Imagine a coin-tossing game. On each turn, players toss a fair coin 500 times. As they do so, they record all runs of heads, so that if they toss H T T H H H T H T T H H H H T T T, they will record: 1, 3, 1, 4, representing the number of heads in each run.
At the end of each round, each player computes the product of their runs-of-heads. The person with the highest product wins.
In addition, there is a House jackpot. Any person whose product exceeds 1060 wins the House jackpot.
There are 2500 possible runs of coin-tosses. However, I’m not sure exactly how many of that vast number of possible series would give a product exceeding 1060. However, if some bright mathematician can work it out for me, we can work out whether a series whose product exceeds 1060 has CSI. My ballpark estimate says it has.
That means, clearly, that if we randomly generate many series of 500 coin-tosses, it is exceedingly unlikely, in the history of the universe, that we will get a product that exceeds 1060.
However, starting with a randomly generated population of, say 100 series, I propose to subject them to random point mutations and natural selection, whereby I will cull the 50 series with the lowest products, and produce “offspring”, with random point mutations from each of the survivors, and repeat this over many generations.
I’ve already reliably got to products exceeding 1058, but it’s possible that I may have got stuck in a local maximum.
However, before I go further: would an ID proponent like to tell me whether, if I succeed in hitting the jackpot, I have satisfactorily refuted Dembski’s case? And would a mathematician like to check the jackpot?
I’ve done it in MatLab, and will post the script below. Sorry I don’t speak anything more geek-friendly than MatLab (well, a little Java, but MatLab is way easier for this).
Umm first you have to understand CSI…
Joe G,
I have a problem understanding the “Specification” part of CSI.
Why is it done after the fact?
ID says if a part serves a function, that means it is specified, but that to me looks circular.
Looking forward towards a target I could accept as a specification, but looking back from a target and saying, “This must be what we were aiming for”, is a hard sell.
As I understand Dembski’s paper, he is trying to find a way of “specifying” a “special” subset post hoc, so that we can look at a pattern, without knowing what the “intended” target is, and yet recognise that the pattern is one of a small but special subset.
His proposal is to use “compressibility” – if a pattern can be described as or more simply than the observed pattern, then it is part of the “special” subset. Thus we can estimate the size of the subset of which the observed pattern forms a part, and the proportion that subset forms of the whole. And if that proportion is so small that hitting a member of it by some process in which all patterns are equiprobable is unlikely given the number of events in the universe, then we can infer that it must have been designed.
I have in fact given a pre-specification to make life easier (not for me – for ID proponents!) in that I have said that my target is a member of that small subset of coin-toss series in which the product of the size of each run of heads exceeds a very large number.
I don’t, in advance, know what the jackpot series is, but I do know that I’m unlikely to hit it without natural selection. However, using natural selection, I’m confident I can find one well before I’ve used up the probabilistic resources of the universe!
Right now I’m up to 3.8319e+58.
Improvements are becoming rarer. I forgot to output the number of generations, so I don’t know how long I’ve been going. I’ll check on it in a while.
One way to get products greater than 10^60 is to have a run 100 groups of THHHH; in which case you would have a score of 4^100 = 1.6 x 10^60.
Yes, that seems to be one that gets over the bar. I don’t know how many others there are though.
Current best is: 4.7898e+58
Perhaps by CSI above you mean reaching the CSI threshold that indicates a design inference is warranted.
Given that, I don’t see anything wrong in principle with your reasoning or your use of CSI in this example. I’m just not sure what claim of Dembski’s you think your example refutes, or what you think a process of culling the bottom performers is representative of. Given the title of the thread, it appears you are equating that culling process with NS, but it’s certainly not representative of natural selection; you’ve described a process deliberately designed to increase the CSI to a maximum potential.
Natural selection doesn’t cull the CSI-producing underachievers; it’s culling process has nothing to do with CSI per se.
I would have no problem with the term “Described” as opposed to “Specified” but Dembski and other IDists can use “Specified” to hint that a “specific” target was intended.
