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  1. #21
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    Quote Originally Posted by ygolo View Post
    I still wonder, though, if there aren't other techniques which are being left unexplored, that would allow previously intractable problems, to be tackled. For instance, the techniques that come from complexity and chaos theory.
    If you read through the first post I made you'll see that I state in science it is easy to come to the wrong conclusion quite logically when you aren't able to predict every system your idea will encounter. This leads to situations where you predict something and it works in 99.9% of the systems you apply it to. Leaving the first principle theories you used to construct the idea intact and the overall design of your idea (built up on first principles and well established theories) correct, but still quite useless in 1 out of 100,000 systems, due to our inability to accurately define all parameters in every system that the idea/general concept will encounter.

    It's like saying that the theory of gravity is wrong because you droped a ball from a building and when you went to the street expecting to find the ball on the ground it wasn't there. Then everyone is like holy shit where is the ball? There is no evidence of it hitting the ground, say it was covered in paint to mark the exact point of impact. Too bad nobody saw the child who ran by and caught the ball in mid air before it hit the ground. The issue with trying to account for variables like this is simply that most of them are completely unrelated to the system that we are studying and infinite in their possibilities. There is a difference between a known parameter whose quantity can be random and multiple unknown parameters that are all random in every way that can possibly be conceived. This is what happens when large molecules adopt unexpected shapes, or unexpected enzymes or metabolites interact with a drug in the body due to diet, preexisting disease, or some other unknown factor that couldn't have possibly been predicted.

    The use of chaos theory is common in electrochemistry as well as other areas of chemisry and science in general - Polynomial chaos in simulation and engineering applications F.

    but it cannot be realistically applied to many problems in engineering, physics, chemistry, etc especially in situations where the products being made are used for manufacturing or in the human body. It really isn't possible to know what kinds of alterations in the parameters will be forced by the environment... It's like asking why we aren't using chaos theory to predict the exact time of death of every individual on the planet, or why we aren't using chaos theory to predict who will be elected for president 50 years from now. Chaos theory is not psychic, so there are limitations to what it can do for certain areas of research because the randomness is compounded and therefore not easily defined.

    edit: so in short this is why we need experimentation in science so that we can a.) be sure of cause and effect and b.) be sure that the ideas we have and are testing are repeatable in all systems c.) and when there are exceptions we need to understand why (this is better done with experimentation because it has a way of revealing parameters that hundreds of years of theorizing and philosophizing would have never eluded to.

  2. #22

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    I agree with the facts you tell me. I read your posts and I agree with almost everything you say.

    Even in my very first post, I said I am not proposing a dichotomy. Then in my second post I said I believe a balance is needed.

    I am not implying that theory is applicable everywhere. I believe experiment is more expedient in the cases you provide.

    There is only a very small sticking point, and I believe that has to do with the way we are using the word "logical."

    When I am talking about a logical process that arrives at a conclusion, I am talking about the use of premises and the formal rules of inference to arrive at a conclusion.

    Notice that there are only 8 rules of inference. If you were to put your "logical" process into formal logic form, you would find that most of the "steps" are premises not a use of the rules of inference.

    Remember, in making a formal arguments, you are allowed to use only the eight rules of inference. Anything that cannot be derived with the rules of inference has to become a premise.

    Quote Originally Posted by spin-1/2-nuclei View Post
    If you read through the first post I made you'll see that I state in science it is easy to come to the wrong conclusion quite logically when you aren't able to predict every system your idea will encounter. This leads to situations where you predict something and it works in 99.9% of the systems you apply it to. Leaving the first principle theories you used to construct the idea intact and the overall design of your idea (built up on first principles and well established theories) correct, but still quite useless in 1 out of 100,000 systems, due to our inability to accurately define all parameters in every system that the idea/general concept will encounter.

