Annoying creationists
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Paul C. Anagnostopoulos
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I don't know, same reason you use a computer to balance your checkbook? Why not do a lot of stuff with paper and pencil?T'ai said:
Seriously, here's the problem:
Creationist: Not enough time, 2nd law, fossil record, blah, blah, blah. You can't creation information.
Schneider: Sure you can. Look at genomes.
Creationist: No information created. God put it there.
Schneider: Let me write this program ... Look! Did God put the information in my simulated genome?
Dembski: You snuck it in somehow!
http://www.ccrnp.ncifcrf.gov/~toms/paper/ev/dembski/claimtest.html
~~ Paul
Paul C. Anagnostopoulos
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Not evolution as a whole, but macroevolution. Whatever macroevolution is, there is not enough time for it to happen, because Ev's simulation of 1/1000 of 1% of the evolutionary landscape shows that it can't happen in the unspecified timeframe of punctuated equilibrium. Something like that.Level said:
~~ Paul
Annoying Creationists
Warning, this ride is not for evolutionists who are faint of heart.
For the fearless evolutionist, here is the URL: where you can access and run the program:
http://www.ccrnp.ncifcrf.gov/~toms/paper/ev/evj/evjava/index.html
Use this program at your own risk.
That’s a simulation written by evolutionist Dr Tom Schneider, head of computational molecular biology at the National Cancer Institute and peer reviewed and published in the Oxford University Press journal Nucleic Acids Research. The java version of the program is available online and was written by Moderator Paul Anagnostopoulos. Paul is an excellent java programmer but we have got to work on his arithmetic.Level said:
Warning, this ride is not for evolutionists who are faint of heart.
For the fearless evolutionist, here is the URL: where you can access and run the program:
http://www.ccrnp.ncifcrf.gov/~toms/paper/ev/evj/evjava/index.html
Use this program at your own risk.
You almost got it right. This is a three trick pony. Ev shows that huge populations do not markedly accelerate evolution by random point mutations and natural selection. Ev shows that evolution by random point mutations and natural selection is far too slow for punctuated equilibrium as described by Stephen Gould to be mathematically possible. Ev shows that macroevolution by point mutations and natural selection is mathematically impossible.Level said:
Paul C. Anagnostopoulos
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With sufficient computing resources, you could run Ev with huge populations and determine how many generations are required. Neither you nor I have run Ev with any more than about 128,000 creatures. Furthermore, Ev does not simulation multiple nearly-independent populations.Kleinman said:
This is absurd. How long did Gould say that punctuated equilibrium episodes last, what sort of biological mechanisms was he talking about, and how many generations of Ev simulation is equivalent to that time period?
This is so absurd it's not even worth analyzing.
~~ Paul
Thank you for the link. So if this proves, as you keep claiming, that large-scale evolution is impossible, what steps will you be taking in order to prove this to the scientific community? Are you going to organize a formal argument or is posting in a few online forums the extent of your evolution rebuttal?
Annoying Creationists
My plan is to give an explanation of how Dr Schneider’s program works, give a little bit of the theory behind the model and then present series of cases that demonstrates the mathematical behavior of the model. In addition, I will mix in some of the voluminous quotes Dr Schneider and Paul have made about the model. This really annoys Paul but it reveals how convoluted evolutionist thinking has become. I don’t know whether I will write a formal paper. I don’t think any mainline scientific journals would take it at this time. The evolutionist community has a real problem with this computer simulation. This is not a model written by IDers. In fact IDers have been attacking this model for years. When I first saw this model the evolutionist community thought they had the mathematical proof for the theory of evolution. I happen to believe the model is a plausible simulation for random point mutations and natural selection unlike the IDers who have criticized the model. The problem for evolutionists is that one of their own has produced a mathematical model that puts their theory in a mathematical vise that I don’t think they can get out of without discrediting 20 years of work by the head of the computational molecular biology lab at the National Cancer Institute. It also reveals a very poor peer review process at Nucleic Acids Research. Tomorrow I’ll post the first steps of my proof and walk the readers through this very interesting computer model.
