I meant to imply that this would be all that they would use proofs for, not that they don't. My bad.
That's the sign of a good model, though - when you deductively derive conclusions, and they turn out to be right.
1) MWI rids itself of the deductively derived conclusions
2) Knowing how your model relates to your system is even more important
classical models are so intuitive and so easy to understand that we don't think of them as models
I can link you to a particular event
devoted to models,
journals devoted to modeling in particular fields, conferences that are always on
modeling, and which run the gamut from nanotechnology to musicology, all without looking at my bookshelves. It seems people are pretty aware that classical models are models.
To start, a simple definition (from the source I'll use as an example):
"A model is a simplified mathematical representation of a system" Boccara's
Modeling complex systems (
Graduate Texts in Physics).
I'll use their intro example: predator-prey models
Every variable corresponds with something that was either observed to construct the model and/or will be replaced by values for specific observations when used.
H, for example, is for herbivores, K for carrying capacity, -cP is the rate at which predators will die, etc. There are many such models, and the way one would determine which to use (of if a new model was needed) is by e.g., going out to some place, setting up observation equipment, and seeing how well the model describes what we actually see.
This doesn't change if we get really big (stellar astrophysics) or much smaller, like a neuron:
Why is it that I can take a book I have on modeling in neuroscience, climate science, systems science, or computer science and find so much of the same stuff, yet none of them treat models like QM? It's because to build a model of something, from
cloud seeding to cloud computing, we observe the system and extract the relevant properties or features involved in the processes we're interested in. Then we can see not only if it works, but also alter, eliminate, or add features of the system to see if it improves the model. If I predict a neuron will fire at around x threshold, and it doesn't, I can check to see what I left out and/or if the various components of the model weren't adequate approximations of the system's components.
That's what correspondence means. I can't do that with QM. If I prepare a system and transcribe it into a model using e.g., Schroedinger's wave function, I cannot say even say
why I describe the state as I do because pretty much all quantum "weirdness" is a logical consequence of using amplitudes to calculate probabilities. Also, whether or not it is an accurate interpretation, my model at least
appears to describe one system as having more than one state at the same time. And because nobody knows how the "state" of the system corresponds to the model, it's impossible to say whether it does or doesn't.
The Newtonian solar system? The concept of moving masses in 3D flat space requires a model defining what "mass," "3D space" and "time" all are
"A model is a simplified mathematical representation of a system"
In QM, the model is the system. Which, if we are comparing to classical physics, either means it isn't a model, or that their isn't a system it models.
However, we aren't dealing with classical physics, but as you seem to think there's no difference, then perhaps you can explain why the definition given doesn't apply in QM.
When computational neuroscientists develop models, they observe neurons (mostly via imagining equipment rather what Hodgkin and Huxley had available). They then construct a model which they think captures the essential features of this type of neuron. Finally, they (and others) can repeatedly test this model to see if the parts and processes of the neurons it describes are good approximations of what actually happens.
Schroedinger's wavefunction, Heisenberg's matrix mechanics, and every other formulation of QM systems are constructed through outcomes alone. That's because in order to prepare a quantum system, we've disturbed it in non-trivial ways we can only approximate.
Talking about a physical system without some model
Models are essential. Recall my mention of Rosen's metabolic-repair model that (according to many, and in disagreement with many) purportedly shows biological systems aren't computable. You objected to the model because it made use of a function that wasn't explicitly related to the system. That's all of QM.
I would expect it to be mathematically identical to QM - by definition, anything that isn't is a seperate theory, not merely an interpretation of one.
One could argue that QM isn't a theory, but I don't think that's useful. It's much more important to note that it isn't an interpretation of QM, and cannot be. Because QM uses a particular set of postulates which include the very thing MWIs wish to be rid of: the variously described projection postulate. From Everett onwards, the goal involved solving the measurement problem. None have.
did you disagree with the explanation's validity for some reason?
Yes. We derived a way to get experimental outcomes (a model) that is very successful at telling us what we will find under the assumption that the same preparation will result in a system that is similar enough to give us similar outcomes. It works. Once you say that every measurement is simply a new branch, then the assumption that systems which are prepared in the same way will tend to give the same results has no basis. We know for a fact that for a given preparation our predictions can be way off. Otherwise we would have a statistical mechanics analogue to QM, rather than QM as a statistical mechanics without any classical mechanics analogue.
In particular, there is no classical analogue to the QM measurement process. Probability is at the heart of QM, as is probabilistic reasoning: given that I've prepared this system in this way, I should tend to get the same results as others do when they prepare it (akin to statistical mechanics). If I build a model of a neuron based on measurements of that neuron, the reason I expect my model to be reliable is because I'm assuming that repeatedly observing the neuron means repeatedly observing that neuron.
There is no reason for this assumption in any MWI. It's as if every time I measure the point at which a neuron fires, I'm measuring a different neuron without any reason for thinking it should behave the same as another (and they don't).
and it makes no sense to ask how we are applying that same model to the system.
Because all we have is a model that we call our system. Where else is this done?
We prepare a particular system in a particular way under the assumption that it will tend to result in outcomes similar to identical systems and preparations, and under that assumption we're right. However, MWI tells us that we aren't getting any outcomes particular to a particular system prepared in a particular way, because there are no outcomes. Just a resulting branch. There aren't even particular systems, as we can't prepare a quantum system if that's all that exists.
I suspect you misunderstand MWI then.
Possible. But you've said similar things about physicists:
...Those are completely different statements...they're not two ways of saying the same thing at all.
And
physicists have said much the same as I have:
"no known version of the theory (unadorned by extra ad hoc postulates) can account for the appearance of probabilities and explain why the theory it was meant to replace, Copenhagen quantum theory, appears to be confirmed, or more generally why our evolutionary history appears to be Born-rule typical."
perhaps you could explain what I misunderstand, and one or more sources you use so that I can understand it as you do.