Reply To: Are retrocausal accounts of entanglement unnaturally fine-tuned?

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#2810

Hi Ken,

There’s one point which has been nagging at the back of my mind these last few days: When I said “good” retrocausal models, what I meant is that they should be clear about what the ontic variables and the epistemic variables are, and that there would be a natural way to take the log of the number of possible ontic states and associate it with an entropy. Intuitively, I think in my model lambda does not represent an ontic variable – it is an angle which seems to divide the available phase-space into parts which lead to different outcomes. When the parameter settings are the same, there are just two relevant parts. When Alice and Bob choose different settings, apparently the structure of the available phase-space is different. You could think that it’s only the way the phase-space is subdivided, but you can’t go too far in that direction and here’s why: If the structure of the phase space is not changed then there’s no apparent reason for the probability density to change, and for such models Bell’s original analysis works (with lambda representing the phase-space variable), so they cannot violate the inequality and won’t explain anything.

Now I’m not saying it’s going to be simple, if the parameter settings affect the structure of the phase space, but I think that’s a thing to explore. Also, in this sense your model is different, because you do have an explicit description of something rotating along the path, so it looks very much like an ontic variable. Again, you can think of my lambda as the value of that variable at the point along the path which correponds to the source. I think that’s just one way that you can subdivide a phase-space: the space of functions is clearly divided into classes which share the same value at a point. But intuitively I think that that’s not the relevant subdivision. I would think that the entropy would refer to the phase space of the values of the ontic variable at the source, at just one instant. So we have to keep looking.

Best, Nathan.

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