soames, "The Value of Vagueness." Chapter 2 in: Andrei Marmor & Scott Soames, Philosophical Foundations of Language in the Law. Oxford: Oxford University Press, 2013, pp. 26-43, at p. 34.</ref>

Mathematics

The definitional disputes about fuzziness remain unresolved so far, mainly because, as anthropologists and psychologists have documented, different languages (or symbol systems) that have been created by people to signal meanings suggest different ontologies.<ref>Alfred Korzybski, Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics (5th ed.). Forest Hills, N.Y.: Institute of General Semantics, 1995. Gregory Bateson, Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology. Chicago: University Of Chicago Press, 1972.</ref> 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.<ref>Vassos Argyrou, "Anthropology of Magic". In: James D. Wright (ed.), International Encyclopedia of the Social & Behavioural Sciences. Amsterdam: Elsevier, 2015, 2nd edition, Vol. 14, p. 438. Dominic Hyde, Vagueness, Logic and Ontology. Aldershot: Ashgate Publishing Ltd, 2008. See also object-oriented ontology.</ref> 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.<ref>Alfred Korzybski, Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics (5th ed.). Forest Hills, N.Y.: Institute of General Semantics, 1995.</ref>

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."<ref>Tanya Lewis, "What's the Universe Made Of? Math, Says Scientist." Live Science, 30 January 2014.[68]</ref>

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 mathematical universe hypothesis). 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 (1) the steps in this argument do not necessarily follow, (2) no conclusive proof or test is possible for the claim that such an exhaustive mathematical expression or reduction is feasible, and (3) 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.<ref>See also Raphael van Riel & Robert Van Gulick, "Scientific reduction". In: Stanford Encyclopedia of Philosophy, 2014.[69]</ref>

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<ref>Edward N. Zalta, Abstract Objects. An introduction to axiomatic metaphysics. Dordrecht: D. Reidel Publishing Company, 1983.</ref> 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,<ref>Norman Gulley, Plato's theory of knowledge[1962]. Milton Park: Routledge, 2013, chapter 4.</ref> which expresses confidence in the ability of science to conceptualize the world exactly.

Platonism

The Platonic-style interpretation was critiqued by Hartry H. Field.<ref>Hartry H. Field, Science without numbers. A defense of nominalism. Second edition, Oxford: Oxford University Press, 2016.</ref> 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.<ref>Mark Balaguer, Platonism and Anti-Platonism in Mathematics. Oxford: Oxford University Press, 1998.</ref> 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.<ref>"Unlike the platonic epistemology required by the classic Frege-Russell account... the epistemology of naturalized propositions sees acquaintance with, and knowledge of, propositions as rooted in acquaintance with, and knowledge of, acts and events that make up one's cognitive life" - Scott Soames, What is meaning?. Princeton: Princeton University Press, 2010, p. 106.</ref> As another philosopher put it,

"The question of how we can know the world around us is not entirely unlike the question of how it is that the food our environment provides happens to agree with our stomachs. Either can become a mystery if we forget that minds, like stomachs, originated in and have been conditioned by a pre-existent natural order."<ref>William Ashley, Marxism and moral concepts. New York: Monthly Review Press, 1964, pp. 4-5. Similarly, Paul Lafargue had written in his essay "The Origin of Abstract Ideas" (1900) that "The brain has the property of thinking as the stomach has that of digesting. It cannot think but by the aid of ideas, which it fabricates with the materials furnished it by the natural environment and the social or artificial environment in which man evolves." [70] </ref>

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.<ref>John Coates, The claims of common sense; Moore, Wittgenstein, Keynes and the social sciences. Cambridge: Cambridge University Press, 1996.</ref>

