This is in response to the video entitled, “Evolution CAN Increase Information (Classroom Edition).”
I agree with the basic presentation of Shannon’s work in the video, along with its evaluation of Information Theory, the Information Theory definition of “information,” bits, noise, and redundancy. I also accept the fact that new genes evolve, as described in the video. So far, so good.I have some objections to the video, including the underlying premise, which I consider to be a strawman.
Before I outline my dissent, here’s what I think the problem is. This is likely the result of creationists hijacking work done by ID scientists, in this case William Dembski, and arguing against evolution using flawed reasoning that misrepresents ID scientists. I have no doubt that there are creationists who could benefit by watching this video and learn how they were mistaken in raising the argument the narrative in the video refutes. But, that flawed argument misinterprets Dembski’s writings.
ID Theory is grounded upon Dembski’s development in the field of informatics, based upon Shannon’s work. Dembski took Shannon Information further, and applied mathematical theorems to develop a special and unique concept of information called COMPLEX SPECIFIED INFORMATION (CSI), aka “Specified Information.” I have written about CSI in several blog articles, but this one is my most thorough discussion on CSI.
I often am guilty myself of describing the weakness of evolutionary theory to be based upon the inability to increase information. In fact, my exact line that I have probably said a hundred times over the last few years goes like this:
“Unlike evolution, which explains diversity and inheritance, ID Theory best explains complexity, and how information increases in the genome of a population leading to greater specified complexity.”
I agree with the author of this video script that my general statement is so overly broad that it is vague, and easily refuted because of specific instances when new genes evolve. Of course, of those examples, Nylonase is certainly an impressive adaptation to say the least.
But, I don’t stop there at my general comment to rest my case. I am ready to continue by clarifying what I mean when I talk about “information” in the context of ID Theory. The kind of “information” we are interested is CSI, which is both complex and specified. Now, there are many instances where biological complexity is specified, but Dembski was not ready to label these “design” until the improbability reaches the Universal Probability Bound of 1 x 10^–150. Such an event is unlikely to occur by chance. This is all in Dembski’s book, “The Design Inference” (1998).
According to ID scientists, CSI occurs early, in that it’s in the very molecular machinery required to comprise the first reproducing cell already in existed when life originated. The first cell already has its own genome, its own genes, and enough bits of information up front as a given for frameshift, deletion, insertion, and duplication types of mutations to occur. The information, noise, and redundancy required to make it possible for there to be mutations is part of the initial setup.
Dembski has long argued, which is essentially the crux of the No Free Lunch theorems, that neither evolution or genetic algorithms produce CSI. Evolution only smuggles CSI forward. Evolution is the mechanism that includes the very mutations and process to increase the information as demonstrated in the video. But, according to ID scientists, the DNA, genes, start-up information, reproduction system, RNA replication, transcription, and protein folding equipment were there from the very start, and that the bits and materials required in order for the mutations to occur were front-loaded in advance. Evolution only carries it forward into fruition in the phenotype. I discuss Dembski’s No Free Lunch more fully here.
“Consider a spy who needs to determine the intentions of an enemy—whether that enemy intends to go to war or preserve the peace. The spy agrees with headquarters about what signal will indicate war and what signal will indicate peace. Let’s imagine that the spy will send headquarters a radio transmission and that each transmission takes the form of a bit string (i.e., a sequence of 0s and 1s ). The spy and headquarters might therefore agree that 0 means war and 1 means peace. But because noise along the communication channel might flip a 0 to a 1 and vice versa, it might be good to have some redundancy in the transmission. Thus the spy and headquarter s might agree that 000 represents war and 111 peace and that anything else will be regarded as a garbled transmission. Or perhaps they will agree to let 0 represent a dot and 1 a dash and let the spy communicate via Morse code in plain English whether the enemy plans to go to war or maintain peace.
“This example illustrates how information, in the sense of meaning, can remain constant whereas the vehicle for representing and transmitting this information can vary. In ordinary life we are concerned with meaning. If we are at headquarters, we want to know whether we’re going to war or staying at peace. Yet from the vantage of mathematical information theory, the only thing that’s important here is the mathematical properties of the linguistic expressions we use to represent the meaning. If we represent war with 000 as opposed to 0, we require three times as many bits to represent war, and so from the vantage of mathematical information theory we are utilizing three times as much information. The information content of 000 is three bits whereas that of 0 is just one bit.” [Source: Information-Theoretic Design Argument]
My main objection to the script is toward the end where the narrator, Shane Killian, states that if anyone has a different understanding of the definition of information, and prefers to challenge the strict definition that “information” is a reduction in uncertainty, that their rebuttal should be outright dismissed. I personally agree with Shannon, so I don’t have a problem with it, but there are other applications in computer science, bioinformatics, electrical engineering, and a host of other academic disciplines that have their own definitions of information that emphasize different dynamics than Shannon did.
Shannon made huge contributions to these fields, but his one-way radio/telephone transmission analogy is not the only way to understand the concept of information. Shannon discusses these concepts in his 1948 paper on Information Theory. Moreover, even though that Shannon’s work was the basis of Dembski’s work, ID Theory relates to the complexity and specificity of information, not just in quantification of “information” alone per se.