Jerry Bergman and Doug Sharp have produced a MS Access program to refute Richard Dawkin's computer simulation claiming to show that random events can produce something meaningful.
I do not have MS Access, does anyone know of some Open Source software that could run Bergman and Sharp's example?
http://www.rae.org/MutationProgram.htm
Dawkins? Blind Watchmaker Thesis Refuted
By Jerry Bergman and Doug Sharp
Download Simulation Program
(requires Microsoft Access 2000 or XP)
Microsoft Access 97 Version
Abstract: In his book, The Blind Watchmaker, Richard Dawkins produced a computer simulation to show that within a relatively small number of 43 generations random mutations can produce a meaningful result. He set as the goal for the program to produce the phrase: ?Methinks it is a weasel.? We recreated this simulation, and introduced a number of other factors that Dawkins probably did not consider. First of all, we make an assumption that when a letter (or gene) matches a desired goal, there is something that prevents it from further mutation, even though there is no biochemical evidence or process to back up this assumption. This way we give the process the benefit of the doubt way beyond what the evidence calls for. Second, if we presume that assumption to be true, then once the organism achieved the ?goal? it is forever prevented from further evolution. His thesis (and therefore evolution) is refuted both ways, for if we take away this ?mutation save? function, our simulation goes on for millions of generations without achieving the goal. Without the source code to Dawkins? program, we can only run a simulation that gives the benefit of the doubt far beyond what is reasonable, and establish limits within which evolution can occur.
In addition to testing the results both with and without the save function, our computer simulation adds into the mixture several more factors involved in mutations that serve to increase the number of generations. Our simulation saves the results in a table if the results match a specified number of letters in the goal. When we ran the simulation without the save function for more that 7 million generations, we could only produce 6 matches on 9 letters out of 28.
We also found that there were several ways to do the simulation with the save function. Originally, within one generation, if the random mutation fell on a letter that was ?saved,? the mutation simply didn?t occur, and the program went on to the next generation. The results in that case were that it took between 2500 and 4500 generations to produce the goal sentence.
Next, to try to achieve the results Dawkins claimed, we presumed that if a mutation fell upon a ?saved? letter, the program went on to try and find another letter to mutate within the same generation. The number of generations was reduced down to 500 ? 700. This still does not match the results Dawkins got. The only way I can see for him to get it down to 43 generations would be for him to mutate EVERY gene EVERY generation. This is totally unrealistic. We distorted the experiment in the following manner to give Dawkins every benefit of the doubt:
1. Mutations are saved if it matches the goal
2. Within the same generation, if a mutation is tried on a letter that matches a goal, mutations continue to occur until one is found that doesn?t match.
3. Within the same generation, every letter is mutated.
We found that if you remove these false assumptions and allow random mutations to occur freely whether they match the goal or not, you can run the simulation for millions of generations without ever getting close to the goal.
The ?save function,? therefore, is the overriding factor that might allow Dawkins? conclusions to be possible.
In addition, we know the following to be true about mutations:
1. Mutations may be inserted, moving the entire string over one gene.
2. Mutations favor ?hotspots? and rarely occur elsewhere.
3. Mutations heavily favor the T gene.
4. Mutations may result in unusable or meaningless code, represented by special characters in our simulation.
5. Mutations may kill the organism with a ?poison factor? and stop the process.
We have added these factors in our simulation so that we can give a more realistic representation of what actually happens with mutations. Even with the save function turned on, the addition of these factors causes the mutations to further destabilize. With all of the factors turned on, it either died with the poison factor, or it took 23,000 generations to match the goal.
But the most amazing revelation we had when we were doing this simulation is that if Dawkins? save function were true, it would prevent the organism from ever evolving again, not to mention that implicitly if you try to match a goal, you are introducing intelligent design into the equation.
This computer simulation serves to quantify the problem in a concrete, demonstrable manner.