Sociology and linguistics

In a 1973 paper, George Lakoff analyzed hedges in the interpretation of the meaning of categories.<ref>George Lakoff, "Hedges: A Study in Meaning Criteria and the Logic of Fuzzy Concepts." Journal of Philosophical Logic, Vol. 2, 1973, pp. 458–508.[71]</ref> Charles Ragin and others have applied the idea to sociological analysis.<ref>Charles Ragin, Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press, 2008. Shaomin Li, "Measuring the fuzziness of human thoughts: An application of fuzzy sets to sociological research". The Journal of Mathematical Sociology, Volume 14, Issue 1, 1989, pp. 67–84; Mario Quaranta, "Fuzzy set theory and concepts: a proposal for concept formation and operationalization". Comparative Sociology Vol. 12, issue 6, 2013, pp. 785-820.</ref> For example, fuzzy set qualitative comparative analysis ("fsQCA") has been used by German researchers to study problems posed by ethnic diversity in Latin America.<ref>Michael Stoiber, Frederik Caselitz, Marie Sophie Heinelt, "How to deal with socio-ethnic conflicts in Latin America? Analysing conditions on multiple levels with fsQCA." Paper for the conference "QCA. Applications and Methodological Challenges", November 22–23, 2013, Goethe University Frankfurt.[72]</ref> 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.<ref>Robert Draeseke & David E.A. Giles, "A fuzzy logic approach to modelling the New Zealand underground economy." Mathematics and Computers in Simulation, Vol. 59, No. 1, 2002, pp. 115–123. Tiffany Hui-Kuang Yu, David Han-Min Wang and Su-Jane Chen, "A fuzzy logic approach to modeling the underground economy in Taiwan". Physica Vol. 362, No. 2, 2006, pp. 471–479. Mohammad Hossien Pourkazemi, Mohammad Naser Sherafat and Zahra Delfan Azari, "Modeling Iran's Underground Economy: A Fuzzy Logic Approach". In: Iranian Economic Review, Volume 19, Issue 1, Winter 2015, Page 91-106; Kristina Marsic & Dijana Oreski, "Estimation and Comparison of Underground Economy in Croatia and European Union Countries: Fuzzy Logic Approach". In: Journal of Information and organizational Sciences, Vol. 49, No. 1, 2016, pp.83-104.</ref> 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.<ref>Kofi Kissi Dompere, Fuzziness, democracy, control and collective decision choice system: a theory on political economy of rent-seeking and profit-harvesting. Heidelberg: Springer, 2014.</ref>

Thomas Kron uses fuzzy logic to model sociological theory. On the one hand, he has presented an integral action-theoretical model with the help of fuzzy logic. With Lars Winter he works on the extension of the system theory of Niklas Luhmann by means of the "Kosko-Cube". Furthermore, he has explained transnational terrorism and other contemporary phenomena with the help of fuzzy logic, e.g. uncertainty, hybridity, violence and culture. <ref>Thomas Kron, Reflexiver Terrorismus. Weilerswist: Velbrück, 2015. Fuzzy-Systeme und die "Corona-Krise. In: Zeitschrift für Theoretische Soziologie, Special Issue "Corona-Krise und Differenzierungslagen", 2021 (with Lars Winter). Die Vagheit der Kultur. In: interculture journal: online journal for intercultural studies, 2021, Bd. 19, H. 34 (with Anna-Maria Weihrauch). Gewalt und emotionale Energie. In: Österreichische Zeitschrift für Soziologie, Special Issue "Bestandsaufnahme soziologischer Gewaltforschung" (ed. Andreas Braun/Thomas Kron), 2020: 113-134. Die (Re)Produktion des Terrors – Unterscheidungen und Vagheiten. In: Soziale Systeme, Special Issue "Terrorismus – fuzzy logisch und formtheoretisch", 2018, H. 1: 15-41 (with Lars Winter). Autopoiesis und Hybride - zur Formkatastrophe der Gegenwartsgesellschaft. In: Zeitschrift für Theoretische Soziologie, H.2, 2014: 220-252. Integrale Akteurtheorie – zur Modellierung eines Bezugsrahmens für komplexe Akteure. In: Zeitschrift für Soziologie, 2006, H. 3: 170-192. Fuzzy Systems - Überlegungen zur Vagheit sozialer Systeme. In: Soziale Systeme, 2005, H. 2: 370-394 (with Lars Winter). Fuzzy-Logik für die Soziologie. In: Österreichische Zeitschrift für Soziologie, 2005, H. 3: 51-89. Logik der in der Soziologie. In: Klimczak, Peter/Thomas Zoglauer (ed.): Logik in den Wissenschaften. Münster: Mentis 2017 (with Lars Winter), 181-198. Terrok – A hybrid perpetrator in individualized terrorism warfare. In: Deflem, Mathieu (ed.): Terrorism and Counterterrorism Today. Bingley: Emerald, 2015, 131-149 (with Andreas Braun and Eva Heinke). Fuzzy Thinking in Sociology. In: Seising, Rudi (ed.): Views on Fuzzy Sets and Systems From Different Perspectives. Philosophy and Logic, Criticisms and Applications. Berlin, Heidelberg: Springer, 2009 (with Lars Winter): 301-320.</ref>

