# Fuzzy Logic

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## Full Title or Meme

A logic that goes beyond the two-valued "true" and "false" of Boolean logic to a continuum, which could be imaged as a probability between 0 to 1.

## Context

By no means is “fuzzy logic” new. The introduction to this approach was Professor Lotﬁ Zadeh back in 1965.[1] As described in brief by the developer, Zadeh: “Fuzzy logic is not fuzzy. Basically, fuzzy logic is a precise logic of imprecision and approximate reasoning. More speciﬁcally, fuzzy logic may be viewed as an attempt at formalization/mechanization of two remarkable human capabilities. First, the capability to converse, reason and make rational decisions in an environment of imprecision, uncertainty, incompleteness of information, conﬂicting information, partiality of truth and partiality of possibility – in short, in an environment of imperfect information. And second, the capability to perform a wide variety of physical and mental tasks without any measurements and any computations, from computing with numbers to computing with words-from manipulation of measurements to manipulation of perceptions”[2]

According to Turner (2021) “Dual process models in sociology are faced with a generic problem of computationalist approaches to cognitive science about generalization: if the processes are machine or computer-like, these processes don’t generalize to near cases very well. The idea that fuzzy logic might help is a reasonable response to this problem.” Indeed, among its varied attributes, fuzzy logic has a high power of cointensive precisiation. Informally, precisiation is an operation which transforms an object, p into an object, p*, which in some speciﬁed sense is deﬁned more precisely than p (Zadeh, 2008). This power is needed fora formulation of cointensive deﬁnitions of scientiﬁc concepts and cointensive formalization of human-centric ﬁelds such as economics, linguistics, law, conﬂict resolution, psychology and medicine (Zadeh, 2008). Like all constructs of knowledge fuzzy logic has its limitations but its adaptability to be used in a wide array of applications garners support that such holds potential to eﬀectively serve to augment dual- process modelling initiatives. The potential beneﬁt of fuzzy logic to that of sociology and the social sciences in general may be examined further in Kosko (1999, 1994); Bunge (1983); and Uddin (2017).As quoted by Edgar Degas: “Art is not what you see but what you make others see”(Schenkel, 2004). By replacing the word “art”, this same line of thinking may hold as a goal of theory. In so doing, new creative approaches and added bridges are enabled to help connect existing and developing disciplines, plus constructs of knowing in general. To this end and beyond, dual-process models hold potential to further expand our thinking, or as Leschziner(2019) would say, help explicate social action. Finding new in-roads towards this quest such as the potential incorporation of fuzzy logic, requires that we sharpen our creativity(Willison,2017), curiosity and sociological imagination (Mills, 1959).Overall, the domain of sociology has expanded by an emphasis on integrative practices as we witness and beneﬁt from such emerging ﬁelds as: cognitive sociology, environmental sociology, computational sociology and sociological social psychology, and so forth, to name but a few relatively new developments.[3]

## References

1. L. A. Zadeh, Fuzzy sets. Information and Control. San Diego. (1965-06) 8 (3): 338–353. doi:10.1016/S0019-9958(65)90241-X. ISSN 0019-9958. Wikidata Q25938993.
2. L. A. Zadeh Is there a need for fuzzy logic?. Information Sciences Volume 178, Issue 13, (2008-07-01), pp. 2751-2779
3. Willison, K. (2021). Dual-Process Models in Sociology: Reﬂections and Potential. Academia Letters,Article 201. https://doi.org/10.20935/AL201 https://www.academia.edu/45050564/Dual_Process_Models_in_Sociology_Reflections_and_Potential?email_work_card=thumbnail