{"id":512,"date":"2007-09-17T09:00:00","date_gmt":"2007-09-17T09:00:00","guid":{"rendered":"http:\/\/scientopia.org\/blogs\/goodmath\/2007\/09\/17\/a-glance-at-the-work-of-dembski-and-marks\/"},"modified":"2017-01-10T09:57:03","modified_gmt":"2017-01-10T14:57:03","slug":"a-glance-at-the-work-of-dembski-and-marks","status":"publish","type":"post","link":"http:\/\/www.goodmath.org\/blog\/2007\/09\/17\/a-glance-at-the-work-of-dembski-and-marks\/","title":{"rendered":"A Glance at the Work of Dembski and Marks"},"content":{"rendered":"

Both in comments, and via email, I’ve received numerous requests to take a look at
\nthe work of Dembski and Marks, published through Professor Marks’s website. The site is
\ncalled the “Evolutionary Informatics Laboratory”. Before getting to the paper, it’s worth
\ntaking just a moment to understand its provenance – there’s something deeply fishy about
\nthe “laboratory” that published this work. It’s not a lab – it’s a website; it was funded
\nunder very peculiar circumstances, and hired Dembski as a “post-doc”, despite his being a full-time professor at a different university. Marks claims that his work for
\nthe EIL is all done on his own time, and has nothing to do with his faculty position at the university. It’s all quite bizarre. For details, see here<\/a>.<\/p>\n

On to the work. Marks and Dembski have submitted three papers. They’re all
\nin a very similar vein (as one would expect for three papers written in a short period
\nof time by collaborators – there’s nothing at all peculiar about the similarity). The
\nbasic idea behind all of them is to look at search in the context of evolutionary
\nalgorithms, and to analyze it using an information theoretic approach. I’ve
\npicked out the first one listed on their site: Conservation of Information in Search: Measuring the Cost of Success<\/a><\/p>\n


\n There’s two ways of looking at this work: on a purely technical level, and in terms of its
\npresentation.<\/p>\n

On a technical level, it’s not bad. Not great by any stretch, but it’s entirely reasonable. The idea
\nof it is actually pretter clever. They start with NFL. NFL says, roughly, that if you don’t know anything about the search space, you can’t select a search that will perform better than a random walk.
\nIf we have a search for a given search space that does<\/em> perform better than a random walk,
\nin information theoretic terms, we can say that the search encodes<\/em> information
\nabout the search space. How can we quantify the information encoded in a search algorithm
\nthat allows it to perform as well as it does?<\/p>\n

So, for example, think about a search algorithm like Newton’s method. It generally homes in extremely
\nrapidly on the roots of a polynomial equation – dramatically better than one would expect in a random
\nwalk. For example, if we look at something like y = x2<\/sup> – 2, starting with an approximation of a
\nzero at x=1, we can get to a very good approximation in just two iterations. What information is encoded
\nin Newton’s method? Among other things, it’s working in a Euclidean space on a continuous, differentiable
\ncurve. That’s rather a lot of information. We can actually quantify that in information theoretic
\nterms by computing the average time to find a root in a random walk, compared to the average time
\nto find a root in Newton’s method.<\/p>\n

Further, when a search performs worse<\/em> than what is predicted by a random walk, we can
\nsay that with respect to the particular search task, that the search encodes negative<\/em> information – that it actually contains some assumptions about the locations of the target that
\nactively push it away, and prevent it from finding the target as quickly as a random walk would.<\/p>\n

That’s the technical meat of the paper. And I’ve got to say, it’s not bad. I was expecting something really awful – but it’s not. As I said earlier, it’s far from being a great paper. But technically, it’s reasonable.<\/p>\n

Then there’s the presentation side of it. And from that perspective, it’s awful. Virtually every
\nstatement in the paper is spun in a thoroughly dishonest way. Throughout the paper, they constantly make
\nstatements about how information must be<\/em> deliberately encoded into the search by the programmer.
\nIt’s clear the direction that they intend to go – they want to say that biological evolution can
\nonly work if information was coded into the process by God. For example, an evolution to use beta alanine<\/span><\/a> as a catylist for digestion would of been already predisposed by information placed in DNA. Here’s an example from the first
\nparagraph of the paper:<\/p>\n

Search algorithms, including evolutionary searches, do not
\ngenerate free information. Instead, they consume information,
\nincurring it as a cost. Over 50 years ago, Leon Brillouin, a
\npioneer in information theory, made this very point: “The
\n[computing] machine does not create any new information,
\nbut it performs a very valuable transformation of known
\ninformation” When Brillouin’s insight is applied to search
\nalgorithms that do not employ specific information about the
\nproblem being addressed, one finds that no search performs
\nconsistently better than any other. Accordingly, there is no
\n“magic-bullet” search algorithm that successfully resolves all
\nproblems.\n<\/p><\/blockquote>\n

That’s the first one, and the least objectionable. But just half a page later, we find:<\/p>\n