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.<ref>Edward A. Shils & Henry A. Finch (eds.), Max Weber on the methodology of the social sciences. Glencoe, Ill.: The Free Press, 1949, p. 93.</ref> 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".<ref>Ann Markusen, "Fuzzy Concepts, Scanty Evidence, Policy Distance: The Case for Rigour and Policy Relevance in Critical Regional Studies." In: Regional Studies, Volume 37, Issue 6-7, 2003, pp. 701–717.</ref> 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:

• a handbook of sociology states that "The theory of interaction rituals contains some gaps that need to be filled and some fuzzy concepts that need to be differentiated."<ref>Jörg Rössel and Randall Collins, "Conflict theory and interaction rituals. The microfoundations of conflict theory." In: Jonathan H. Turner (ed.), Handbook of Sociological Theory. New York: Springer, 2001, p. 527.</ref> The idea is, that if finer distinctions are introduced, then the fuzziness or vagueness would be eliminated.
• a book on youth culture describes ethnicity as "a fuzzy concept that overlaps at times with concepts of race, minority, nationality and tribe".<ref>Carol Jenkins, "Ethnicity, culture, drugs and sex". In: Peter Aggleton, Andrew Ball and Purnima Mane (eds.), "Sex, Drugs and Young People: International Perspectives." London: Routledge, 2006, p. 48.</ref> In this case, part of the fuzziness consists in the inability to distinguish precisely between a concept and a different, but closely related concept.
• a book on sociological theory argues that the Critical Theory of domination faces the problem that "reality itself has become a rather meaningless, fuzzy concept."<ref>Elizabeth Chaplin, Sociology and visual representation. London: Routledge, 1994, p. 130.</ref> The suggestion here is, that the variations in how theoretical concepts are applied have become so large, that the concepts could mean all kinds of things, and therefore are crucially vague (with the implication, that they are not useful any longer for that very reason).
• A history book states: "Sodomy was a vague and fuzzy concept in medieval and early modern Europe, and was often associated with a variety of supposedly related moral and criminal offenses, including heresy, witchcraft, sedition, and treason. St Thomas Aquinas... categorized sodomy with an assortment of sexual behaviours "from which generation [i.e. procreation] cannot follow".<ref>Stephen J. Lynch (ed.), Christopher Marlowe: Edward II, with related texts. Indianapolis: Hackett Publishing Company, 2015, p. xix.</ref> In this case, because a concept is defined by what it excludes, it remains somewhat vague what items of activity it would specifically include.

Mass media

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 (although we know that they have magnitudes and proportions), among other things because they are interactive and reflexive (the observers and the observed mutually influence the meaning of events).<ref>Loïc Wacquant, "The fuzzy logic of practical sense." in: Pierre Bourdieu and Loïc Wacquant, An invitation to reflexive sociology. London: Polity Press, 1992, chapter I section 4.</ref> Those human characteristics can be usefully expressed only in an approximate way (see reflexivity (social theory)).<ref>Ph. Manning "Fuzzy Description: Discovery and Invention in Sociology". In: History of the Human Sciences, Vol. 7, No. 1, 1994, pp. 117–23.[73]</ref>

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 (e.g. money or time), 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".<ref>Philip Shenon, "Their prince is back: Cambodians are baffled." New York Times, 6 June 1993.</ref>

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:

"I expected people in the social sciences – economics, psychology, philosophy, linguistics, politics, sociology, religion and numerous other areas to pick up on it. It's been somewhat of a mystery to me why even to this day, so few social scientists have discovered how useful it could be."<ref>Betty Blair, "Interview with Lotfi Zadeh, Creator of Fuzzy Logic". Azerbaijan International, Winter 1994, pp. 46–47.[74]</ref>

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."<ref>Johan van Benthem et al. (eds.), The age of alternative logics. Assessing philosophy of logic and mathematics today. Dordrecht: Springer, 2006, p. 203.</ref> 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 (using programs such as SPSS and Excel) 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 (especially if they refer to a process in motion, or a process of transformation where something is "in the process of turning into something else"). In that case, they do not provide a clear orientation for action or decision-making ("what does X really mean, intend or imply?"); reducing fuzziness, perhaps by applying fuzzy logic,<ref>Kofi Kissi Dompere, Fuzziness and approximate reasoning; epistemics on uncertainty, expectation and risk in rational behaviour. Berlin: Springer, 2009.</ref> might generate more certainty.

