Fuzzy logic and the Liar paradox

In summary, the Economist article discusses a paper which suggests that fuzzy logic solves the liar paradox by assigning the liar sentence a truth value N, other than T or F, with [[A]] = N ⇒[[~A]] = N. However, I don't see that this gets around the essential point of the liar: the liar uses a predicate ~T, and the assumption of the existence of a predicate ~T leads to a contradiction, for example quickly with the Diagonal Lemma. So if you could build the liar sentence, then the fuzzy logic would be of use to not make it a paradox, granted, but you can't even build the liar sentence in the first place.
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nomadreid
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I have once again (this time in http://www.economist.com/node/2099851) come across the argument that a fuzzy logic solves the liar paradox by assigning the liar sentence a truth value N, other than T or F, with
[[A]] = N ⇒[[~A]] = N. However, I don't see that this gets around the essential point of the liar: the liar uses a predicate ~T, and the assumption of the existence of a predicate ~T leads to a contradiction, for example quickly with the Diagonal Lemma. So if you could build the liar sentence, then the fuzzy logic would be of use to not make it a paradox, granted, but you can't even build the liar sentence in the first place. What am I missing?
 
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Here's the arxiv preprint of the paper that the Economist article refers to:

http://arxiv.org/pdf/cs/0309046v1.pdf

I haven't read the whole thing in depth, but basically, they look at specific instances of "The truth value of B is b." At the first level, you have a set of propositions B∈S1, which can be assigned a certain truth value Tr(B) (N.B.--in fuzzy logic, Tr(B)∈[0,1]; cf. Boolean logic, where Tr(B)∈{0,1}), and at the second level you have the set of statements C∈S2 about the truth value of B, namely C = "Tr(B) = b." [It's important to note that S2⊆S1.] Of course, Tr(B) is independent of b, but our intuition says that C is true when Tr(B) is actually equal to b and false when Tr(B) and b differ by exactly 1. So they define Tr(C) = 1-|Tr(B)-b|.

The tricky part comes when you have a self-referential formula, like B = "Tr(B) = b." The authors model the Liar sentence as A = "A is false," or A = "Tr(A) = 0," where A∈S1. But since A is of the same form as C (above), it's also the case that A∈S2. So the sentence can be recast as C = "Tr(A) = 0." Since A=C, we also have Tr(A)=Tr(C). Taking the definition of Tr(C), you get:

Tr(C) = 1-|Tr(C)-0| = 1-Tr(C) since Tr(C) ≥ 0
2Tr(C) = 1
Tr(C) = 0.5

Whether you agree with it philosophically or not, it does seem to be a consistent way to treat the problem. But it hinges on two things: how you model the Liar sentence, and how you define the truth value of sentences from the set S2.
 
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Many thanks, TeethWhitener, both for the article and your excellent summary! Fascinating article, not only the part that you so splendidly summarized, but the rest of it as well. I am still going through it, but the essential part which you explained is indeed rather elegant: a bit like having nested models reflected down to a single syntactic level. Philosophically? I do not see any philosophical objection problem with breaking a truth value assignment into two parts. More of interest are the open questions which the author leaves "for future research", especially: is the set of solutions the base set for a lattice? Thanks again!
 

1. What is fuzzy logic?

Fuzzy logic is a type of mathematical logic that allows for the representation of imprecise or vague concepts. It is based on the idea that things in the real world cannot always be perfectly categorized as true or false, but rather exist on a spectrum of truth values. Fuzzy logic uses linguistic variables and membership functions to handle this uncertainty.

2. How does fuzzy logic relate to the Liar paradox?

The Liar paradox is a logical paradox that arises when a statement claims to be false, but if it is true, then it must be false, and if it is false, then it must be true. Fuzzy logic can help to resolve this paradox by allowing for statements to have a truth value between 0 and 1, rather than just being strictly true or false. This allows for more nuanced and flexible reasoning, which can avoid the contradiction in the Liar paradox.

3. What are some applications of fuzzy logic?

Fuzzy logic has many practical applications, including in artificial intelligence, control systems, and decision-making processes. It is commonly used in systems that deal with imprecise or uncertain data, such as in medical diagnosis, weather forecasting, and image recognition. Fuzzy logic can also be applied in fields such as economics, linguistics, and psychology.

4. Are there any limitations to fuzzy logic?

Like any mathematical model, fuzzy logic has its limitations. It may not always accurately represent complex or contradictory situations, and it relies on human input to define membership functions and linguistic variables. Additionally, fuzzy logic can be computationally expensive and may not always provide a unique solution to a problem.

5. How does fuzzy logic differ from traditional binary logic?

Fuzzy logic differs from traditional binary logic in that it allows for intermediate truth values between 0 and 1, rather than just true or false. This allows for more flexible and nuanced reasoning, which can better reflect the real world. Additionally, traditional binary logic operates on the principle of bivalence, which states that a statement is either true or false, while fuzzy logic allows for statements to have a degree of truthfulness.

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