A “Specification” in engineering describes an intended “pre-design” outcome while ID uses it to describe an outcome after the fact.
This is misleading.
Walk us through an example with calculated values Joe, so we’re all on the same page.
Since this is a thread on GAs I will post a link to my word generator. Unlike the basic Weasel program it does not have a target. Its fitness function is based on whether letters appear in positions that occur in dictionary words. There is no preference given to matching complete words, but it does score increased fitness for combinations of letters that appear in words.
Because it does not score fitness for completed words, it is as likely to produce pronounceable neologisms as it is to produce dictionary words. I’m rather biased, but I think the ability to produce neologisms is evidence that GAs can invent.
It’s produced several drug trade names that are not in its scoring dictionary.
http://itatsi.com
So I guess with this sequence of 100 single tails and four heads we could have THHHH or HHHHT; in which case there are 2 out of 2^500 chances of getting 10^60.
So that is a probability of 2^(-499) = 6.1 x 10^(-151) of obtaining that score.
No. Liz has described a process deliberately designed to increase the runs of H’s. This is a simulation that is exactly representative of natural selection, where rounds with the phenotype *high product of H-runs* have higher fitness than rounds with the phenotype *low product of H-runs*. The fitter phenotypes also have higher CSI, i.e the two traits (*high product of H-runs* and *high CSI*) are mechanistically correlated, and thus selection acts jointly on both traits. Just like an individual with a functioning immune system may have higher fitness than an individual without a functioning immune system, where the trait that confers higher fitness (a functioning immune system) may also have higher CSI.
Natural selection culls the CSI-producing underachievers in all those situations where the traits that confer higher fitness also have higher CSI. That’s exactly the example Liz is using.
Strange, if you could just support your position you wouldn’t even have to worry about any part of CSI
How do you think archaeologists and forensic scientists do it? “Minority Report” with precogs is science-fiction.
Do you think we were there with the designer(s)?
Unfortinately that isn’t all what ID says about that. For example if any ole DNA sequence could produce any ole protein, there wouldn’t be any specificity. However if there is a protein of say 200 amino-acids that can tolerate only one AA substitution, then that has a high specificity.
Well that is how science operates- we observe some result and try to explain it- what it is, what it does and how it came to be the way it is.
I have been pointing this out to IDists like Joe G for years. What ID calls a ‘specification’ is actually only an after-the-fact description of a biological entity. To be a specification it must be a before-the-fact design document that the end product is constructed to meet.
Arguing this point was one of the things that got me banned at UD. I’ve yet to see any IDist honestly address the problem.
How about this example:
http://www.antievolution.org/cgi-bin/ikonboard/ikonboard.cgi?s=4f60ec0f85525598;act=ST;f=14;t=7297;st=0
?
If that is what she means in her example, where does she represent situations where greater fitness = decreased CSI?
I said: “Natural selection culls the CSI-producing underachievers in all those situations where the traits that confer higher fitness also have higher CSI. That’s exactly the example Liz is using.”
Who said that she does? Why would she need to represent that? She is trying to show that NS *can* produce CSI. Simulating a situation where higher fitness is correlated to higher CSI is completely sufficient to achieve that. Unless you think that this situation can never occur in living organisms?
That would mean that “any” bit pattern is specific since if you change any bit, the “information” is not the same.
On the other hand, I could show you JPEGs of Mitt Romney at different compression ratios, but all of them are recognizable, yet they all have completely different bit patterns.
With your definition of CSI, a compressed picture of Mitt Romney is not “specifically” a picture of him since all bits in the “information” can change without changing the image of Romney from one JPEG to another.
In the first place, no new complexity or specification is being added by the culling process; that is being added by her point mutation process. IOW, if there are no point mutations, the culling process only culls the bottom performers until virtually nothing is left. But, if we allow Elizabeth to sneak her point mutations in as “part of the NS process”, then she can only accompllish her goal through an iterative culling process in conjunction with random* point mutations.