Relevance

However, this is not necessarily always so.<ref>Masao Mukaidono, Fuzzy logic for beginners. Singapore: World Scientific Publishing, 2001.</ref> 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.<ref>Karen Armstrong, The Case for God. New York: Anchor, 2010.</ref>

Observer effect

In physics, the observer effect and Heisenberg's uncertainty principle<ref>David Bohm, Wholeness and the implicate order. London: Routledge & Kegan Paul ARK paperback edition, 1983, p. 86f.</ref> 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 (or being familiar with the way in which a term is normally used, or what it is associated with).

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 (see also Imprecise language). Bertrand Russell regarded ordinary language (in contrast to logic) as intrinsically vague.<ref>Bertrand Russell. "Vagueness". In: Australasian Journal of Psychology and Philosophy, Vol. 1, pp. 84–92, 1923. Reprinted in: Bertrand Russell Papers, Vol. 9, pp. 147–54. Nadine Faulkner, "Russell and vagueness." Journal of Bertrand Russell Studies, Summer 2003, pp. 43–63.</ref>

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" (or, more cynically, manipulated). 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.<ref>A critique of Grice is provided by Wayne A. Davis, Implicature: intention, convention, and principle in the failure of Gricean theory. Cambridge: Cambridge University Press, 1998. An example of a specific application of Gricean theory is: Penelope Brown & Stephen C. Levinson, Politeness: some universals in language use. Cambridge: Cambridge University Press, 1987.</ref> 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 (perhaps a sort of code), 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,<ref>Patrick Hughes & George Brecht, Vicious Circles and Infinity. An anthology of Paradoxes. Penguin Books, 1978. Nicholas Rescher, Epistemological Studies. Frankfurt: Ontos Verlag, 2009, chapter 3.</ref> even although in other respects they conform to logical rules (see Russell's paradox). 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". <ref>Andrea Cantini, "Paradoxes and Contemporary Logic", Stanford Encyclopedia of Philosophy 30 April 2012.[75]</ref>

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 (see also Chinese room).<ref>See further Radim Bělohlávek & George J. Klir (eds.) Concepts and Fuzzy Logic. MIT Press, 2011. John R. Searle, "Minds, brains and programs". The behavioral and brain sciences, Vol. 3, No. 3, 1980, pp. 417–457. Robert Epstein, "The empty brain", Aeon, 18 May 2016.[76]</ref>

• While ordinary computers use strict binary logic gates, the brain does not; i.e., it is capable of making all kinds of neural associations according to all kinds of ordering principles (or fairly chaotically) in associative patterns which are not logical but nevertheless meaningful. For example, a work of art can be meaningful without being logical. A pattern can be regular, ordered and/or non-arbitrary, hence meaningful, without it being possible to describe it completely or exhaustively in formal-logical terms.
• Something can be meaningful although we cannot name it, or we might only be able to name it and nothing else.<ref>Harry Collins, Tacit & explicit knowledge. Chicago: University of Chicago Press, 2013.</ref>
• Human brains can also interpret the same phenomenon in several different but interacting frames of reference, at the same time, or in quick succession, without there necessarily being an explicit logical connection between the frames (see also framing effect).<ref>Amos Tversky and Daniel Kahneman, "The framing of decisions and psychology of choice". Science, Vol. 211, No. 4481, January 1981, pp. 453-458.</ref>

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" (see also: Problem of multiple generality).<ref>C. J. Brainerd and V. F. Reyna, "Gist is the grist: fuzzy-trace theory and the new intuitionism". Developmental Review, Vol. 10, No. 1, March 1990, pp. 3-47, at p. 39.</ref>

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 (or not).

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".<ref>Arthur S. Reber, Implicit learning and tacit knowledge. An essay on the cognitive unconscious. Oxford: Oxford University Press, 1993, pp. 137-138.</ref>

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.<ref>Ronald A. Havens (ed.), The wisdom of Milton H. Erickson, Volume II: human behavior & psychotherapy. New York: Irvington Publishers, 1992, chapter 3.</ref>

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.<ref>A. Cornelius Benjamin, "Science and vagueness". In: Philosophy of science, Vol. 6 No. 4, 1939, pp. 422-431.</ref> 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 ("you know what I mean").

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.

• Between black and white, we are able to detect only a limited number of shades of gray, or colour gradations (there are "detection thresholds").<ref>Kenneth Knoblauch, "Color Vision", in: Steven's handbook of experimental psychology, Vol 1: sensation and perception (3rd ed.). New York: John Wiley & Sons, 2002, p. 48.</ref>
• Motion blur refers to the loss of detail when a person looks at a fast-moving object, or is moving fast while the eyes are focused on something stationary. In a movie reel, the human eye can detect a sequence of up to 10 or 12 still images per second. At around 18 to 26 frames per second, the brain will "see" the sequence of individual images as a moving scene.<ref>Andrew Tarantola, "Why Frame Rate Matters". Gizmodo.com, 14 January 2015.[77]</ref>

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 (yet) be sharply defined given the background knowledge available, and by known distinctions, associations or generalizations.

