What percentage of observations that we make can be predicted?
Let's say you had unlimited computational power, and all of the world's most predictive scientific models were translated into algorithms. How much of what is happening in the real world would you then be able to predict?
>>8105802
lel, I'll bet some >>>/biz/mackbiyombos have something to say about it
>>8105802
Given the right algorithms, everything.
>>8105825
>Given the right algorithms
Is everything algorithmable?
>>8105842
no, you can't even find an algorithm that tells you if [math]\pi^{\pi^{\pi^{\pi}}}[/math] is an integer.
>>8105842
>Is everything algorithmababble?
Not even.
>>8105802
I don't know about percentage(probably it can't be calculated), but there are observations that can't be predicted no matter how much power and information you have. In quantum physics there is plenty of fundamental randomness.
>>8105847
prove it
>>8105894
burden of proof is on you.
>>8105802
Even if the world is completely deterministic, you can't predict accurately events which your prediction effects. So it would not be very useful to attempt to predict everything, because your prediction would effect something, and probably fail.
>>8105802
100%
The problem is less about processing the laws and more about obtaining accurate initial conditions.
Your model will necessarily have errors: pigeon hole principle for starters, past a few more issues involving chaotic sysyems, all the way down to Heisenberg uncertainty. These errors well up, magnify, and cascade of control quickly. I.e. chaos theory.
Look at any weather model. It's pretty good for 24h. But after that the predictive quality goes straight into the shitter. The math still works, if course. But you're no longer in reality.
TL;DR see: Laplace's demon
>>8105847
It is the integer 1 in base pi
