> On 8 Feb 2020, at 02:53, Bruce Kellett <bhkellet...@gmail.com> wrote:
> 
> On Sat, Feb 8, 2020 at 12:23 PM 'Brent Meeker' via Everything List 
> <everything-list@googlegroups.com <mailto:everything-list@googlegroups.com>> 
> wrote:
> On 2/7/2020 2:16 PM, Bruce Kellett wrote:
>> 
>> The point of Kent's argument is that in MWI where all outcomes occur, you 
>> will get the same set of sequences of results whatever the intrinsic 
>> probabilities might be. So you cannot use data from any one sequence to test 
>> a hypothesis about the probabilities: the sequences obtained are independent 
>> of any underlying probability measure.
> 
> Why not?  Most copies of me will see sequences with approximately equal 
> numbers of H and T.
> 
> You are making the mistake that many commentators make: you are thinking of 
> the distribution over the set of all possible sequences, and then assuming 
> that we sample at random from this set. But that is not how experiments are 
> done. We run the experiment N times and obtain some sequence of results. We 
> then use the data so obtained to compare with our theory. There is no random 
> selection from the set of all possible sequences. In fact, in MWI, there is 
> one observer for every possible sequence, and we have to consider what each 
> of them, in isolation, will conclude. Many will see the Born rule 
> disconfirmed.
> 
> 
>  
>   In fact we do use data from one sequence, which ever one our accelerator 
> produces, even though the theory we're testing predicts that all sequences 
> are possible.  But we don't compare sequences; we compare statistics on the 
> sequences and compare those to predicted probabilities.
> 
> That is just a fantasy made up to get out of a difficulty. That is not how 
> science proceeds.
> 
> Of course, if many-worlds is correct and every possible outcome occurs for 
> every trial, then given the probability deduced from one set of N trials, we 
> can always attempt to confirm this result by doing another set of trials. The 
> problem is that the second set of trials is quite like to give a different 
> result from the first. That also would count as a disconfirmation of the 
> theory.
> 
> Whether sequences are independent of "underlying probabilities" is a 
> different problem.  First, one can't legitimately assume underlying 
> probabilities when trying to justify the existence of a probability measure.
> 
> In the first instance, we are not trying to justify the existence of a 
> probability measure. We are trying to see if experimental data can confirm a 
> particular theory.
> 
>  
> Second, the simple way to postulate a measure is just counting branches, 
> which means that there must be many repetitions of the same sequence on 
> different branches in order to realize probability values that aren't integer 
> ratios
> 
> 
> Branch counting has a bad reputation as a basis for a probability measure. 
> One problem, as Wallace for instance points out, is that the number of 
> branches is never well-defined, so no clear count is available. There are 
> other problems, which have led to the abandonment of this approach to 
> probability.

With QM, the number of branch is arguably equal to 2^aleph_0, but with 
Mechanism it might need to be bigger than that.

Bruno



> 
> Bruce
> 
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