\nThe significance of COI [MarkCC: Conservation of Information – not Dembski’s version, but from someone
\nnamed English]<\/em> has been debated since its popularization through the NFLT [30]. On the one hand, COI has a
\nleveling effect, rendering the average performance of all algorithms equivalent. On the other hand,
\ncertain search techniques perform remarkably well, distinguishing themselves from others. There is a
\ntension here, but no contradiction. For instance, particle swarm optimization [10] and genetic algorithms
\n[13], [26] perform well on a wide spectrum of problems. Yet, there is no discrepancy between the
\nsuccessful experience of practitioners with such versatile search algorithms and the COI imposed inability
\nof the search algorithms themselves to create novel information [5], [9], [11]. Such information does not
\nmagically materialize but instead results from the action of the programmer who prescribes how knowledge
\nabout the problem gets folded into the search algorithm.\n<\/p><\/blockquote>\n

That’s where you can really see where they’re going. “Information does not magically materialize, but
\ninstead results from the action of the programmer”. The paper harps on that idea to an
\ninappropriate degree. The paper is supposedly about quantifying the information that
\nmakes a search algorithm perform in a particular way – but they just hammer on the idea
\nthat the information was deliberately put there<\/em>, and that it can’t come from
\nnowhere.<\/p>\n

It’s true that information in a search algorithm can’t come from nowhere. But it’s
\nnot a particularly deep point. To go back to Newton’s method: Newton’s method of root
\nfinding certainly codes all kinds of information into the search – because it was created
\nin a particular domain, and encodes that domain. You can actually model orbital dynamics
\nas a search for an equilibrium point – it doesn’t require anyone to encode in
\nthe law of gravitation; it’s already a part of the system. Similarly in biological
\nevolution, you can certainly model the amount of information encoded in the process – which
\nincludes all sorts of information about chemistry, reproductive dynamics, etc.; but since those
\nthings are encoded into the universe<\/em>, you don’t need to find an intelligent agent
\nto have coded them into evolution: they’re an intrinsic part of the system in which
\nevolution occurs. You can think of it as being like a computer program: computer programs
\ndon’t need to specifically add code into a program to specify the fact that the computer they’re going to run on has 16 registers; every<\/em> program for the computer has that wired into
\nit, because it’s a fact of the “universe” for the program. For anything in our universe, the basic
\nfacts of our universe – of basic forces, of chemistry, are encoded in their existence. For anything on earth, facts about the earth, the sun, the moon – are encoded into their very existence.<\/p>\n

Dembski and Marks try to make a big deal out of the fact that all of this information is quantifiable.
\nOf course<\/em> it’s quantifiable. The amount of information encoded into the structure of the universe
\nis quantifiable too. And it’s extremely interesting to see just how you can compute how much information
\nis encoded into things. I like that aspect of the paper. But it doesn’t imply anything about
\nthe origin of the information: in this simple initial quantification, information theory cannot distinguish between environmental information which is inevitably encoded, and information
\nwhich was added by the deliberate actions of an intelligent agent. Information theory can
\nquantify information – but it can’t characterize its source.<\/p>\n

If I were a reviewer, would I accept the paper? It’s hard to say. I’m not an information theorist; so
\nI could easily be missing some major flaw. The style of the paper is very different from any other
\ninformation theory paper that I’ve ever read – it’s got a very strong rhetorical bent to it which is very
\nunusual. I also don’t know where they submitted it, so I don’t know what the reviewing standards are – the
\nreviewing standards of different journals are quite different. If this were submitted to a theoretical
\ncomputer science journal like the ones I typically read, where the normal ranking system is (reject\/accept
\nwith changes and second review\/weak accept with changes\/ strong accept with changes\/strong accept), I
\nwould probably rank it either “accept with changes and second review” or “weak accept with changes”.<\/p>\n

So as much as I’d love to trash them, a quick read of the paper seems to show
\nthat it’s a mediocre paper, with an interesting idea. The writing sucks: it was
\nwritten to try to make a point that it can’t make technically, and it makes that point with all the subtlety of a sledgehammer, despite the fact that the actual technical content of the paper can’t support it.<\/p>\n","protected":false},"excerpt":{"rendered":"

Both in comments, and via email, I’ve received numerous requests to take a look at the work of Dembski and Marks, published through Professor Marks’s website. The site is called the “Evolutionary Informatics Laboratory”. Before getting to the paper, it’s worth taking just a moment to understand its provenance – there’s something deeply fishy about […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":true,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[30,31],"tags":[],"class_list":["post-512","post","type-post","status-publish","format-standard","hentry","category-information-theory","category-intelligent-design"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p4lzZS-8g","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/512","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/comments?post=512"}],"version-history":[{"count":3,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/512\/revisions"}],"predecessor-version":[{"id":3383,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/512\/revisions\/3383"}],"wp:attachment":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/media?parent=512"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/categories?post=512"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/tags?post=512"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}