Evolution, Information and the Mystery of the Human Mind

Hartmut Ising

The evolutionary model of Dawkins

Richard Dawkins describes natural selection as an automatic and blind process that, on the one hand, is not accidental but, on the other hand, is not geared towards any higher developmental goal. If this process can be attributed to the role of a watchmaker in nature, then that of a "blind watchmaker" – hence the title of his book (1).

In this book, Dawkins seeks to correct a common misconception of biological evolution according to which, for example, the organisation of DNA sequences is considered a mere product of chance. Instead of achieving the goal in a single practically impossible " jump by chance", he describes how he "breaks up the improbability into small, handy parts and thus outwits the chance; he goes to the back of the mountain of improbability and crawls up the gentle slopes, one million-year-inch after the other. "

This image demonstrated by Dawkins in a computer simulation that develops the words of Shakespeare "METHINKS IT IS LIKE A WEASEL" in just 43 steps – consisting of random mutations and selection of useful changes (selection).

The probability of generating this sentence directly as a unique “jump by chance” is

1/2728 or about 1/1040. Therefore, a direct " jump by chance " can’t explain the generation of this sentence.

Fig. 1 A “jump by chance” to the height H of the mountain of improbability is practically impossible.

By contrast, the rise in many individual steps (indicated on the "gentle slope" on the left) consisting

of chance and selection seems to be easily possible.

Dawkins now explains how his computer model easily handles the problem: It starts with a random selection of 28 letters (Generation 1). The computer then checks which letters match the target sentence. These are recorded and the remainder randomly chosen (Generation 2). This process leads to the goal after only 43 steps – consisting of chance and subsequent selection.

Generation  1: WDLMNLT DTJBKWIRZREZLMQCO P 

Generation  2: WDLTMNLT DTJBSWIRZREZLMQCO P

Generation 20: MELDINLS IT ISWPRKE Z WECSEL

Generation 40: METHINKS IT IS LIKE I WEASEL

Generation 43: METHINKS IT IS LIKE A WEASEL

However, in Dawkins' model, the required sentence must first be entered into the computer. Moreover, the comparison with the target is certainly not comparable to a blind natural process. These problems were also recognized by Dawkins, he writes:

Although the monkey/Shakespeare model is useful for explaining the distinction between single-step selection and cumulative selection, it is misleading in important ways. One of these is that, in each generation of selective 'breeding', the mutant 'progeny' phrases were judged according to the criterion of resemblance to a distant ideal target, the phrase METHINKS IT IS LIKE A WEASEL. Life isn't like that. Evolution has no long-term goal. There is no long-distance target, no final perfection to serve as a criterion for selection, although human vanity cherishes the absurd notion that our species is the final goal of evolution. In real life, the criterion for selection is always short-term, either simple survival or, more generally, reproductive success (1, p.50).

In his book "The God Delusion" (2), Dawkins attempts to refute the argument of irreducible complexity. As an example, he does not use Behe’s mouse trap (3) but a rather unusual combination lock:

Another favourite metaphor for extreme improbability is the combination lock on a bank vault. Theoretically, a bank robber could get lucky and hit upon the right combination of numbers by chance. In practice, the bank's combination lock is designed with enough improbability to make this tantamount to impossible - almost as unlikely as Fred Hoyle's Boeing 747. But imagine a badly designed combination lock that gave out little hints progressively- the equivalent of the 'getting warmer' of children playing Hunt the Slipper. Suppose that when each one of the dials approaches its correct setting, the vault door opens another chink, and a dribble of money trickles out. The burglar would home in on the jackpot in no time (2).

Here, Dawkins seeks to argue away the proven existence of irreducible complexity in nature and to replace it with gradual adaptation to adverse environmental conditions. An example of the latter is the resistance development of bacteria to a new antibiotic.

In technology, a method of gradual adaptation has been successfully applied. It is the "evolutionary strategy" developed by Rechenberg. He demonstrated in 1964 how with this method the flow resistance of a complex system can be minimized by using blind natural processes. The parameters of the system are changed randomly at each "evolutionary step" with subsequent determination of the flow resistance. If the change leads to an increase in the resistance, it will be reversed by the program control, whereas if the resistance is reduced it will be retained and used as a starting point for the next "evolutionary step". Rechenberg (4) was able to prove that this method is the fastest way to the optimum for systems with many degrees of freedom. This "evolution strategy" is now being used successfully in technical optimization problems. For this reason, the conditions and limits of the procedure are well known.

The application of the evolution strategy presupposes the existence of a parameter by which optimization can reached step-by-step – in Rechenberg's original example this was the flow resistance. For the purpose of "minimizing flow resistance," the model tests all possible randomly-varied shapes, eventually leading to the previously unknown, optimal shape.

In biology, evolutionary strategy may explain gradual changes of organs, but it also shows the limits of evolution by random changes and blind selection. In principle, no real existing combination lock can be opened with this method. – The problem of irreducible complexity remains unresolved. The assumed breaking up of improbability into small handy parts proves to be an illusion, as does the climbing of the mountain of improbability (see Fig.2).