She’s trying to get to a goal “within the lifetime of the universe”. If we have infinite time and reasources, then yes, eventually monkeys will type shakespeare; but the reason the culling process is important is because that speeds the monkey to bard process up. Nobody is arguing that chance* ***cannot*** produce CS; they argue that in principle it is not a sound explanation for CSI above a certan level, given limited time and resources.
This makes how the culling process is characterized very important, because if the culling process is also culling out CSI overachievers on occasion (where increased fitness matches decreased CSI), that lengthens the amount of time necessary for the process to acquire the target level of CSI that implies a design inference. In fact, it makes that amount of time required (iterative generations) indistinguishable form chance, unless the culling process is, for some reason, gamed (skewed) towards increased CSI.
Well, Dembki’s chi metric actually incorporates his universal probability bound, and he suggest that if the value of chi exceeds 1, we can reject the non-Design null.
So, yes, I’m trying to get to that threshold. I think I’m there, actually, but the iterations haven’t quite hit my jackpot yet. I’m not sure whether they will, with the deterministic culling system I’ve got. Stochastic factors sometimes get a a promising failure into play. But I’ll leave it running overnight.
That if we observe a pattern that exhibits CSI>1 we must reject non-Design.
Natural selection i.e. heritable variance in reproductive success. The poor performers don’t get to reproduce. Consider a population with limited resources, in which only the best foragers survive to breed. The better everyone gets, the better the best have to be to survive.
Yes, of course.
It’s exactly representative of natural selection.
No. I’ve designed a process that maximises the product of the sizes of the runs of heads. That’s the function I specified. And very few series will have a larger function that my jackpot threshold. That means, automatically, that the CSI of any winning series will be very high. Just as, if we specified that a sequence of nucleotides had to result in a highly effective protein, there might be very few sequences that coded for that protein above a given threshold. Dembski suggest that when we observe a pattern, we compute the number of patterns that perform as well, or better, and compute the proportion they are of the total number of theoretically possible patterns.
That’s exactly what I’m doing.
Indirectly it does, because what specification the “semiotic agent” as Dembski calls it will be related to the function of interest. If it’s a protein, then it will be the function of that protein, so if we select for that protein, we will also,by definition, be selecting for CSI, where the S part is the function of that protein.
In my little exercise, I have a “genome” – the series of coin tosses, a “phenotype” – the string of head-run-totals, and a “function” – the product of that string. I’m selecting for that function. Only a very small subset of possible genomes can perform that function above the threshold I set. That means that if I manage to find one that does, I have a genome that exhibits CSI.
That’s my comment you were quoting, Rich. I’m happy to walk you through it, though.
An example of where increased fitness might result in reduced CSI calculated on a DNA sequence could be where a protein is redundant in the current environment, and therefore uses resources that could be better used on useful proteins. And there are might be a lot of mutations that would disable the protein, and very few that did not.
So if you calculated CSI on basis of the small number of sequences that produced a viable protein, as against the many sequences that did not, an organism with a corrupted sequence might be fitter than one with an intact sequence, yet the CSI, calculated on the protein, would be reduced.
Of course this raises a key conceptual issue with regard to CSI – if the specification is simply “a fitter organism” then of course anything that increases fitness will increase CSI. But if you calculate it on some sub-function, like protein synthesis, then you could have greater fitness in organisms with less CSI.
Here, I am computing Functional Complexity, where the better the function is performed, the fitter my “phenotypes”.
But that does not invalidate what I am doing by Dembski’s criteria, and in fact, because my specification is a function that serves fitness, I’m doing something more biologically relevant,than, say, computing the Functional Complexity of a protein that is not necessarily contributing to the fitness of the phenotype.
Sorry, my statement was imprecise, my bad! What I meant was this: Liz is trying to show that random point mutations AND natural selection *can* produce CSI. And Liz isn’t *sneaking* anything in anywhere. She clearly states in the OP: “…starting with a randomly generated population of, say 100 series, I propose to subject them to random point mutations and natural selection,…”
I have no idea what you mean by this or how it pertains to Lizzie’s OP. It is quite obvious, and you acknowledge this in your first comment on this thread that by CSI she means reaching a probability threshold that Dembski claims to indicate that a design inference is warranted.
