Open Challenge to ID Advocates


(S. Joshua Swamidass) #1

Continuing the discussion from Eric Holloway: Algorithmic Specified Complexity:

It is commonly asserted that CSI, a type of mutual information, is a unique signature of intelligence, and that the CSI content of any object can be easily measured with metrics ASC. The argument continues, we observe CSI in life, so therefore it must be intelligently designed. As @EricMH, one of Robert Marks’s PhD students, explains…

So I have a puzzle for ID advocates to solve that seems impossible to me. I know the answer, just because I constructed the strings. Who can can solve this puzzle? If any ID advocate can, I will be truly impressed, and have much to learn from them.

Good luck, and share this widely in your networks.

Halting Oracles And Law of information Non Growth
Eric Holloway: Wrap up Experiment on Mutual Information
(Eric Michael Holloway) #2

Could you explain why this follows?

(S. Joshua Swamidass) #3

I should rephrase:

If you can’t solve this puzzle with an objective metric, it means you can’t objectively measure the CSI of arbitrary objects.

If that is the case, there is no way to objectively demonstrate the unique signature of minds on arbitrary objects, such as DNA. Of course, as I have explained elsewhere, MI is not unique signature of minds any ways.

(Eric Michael Holloway) #4

This is fallacious reasoning. Just because we cannot measure ASC in some instance, i.e. encrypted text, does not mean we cannot measure ASC in some other instance.

(S. Joshua Swamidass) #5

Correct! I’ve always agreed with this.

However, Dembski, Durston, and Marks have been arguing they can use ASC on DNA to quantify FI, ignoring how it was produced, because they argue that ASC is always an underestimate of the true CSI. We’ve already clarified that this only true if they use the right P. Durston, however, does not even attempt to improve upon a uniform distribution. It is clearly the the wrong P, and therefore overestimates CSI. They were hoping to avoid this critique by ignoring the impact of picking the wrong P altogether.

So, this is a substantial set back for their current best argument.

So now, if can continue to identify example after example where their metric fails, we can understand what characteristics cause problems for them. You hit on one of them with “encryption”. DNA is much more like an encrypted program than a clear text program. There is more though. That is not the whole story. We will several layers of qualities that DNA have that frustrate application of ASC like metrics to determine the difficulty of evolving something.

In the end, we can still measure ASC in carefully selected toy examples. The key question is if it works in real world biology. The honest answer is not yet, and maybe not ever.

(Eric Michael Holloway) #6

A uniform distribution is not clearly wrong. Especially if DNA cannot be generated altogether by naturalism and thus has a probability of zero.

And even if it is wrong, that doesn’t invalidate what they are doing. It just means they have more work to do. It also means that ID makes empirically workable claims that could be verified or falsified, and thus is a science.

(S. Joshua Swamidass) #7

Go understand the cancer thread, and see then if you understand why it is clearly wrong.

(George) #8

@swamidass, wow!