1) We evaluate the Bayesian fine-tuning argument, which claims that there is an astronomically small chance P(I) that life emerged intranaturalistically.

2a) We observe that life emerged intranaturalistically: P(I) = 1. (begging the question = circular reasoning)

2b) We observe that life emerged: P(L) = 1. (ambiguity = two definitions of life friendly)

3) Bayesian theory: P(I|L) differs from P(L|I) in general. (obfuscation = irrelevant theory)

4) We conclude through Bayesian reasoning that P(I) can be very close to 1. (only valid for 2a)

5) The smaller P(I) is claimed to be, the more the fine-tuning argument is faulty.

It is good to know that the argument via 2a is valid, but not sound, because 2a is simply false.

Best regards,

Ward Blondé

See: https://groups.google.com/forum/#!topic/atvoid/l2vusPDOW6E

]]>The shot was fired early on in the article.

Quote: “Others have argued against the assumption that the universe must have very narrowly constrained values of certain physical constants for life to exist in it. They have argued that life could exist in universes that are very different from ours, but it is only our insular ignorance of the physics of such universes that misleads us into thinking that a universe must be much like our own to sustain life. Indeed, virtually nothing is known about the possibility of life in universes that are very different from ours. It could well be that most universes could support life, even if it is of a type that is completely unfamiliar to us. To assert that only universes very like our own could support life goes well beyond anything that we know today.”

The authors decided they wanted a duel instead of just shooting the swordsman dead.

Quote:”While recognizing the force and validity of these arguments, the main points we will make go in quite different directions,”

ROTFLMAO.

The rest of the article was the duel, completely moot, as are you positions on “fine tuning” and the possibility of life.

It’s like the other side were overly good sports and wanted to give you a fair chance so that there is at least something for spectators to watch instead of a knockout at the start of the bell.

]]>I came accross this article:

http://www.utm.utoronto.ca/~weisber3/articles/TAfDI.pdf

titled ‘The Argument from Divine Indifference’ by Jonathan Weisberg. It seems that he argues that learning:

“S: the fact that the laws of our universe are stringent, i.e. that they will only support intelligent life on a few settings of the constants and initial conditions (this would be equivalent to your. “F” i think)

.. may amplify the evidential support O (where: O stands for the fact that life exists) lends to D (design), this does not mean that learning S in addition to O increases the net support for D. For S may simultaneously be evidence against D, so that the amplification of O’s support is drowned out by the disconfirmation effected by S.”

Any thoughts?

]]>And btw, with my last example, we are precisely conditioning on L so no objection based on that should arrise.

]]>I think a case can be made that P(L|Theism) is high or at least not low. Therefore the fact of L supports theism over naturalism. That we already knew that we were alive doesn’t seem to be a problem. Just like you said ” If I flip two coins and see that both are heads I already know the result, but I can still calculate the chance that this would have happened given fair coins.”

which is confirmed by: https://letterstonature.wordpress.com/2013/11/18/probability-myth-weve-observed-x-so-the-probability-of-x-is-one/

Moreover, for someone who thinks that P(N) is roughly the same as P(T) (thus thinking P(T)/P(N)=1 he can reason:

P(T|L)/P(N|L) = (P(L|T)*P(T)/P(L)) / (P(L|N)*P(N)/P(L)) = P(L|T) / P(L|N) > P(T)/P(N) = 1

Thus L supports T over N. Anyway it’s late here, tomorrow I’ll rethink things true and check if there aren’t any mistakes.

]]>Think of Bayes theorem. Both E and B are known, are “data”. And yet in Bayes theorem we calculate the likelihood p(E |HB), which doesn’t treat E as known. That’s a *feature* of Bayes theorem. We can write probabilities we need in terms of probabilities we have.

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