You completely misunderstand the example, and my explanations of it. In a situation where the trait that confers higher fitness also has higher CSI, the culling process of natural selection CANNOT cull CSI-overachievers. Not even occasionally. By the very definition and the relation of the terms *fitness* and *natural selection*.
So back to the topic- what does the OP have to do with natural selection, which pertains to biological populations?
All you’re doing, Elizabeth, is equivocationg fitness with CSI. If you’re going to assume “increased fitness” = “increased CSI” (which is all your example does, because the only distinguishing quality of your target is a CSI value, and the only consideration of your culling process is to achieve that goal), then all you have created here is an example of how an intelligently designed process can acquire high levels of CSI even utilizing random mutations by gaming the system in favor of high CSI outcomes.
William- it is a waste of time trying to explain this stuff to these people.
Madbat,
I didn’t misunderstand you. I understand that in the case of the higher CSI = better fitness, the higher CSI will be chosen. What her example ignores are the cases of lower CSI = better fitness and the cases where “no variation in CSI” = better fitness.
This is where you misunderstand ID’s requirement that CSI is linked to “specific” complexity.
Better fitness implies a higher degree of “specified” information since by definition any “random” bit pattern would not be as “specific”.
The “bit window”, has NOT changed and therefore the better the “fitness”, the more “specific” the “information”.
Here is a plot I made of sequences with high “products” where all the runs-of-heads are the same length. There will also be sequences that mix runs of different sizes, but this gives an idea of the odds:
William, give me a calculated example of a case where lower CSI=better fitness. I don’t necessarily need to see actual numbers, just tell me what would have to be true for this to be the case.
Again: No, it doesn’t ignore anything. The cases where lower CSI = better fitness are irrelevant to her goal, which is to show that random point mutation and natural selection *can* produce CSI: she argues that it does so in cases where higher CSI = better fitness. That there are other cases where random point mutations and natural selection will not produce CSI is not contested here by anyone and is completely irrelevant to her argument.
As I’ve tried to explain before, William, it makes no sense to separate these two processes. It’s like saying all the clapping is being done by one hand.
If I do not cull, and I can easily drop that part of my program, I will simply generate, rather ineffectually, a lot of sequences that are no more likely to have high products than any other sequence, although they are quite likely to have a product close to that of their parent. I can easily do this, and show you the result. But the chances of me producing a high-product sequence will be extremely small, and therefore my chances of producing a sequence that exhibits a chi >1 will also be extremely small. In fact that’s pretty well Dembski’s null hypothesis.
But by adding Natural Selection, i.e. by culling the poor performers, parts of sequences that have high products accumulate, and so I am much more likely to achieve chi>1.
If I demonstrate that this is so, i.e. that no culling doesn’t give me chi>1 and culling does, will you be convinced?
Obviously we need the point mutations – that’s the other hand in the clap. Without new variation, no genome can be better than the best of the starting population, which was randomly generated! However, note that I cull the bottom 50, then “breed” from the top. If I do this with no mutation then eventually I will end up with a population that is identical to the best performer of the original randomly generated sequences. I can demonstrate all these if you like: NS but no RM; RM, but no MS, neither NS nor RM. But only RM+NS will generate a sequence with chi>1.
And the take home message, IMO, is that CSI is a potentially useful concept, but it’s not the “pattern that signifies intelligence”, it’s the pattern that signifies heritable variance with reproductive success, of which intentional intelligence, I suggest is a subset.
Of course. But I’m not “sneaking” in anything. I’m simply showing that RM alone can’t produce high CSI, but that RM+NS can. Obviously NS alone, can’t, because NS is heritable variation in reproductive success, and that variance has to come from somewhere! In reality you can’t have NS without some source of variance. In my exercise we start with a population of 100 variants, but I could, if you like, start with 100 identical genomes. Clearly, then, without RM, there will be no NS, because all the genomes will have exactly the same fitness. That’s why I say that separating them is so misleading.