"Crisis management plans cannot be put 'on the fly' after the crisis occurs. At the outset, information is often vague, even contradictory. Events move so quickly that decision makers experience a sense of loss of control. Often denial sets in, and managers unintentionally cut off information flow about the situation" - L. Paul Bremer.<ref>L. Paul Bremer, "Corporate governance and crisis management", in: Directors & Boards, Winter 2002</ref>

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.<ref>Jean Piaget & Bärbel Inhelder, The Growth of Logical Thinking from Childhood to Adolescence. New York: Basic Books, 1958; Philip J. Kelman & Martha E. Arterberry, The cradle of knowledge: development of perception in infancy. Cambridge, Mass.: The MIT Press, 2000.</ref>

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 (yet) 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

• In meteorology, where changes and effects of complex interactions in the atmosphere are studied, the weather reports often use fuzzy expressions indicating a broad trend, likelihood or level. The main reason is that the forecast can rarely be totally exact for any given location.
• In biology, protein complexes with multiple structural forms are called fuzzy complexes. The different conformations can result in different, even opposite functions. The conformational ensemble is modulated by the environmental conditions. Post-translational modifications or alternative splicing can also impact the ensemble and thereby the affinity or specificity of interactions. Genetic fuzzy systems use algorithms or genetic programming which simulate natural evolutionary processes, in order to understand their structures and parameters.
• In medical diagnosis, the assessment of what the symptoms of a patient are often cannot be very exactly specified, since there are many possible qualitative and quantitative gradations in severity, incidence or frequency that could occur.<ref>Rudolf Seising, "On the absence of strict boundaries — Vagueness, haziness, and fuzziness in philosophy, science, and medicine". Applied Soft Computing, Vol 8, 2008, pp. 1232–1242, at p. 1235.</ref> Different symptoms may also overlap to some extent. These gradations can be difficult to measure, it may cost a lot of time and money, and so the medical professionals might use approximate "fuzzy" categories in their judgement of a medical condition or a patient's condition. Although it may not be exact, the diagnosis is often useful enough for treatment purposes. Fuzzy logic is increasingly employed in diagnostic and medical equipment capable of measuring gradations of a condition.<ref>Kazem Sadegh-Zadeh "The Fuzzy Revolution: Goodbye to the Aristotelian Weltanschauung". In: Artificial Intelligence in Medicine, 21, 2001, pp. 18–19.[78]</ref>
• In information services, fuzzy concepts are frequently encountered because a customer or client asks a question about something which could be interpreted in different ways, or, a document is transmitted of a type or meaning which cannot be easily allocated to a known type or category, or to a known procedure. It might take considerable inquiry to "place" the information, or establish in what framework it should be understood.
• In phenomenology, which aims to study the structure of subjective experience without preconceptions,<ref>Stephen Priest, Theories of the mind. London: Penguin Books, 1991, p. 183.</ref> an important insight is that how someone experiences something can be influenced both by the influence of the thing being experienced itself, but also by how the person responds to it.<ref>Michael Hammond, Jane Howarth and Russell Keat, Understanding Phenomenology. Oxford: Blackwell, 1991.</ref> Thus, the actual experience the person has, is shaped by an "interactive object-subject relationship". To describe this experience, fuzzy categories are often necessary, since it is often impossible to predict or describe with great exactitude what the interaction will be, and how it is experienced.
• In translation work, fuzzy concepts are analyzed for the purpose of good translation. A concept in one language may not have quite the same meaning or significance in another language, or it may not be feasible to translate it literally, or at all.<ref>Cornelia Griebel, "Fuzzy concepts in translators' minds". In: Valérie Dullion, Between specialised texts and institutional contexts – competence and choice in legal translation. Amsterdam: John Benjamins Publishing Company, 2017. (Special issue of Translation and Translanguaging in Multilingual Contexts, Vol. 3, No. 1, 2017).</ref> Some languages have concepts which do not exist in another language, raising the problem of how one would most easily render their meaning. In computer-assisted translation, a technique called fuzzy matching is used to find the most likely translation of a piece of text, using previous translated texts as a basis.
• In hypnotherapy, fuzzy language is deliberately used for the purpose of trance induction. Hypnotic suggestions are often couched in a somewhat vague, general or ambiguous language requiring interpretation by the subject. The intention is to distract and shift the conscious awareness of the subject away from external reality to her own internal state. In response to the somewhat confusing signals she gets, the awareness of the subject spontaneously tends to withdraw inward, in search of understanding or escape.<ref>Ronald A. Havens (ed.), The wisdom of Milton H. Erickson, Volume I: hypnosis and hypnotherapy. New York: Irvington Publishers, 1992, p. 106. Joseph O'Connor & John Seymour (ed.), Introducing neuro-linguistic programming. London: Thorsons, 1995, p. 116f.</ref>
• In business and economics, it was discovered that "we are guided less by a correct exact knowledge of our self-interest than by a socially learned, evolved, intuitive grasp derived from mental shortcuts (frames, reference points, envy, addiction, temptation, fairness)".<ref>Francese Trillas, "Fuzzy logic and modern economics." In: Rudolf Seising, Enric Trillas & Janusz Kacprzyk (eds.), Towards the future of fuzzy logic. Basel: Springer International Publishing, 2015, p. 56.</ref> Thus, economic preferences are often fuzzy preferences, a highly important point for suppliers of products and services. Fuzzy set empirical methodologies are increasingly used by economic analysts to analyze the extent to which members of a population belong to a specific market category, because that can make a big difference to business results.