    It's like saying that the theory of gravity is wrong because you droped a ball from a building and when you went to the street expecting to find the ball on the ground it wasn't there. Then everyone is like holy shit where is the ball? There is no evidence of it hitting the ground, say it was covered in paint to mark the exact point of impact. Too bad nobody saw the child who ran by and caught the ball in mid air before it hit the ground. The issue with trying to account for variables like this is simply that most of them are completely unrelated to the system that we are studying and infinite in their possibilities. There is a difference between a known parameter whose quantity can be random and multiple unknown parameters that are all random in every way that can possibly be conceived. This is what happens when large molecules adopt unexpected shapes, or unexpected enzymes or metabolites interact with a drug in the body due to diet, preexisting disease, or some other unknown factor that couldn't have possibly been predicted.
    I agree with everything here except for the use of the term "logically" (note I know it a valid use of the term, it just isn't what I was referring to when I was talking about the use of logic).

    To illustrate what I mean, I will use a formal argument to arrive at the conclusion that we should have found the ball on the ground.

    So as to not use special characters I will use the following:
    • "A || B" means "either statement A is true or statement B is true or both statements A and B are true."
    • "A && B" means "Both statements A and B are true."
    • "A -> B" means "Statement A being true implies statement B is true."
    • I may also use parentheses to indicate order of operations.


    I will replace the following symbols for the corresponding statements:
    1. S1:Gravity pulls things towards the earth
    2. S2:The ball we drop will hit the earth
    3. S3:We will find the ball on the ground.


    Premises:
    P1:S1
    P2:S1->S2
    P3:S2->S3
    ---------------
    L1:S2 (modus ponens using P1 and P2)
    C1:S3 (modus ponens using L1 and P3)

    But through observation, we have found C1, the conclusion of the argument to be wrong. So some step in the argument is wrong.

    Here is the list of things we can choose from:
    1. modus ponens
    2. P1
    3. P2
    4. P3


    1. Modus ponens has been used so often and held to be true so much, that this is a poor choice.
    2. Now P1 is S1 which states that gravity pulls things towards the earth. This seems like a bad choice due to the seeming universality of it.
    3. Now P2 may be a good choice, since we can imagine things going wrong here (an awning catching the ball, the ball falling into a car through a sun roof, etc.)
    4. P3 also seems like a good choice, since again we can imagine things going wrong, like a child picking up the ball after it hits the ground, but before we look at it. In fact, in the scenario you proposed, P3 is what was wrong. The only way we could truly confirm that this was wrong is if we had some empirical evidence, like eye-witnesses or a confession from the child.


    Based on prior experience, P2 and P3 are good choices for disbelief, while modus ponens and P1 are poor choices.

    Quote Originally Posted by spin-1/2-nuclei View Post
    The use of chaos theory is common in electrochemistry as well as other areas of chemisry and science in general - Polynomial chaos in simulation and engineering applications F.
    That's cool to find out. What about complexity science?...like what the Santa Fe Institute does? Also, how prevalent is the use of chaos theory? Do you believe it can be applied for more than it is being applied to now?

    Quote Originally Posted by spin-1/2-nuclei View Post
    but it cannot be realistically applied to many problems in engineering, physics, chemistry, etc especially in situations where the products being made are used for manufacturing or in the human body. It really isn't possible to know what kinds of alterations in the parameters will be forced by the environment... It's like asking why we aren't using chaos theory to predict the exact time of death of every individual on the planet, or why we aren't using chaos theory to predict who will be elected for president 50 years from now. Chaos theory is not psychic, so there are limitations to what it can do for certain areas of research because the randomness is compounded and therefore not easily defined.
    Compounded randomness is what complexity science studies. It has been helpful in proving wrong many long held theories of economics, like the idea that stock movement is due to Brownian motion (it turns out the order book is an incredibly large factor), or that the amount of business a place gets is random around a fixed mean (it has more of a cellular automata structure).

    Quote Originally Posted by spin-1/2-nuclei View Post
    edit: so in short this is why we need experimentation in science so that we can a.) be sure of cause and effect and b.) be sure that the ideas we have and are testing are repeatable in all systems c.) and when there are exceptions we need to understand why (this is better done with experimentation because it has a way of revealing parameters that hundreds of years of theorizing and philosophizing would have never eluded to.
    I agree with all of this.

