On Sun, Feb 9, 2020 at 6:38 AM 'Brent Meeker' via Everything List < [email protected]> wrote:
> On 2/7/2020 11:00 PM, Bruce Kellett wrote: > > > It is an indexical theory. The problem is that in MWI there will always be > observers who see the sequences that are improbable according to the Born > rule. This is not the case in the single-world theory. There is no random > sampling from all possibilities in the single-world theory. > > > ?? There's something deterministic in single-world QM? You seem to have > taken the position that MWI is not just an interpretation, but a different > theory. > That is a possibility. I do think that MWI has difficulty with probability, and with accounting for the results of normal observation. That some very improbable results cannot occur in SW QM. I think you are > mistaken. > I don't know where you got the idea that I might think this. No matter how low a probability the Born rule assigns to a result, that > result could occur on the first trial. > Yes, but in SW the probability of that is very low: in MWI the probability for that is unity. However, we seem to be in danger of going round in circles on this, so it might be time to try a new tack. As I said, I have difficulty understanding how the concept of probability can make sense when all results occur in every trial. If you have N independent repetitions of an interaction or experiment that has n possible outcomes, the result, if every outcome occurs every time, is a set of n^N sequences of results. The question is "How does probability fit into such a picture?" In any branch, when the experiment is performed, that branch is deleted and replaced by n new branches, one for each possible outcome of the experiment. This is clearly independent of any model for the probability associated with each outcome. In the literature, people speak about "weights of branches". But what does this mean? -- that there are more of some types of branch?, or that some branches are more 'important' that others? It does not seem clear to me that one can assign any operational meaning to such a concept of "branch weights". In this situation, the set of n^N sequences of results for this series of trials is independent of any a priori assignment of probabilities to individual outcomes: whatever the probabilities or weights, the set of sequences of results is the same. In other words, for the experimentalist, the data he has to work with is the same for any presumed underlying probabilistic model. Consequently, experimental data cannot be used to infer any probabalistic model. In particular, experimental data cannot be used to test any prior theory one might have about the probabilities of particular outcomes from individual experiments. The conclusion would be that such a model is unable to account for standard scientific practice, in which we definitely use experimental data to test our theories, and as the basis for developing new and improved theories. This is impossible on the above understanding of MWI. So this understanding of MWI is presumably flawed. But how? I do not see any other realistic way to implement the idea that all possible results occur in any trial. Talking about branch weights and probabilities seems to be entirely irrelevant because these things have no operational significance in such a model. Bruce -- You received this message because you are subscribed to the Google Groups "Everything List" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/everything-list/CAFxXSLQLpAfGvB5MA%2BhHE2muLxDY5uh0Daf9OcnpbP%3Dadr9G%3DA%40mail.gmail.com.