That’s not quite accurate, I have been able to run a 1000 base genome with a population of just over 1,000,000 using the pascal version of ev. In theory you can run larger cases but my computer only has 512 meg of memory and larger cases use a disk page file which slow the calculations a 1000 fold and would probably destroy my hard drive.Kleinman said:
I posted Gould’s estimates for punctuated equilibrium, earlier on this thread. He uses a range of values up to 20 million years but as low as 10-100,000 years. In one of Gould’s publications he even supposes a length of time of only 5-10,000 years. Read the quotes I posted from Dr Schneider’s references from his ev publication. If you consider your case of the evolution of 16 binding sites (96 loci) on a 100,000 base genome in 200,000,000 generations (over 500,000 years at one generation per day), you have already exceeded a large portion of the time span that Gould proposes and you still haven’t reached a realistic genome length. If you are talking about reptiles evolving into birds, not only will you have a genome much larger than 100,000 bases, you will have much longer generation times than 1 per day.Kleinman said:
That’s ok, I’ll post the data from ev that shows this.Kleinman said:
The first thing I did was discuss this directly with Dr Schneider and Paul Anagnostopoulos who is Dr Schneider’s java programmer for ev. Paul is the one who started this thread. I then asked Dr Schneider if he was willing to discuss this publicly because he had in the past but said no to my request, however Paul was willing to take up the banner. Dr Schneider had started a thread on the Evolutionisdead web site so I went ahead and started a discussion there. That went on for several months and there were a couple of evolutionists (including Paul) who were willing to debate this issue but they ran out of ideas on how to counter the data that was coming out of the model. Paul’s argument has gone from saying that ev simulates reality to it simulates a small portion of the rich evolutionary landscape. I also contacted the editors of Nucleic Acids Research who originally published Dr Schneider’s results based on unrealistic parameters in his model. I hoped to submit a letter to the editor. I told them that when realistic parameters are used in his model that it predicts that random point mutation and natural selection is too slow to account for macroevolution. They gave the usual evolutionist argument and said I was setting up a strawman, in addition they don’t take letters to the editor and to publish in their journal costs over $1000. So here we are James Randi educational forum.Level said:
My plan is to give an explanation of how Dr Schneider’s program works, give a little bit of the theory behind the model and then present series of cases that demonstrates the mathematical behavior of the model. In addition, I will mix in some of the voluminous quotes Dr Schneider and Paul have made about the model. This really annoys Paul but it reveals how convoluted evolutionist thinking has become. I don’t know whether I will write a formal paper. I don’t think any mainline scientific journals would take it at this time. The evolutionist community has a real problem with this computer simulation. This is not a model written by IDers. In fact IDers have been attacking this model for years. When I first saw this model the evolutionist community thought they had the mathematical proof for the theory of evolution. I happen to believe the model is a plausible simulation for random point mutations and natural selection unlike the IDers who have criticized the model. The problem for evolutionists is that one of their own has produced a mathematical model that puts their theory in a mathematical vise that I don’t think they can get out of without discrediting 20 years of work by the head of the computational molecular biology lab at the National Cancer Institute. It also reveals a very poor peer review process at Nucleic Acids Research. Tomorrow I’ll post the first steps of my proof and walk the readers through this very interesting computer model.
Pardon me if I'm a bit confused here, but...
It seems to me that it is much easier to prove that something is possible with a simulation than to prove it is impossible.
In the case of Ev, its author has (in my understanding) said, "This, this and this characteristic and behaviour can create result X."
The fact that Ev can't produce result Y does not mean that Y is impossible. It could simply be that Ev doesn't model the physical world accurately enough to simulate that.
Ev demonstrates one way things _could_ work. To prove that something is _impossible_ requires that you prove there is _no_ way it could work. This discussion reminds me of another poster on this forum, who's trying to use a physical modelling program to design a perpetual motion machine.
All models have limits. They can be very useful when used within their limits, but if you go outside the limits, the results are usually utterly meaningless.
To argue that Ev disproves the possibility of macroevolution requires two things:
1. You must demonstrate that a simulation done with Ev shows the impossibility of macroevolution.
2. You must demonstrate that none of the differences between Ev and the real world are significant enough to matter.
All of the discussions I have seen so far have centered on (1). The only arguments I have seen for (2) reference that Ev has been accepted as a reasonable modelling tool for microevolution. Can somebody justify the use of Ev for macro-evolution?
It seems to me that it is much easier to prove that something is possible with a simulation than to prove it is impossible.
In the case of Ev, its author has (in my understanding) said, "This, this and this characteristic and behaviour can create result X."
