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Eye_in_the_Sky said:
At least one of the instruments KNOWS THE VALUE of both the setting and the outcome of the other instrument.
I am saying that this statement is true no matter what. Otherwise, the quantum correlations cannot happen as they do.
You are saying that when we employ 'realistic variables' in the description, we are forced to accept such a statement. But when we don't do that, the matter is left open and it depends upon interpretation.
... Did I get that right?
Sort of - but I think there's always going to be a struggle pinning everything down with 'precise enough' words. In my opinion we do have to be a bit careful using words like 'know' and even 'information'. I've tried to be careful about this in my posts above so that I would say that in a
realistic variable treatment the information about what's happening at B has to be
in some sense available to A. And here I'd hope that the use of 'in some sense' would indicate that I'm not being overly precise but rather attempting to convey an intuition.
If we had some probability distribution P(X,Y) where X and Y are 2 random variables then we could determine from this the various marginal and conditional probabilities, like P(X | Y), for example. If X and Y are completely uncorrelated then we have that
P(X) = P(X | Y). In information terms we would say that information about Y gives us no information about X. If they were correlated so that
P(X | Y) ≠ P(X)
then knowledge of Y gives us some information about X.
In the Bell inequality set-up we have a slightly more complicated conditional probability - but it can be understood in exactly the same way we'd approach the more simple distribution for X and Y above.
So, for example, in the Bell case we might want to examine a conditional probability distribution
P(A | a, b)
and this means something like the "probability that the result A is some value given that the settings of the measuring instruments are a and b". It's certainly one that we can measure from actually performing the experiment.
So here we're assuming that there is some functional dependence on a and b. In a loose sense we might then say that the outcome at A 'knows' that the settings are a and b. More technically we might make the statement that knowledge of the settings at a and b gives us additional information about the probabilities of A.
If there were no such functional dependence on b, say, then we would write
P(A | a, b) = P(A | a)
This latter identification is, of course, a critical component of the Bell proof - it's the imposition of the locality condition. It's an assumption that says that the probability of the outcomes at A is not conditioned upon the settings at b. Expressed another way, we could say that knowledge of the settings at b confers no additional information pertinent to the results at A.
With this locality condition and the assumption of CFD then we can show that the probability functions we measure are constrained by the Bell inequality.
If we don't want them to be so constrained (i.e. have the possibility to violate the inequality) then at least one of these assumptions has to go. If we choose to retain CFD then we have to lose locality, if we want to have the possibility to break those constraints.