After all, he argues, random processes are used all the time to model things in science:

When we test a sequence of numbers for randomness, we are essentially testing how easy it is to predict the sequence of numbers. One of the simplest tests is to measure how frequently heads and tails occur during a series of coin flips. If the distribution is heavily skewed one way or the other after a large number of flips, then we can be pretty certain the coin is not fair. We cannot be absolutely certain, since there is always a small probability for a really long run of heads, but as the run lengthens, the probability of achieving the run with a fair coin drops exponentially. If we cannot find any predictable patterns in a series of numbers, then we say the series is at least pseudo random.

However – and this is the really important point, so pay attention – we can never say a series is truly random just by examining it, since we would have to run an infinite number of randomness tests to look for all conceivable patterns. Thus, without actually knowing the original cause of a number sequence, the best we can ever say is a sequence is pseudo random with regard to the set of randomness tests that we have run. This conclusion is mathematically provable with Kolmogorov complexity.

Now we come to the second really important point, so don’t switch to YouTube just yet! Observe that the reverse is not true. Once we have detected a predictable pattern in a number sequence, then we are able to say, at least with some confidence, the sequence is not random. And the longer the sequence and higher the predictability, the greater our confidence grows.

Eric Holloway, “Why Is randomness a good model, but not a good explanation?” atMind Matters News

All decisions are per definition random, because objectively any choice can turn out either A or B.

And it is proven that some qm things are inherently random. For example it was proven by searching a database, by exploiting the mere possibility that a search algorithm could have run.

So the search algorithm is not run, but the experiment is set up to detect what would happen if the search algorithm did run. Basically it means possibilities are real, and you can do stuff with them.

The experiment was set up for a photon to split to 2 possible routes. Then the 2 routes converge at a second split.

Strangely, if both routes to the second split are open, then the photon would always go right at the second split, and never go left. So that it doesn’t function as a split then, eventhough it is exactly the same as the first split.

But if one of the routes is closed, then the photon could go either left or right at the second split. (ofcourse if the photon takes the closed route, then it never arrives at the second split)

The experiment was set up that the search algorithm was at the second route, and that the search mechanism would stop a photon from proceeding to the second split, if the variable was found in the database.

So then when the photon went left at the second split, then they would know that the search algorithm would have found the variable, if the search algorithm had run.

Thus proving that decison, randomness, possibilities, are real things.

Mohammadnursyamsu,

You might want to take a closer look at Bayes Theorem on likelihoods and Chaos theory as an alternative to randomness.

-Q

Chaos theory is one explanation for why something might not be random when it is.

When there are a lot of processes that lead to the data, it is a lot simpler to treat them as random, even if the processes aren’t themselves chaotic.

Bayesian probability theory (which is a bit more than just Bayes’ Theorem) is a way of defining randomness as uncertainty (including epistemic uncertainty). Essentially, it says that when we say something is random, we’re saying we’re not certain about the process that lead to the data.

Bob O’H,

Sorry, but you clearly don’t understand anything what you wrote. No, I don’t want to explain where you’re wrong. These are interesting subjects and you might want to study them first. For example, here’s an article on an application of Chaos Theory: https://news.mit.edu/1998/chaos

Notice that a tiny change in initial conditions make a huge difference very quickly in a chaotic system while random systems have a central tendency that’s

oppositeof a chaotic system.-Q