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A Bit Confused
Skeptical Inquirer, March, 2001 by David Roche
Creationism and Information Theory
The argument of some creationists that modern information theory refutes Darwinian evolution is based on a confusion between two distinct information concepts. At the heart of the Darwinian thesis is not information, but complexity.
In recent years, the notion of "information" has crept into the arguments of creationists and other critics of evolution, particularly among proponents of Intelligent Design (ID) (see Edis, this issue). According to such arguments, information theory refutes Darwinian evolution. Carl Wieland (1997) sums up the argument nicely.
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What mechanism could possibly have added all the extra information required to transform a one-celled creature progressively into pelicans, palm trees, and people? Natural selection alone can't do it-selection involves getting rid of information. A group of creatures might become more adapted to the cold, for example, by the elimination of those which don't carry enough of the genetic information to make thick fur. But that doesn't explain the origin of the information to make thick fur. [And] mutations ... are accidental mistakes as the genetic information ... is copied from one generation to the next. Naturally, such scrambling of information will tend to either be harmful, or at best neutral. ... Rather than adding information, they destroy information, or corrupt the way it can be expressed (not surprising, since they are random mistakes).
In other words, so goes the argument, the Darwinian process is inadequate for explaining the origins of biological (e.g., genetic) information. Natural selection cannot produce any new information; it merely shuffles or in some cases eliminates the information that was already there. And mutation cannot create new information either, because mutation is essentially a random process. So although variation occurs in nature and natural selection may operate on this variation, evolution leads to neutral or even degenerative change. It does nor provide the "progressive" component required to explain the origins of organisms with lots of information. Have you spotted the flaw in this argument? It's not as simple as you might think.
In one sense, proponents of this argument are right. Both natural selection and random mutations can be thought of as leading to a reduction in information. However--and herein lies the flaw--the type of information is different in each case. Information comes in different forms, so we need to be clear regarding what sort of information we are talking about.
One type of information we might call Shannon information. This is the type of information concept introduced by the Bell engineer Claude Shannon in 1948 when he laid the foundations of the modern science of information theory (Shannon and Weaver 1949). Shannon defined information in terms of reduction in uncertainty. So if I sent you the string of binary digits "010," I have specified one of eight possibilities. Assuming equiprobable digits, I have reduced your level of uncertainty by a factor of eight. Information is typically measured as the base-2 logarithm of the reduction in uncertainty, which in this case translates into three bits of information.
The Shannon information content of any system can be thought of as the reduction in uncertainty resulting from a complete specification of that system. In other words, you can think of Shannon information content in terms of the length of a concise and fully detailed description of the system. Such a definition therefore accounts for redundancy. A book consisting only of the letter "A" repeated 100,000 times is easy to describe. It has lots of redundancy and therefore little Shannon information. If we converted it into a computer file and ran it through a compression algorithm, we would end up with something much smaller than what we started with. A book of 100,000 completely random letters, in contrast, has no redundancy and therefore lots of Shannon information. There is no shorter description of a book of random letters than the book itself, so running it through a computer compression algorithm has little affect on its size. Shakespeare's play The Tempest also contains about 100,000 letters. Its Shannon information content is intermediate between the book of A's and the book of random letters. If we ran it through a computer-compression algorithm, we would end up with something somewhat smaller than what we starred with, but not drastically so. The Tempest contains more Shannon information than a book of 100,000 A's but less Shannon information than a book of 100,000 random letters. [1]
Another type of information concept is complexity. Physicist Murray Gell-Mann has helped in recent years to clarify this concept (Gell-Mann 1994; 1995). According to Gell-Mann, you can think of complexity as a measure of how difficult it would be to describe the regularities of something in complete detail. Mathematically, it is the difference between something's maximally compressed Shannon information content and its "incompressible" information content--the information content of those elements of the system that are truly random (Gell-Mann and Lloyd 1996). A book of 100,000 A's has little complexity. It does not have much incompressible information, while its compressible information is highly compressible. The regularities of the book can be completely described in one short sentence ("A book of 100,000 A's"). A book of random letters also has little complexity. It has lots of Shannon information, but virtually all of this information is incompressible because there are no regularities to compress. In c ontrast, The Tempest has lots of complexity. It has lots of regularities (e.g., words, rules of grammar, aspects of plot development etc.) and so virtually all of its information content is compressible. Yet once fully compressed, it is still quite large.
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