For any observed phenomenon, there can be many possible explanations. A chance-based mechanism could be one explanation. We usually associate chance as being indifferent to purpose, and intelligent design as being with a purpose. When a mechanism appears to be unconcerned with or incognizant of any possible outcome, we term it a chance-based, or random mechanism. In other words, chance is invoked when there appears to be no preference for one particular outcome over another.
While a chance-based mechanism can be one of the possible explanations for a phenomenon, we only consider it seriously when the probability of the proposed mechanism is significant. When the probability is insignificant, we must eliminate this possibility and consider other options. Otherwise, we may fall into what the mathema-tician/philosopher Dembski calls the 'chance-of-the-gaps' fallacy:
'Statistical reasoning must be capable of eliminating chance when the probability of events gets too small. If not, chance can be invoked to explain anything. Scientists rightly resist invoking the supernatural in scientific explanations for fear of committing a god-of-the-gaps fallacy (the fallacy of using God as a stop-gap for ignorance). Yet, without some restriction on the use of chance, scientists are in danger of committing a logically equivalent fallacy'one we may call the 'chance-of-the-gaps fallacy'. Chance, like God, can become a stop-gap for ignorance.' [Demski, 98]
To illustrate this principle let us consider three examples. The first is from a movie, related by the same author. The event related concerns the supposed spontaneous combustion of a person.
'In the movie 'This is Spinal Tap', the lead singer remarks that a former drummer in the band died by spontaneously combusting. Any one of us could this instant spontaneously combust if all the most rapidly moving air molecules in our vicinity suddenly converged on us. Such an event, however, is highly improbable, and we don't give it a second thought.' [Dembski, 98]
Let us consider two other examples: footprints in the desert and a novel placed next to a typewriter.
Footprints in the Desert
When a bedouin sees camel footprints in the desert, he does not attribute these to chance. He does not think that the wind, by chance, formed those patterns on the sand. Instead, he interprets them as signs that a camel has recently walked along this way. The probability of the chance-based scenario is simply too small. Having seen the patterns characteristic of the wind, the bedouin can be almost sure that the camel explanation is the correct one, even if he has not seen the camel itself.
Novel Placed Next to a Typewriter
Imagine you find a novel left next to a typewriter, next to which sits a monkey. One explanation for who might have written the novel could be that the monkey typed the novel, making words and sentences that make sense, solely by chance. We can view this as a chance-based explanation; up to now we have not witnessed any monkey that can appreciate human literature. To make this explanation more plausible, let us assume that when the monkey completes a page, a human checks the page and if it does not make any sense the page is thrown away. If there were an unlimited supply of paper and ink, and if the monkey were replaced by another monkey when it died, you might expect to see a few lines of meaningful literary work after thousands of generations. In the process you would expect to see mountains of thrown-out pages, containing meaningless sequences of letters. If you knew that the monkey could not live long enough to produce a novel, if you knew that it did not have access to enough paper, or if you could not find any thrown-out pages, you would simply have to eliminate the monkey hypothesis. You would consider other explanations, such as that there was a person who was capable of producing literary work, and this person typed the novel and left it next to the typewriter. The idea that a monkey could produce a novel by typing randomly, without regard to content (i.e. by chance) is simply too small a probability. It is not worthy of serious consideration.
These examples illustrate a general principle; highly improbable and 'preferred' (independently specified) patterns cannot be generated by chance-based mechanisms. The footprints in the first example illustrate highly improbable events, and shows us how one arrives at a conclusion by preference or purpose. Since Darwinian evolution relies on chance, there is only one way it can produce the diversity of life found on earth. This can be done by exhausting a significant proportion of the possibilities, producing useless organisms in the process, leaving the remains of the unsuccessful organisms on the way and by eventually producing a useful organism after using up a considerable amount of time, matter and space.
Blind or Not Blind?
Some evolutionists argue that evolutionary algorithms are not the same as a blind search, because they include a fitness function which favors certain outcomes over others. In a way, this imagined 'fitness function' evaluates which organisms are the most promising in each generation. To understand the concept of a fitness function, consider the selective breeding of animals. The breeders select the members that have the most desirable properties and continue to breed them. They might breed from only the most fertile chickens or the woolliest sheep and succeed in altering the characteristics of these animals. So, the selection criteria of the breeders can be regarded as a fitness function. However, in the case of selective breeding, the human breeder is the one who defines and applies this function. In the case of Darwinian evolution, there is no room for an intelligent being. So, the imagined fitness function must be a result of the physical laws of the universe. It must be a result of the conditions on earth
What the evolutionary algorithms do is to exploit the information already encoded in the fitness function. So, evolutionary algorithms simply 'shift' the problem into a different space. If we assume that this fitness function is a result of the conditions on earth, we have to remember that the conditions on earth are highly improbable and specified. So, if this fitness function is assumed to be capable of design, one must explain how it came to be in the first place. Secondly, this function is ill-defined. Even evolu- tionists themselves have difficulty defining what this function is or how it operates. Various attempts to define this function by evolutionists amount to a tautology in order to justify it. 'In this formulation, the theory predicts that the fittest organisms will produce the most offspring, and it defines the fittest organisms as the ones which produce the most offspring.' [Johnson, 91]. Noting this trend, the famous philosopher of science Karl Popper once wrote, 'some of the greatest contemporary Darwinists themselves formulate the theory in such a way that it amounts to the tautology that those organisms that leave the most offspring leave the most offspring.' Since the fitness function is not well defined, it is also not possible to demonstrate how this function is capable of producing this diversity of life, neither scientifically nor mathematically. In experiments where evolutionary mechanisms were tested, they tend to favor simplicity, whereas in life we see increasing complexity. Limits of the Universe The famous physicist Carl Sagan once said, in reference to evolutionary processes, �given enough time, chance will work miracles.� While this statement can be true theoretically, one should also consider that chance, given enough time, will produce a disproportionately high ratio of useless outcomes before coming up with a miracle. Furthermore, the universe, as we have observed, has a limited age and a limited amount of matter. So the amount of time that we can assume chance has is limited. The amount of material to be used by the chance-based pro- cesses is also limited. So the next question is, given the age of the universe, the amount of matter in the universe and all the possible sequences of changes that can take place, what is the likelihood of chance producing a single living cell? The answer to this question is important because it will determine whether the chance hypothesis is worthy of our attention. Dembski explains that within the observed universe any probability below a universal probability bound remains improbable, even if it is assumed that all the resources available were exhausted in order to try out all the possibilities. He calculates this number as 10-150, that is 1 over 10 to the 150th power. The details of this calculation and what it means are explained by him as follows:
|"It would take at least 1067 times the current lifetime of the universe for the universe to manage to make all possible proteins of length 200 at least once."|