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.
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:
- S1:Gravity pulls things towards the earth
- S2:The ball we drop will hit the earth
- 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:
- modus ponens
- P1
- P2
- P3
- Modus ponens has been used so often and held to be true so much, that this is a poor choice.
- 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.
- 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.)
- 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.
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?
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).
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.