The fact that Ev can't produce result Y does not mean that Y is impossible. It could simply be that Ev doesn't model the physical world accurately enough to simulate that.
Ev demonstrates one way things _could_ work. To prove that something is _impossible_ requires that you prove there is _no_ way it could work. This discussion reminds me of another poster on this forum, who's trying to use a physical modelling program to design a perpetual motion machine.
All models have limits. They can be very useful when used within their limits, but if you go outside the limits, the results are usually utterly meaningless.
To argue that Ev disproves the possibility of macroevolution requires two things:
1. You must demonstrate that a simulation done with Ev shows the impossibility of macroevolution.
2. You must demonstrate that none of the differences between Ev and the real world are significant enough to matter.
All of the discussions I have seen so far have centered on (1). The only arguments I have seen for (2) reference that Ev has been accepted as a reasonable modelling tool for microevolution. Can somebody justify the use of Ev for macro-evolution?
What does any of this have to do with what I posted?
You suggested that the fact that dogs tend to revert to a more wolf-like state when they are put back into an ancestral "pre-dog" environment suggests that new traits picked up by micro-evolution will also tend to be lost quickly if the selective pressure that favoured those traits goes away.
I pointed out how and why that won't always be the case. I also suggested that for dogs, there simply hasn't been enough time for the other factors that I'm talking about to come into play. To which you responded:
I may be stupid, but I don't see how that addresses what I said at all.
It's quite possible that I just need things spelled out for me. It's happened before.
Although you must realize a computer spreadsheet is not a simulation of a checkbook- it is in fact doing the same exact thing as a checkbook. Programs that model real world evolution, on the other hand, have many assumptions built in for simplification purposes.
Why would you shy away from doing experiments on real biology to prove your point? Wouldn't that be more effective than appealing to an intelligently designed assumption laden simulation?
The experiments are "intelligently designed" as well. The reason to use a computer simulation is that there are some things that are much easier to do with a computer. You just have to know what it is you're trying to show, and the limits of the model.
Do you object to computer models in general?
Kleinman, I suggest you read [URL="http://www.internationalskeptics.com/forums/showthread.php?t=48951]this thread[/URL]. I would be interested to know your views on AgingYoung, and if you believe a perpetual motion machine is possible. The argument is exactly the same as this one, that a simulation of one small part of the world is more accurate at representing the world than the world is itself.
Paul C. Anagnostopoulos
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Ooh, cool. Exactly which parameters did you use and how many generations did it take?Kleinman said:
~~ Paul
Annoying Creationists
An Introduction to the ev computer program
In the following postings, I will introduce the reader to the ev computer simulation of evolution of binding sites by random point mutations and natural selection. This initial discussion will be limited to a description of the model, how the model is used and some of the key parameters that are used in the model. Later, I will discuss some of the theory behind the derivation of the model and give series of cases which show that this model of evolution by random point mutations and natural selection is far too slow to explain macroevolution and the results from this computer model also contradict Stephen Gould’s hypothesis of punctuated equilibrium.
As a starting point, I refer readers to the following URL:
http://www.ccrnp.ncifcrf.gov/~toms/paper/ev/evj/evjava/index.html
This link will take you to the online version of ev written in the java language by the very capable programmer, a moderator on this forum and originator of this thread but confoosed evolutionist Paul Anagnostopoulos. If you only have a low speed internet hookup, you can still run longer cases with ev. Once you have started ev, you can disconnect from the net and continue running your cases.
I will not post screen shots of the program, I will assume you have followed the above link and now have the applet available to be viewed. There are two basic screens to this applet. The initial screen which presents when starting the applet is the program execution screen. The second screen is accessed by pushing the New button at the top left of the execution screen.
The layout of the first screen gives several controls, some of which are user modifiable and others which are for display purposes only. I will start from the top left and describe the controls.
