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Adam Savage's Tested

https://youtu.be/QwWOUYpwi1I?t=71

On Mythbusters we were regularly trying to quantify what "dead" was. And, just like everything else in science, the closer you look the blurrier things get.

Frequently when we try to apply crisp distinctions (distinct terms) to observations in the world, as in this example, the distinction between dead and alive seems to become vague, fuzzy, or indistinct rather than crisp. Should we conclude that human knowledge is inherently fuzzy? Or should we attribute this common experience of uncertain knowledge to some other philosophical concept?

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    there's a lot of sayings like the map is not the territory or all models are wrong (but some are useful) which don't necessarily get at the core of this question, but seem related
    – Kaia
    Commented Jul 9 at 16:58
  • Tony Stark marrying Pepper Potts in the MCU is knowledge and not vague.
    – J Kusin
    Commented Jul 9 at 18:08
  • Is a guerilla a freedom fighter or a terrorist? Knowledge isn't absolute, with fixed definitions. Commented Jul 9 at 20:42

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Yes, this is what fuzzy logic is for.

It doesn't necessarily apply to all human knowledge. 1+1=2 is (at least arguably) not a fuzzy statement.

But fuzziness does apply to practically all terms that we use to describe macroscopic conditions in the real world. This is because of the Sorites paradox; it applies to practically everything. What is a chair? There are border cases where it is difficult to say whether something is a chair or not. Consider a succession of objects as we gradually "morph" a chair into, say, a small rock. At some point in this succession we would stop saying the object is a chair, but is that at 23% rock 77% chair, or is at 23.3% rock 76.7% chair? No way to say.

This applies to virtually any noun. Chair, giraffe, automobile, basalt. Although, there are many things that we would definitely 100% call chairs, so the fuzziness does not always come to the surface, it's just always "lurking" in the edge cases.

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    Bertrand Russell argued that the very concepts of true and false are vague, as must be all human language. Commented Jul 9 at 21:55
  • Could we not say that all fuzzy logic is ultimately grounded in a binary? That is, for any claim about X being fuzzy it seems on its face that the claim itself about X being fuzzy resolves about "yes, it is fuzzy"(like the answer you gave). If someone were to problematize this saying the "yes" itself is fuzzy, then this just pushes back the question and so we must ask "is THAT claim yes/no or fuzzy?". So beyond the very claim that the yes is fuzzy requires a binary, further problematization could seemingly only be resolved by binary logic.
    – Sismetic
    Commented Jul 10 at 1:16
  • @Sismetic Normally yes, we do use more conventional binary logic to model fuzzy logic. However, this is only what we "normally" do. Which logic you use is a question of which algorithm you use in your brain or in a computer to come up with conclusions. It's conceivable that you could design an analog computer that uses only fuzzy logic, based entirely on continuous values, so that it would be fundamentally fuzzy with no dependence on binary logic. It's plausible that the human brain works this way on a neural level.
    – causative
    Commented Jul 10 at 1:22
  • @causative Interesting. I don't know much of this topic, and yet it makes little sense to me. As far as I know, even advocates of fuzzy logic must do so on top of binary logic and not as an alternative logic(an extension of it, if you will). But I'm ignorant. I just fail to see how fuzzy logic can have meaning absent a binary base or a stable base. It's the same kind of issue of trying to push induction absent any ground. It seems to me to speak of a groundless logic, in which case I always wonder how is it a logic, and whether we treat it as vaild or invalid, does not reproduce the binary?
    – Sismetic
    Commented Jul 10 at 1:37
  • @Sismetic - Consider the fuzzy items hot, warm, and cold. There are three distinct (crisp) words associated with vague perceptions and concepts. Language compels the use of crisp terms that are often coupled to otherwise fuzzy (vague) interpretations. Knowledge seems to have both crisp and fuzzy attributes. Scientific investigation often reproduces the experience of terms becoming vague or fuzzy the more we investigate. Either nature is inherently vague of fuzzy, or human recognition generates vague or fuzzy experience, or some combination of those two concepts. Let the dead bury their dead. Commented Jul 10 at 2:18

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