Exactly. Which is why monkeys-with-typewriters is irrelevant to any criticism of Darwinian evolution!
Well, if you are using “chance” to mean “no-design” (as per your earlier definition) then, yes, people – most ID proponents – are arguing precisely that. It’s the basis of the whole CSI argument. However, if you are using “chance” to mean “random mutation” then, yes, random mutation alone won’t produce high levels of CSI, if the variants never vary in reproductive success i.e. if there is no NS. However, if the variants do confer variation in reproductive success, then we have NS, and a substantial probability of high CSI.
The culling process will always be “skewed” towards CSI, if CSI is calculated on the function the sequence serves the phenotype. If it isn’t, then not necessarily. But the whole point of CSI as a concept is to explain the extraordinarily complex functions we see being carried out in organisms to the benefit of that organism. So why would you want to specify (for your S) something irrelevant to phenotypic function?
William, I fear you will think I am “obfuscating” above, but I am not. Please read my post very carefully. The points I am making go to the heart of the ID vs Evo debate.
And if you’d like me to demonstrate those alternative runs, I’m more than willing to do so.
So what are you doing here?
Let me say it again, because it didn’t seem to sink in the first time: The distinguishing quality of the target, i.e. the selected trait, is NOT CSI, it is: *high product of H-runs*. Liz has described a process deliberately designed to increase the product of H-runs. This is a simulation that is exactly representative of natural selection, where rounds with the phenotype *high product of H-runs* have higher fitness than rounds with the phenotype *low product of H-runs*. The fitter phenotypes also have higher CSI, i.e. the two traits (*high product of H-runs* and *high CSI*) are mechanistically correlated, and thus selection acts jointly on both traits.
So now we’re just going to assume that higher CSI = more fit, and I have to demonstrate the converse? Wheee! The burden has been shifted! We go from you making your case, to me having to prove otherwise?
Here we go: lower CSI = some low form of microbial life. Higher CSI = mammals, reptiles, avians. Environmental pressure = introduction of toxic gas that poisons & kills off all creatures that breathe via lungs. It just so happens that the fittest organisms in that environment are ones with much, much less CSI. Note: the lack of CSI isn’t per se what saved them (all sorts of non-lung CSI would have been fine), it was just the increased CSI that happend to develop lung breathing that killed off trillions of high CSI organisms because of a new environmental pressure.
Joe,
It’s a waste of time if you’re trying to get them to understand. It’s not a waste of time if it’s the most effective way of killing time during slow periods at work.
Could we prove that CSI exists, first and foremost? I don’t know if we’re looking at the emperor’s pants or shirt.
It’s a waste of time if all any of us are doing are “trying to get them to understand”. Let’s try equally hard to understand each other.
WOW!
Are you now suggestion that microbes and animals with lungs are in the same gene pool?
And just what does your “counterexample” have to do with what Elizabeth is demonstrating?
Can any of you ID followers actually explain what CSI is?
What does CSI have to do with anything in science?
No. You don’t have to assume that at all. But if you are going to calculate the CSI on some specification other than phenotypic fitness (which is perfectly possible – you could do it on efficiency at generating some protein, or on the efficiency with which that protein performs some cellular function, even though that protein had no effect – or a deleterious effect – on phenotypic fitness).
But Dembski’s claim is that CSI of chi>1 can only be generated by Design. I am demonstrating that it can be generated by evolutionary processes, namely heritable variance in reproductive success.
Therefore, if I succeed, I have supplied a practicable and replicable falsification of Dembski’s claim.
Attempting to imply that I have cheated by using natural selection is clearly absurd because natural selection is precisely the mechanisms Darwinist propose for generating CSI. Saying that Natural Selection can’t do it without mutations is also pointless because without mutations there can be no Natural Selection. Saying that it’s cheating to select for CSI is absurd because the whole point of the CSI concept it to claim the kind of beneficial functions in biology that are said to exhibit CSI cannot be produced by Darwinian mechanisms, i.e. by Natural Selection.