The Restart button does as the title suggest, it restarts the computation from its initial point. The New button takes you to a form which allows the user to change input parameters to the model. The Help button is not functional but there is more than enough information on the screen to understand how to use this program with minimal study. The About button should be pushed and the window read. The Run/Pause button allow you to start and pause a case at any point and then continue the execution. The Step button allows the user to execute a single generation at a time. Below the Run/Pause and Step buttons are the Speed spinning list control which allows the user to vary the rate at which the program executes. If you are doing larger cases, I suggest you set this control to its maximum value of 21, however it will use 100% of your CPU time and make any other applications you happen to be running execute very slowly. Below the Speed control is the Generations display which tells the user the number of generations that have been calculated to that point. Below that is a Cycles to run control which allows the user to specify the maximum number of generations to execute and below that is a graphical gauge that displays the number of generations that have been run and will also display a message if the program has stopped due to satisfying a convergence criteria.
To the right of the controls just described are 6 controls, 5 of these controls are display only and consist of Population which is the number of creatures or genomes used in the simulation. Potential Sites which is more commonly called genome size in our discussion and is represented by the letter “G” in Dr Schneider’s calculations. This is the number of bases or loci in the entire genome. binding sites which are the portion of the genome that is evolving. This parameter is described by the Greek letter gamma in Dr Schneider’s computation. The only thing I will say about binding sites at this time is that it is the portion of the genome that resides before a gene and must be identified by the organism’s genetic control system in order for the gene to be transcribed. If you are a fisherman, you might think of the binding site as the leader on the fishing line and the fishing line as the gene. Gene are not evolving, only the binding sites for the genes are evolving.Site width is the number of bases or loci in each binding site. Note that Gamma * site width gives the total number of loci that are evolving on the genome. Mutations displays the number of mutations per genome per generation being used in the particular case you are running. The mutation rate can cause some confusion by its definition. There are several ways you can define mutation rate. If you google the terms “bacteria” & “mutation rates”, you will find papers that usually describe mutation rates as 10^-6 or 10^-8 and so on. These rates represent the probability that a mutation will occur at a certain locus per generation. Dr Schneider has used two slightly different definitions than above for programming ease and reduced computational time. I don’t believe these approximations have significant affect on the results of the simulation but you need to be aware which definition you are using. The two definitions that Dr Schneider uses is “m” mutations per genome per generation and 1 mutations per “b” bases per generation. The first definition requires genome lengths (G) of 500,000 or so to give realistic mutation rates, the second definition allows you to approximate realistic mutations on smaller genomes. A per locus rate of 10^-6 would be approximately the same as 1 mutation per 1,000,000 bases per generation. The final control in this column is the Perform selection check box which enables the user to run the simulation with or without selection. To the right of this column is a graphical control which dynamically displays the acquisition of information in a binding site. The last controls on the top portion of the screen, which are labeled at the top with Best and Worst consist of ID which you have to ask Paul what these are for, Age which gives the number of generations that that the best and worst creatures have survived, Mistakes which give the number of mistakes for the best and worst creature. Mistakes is the parameter which is used for selection. Rsequence is the amount of information that has evolved in the binding site. At the start of the computation Rsequence should be zero and according to Dr Schneider’s theory, Rsequence should evolve to the value of Rfrequency which is the amount of information required to find the binding sites. Rfrequency is computed using G and gamma and does not change during the evolutionary process. There are four user modifiable controls below which allow the user to specify convergence conditions including when a Perfect creature has evolved which occurs when the random point mutations and natural selection process has located all gamma number of binding sites on any genome without erroneously identifying a binding site where one should not exist. The Rseq>=Rfreq check box will pause the simulation if the amount of information in the binding sites in any creature equals or exceeds the Rfrequency and the third check box requires that both conditions be met to pause the program. Any none or all boxes can be checked, however Paul seems to have settled on the Perfect Creature criteria as the best convergence criteria. This sometimes gives different results than the Rseq>=Rfreq convergence condition but I see no reason at this time to argue this issue. The last user modifiable control is the Update every generation spin box. This control allows the user to vary the number of times the screen display is updated. I find if the control is set to a low number, it slows the computation, however if you are an old hippie and like to see a colorful display change rapidly, leave this spin box control at a low number. The bottom half of the screen displays the progress of the evolution on one of the genomes with lots of pretty colors.
