If life force is going to be the same sort of thing as g, it might be useful in medicine, which to a substantial and increasing extent is based on statistical trials with little knowledge of mechanisms. But I don’t see it as useful for research into how things work.
I think that “finding out how things work” should not be the goal of science. The goal should be to develop models that provide reliable and useful predictions.
Newton postulate gravitation as a force without telling his audience how gravity works. The fact that Newton couldn’t explain that slowed down adoption of his model, yet accepting his model brought science a huge step forward. Even on many issues that are about research into how things work.
Theories that provide additional predictive power help science advance even if their proponents can’t explain everything from the ground up.
To get back to system theory. It allows us to say: “Emergence” when we don’t know how something come about and still work with what comes about.
When someone tells you that homeopathy doesn’t work because there are no infinitively small numbers of atoms he has a valid argument. Our ontological framework doesn’t allow the infinitively small numbers of atoms. People who have never heard of systems theory and subfield of it like control theory will have a similar reaction to the Shangri-La diet as to homeopathy. The ontology doens’t allow for it.
System theory then allows for an ontology in which it can happen. That’s valuable. When you go through a specific example you can also think about what the various words of system theory might be when you apply it to the system you study. That provides you with a structure to model the problem even if you don’t have enough data for mathematical modelling.
We have no idea how the set point for blood pressure is that in the human body, but it’s worthwhile to think of blood pressure regulation as a sytem that has a set point even if we don’t know how that is set.
From a medical standpoint we can think differently about the system through looking at it with the lense of system theory.
To get back to the life force, it’s good when we get more free and focus on increasing the predictive power of our models without worrying too much about whether we know at the moment the mechanism behind a certain value.
Sometimes it can even be useful to free our concepts from wanting to explain mechanisms. A term like Shaken Baby syndrome can be quite problematic if you find out that 1% of the cases of babies with “Shaken baby syndrome” weren’t shaken.
The thing for a scientist to do is to discover the mechanism
If you believe that’s the only thing scientists are allowed to do then they won’t be able to do work where predictions can be made but where the underlying mechanism is illusive.
“Discover”, not “have discovered”. Newton’s work was a step; Einstein finding more of a mechanism was a further step.
I think that “finding out how things work” should not be the goal of science. The goal should be to develop models that provide reliable and useful predictions.
It’s difficult to get the latter without the former, if you want to make successful way-out-of-sample predictions. Otherwise, you’re stuck in the morass of trying to find tiny signals and dismissing most of your data as noise.
It’s difficult to get the latter without the former, if you want to make successful way-out-of-sample predictions.
I think you can do a lot of successful predictions with IQ without knowing the mechanism of IQ. I don’t think you build better IQ tests by going into neuroscience but giving the tests to people and seeing how different variables correlate with each other.
Otherwise, you’re stuck in the morass of trying to find tiny signals and dismissing most of your data as noise.
I don’t think that’s true. The present approach of putting compounds through massive screening arrays based on theoretical reasoning that it’s good to hit certain biochemical pathways is very noise-laden and produces a lot of false positives. >90% of drug candidats that get put into trials don’t work out.
I think that “finding out how things work” should not be the goal of science. The goal should be to develop models that provide reliable and useful predictions.
Newton postulate gravitation as a force without telling his audience how gravity works. The fact that Newton couldn’t explain that slowed down adoption of his model, yet accepting his model brought science a huge step forward. Even on many issues that are about research into how things work. Theories that provide additional predictive power help science advance even if their proponents can’t explain everything from the ground up.
To get back to system theory. It allows us to say: “Emergence” when we don’t know how something come about and still work with what comes about. When someone tells you that homeopathy doesn’t work because there are no infinitively small numbers of atoms he has a valid argument. Our ontological framework doesn’t allow the infinitively small numbers of atoms. People who have never heard of systems theory and subfield of it like control theory will have a similar reaction to the Shangri-La diet as to homeopathy. The ontology doens’t allow for it.
System theory then allows for an ontology in which it can happen. That’s valuable. When you go through a specific example you can also think about what the various words of system theory might be when you apply it to the system you study. That provides you with a structure to model the problem even if you don’t have enough data for mathematical modelling.
We have no idea how the set point for blood pressure is that in the human body, but it’s worthwhile to think of blood pressure regulation as a sytem that has a set point even if we don’t know how that is set. From a medical standpoint we can think differently about the system through looking at it with the lense of system theory.
To get back to the life force, it’s good when we get more free and focus on increasing the predictive power of our models without worrying too much about whether we know at the moment the mechanism behind a certain value. Sometimes it can even be useful to free our concepts from wanting to explain mechanisms. A term like Shaken Baby syndrome can be quite problematic if you find out that 1% of the cases of babies with “Shaken baby syndrome” weren’t shaken.
“Discover”, not “have discovered”. Newton’s work was a step; Einstein finding more of a mechanism was a further step.
It’s difficult to get the latter without the former, if you want to make successful way-out-of-sample predictions. Otherwise, you’re stuck in the morass of trying to find tiny signals and dismissing most of your data as noise.
I think you can do a lot of successful predictions with IQ without knowing the mechanism of IQ. I don’t think you build better IQ tests by going into neuroscience but giving the tests to people and seeing how different variables correlate with each other.
I don’t think that’s true. The present approach of putting compounds through massive screening arrays based on theoretical reasoning that it’s good to hit certain biochemical pathways is very noise-laden and produces a lot of false positives. >90% of drug candidats that get put into trials don’t work out.