• In sexology, sex and gender are conceptualized by gender pluralists as a spectrum or continuum, or a set of scaled characteristics.<ref>Surya Monro, Bisexuality. Houndmills, Basingstoke: Palgrave Macmillan, 2015, p. 49.</ref> Thus, the idea that people are either heterosexual men, heterosexual women, gay, lesbian, bisexual or transsexual is far too simplistic; gender identity is a matter of degree, a graded concept, which for that very reason is a fuzzy concept with unsharp boundaries. For example, somebody who is "mainly" heterosexual, may occasionally have had non-heterosexual contacts, without this warranting a definite "bisexual" label. A great variety of sexual orientations are possible and can co-exist. In the course of history, typical male or female gender roles and gender characteristics can also gradually change, so that the extent to which they express "masculine" or "feminine" traits is, at any time, a matter of degree, i.e. fuzzy.
• In politics, it can be highly important and problematic how exactly a conceptual distinction is drawn, or indeed whether a distinction is drawn at all; distinctions used in administration may be deliberately sharpened, or kept fuzzy, due to some political motive or power relationship.<ref>Bart Kosko, "Yes, Candidates, There Is a Fuzzy Math". New York Times, 7 November 2000.</ref> Politicians may be deliberately vague about some things, and very clear and explicit about others; if there is information that proves their case, they become very precise, but if the information doesn't prove their case, they become vague or say nothing.
• In statistical research, it is an aim to measure the magnitudes of phenomena. For this purpose, phenomena have to be grouped and categorized, so that distinct and discrete counting units can be defined. It must be possible to allocate all observations to mutually exclusive categories, so that they are properly quantifiable. Survey observations do not spontaneously transform themselves into countable data; they have to be identified, categorized and classified in such a way, that identical observations can be grouped together, and that observations are not counted twice or more.<ref>Russel Gordon & David Bendien, "Standard classifications". New Zealand Statistics Review, September 1993, p. 20.</ref> A well-designed questionnaire ensures that the questions are interpreted in the same way by all respondents, and that the respondents are really able to answer them within the formats provided. Again, for this purpose, it is a requirement that the concepts being used are exactly and comprehensibly defined for all concerned, and not fuzzy.<ref>Paul C. Bauer et al., "Vague concepts in survey questions. A general problem illustrated with the left-right scale." SSRN Electronic Journal, April 2014.[79]</ref> There could be a margin of measurement error, but the amount of error must be kept within tolerable limits, and preferably its magnitude should be known.
• In theology an attempt is made to define more precisely the meaning of spiritual concepts, which refer to how human beings construct the meaning of human existence, and, often, the relationship people have with a supernatural world. Many spiritual concepts and beliefs are fuzzy, to the extent that, although abstract, they often have a highly personalized meaning, or involve personal interpretation of a type that is not easy to define in a cut-and-dried way. A similar situation occurs in psychotherapy. The Dutch theologian Kees de Groot has explored the imprecise notion that psychotherapy is like an "implicit religion", defined as a "fuzzy concept" (it all depends on what one means by "psychotherapy" and "religion").<ref>C.N. de Groot, "Sociology of religion looks at psychotherapy." Recherches sociologiques (Louvain-la-Neuve, Belgium), Vol. 29, No. 2, 1998, pp. 3–17 at p. 4.[80] Archived 2013-05-23 at the Wayback Machine</ref> The philosopher of spirituality Ken Wilber argued that "nothing is 100% right or wrong", things merely "vary in their degree of incompleteness and dysfunction"; no one and nothing is 100% good or evil, each just varies "in their degree of ignorance and disconnection". This insight suggests, that all human valuations can be considered as graded concepts, where each qualitative judgement has at least implicitly a sense of quantitative proportion attached to it.<ref>Mark Manson, "The rise and fall of Ken Wilber", markmanson.net, 4 June 2012.[81]</ref>
• In the legal system, it is essential that rules are interpreted and applied in a standard way, so that the same sorts of cases and the same sorts of circumstances are treated equally. Otherwise one would be accused of arbitrariness,<ref>That is, in applying rules, the rules are not consistently followed, and the pattern of their application therefore does not follow rules.</ref> which would not serve the interests of justice. Consequently, lawmakers aim to devise definitions and categories which are sufficiently precise, so that they are not open to different interpretations. For this purpose, it is critically important to remove fuzziness, and differences of interpretation are typically resolved through a court ruling based on evidence. Alternatively, some other procedure is devised which permits the correct distinction to be discovered and made.<ref>For more information see e.g. Ralf Posche, "Ambiguity And Vagueness In Legal Interpretation", in: Lawrence M. Solan & Peter M. Tiersma (eds.), The Oxford Handbook of Language and Law. Oxford University Press, 2012, pp. 128-144.</ref>
• In administration, archiving and accounting, fuzziness problems in interpretation and boundary problems can arise, because it is not clear to what category exactly a case, item, document, transaction or piece of data belongs. In principle, each case, event or item must be allocated to the correct category in a procedure, but it may be, that it is difficult to make the appropriate or relevant distinctions.<ref>David Henry, "Fuzzy Numbers', Bloomberg Businessweek, 3 October 2004.[82]</ref>