    Accept the past. Live for the present. Look forward to the future.
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    "[A] scientist looking at nonscientific problems is just as dumb as the next guy." Richard Feynman
    "[P]etabytes of [] data is not the same thing as understanding emergent mechanisms and structures." Jim Crutchfield

  3. #23
    ^He pronks, too! Magic Poriferan's Avatar
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    Quote Originally Posted by ygolo View Post
    I am not proposing a dichotomy.

    Nevertheless, there does seem to be some hostility from people who pride themselves on empiricism towards the value of logical argument (from axioms to theorems).

    I want to discuss this. I believe this is the correct forum. The philosophy forum would not invite as any empiricists.

    People seem to believe that the heart of science is in its empiricism. I think the empirical approach is important. But I see a lot of people dismiss the logical-philosophical aspects of science due to the "lack of empiricism."
    I guess you were looking for people of the other mind set, but I want to say that I agree entirely. The hypothetico-deductive element of the scientific method is often over-looked. And science in its entirety is limited in its abilities (as you pointed out with geometry, which is not science in any sense) and itself dependent on logic both inductive and deductive.

    I have encountered a few strong proponents of science in my day who looked down on philosophy without realizing how dependent science itself is upon it (if one understands logic to be a form of philosophy, which it is usually considered to be).

    I think this causes a recent phenomenon that I have complained in the past, where most of the emphasis as gone into quantitative analysis, and qualitative analysis is being neglected.
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  4. #24
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    Quote Originally Posted by ygolo View Post
    That's cool to find out. What about complexity science?...like what the Santa Fe Institute does? Also, how prevalent is the use of chaos theory? Do you believe it can be applied for more than it is being applied to now?

    Compounded randomness is what complexity science studies. It has been helpful in proving wrong many long held theories of economics, like the idea that stock movement is due to Brownian motion (it turns out the order book is an incredibly large factor), or that the amount of business a place gets is random around a fixed mean (it has more of a cellular automata structure).
    Complexity science is useful in some areas of research, medicine is one area where it is having increased success. Unfortunately it isn't as useful for predicting unforeseen problems in biological systems and chemical/molecular behavior as hoped. Progress is being made but still we mostly get descriptions of general patterns, but nothing to do with unexpected interactions that are caused by parameters that cannot be defined beforehand and are largely related to unexpected changes in the external environment whereby unforeseen parameters interact with the system and thus introduce unexpected changes a very small percentage of the time. This is mostly due to the fact that these "random parameters" are not related to attractors etc that are typically observed. It is easier if you think of these parameters as contaminants, but when dealing with the human body or materials science it is also extremely important to know when the exceptions to the rules are going to occur, why they occur, and how to prevent unexpected fatalities or catastrophic failures in materials etc and this is why we typically subject products like drugs and engineering materials to rigorous batteries of tests to verify that our ideas of their functions and interactions hold true outside of the vacuum.

    edit: as far as the prevalence of chaos theory and complexity science, there are some research groups trying to incorporate methods utilizing these theories but due to some of the problems I mentioned above it's hard to really know how effective this approach is going to be in the long term.

  5. #25

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    I look at "complexity science" as field similar to how "artificial intelligence" was a while a go. It's really just a hodgepodge of techniques.

    These days people don't work on "Artificial Intelligence," they work on neural networks, fuzzy controls, statistical pattern classification, or whatever specific thing they specialized in.

    Given their relative simplicity, I think cellular automata are extremely powerful. Not only that, but current processors are poorly designed to run cellular automata, but they still beat out other techniques. It wouldn't be very difficult to create custom hardware to run cellular automata at 1000x (depending on the complexity of cell communication of course) the speed that current processors can (which the same silicon technology). I've already worked out designs for such hardware (as I am sure many others have too).

    Just idle dreaming at this point. If I were part of some research group working on cellular automata and they hadn't already considered such hardware, I'd propose it immediately.

    Accept the past. Live for the present. Look forward to the future.
    Robot Fusion
    "As our island of knowledge grows, so does the shore of our ignorance." John Wheeler
    "[A] scientist looking at nonscientific problems is just as dumb as the next guy." Richard Feynman
    "[P]etabytes of [] data is not the same thing as understanding emergent mechanisms and structures." Jim Crutchfield

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