William Dembski writes:
I am reviewing Jason Rosenhouse’s new book, The Failures of Mathematical Anti-Evolutionism (Cambridge University Press), serially. For the full series so far, go here.
Until about 2007, conservation of information functioned more like a forensic tool for discovering and analyzing surreptitious insertions of information: So and so says they got information for nothing. Let’s see what they actually did. Oh yeah, here’s where they snuck in the information. Around 2007, however, a fundamental shift occurred in my work on conservation of information. Bob Marks and I began to collaborate in earnest, and then two very bright students of his also came on board. Initially we were analyzing some of the artificial life simulations that Jason Rosenhouse mentions in his book, as well as some other simulations (such as Thomas Schneider’s ev). As noted, we found that the information emerging from these systems was always more than adequately accounted for in terms of the information initially inputted.
Yet around 2007, we started proving theorems that precisely tracked the information in these systems, laying out their information costs, in exact quantitative terms, and showing that the information problem always became quantitatively no better, and often worse, the further one backtracked causally to explain it. Conservation of information therefore doesn’t so much say that information is conserved as that at best it could be conserved and that the amount of information to be accounted for, when causally backtracked, may actually increase. This is in stark contrast to Darwinism, which attempts to explain complexity from simplicity rather than from equal or greater complexity. Essentially, then, conservation of information theorems argue for an information regress. This regress could then be interpreted in one of two ways: (1) the information was always there, front-loaded from the beginning; or (2) the information was put in, exogenously, by an intelligence.
And no, Darwinian evolution cannot, according to the conservation of information theorems, create information from scratch. The way out of this predicament for Darwinists (and I’ve seen this move repeatedly from them) is to say that conservation of information may characterize computer simulations of evolution, but that real-life evolution has some features not captured by the simulations. But if so, how can real-life evolution be subject to scientific theory if it resists all attempts to model it as a search? Conservation of information theorems are perfectly general, covering all search.
Push Comes to Shove
Yet ironically, Rosenhouse is in no position to take this way out because, as noted in my last post in this series, he sees these computer programs as “not so much simulations of evolution [but as] instances of it.” (p. 209) Nonetheless, when push comes to shove, Rosenhouse has no choice, even at the cost of inconsistency, but to double down on natural selection as the key to creating biological information. The conservation of information theorems, however, show that natural selection, if it’s going to have any scientific basis, merely siphons from existing sources of information, and thus cannot ultimately explain it.
We’ve seen active information before in the Dawkins Weasel example. The baseline search for METHINKS IT IS LIKE A WEASEL stands no hope of success. It requires a completely random set of keystrokes typing all the right letters and spaces of this phrase without error in one fell swoop. But given a fitness function that assigns higher fitness to phrases where letters match the target phrase METHINKS IT IS LIKE A WEASEL, we’ve now got a better search, one that will converge to the target phrase quickly and with high probability. Most fitness functions, however, don’t take you anywhere near this target phrase. So how did Dawkins find the right fitness function to evolve to the target phrase? For that, he needed active information.
My colleagues and I have proved several conservation of information theorems, which come in different forms depending on the type and structure of information needed to render a search successful.
Dembski concludes this regarding Rosenhouse’s evasion of the conservation of information theorems:
For [Rosenhouse] to forgo providing even the merest sketch of the mathematics underlying this work because “it would not further our agenda to do so” (p. 212–213) and for him to dismiss these theorems as “trivial musings” (p. 269) betrays an inability to grapple with the math and understand its implications, as much as it betrays his agenda to deep-six conservation of information irrespective of its merits.
Dembski’s approach to prove the conservation of information is complemented by a generalization of the 2nd Law of Thermodynamics from quantum statistical physics. His theorems are also backed up by common sense and observation. No one has ever observed a closed system ratcheting up its information content with the passage of time.
It takes an intelligence to recognize information. Why does a live rabbit have more information than a bucket of mud? (I hope no one will insult the rabbit by insisting that it contains no more information than a bucket of mud.) The specific arrangement of atoms that make a rabbit is obviously unique. To see this, imagine stirring up the bucket of mud. The particular arrangement of atoms has changed, but it’s still a bucket of mud. On the contrary, stirring up a rabbit will destroy it, since the arrangement of atoms that make a rabbit are unique. Information is related to the comparison of how many arrangements of the rabbit’s atoms there are that don’t result in a rabbit (nearly countless) to how many arrangements there are that do yield a rabbit (a much smaller number). To claim that natural processes can land on the arrangement of atoms that result in a living system composed of even a single cell is to deny scientific understanding, evidence and proof to the contrary.
For further discussion of these ideas, my book, Canceled Science: What Some Atheists Don’t Want You to See, is a resource that speaks to this topic in more depth, specifically in chapter 9.
The full article by Dembski is available at Evolution News.