Generalities

It could be argued that many concepts used fairly universally in daily life (e.g. "love", "God", "health", "social", "tolerance" etc.) 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 (as when, for example, one says "this is what I mean by X" in contrast to other possible meanings). 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:

"...it is always undesirable to make an effort to increase precision for its own sake – especially linguistic precision – since this usually leads to loss of clarity, and to a waste of time and effort on preliminaries which often turn out to be useless, because they are bypassed by the real advance of the subject: one should never try to be more precise than the problem situation demands. I might perhaps state my position as follows. Every increase in clarity is of intellectual value in itself; an increase in precision or exactness has only a pragmatic value as a means to some definite end..."<ref>Karl Popper, Unended quest: an intellectual autobiography. London: Routledge, 2002, p. 22.</ref>

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 (for practical purposes) 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

Lotfi A. Zadeh 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" (tractability means "being easy to manage or operationalize"). Zadeh calls this approach the "Fuzzy Logic Gambit" (a gambit means giving up something now, to achieve a better position later).

In the Fuzzy Logic Gambit, "what is sacrificed is precision in [quantitative] 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.<ref>Lotfi A. Zadeh, "What is fuzzy logic?". IFSA Newsletter (International Fuzzy Systems Association), Vol. 10, No. 1, March 2013. Takeshi Yamakawa "Stabilization of an Inverted Pendulum by a High-speed Fuzzy Logic Controller Hardware System". Fuzzy Sets and Systems, Vol.32, pp. 161–180, 1989.</ref>

Fuzzy vs. Boolean

Common use of this sort of approach (combining words and numbers in programming), has led some logicians to regard fuzzy logic merely as an extension of Boolean logic (a two-valued logic or binary logic is simply replaced with a many-valued 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 (or sets). 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.<ref>See e.g. C. A. Drossos, "Foundations of fuzzy sets: A nonstandard approach". Fuzzy Sets and Systems, Volume 37, Issue 3, 28 September 1990, pp. 287-307.</ref> 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:

