From: d@d.d   
      
   Hello,   
      
      
   More rigorous about precision of computational complexity..   
      
   When we say that 2+2=4, it is a system that inherently contains enough   
   precision that we call enough precision, it is in fuzzy logic that it is   
   100% precision that is a truth that is equal 1   
      
   But when for example we say:   
      
   "If i say that a person is obese, so he has a high risk to get a disease   
   because he is obese."   
      
   What is it equal in fuzzy logic ?   
      
   It is not an exact value in fuzzy logic, but it is not exact precision   
   but it is fuzzy and it is equal to a high risk to get a disease.   
      
   That's the same for time complexities of n*log(n) and n , they   
   are fuzzy, but there level of fuzziness can predict the resistance of   
   the algorithm that it is average (by analogy with material resistance,   
   read below to understand), and i think that it is how can be viewed   
   computational complexity.   
      
   Read the rest of my previous thoughts to understand better:   
      
   Yet about what can we take as enough precision and about computational   
   complexity..   
      
      
   As you have just noticed i said before that 2+2=4 is a system that   
   inherently contains enough precision that we call enough precision,   
   because we have to know that it is judged and dictated by our minds of   
   we humans, but when the mind sees a time complexity of n*log(n) or n ,   
   it will measure them by reference to the other time complexities that   
   exists, and we can notice that they are average time complexities if we   
   compare them with an exponential time complexity and with a log(n) time   
   complexity, so this measure dictates that the time complexities of   
   n*log(n) and n are average resistance (by analogy with material   
   resistance, read below to understand), but our minds of humans will also   
   notice that this average resistance is not an exact resistance, so   
   they are missing precision and exactitude, so like the example that i   
   give below of the obese person, we can call the time complexities such   
   as n*log(n) and n as fuzzy and this look like probability calculations.   
      
   Read the rest of all my previous thoughts to understand:   
      
   I will add again more logical rigor to my post about about computational   
   complexity:   
      
   As you have just noticed in my previous post (read below), i said the   
   following:   
      
   That time complexities such as n*log(n) and n are fuzzy.   
      
   But we have to be more logical rigor:   
      
   But what can we take as enough precision ?   
      
   I think that when we say 2+2=4, it has no missing part   
   of precision, so this fact is enough precision,   
   but if we say a time complexity of n*log(n), there is a missing part   
   of precision, because n*log(n) is dependent on reality that needs   
   in this case more precision about an exact precision about the   
   resistance of the algorithm(read below my analogy with material   
   resistance), so this is why we can affirm that time complexities such as   
   n*log(n) and n  are fuzzy, because there is a missing part of precision,   
   but eventhough there is a missing part of precision, there is enough   
   precision that permits to predict that there resistance in reality are   
   average resistance.   
      
   Read the rest of my previous thoughts to understand:   
      
   I correct again one last typo, here is my final post about computational   
   complexity:   
      
   I continu about computational complexity by being more and more   
   rigorous, read again:   
      
   I said previously(read below) that for example the time complexities   
   such as n*(log(n)) and n are fuzzy, because we can say that n*(log(n) is   
   an average resistance(read below to understand the analogy with material   
   resistance) or we can say that n*log(n) is faster than if it was a   
   quadratic complexity or exponential complexity, but we can not say   
   giving a time complexity of n or n*log(n) how fast it is giving the   
   input of the n of the time complexity, so since it is not exact   
   prediction, so it is fuzzy, but this level of fuzziness, like in the   
   example below of the obese person, permits us to predict important   
   things in the reality, and this level of fuzziness of computational   
   complexity is also science, because it is like probability calculations   
   that permits us to predict, since computational complexity can predict   
   the resistance of the algorithm if it is high or low or average (by   
   analogy with material resistance, read below to understand).   
      
   Read my previous thoughts to understand:   
      
   What is science? and is computational complexity science ?   
      
   You just have seen me talking about computational complexity,   
   but we need to answer the questions of: What is science ?   
   and is computational complexity science ?   
      
   I think that we have to be more smart because there is like   
   higher level abstractions in science, and we can be in those   
   abstractions exact precisions of science, but we can be more fuzzy   
   precisions that are useful and that are also science, to understand me   
   more, let me give you an example:   
      
   If i say that a person is obese, so he has a high risk to get a disease   
   because he is obese.   
      
   Now you are understanding more that with this abstraction we are not   
   exact precision, but we are more fuzzy , but this fuzziness   
   is useful and its level of precision is also useful, but is it   
   science ? i think that this probabilistic calculations are   
   also science that permits us to predict that the obese person   
   has a high risk to get a disease. And this probabilistic calculations   
   are like a higher level abstractions that lack exact precision but   
   they are still useful precisions. This is how look like computational   
   complexity and its higher level abstractions, so you are immediately   
   understanding that a time complexity of O(n*log(n)) or a O(n)   
   is like a average level of resistance(read below to know why i am   
   calling it resistance by analogy with material resistance) when n grows   
   large, and we can immediately notice that an exponential time complexity   
   is a low level resistance when n grows large, and we can immediately   
   notice that a log(n) time complexity is a high level of resistance   
   when n grows large, so those time complexities are like a higher level   
   abstractions that are fuzzy but there fuzziness, like in the example   
   above of the obese person, permits us to predict important things in the   
   reality, and this level of fuzziness of computational complexity is also   
   science, because it is like probability calculations that permits us   
   to predict.   
      
   Read the rest of my previous thoughts to understand better:   
      
   The why of computational complexity..   
      
      
   Here is my previous answer about computational complexity and the rest   
   of my current answer is below:   
      
      
      
   [continued in next message]   
      
   --- SoupGate-Win32 v1.05   
    * Origin: you cannot sedate... all the things you hate (1:229/2)