“The” was just me being bad in English. What I mean is:
There is probably a way to mathematically model true stochasticity. Properly, not as many-worlds.
Math being deterministic shouldn’t be a problem, because the laws of truly stochastic world are not stochastic themselves.
I don’t expect any such model to be simpler than many-worlds model. And that’s why you shouldn’t believe in true stochasticity.
If 1 is wrong and it’s not possible to mathematically model true stochasticity, then it’s even worse and I would question your assertion of true stochasticity being coherent.
If you say that mathematical models turn out complex because deterministic math is unnatural language for true stochasticity, then how do you compare them without math? The program that outputs an array is also simpler than the one that outputs one sample from that array.
How would you formulate this axiom?
Ugh, I’m bad at math. Let’s say given the space of outcomes O and reality predicate R, the axiom would be ∃x∈O:R(x).
“The” was just me being bad in English. What I mean is:
There is probably a way to mathematically model true stochasticity. Properly, not as many-worlds.
Math being deterministic shouldn’t be a problem, because the laws of truly stochastic world are not stochastic themselves.
I don’t expect any such model to be simpler than many-worlds model. And that’s why you shouldn’t believe in true stochasticity.
If 1 is wrong and it’s not possible to mathematically model true stochasticity, then it’s even worse and I would question your assertion of true stochasticity being coherent.
If you say that mathematical models turn out complex because deterministic math is unnatural language for true stochasticity, then how do you compare them without math? The program that outputs an array is also simpler than the one that outputs one sample from that array.
Ugh, I’m bad at math. Let’s say given the space of outcomes O and reality predicate R, the axiom would be ∃x∈O:R(x).