If you have been able to keep up with this discussion to this point, and your head is not spinning too much, I’ll now describe the second screen in the program. This second screen is accessed by pushing the New button on the execution page. This is a form window that allows the user to change parameters in the model. The top line on this form Title allows you to put a title on the case you are running. This can be useful if you decide to run several cases simultaneously. The Title will show up on the taskbar. The left side of the form has 5 spin box/entry fields, Population, Potential Sites or genome length “G”, Binding sites count also called gamma, Weight width which is the mathematical representation of the binding protein which is used to identify binding sites as they evolve. Site Width is the number of loci or bases in each of the gamma binding sites. If you want a description of the control Placement, you will have to ask Paul or Dr Schneider, I have only used the default value. On the right side of this form are the two different choices for setting the mutation rate as described earlier. The Selection Parameters box repeats the same controls as on the execution screen except you can experiment with Tie Scores and vary the selection process when this occurs. The final two entry controls are the cycles to run which performs the same function as on the execution screen control and Random Seed which is used to initiate the random number generator. The buttons on the bottom of the screen are self explanatory. The Ok returns you to the execution screen with your modified parameters loaded into the model. Unless Paul has done more work on this program, you are unable to Save or Load a case.
The basic flow of the simulation once you have defined your input parameters, consists of the following: The program defines a series of random genomes equal to the population. A portion of each genome is set aside to evolve the gamma number of binding sites, each with the site width specified. Random mutations defined by the user are allowed to each genome. If the mutations enable the weight matrix (binding protein) to identify binding sites in the appropriate area of the genome, then these are not considered mistakes. If the weight matrix does not identify binding sites where they should be, these are considered mistakes. The opposite occurs in the non-binding site region of the genome. If binding sites are identified in this region it is considered a mistake. The total number of mistakes determines the selection process. The half of the population with the fewest mistakes is allowed to reproduce, the other half is selected out. The process is then repeated.
I hope I got all the details correct, if I messed up something I’m sure Paul will gently correct me.
If this discussion hasn’t completely bored you out of your mind, try running a few cases with ev. In the coming days, I will discuss how information theory is tied in with this computation and start presenting results of cases that I have run that I believe prove my case that ev shows that macroevolution by point mutation and natural selection is mathematically impossible, Gould’s hypothesis of punctuated equilibrium is mathematically impossible and that huge populations do not accelerate evolution.
An Introduction to the ev computer program
In the following postings, I will introduce the reader to the ev computer simulation of evolution of binding sites by random point mutations and natural selection. This initial discussion will be limited to a description of the model, how the model is used and some of the key parameters that are used in the model. Later, I will discuss some of the theory behind the derivation of the model and give series of cases which show that this model of evolution by random point mutations and natural selection is far too slow to explain macroevolution and the results from this computer model also contradict Stephen Gould’s hypothesis of punctuated equilibrium.
As a starting point, I refer readers to the following URL:
http://www.ccrnp.ncifcrf.gov/~toms/paper/ev/evj/evjava/index.html
This link will take you to the online version of ev written in the java language by the very capable programmer, a moderator on this forum and originator of this thread but confoosed evolutionist Paul Anagnostopoulos. If you only have a low speed internet hookup, you can still run longer cases with ev. Once you have started ev, you can disconnect from the net and continue running your cases.
I will not post screen shots of the program, I will assume you have followed the above link and now have the applet available to be viewed. There are two basic screens to this applet. The initial screen which presents when starting the applet is the program execution screen. The second screen is accessed by pushing the New button at the top left of the execution screen.
The layout of the first screen gives several controls, some of which are user modifiable and others which are for display purposes only. I will start from the top left and describe the controls.
The Restart button does as the title suggest, it restarts the computation from its initial point. The New button takes you to a form which allows the user to change input parameters to the model. The Help button is not functional but there is more than enough information on the screen to understand how to use this program with minimal study. The About button should be pushed and the window read. The Run/Pause button allow you to start and pause a case at any point and then continue the execution. The Step button allows the user to execute a single generation at a time. Below the Run/Pause and Step buttons are the Speed spinning list control which allows the user to vary the rate at which the program executes. If you are doing larger cases, I suggest you set this control to its maximum value of 21, however it will use 100% of your CPU time and make any other applications you happen to be running execute very slowly. Below the Speed control is the Generations display which tells the user the number of generations that have been calculated to that point. Below that is a Cycles to run control which allows the user to specify the maximum number of generations to execute and below that is a graphical gauge that displays the number of generations that have been run and will also display a message if the program has stopped due to satisfying a convergence criteria.
