Submitted by pasticciociccio t3_105oovx in MachineLearning
fakesoicansayshit t1_j3d1jev wrote
How do people assume causality when every measured system contains hidden variables?
BlindOdyssey t1_j3g0kuk wrote
I agree with what I think you’re asserting, that the number of variables that might contribute to triggering any sort of event is incalculable, but as a matter of practicality, I assume we have to view causality as a gradient of probability. In other words, we account for as many variables as we can, and make a “best guess” based on what we know. We can make “predictions” based on that until we know more, and then we redefine our overall system over time.
Edit: a word
fakesoicansayshit t1_j49364c wrote
>gradient of probability
Nicely put.
rottoneuro t1_j3gnbf5 wrote
the issue of hidden variables is "solved' by perturbation approaches... which are often unfeasible... so as a proxy we accept this limitation and we do our best... I think this is also mentioned in the article
fakesoicansayshit t1_j4933r6 wrote
Thank you, 'perturbation' is what I was looking for.
rottoneuro t1_j6pgwnp wrote
which approach in particular? I am also interested, can you share the reference?
rottoneuro t1_j3gtzm8 wrote
did you read the article?
Superschlenz t1_j3et16e wrote
Because the state of the fuse changes less frequently than the state of the switch.
Viewing a single comment thread. View all comments