• 1. Contextualizing the concept by defining the setting or situation in which the concept is used, or how it is used appropriately (context).
• 2. Identifying the intention, purpose, aim or goal associated with the concept (teleology and design).
• 3. Comparing and contrasting the concept with related ideas in the present or the past (comparative and comparative research).
• 4. Creating a model, likeness, analogy, metaphor, prototype or narrative which shows what the concept is about or how it is applied (isomorphism, simulation or successive approximation [83]).
• 5. Probing the assumptions on which a concept is based, or which are associated with its use (critical thought, tacit assumption).
• 6. Mapping or graphing the applications of the concept using some basic parameters, or using some diagrams or flow charts to understand the relationships between elements involved (visualization and concept map).<ref>Guy W. Mineau et al. (eds.), Conceptual graphs for knowledge representation. Berlin: Springer, 1993. Tru Hoang Cao, Conceptual graphs and fuzzy logic. Berlin: Springer, 2010.</ref><ref>V. Rahmati et al. (eds.), A Novel Low Complexity Fast Response Time Fuzzy PID Controller for Antenna Adjusting Using Two Direct Current Motors.</ref>
• 7. Examining how likely it is that the concept applies, statistically or intuitively (probability theory).
• 8. Specifying relevant conditions to which the concept applies, as a procedure (computer programming, formal concept analysis).
• 9. Concretizing the concept – finding specific examples, illustrations, details or cases to which it applies (exemplar, exemplification).
• 10. Reducing or restating fuzzy concepts in terms which are simpler or similar, and which are not fuzzy or less fuzzy (simplification, dimensionality reduction, plain language, KISS principle or concision).
• 11. Trying out a concept, by using it in interactions, practical work or in communication, and assessing the feedback to understand how the boundaries and distinctions of the concept are being drawn (trial and error or pilot experiment).
• 12. Engaging in a structured dialogue or repeated discussion, to exchange ideas about how to get specific about what it means and how to clear it up (scrum method).
• 13. Allocating different applications of the concept to different but related sets (Boolean logic).
• 14. Identifying operational rules defining the use of the concept, which can be stated in a language and which cover all or most cases (material conditional).
• 15. Classifying, categorizing, grouping, or inventorizing all or most cases or uses to which the concept applies (taxonomy, cluster analysis and typology).

An operationalization diagram, one method of clarifying fuzzy concepts.

• 16. Applying a meta-language which includes fuzzy concepts in a more inclusive categorical system which is not fuzzy (meta).
• 17. Creating a measure or scale of the degree to which the concept applies (metrology).
• 18. Examining the distribution patterns or distributional frequency of (possibly different) uses of the concept (statistics).
• 19. Specifying a series of logical operators or inferential system which captures all or most cases to which the concept applies (algorithm).
• 20. Relating the fuzzy concept to other concepts which are not fuzzy or less fuzzy, or simply by replacing the fuzzy concept altogether with another, alternative concept which is not fuzzy yet "works the same way" (proxy)
• 21. Engaging in meditation, or taking the proverbial "run around the block" to clarify the mind, and thus improve precision of thought about the definitional issue (self-care).

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

Main article: 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" (the object) is itself fuzzy and evades definition, or because "one of those" (the concept of the object) is fuzzy and inadequately defined.

Thus, the source of fuzziness may be in (1) the nature of the reality being dealt with, (2) the concepts used to interpret it, or (3) the way in which the two are being related by a person.<ref>cf. Timothy Williamson, Vagueness. London: Routledge, 1996, p. 258.</ref> 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.

See also

• Alternative set theory
• Approximate measures
• Classical logic
• Defuzzification
• Detection theory
• Deviant logic
• Dialectic
• European Society for Fuzzy Logic and Technology
• Fuzzy subalgebra
• Fuzzy logic
• George Klir
• Fuzzy clustering
• Fuzzy mathematics
• Fuzzy measure theory
• Fuzzy set operations
• Identity (Philosophy)
• Interval finite element
• Jakobson's functions of language
• Linear partial information
• Many-valued logic
• Multiset
• Neuro-fuzzy
• Non-well-founded set theory
• Obfuscation
• Opaque context
• Paraconsistent logic
• Phenomenology (psychology)
• Precision
• Referential transparency
• reflexivity (social theory)
• Post-normal science
• Rough fuzzy hybridization
• Rough set
• Semiset
• Sørensen similarity index
• Synchronicity
• Type-2 Fuzzy Sets and Systems
• Uncertainty
• Vague set

References

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External links

• James F. Brule, Fuzzy systems tutorial
• "Fuzzy Logic", Stanford Encyclopedia of Philosophy
• "Vagueness", Stanford Encyclopedia of Philosophy
• Calvin College Engineering Department, Getting Started with Fuzzy Logic
• 2009 Benjamin Franklin Medal Winner: Lotfi A. Zadeh
• Lin Shang, Lecture on fuzzy and rough sets, Nanjing University
• Rudolf Kruse and Christian Moewes on fuzzy set theory