To the right of the controls just described are 6 controls, 5 of these controls are display only and consist of Population which is the number of creatures or genomes used in the simulation. Potential Sites which is more commonly called genome size in our discussion and is represented by the letter “G” in Dr Schneider’s calculations. This is the number of bases or loci in the entire genome. binding sites which are the portion of the genome that is evolving. This parameter is described by the Greek letter gamma in Dr Schneider’s computation. The only thing I will say about binding sites at this time is that it is the portion of the genome that resides before a gene and must be identified by the organism’s genetic control system in order for the gene to be transcribed. If you are a fisherman, you might think of the binding site as the leader on the fishing line and the fishing line as the gene. Gene are not evolving, only the binding sites for the genes are evolving.Site width is the number of bases or loci in each binding site. Note that Gamma * site width gives the total number of loci that are evolving on the genome. Mutations displays the number of mutations per genome per generation being used in the particular case you are running. The mutation rate can cause some confusion by its definition. There are several ways you can define mutation rate. If you google the terms “bacteria” & “mutation rates”, you will find papers that usually describe mutation rates as 10^-6 or 10^-8 and so on. These rates represent the probability that a mutation will occur at a certain locus per generation. Dr Schneider has used two slightly different definitions than above for programming ease and reduced computational time. I don’t believe these approximations have significant affect on the results of the simulation but you need to be aware which definition you are using. The two definitions that Dr Schneider uses is “m” mutations per genome per generation and 1 mutations per “b” bases per generation. The first definition requires genome lengths (G) of 500,000 or so to give realistic mutation rates, the second definition allows you to approximate realistic mutations on smaller genomes. A per locus rate of 10^-6 would be approximately the same as 1 mutation per 1,000,000 bases per generation. The final control in this column is the Perform selection check box which enables the user to run the simulation with or without selection. To the right of this column is a graphical control which dynamically displays the acquisition of information in a binding site. The last controls on the top portion of the screen, which are labeled at the top with Best and Worst consist of ID which you have to ask Paul what these are for, Age which gives the number of generations that that the best and worst creatures have survived, Mistakes which give the number of mistakes for the best and worst creature. Mistakes is the parameter which is used for selection. Rsequence is the amount of information that has evolved in the binding site. At the start of the computation Rsequence should be zero and according to Dr Schneider’s theory, Rsequence should evolve to the value of Rfrequency which is the amount of information required to find the binding sites. Rfrequency is computed using G and gamma and does not change during the evolutionary process. There are four user modifiable controls below which allow the user to specify convergence conditions including when a Perfect creature has evolved which occurs when the random point mutations and natural selection process has located all gamma number of binding sites on any genome without erroneously identifying a binding site where one should not exist. The Rseq>=Rfreq check box will pause the simulation if the amount of information in the binding sites in any creature equals or exceeds the Rfrequency and the third check box requires that both conditions be met to pause the program. Any none or all boxes can be checked, however Paul seems to have settled on the Perfect Creature criteria as the best convergence criteria. This sometimes gives different results than the Rseq>=Rfreq convergence condition but I see no reason at this time to argue this issue. The last user modifiable control is the Update every generation spin box. This control allows the user to vary the number of times the screen display is updated. I find if the control is set to a low number, it slows the computation, however if you are an old hippie and like to see a colorful display change rapidly, leave this spin box control at a low number. The bottom half of the screen displays the progress of the evolution on one of the genomes with lots of pretty colors.
If you have been able to keep up with this discussion to this point, and your head is not spinning too much, I’ll now describe the second screen in the program. This second screen is accessed by pushing the New button on the execution page. This is a form window that allows the user to change parameters in the model. The top line on this form Title allows you to put a title on the case you are running. This can be useful if you decide to run several cases simultaneously. The Title will show up on the taskbar. The left side of the form has 5 spin box/entry fields, Population, Potential Sites or genome length “G”, Binding sites count also called gamma, Weight width which is the mathematical representation of the binding protein which is used to identify binding sites as they evolve. Site Width is the number of loci or bases in each of the gamma binding sites. If you want a description of the control Placement, you will have to ask Paul or Dr Schneider, I have only used the default value. On the right side of this form are the two different choices for setting the mutation rate as described earlier. The Selection Parameters box repeats the same controls as on the execution screen except you can experiment with Tie Scores and vary the selection process when this occurs. The final two entry controls are the cycles to run which performs the same function as on the execution screen control and Random Seed which is used to initiate the random number generator. The buttons on the bottom of the screen are self explanatory. The Ok returns you to the execution screen with your modified parameters loaded into the model. Unless Paul has done more work on this program, you are unable to Save or Load a case.
