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] <javascript:>> 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 ] - 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.
Brent
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