Chalnoth said:(note: you can't prove something to be absolutely true, but if you accept as an axiom that physical laws are immutable, you can prove something to be true within some degree of precision).
Not really. Even if we assume that "physical laws" do not vary with time, and even if we only care about provable accuracy, there always remains the possibility of a "special case" that you can't eliminate.
You need a couple of additional axioms, such as that physical laws do not vary with reference frame and observer, so that where you conduct the experiment, and whose's doing it don't represent a special case.
Even then, I'm not so sure. Imagine that the truly underlying nature of the universe is governed by a type of planck level cellular automata. This automata gives rise to emergent macroscopic observables like momentum and position, that (lucky us) happen to be model-able using simple analytic mathematical equations, and can predict the future output of this cellular automaton with reason accuracy.
But what if the output of this automaton is only predictable because it happens to be periodic and we are in a "stable" region of its output right now, one that happens to be "compressible" into physical laws. What if, under certain rare conditions, it produces a "blip", and blips are not predictable by any model (inherently random) and cause experiment to disagree with theory sometimes, but not others.
We're then left with a scientific theory that works, say, 98% of the time to 1 part in 10 million accuracy. But 2% of the time, it fails to agree with data for some reason.
One way you can view this, is that the cellular automata is not time/space invariant and violates your first axiom. My view is that the automata is invariant, it doesn't change its rules, it just happens to be unpredictable.
I think that our confidence in a scientific theory increases everytime they survive an experimental test, and that the alternatives fail. In principle, this confidence can continue to increase without limit, but it will never be 100%, and I think I can live with that.
Somewhat technical point, but it is rather doubtful that we'll be capable of the computing precision and speed required for such a prediction before it can be done in a lab.
I somewhat agree. Abiogensis starts with a simple molecular soup and studies how more complex organic molecules can form over time. It's actually a simpler problem than say, protein folding, because the molecules you are modeling are alot simpler than a protein.
My idea of how this might work is that simulation searches identify candidate "soup" mixtures and settings which produce probable organic formation. Then lab work takes these search parameters, and conducts physical experiments. Data from physical experiments are fed back into simulations to fine-tune and visualize what's happening. and on and on.