On 10/20/2020 1:20 PM, Jason Resch wrote:
On Tue, Oct 20, 2020 at 1:23 PM 'Brent Meeker' via Everything List <[email protected] <mailto:[email protected]>> wrote:On 10/20/2020 5:39 AM, Bruno Marchal wrote:On 15 Oct 2020, at 20:56, 'Brent Meeker' via Everything List <[email protected] <mailto:[email protected]>> wrote: You should have read Vic Stenger's "The Fallacy of Fine Tuning". Vic points out how many examples of fine tuning are mis-conceived...including Hoyle's prediction of an excited state of carbon. Vic also points out the fallacy of just considering one parameter when the parameter space is high dimensional. But my general criticism of fine-tuning is two-fold. First, the concept is not well defined. There is no apriori probability distribution over possible values. If the possible values are infinite, then any realized value is improbable.I don’t think so. That is why Kolmogorov defines a measure space by forbidding infinite intersection of events. In the finite case the space of events is the complete boolean structure coming from the subset of the set of the possible results. In the infinite domain, the measure space os defined by a strict subset. I miss perhaps something, but the axiomatic of Kolmogorov has been invented to solve that “infinite number of value” problem.That's a non-answer. I was just using infinite (as physicists do) to mean bigger than anything we're thinking of. Kolmogorov just shaped his definition to make the mathematics simpler. There's nothing in Jason's analyses that defines the variables as finite. Jason just helps jimself to an intuition that a value between 7.5 and 7.7 is "fine-tuned". He didn't first justify the finite interval.I admit as much in the article. For most parameters, we don't understand the range or probability distribution for the constants.
Then how can you assert there is fine tuning. Is a value of 20_+_1 qualify? Does it matter whether the possible range was (0,100) or (19,21)?
However, see my explanation for the cosmological constant, a value for which the theory can account for the expected range and probability distribution.
That's right, there is a theory that tells us something about a range and probability distribution. But it's far from an accepted theory, and might well be wrong.
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