The basic flow of the simulation once you have defined your input parameters, consists of the following: The program defines a series of random genomes equal to the population. A portion of each genome is set aside to evolve the gamma number of binding sites, each with the site width specified. Random mutations defined by the user are allowed to each genome. If the mutations enable the weight matrix (binding protein) to identify binding sites in the appropriate area of the genome, then these are not considered mistakes. If the weight matrix does not identify binding sites where they should be, these are considered mistakes. The opposite occurs in the non-binding site region of the genome. If binding sites are identified in this region it is considered a mistake. The total number of mistakes determines the selection process. The half of the population with the fewest mistakes is allowed to reproduce, the other half is selected out. The process is then repeated.
I hope I got all the details correct, if I messed up something I’m sure Paul will gently correct me.
If this discussion hasn’t completely bored you out of your mind, try running a few cases with ev. In the coming days, I will discuss how information theory is tied in with this computation and start presenting results of cases that I have run that I believe prove my case that ev shows that macroevolution by point mutation and natural selection is mathematically impossible, Gould’s hypothesis of punctuated equilibrium is mathematically impossible and that huge populations do not accelerate evolution.
Exactly.
That you so quickly percieved the root of the matter makes me all warm and fuzzy inside. I think I am going to enjoy your contributions to this forum.
An open question:
Why isn't Kleinman simply settling for proving Schnieder wrong? "Look, your response to creationism fails. Try again."
Instead, he wants to use a (possibly) failed argument by an evolutionist as a way to overturn all of evolution.
Is this just more magical thinking - the law of contagion in action (having gained a piece of evolutionary theory, it magically relates to the entire thing)? Or does he think he's doing the same thing we do when we use a Bible contradiction to overturn the inerrancy of the Bible?
Why isn't Kleinman simply settling for proving Schnieder wrong? "Look, your response to creationism fails. Try again."
Instead, he wants to use a (possibly) failed argument by an evolutionist as a way to overturn all of evolution.
Is this just more magical thinking - the law of contagion in action (having gained a piece of evolutionary theory, it magically relates to the entire thing)? Or does he think he's doing the same thing we do when we use a Bible contradiction to overturn the inerrancy of the Bible?
Annoying Creationists
http://www.evolutionisdead.com/forum/viewtopic.php?t=348&postdays=0&postorder=asc&start=285
It is on my Oct 09, 5:39PM post.
That case took about 300Mb of RAM and 4 days of cpu time.
That’s ok, Paul is Dr Schneider’s coworker and he is confoosed as well. Read my post #94 on this thread and I’ll walk you through the simulation.Gopi said:
If you stick with this discussion, this will make sense. You will get a little understanding of Information Theory which Dr Schneider used to write his computer simulation of evolution by random point mutations and natural selection. His simulation shows that it this mechanism can not account for macroevolution. Other evolutionists have tried to counter this finding by saying that recombination will speed up the macroevolutionary processes. Recombination can not create a new gene that is recombination can not add information to the gene pool.Kleinman said:
Cuddles said:
I couldn’t get you link to work. I do not believe in perpetual motion machines. Dr Schneider believes his simulation represents the real world.Cuddles said:
I posted the results on the Evolutionisdead forum on the following page:Kleinman said:
http://www.evolutionisdead.com/forum/viewtopic.php?t=348&postdays=0&postorder=asc&start=285
It is on my Oct 09, 5:39PM post.
That case took about 300Mb of RAM and 4 days of cpu time.
Surely, the point is that there is no perfect creature. By prespecifying an end point you're trying to determine the time taken and probability of reaching that end point. In the real universe, assuming ID isn't correct, there isn't an end point and evolution goes to where it goes to.
Annoying Creationists
I think Dr Schneider’s model is essentially correct. Where Dr Schneider went wrong was using unrealistic parameters in his model. You need to ask Paul or better yet ask Dr Schneider if he believes his arguments and model is correct.
Yahzi said:
I think Dr Schneider’s model is essentially correct. Where Dr Schneider went wrong was using unrealistic parameters in his model. You need to ask Paul or better yet ask Dr Schneider if he believes his arguments and model is correct.
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