On 11/7/2019 3:23 PM, Philip Thrift wrote:
On Thursday, November 7, 2019 at 4:43:51 PM UTC-6, Brent wrote: On 11/7/2019 2:32 PM, Philip Thrift wrote:On Thursday, November 7, 2019 at 3:53:12 PM UTC-6, Brent wrote: On 11/7/2019 1:40 PM, Bruce Kellett wrote:On Fri, Nov 8, 2019 at 6:35 AM 'Brent Meeker' via Everything List <[email protected]> wrote: On 11/7/2019 12:21 AM, Philip Thrift wrote:The mystery is: Why do (according to the science press in the wake of Sean Carroll's book) so many people think Many Worlds is a good scientific idea (or the best idea, according to the author).Because it treats measurement as just another physical interaction of quantum systems obeying the same evolution equations as other interactions. But you can do that (viz. accept that people, and measuring instruments, and everything else are basically quantum mechanical) without adopting the "many worlds" philosophy.ISTM that creates problem for defining a point where one of the probabilities becomes actualized. MWI tries to avoid this by supposing that all probabilities are "actualized" in the sense of becoming orthogonal subspaces. There are some problems with this too, but I see the attraction. Brent I studied probability theory - and statistics - through the 70s - my thesis was in random fields [ def: https://en.wikipedia.org/wiki/Random_field <https://en.wikipedia.org/wiki/Random_field> ] - and T've read much on 'interpretations' of probability and statistics. I'll just say that the vocabulary I see with 'probability' in the way some are describing things like Many Worlds are just baffling to me - probability theory-wise. I know one can have a Bayesian probabilities sense of 'a probability becomes 1.0' as in a prior to posterior probability updating, but I don't think the Many Worlds people are doing this. It's like a hybrid of QBI and MWI maybe.I think of probability as an abstract quantity like "energy". It's a useful concept */because/* it has different interpretations that can be translated from one context to another. So the Born rule gives a measure that satisfies the Kolmogorov axioms, and it's useful because in an operational context it translates into the frequentist meaning, and that's useful because it tells you how to bet in a decision theory problem. BrentAs long as QMists are clear about what type of 'probabilities' they are referring from one day (or paragraph) to the next, It's OK I guess. (I always think first: What is the *sample space [ *https://en.wikipedia.org/wiki/Sample_space ]? What are the *elements* of the sample space?)
A sample space implies statistics and a frequentist interpretation of probability.
I've always been a propensitist [ https://plato.stanford.edu/entries/probability-interpret/#ProInt ].
Fine. But my point is that to connect beliefs, predictions, mathematical theory, observations,...you need to be able to transfer one meaning of probability to another.
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