So far from me shifting the burden, I’m trying as hard as I can to keep it where Dembski himself put it! He said that only Design could result in chi>1. Evos say that Darwinian mechanisms can do this. I’m demonstrating that this is true.
Where is the shifted burden?
Here we go: lower CSI = some low form of microbial life.Higher CSI= mammals, reptiles, avians.Environmental pressure = introduction of toxic gas that poisons & kills off all creatures that breathe via lungs. It just so happens that the fittest organisms in that environment are ones with much, much less CSI. Note: the lack of CSI isn’t per se what saved them (all sorts of non-lung CSI would have been fine), it was just the increased CSI that happend to develop lung breathing that killed off trillions of high CSI organisms because of a new environmental pressure.
Fine. No-one, least of all me, is arguing that CSI is not necessarily associated with increased fitness. I’ve already given examples of where that could be the case.
But nor is anyone arguing that NS must increase CSI. All we are arguing is that Dembski is wrong to assume that chi>1 signifies intelligence. My exercise is not “intelligent” in that it has no foresight. It is purely Darwinian. Yet it’s busy generating CSI with chi>1.
Sounds like you disagree with Dembski then.
Dembski says if I have a 500 bit window, (complexity), it does not on its own infer a designer, but couple that with “specified” functionality, and I can infer a designer.
THEREFORE, if I have a less “fit” organism, it would indicate a lower value of CSI for any level of “complexity”, (number of bits), in my window of interest.
THEREFORE, CSI drops with a decrease in fitness.
The introduction of the toxic gas altered the environmental specification to “no lungs”, whereas before it had included them. NS particularly relates to the “S” in CSI. It arguably increases or tightens the specification in your example.
It is the “S” that Dembski particularly associates with intelligent design.
Ten consecutive heads in coin tosses is supposed to show specification, not complexity. The tails could have been poisoned!
Just to clarify (I’ll post my script shortly): I am evaluating the fitness of my critters on the basis of how large the product of the sizes of their runs-of-heads is.
That is also the specification of interest, just as the specification for a protein-coding sequence for protein would normally be how well it performs its function in the organism that bears it. My example is exactly analogous to that.
And there are a very small number of sequences, out of the total number of sequences that give rise to very high products. Over about 1058 the proportion is so tiny that the sequences that good and better form such a tiny subset of the total possible number of sequences that the probability of randomly generating such a sequence, without natural selection, is unlikely in the history of the universe.
With natural selection it is easy, however – selection, that is, for sequences with high products of sizes of runs-of-heads.
Therefore, high CSI is the pattern that signifies selection, not, as Dembski says,
And just a little clarification about the function you are looking at.
We already know that the Shannon entropy is maximized when all probabilities are equal. So all we are doing is partitioning sets of H’s using the T’s as partitions. Then the probabilities within each “box” will be determined by how many H’s there are in that box. If all the probabilities are equal, the negative average of the logarithms of the probabilities is maximized and that means the product of the number of heads in maximized.
So we know that all the probabilities must be equal, which means that the number of H’s within a partition are all equal. That makes the length of a partition I+1 were I is the number of H’s and the plus one counts the partition boundary for each set of H’s
All we have to do is maximize I^(N/(I+1)) which is the same as maximizing its logarithm
(N/(I+1))ln( I ). We do this for integers, but we can see where the maximum occurs by thinking of I being a continuous variable x, set the derivative equal to zero, solve for x, and then find the nearest integer.
In setting the derivative equal to zero, we must find the solution to
x(1 – ln x) + 1 = 0.
This turns out to be 3.59112… and the nearest integer is 4.
So N = 500, I = 4, and we end up with 4^100 as the maximum.
Well, Dembski doesn’t have a whole lot to say about biology, of course. But the Hazen paper, the one that Joe G likes (and so do I) has an equivalent formulation in which the “specification” is function. But that needn’t be coupled to fitness. For example, a sequence could be very efficient at producing a toxic protein. If we evaluated “function” on the efficiency of protein production, we’d have high functional complexity associated with lower fitness. Arguably this is true, for example of the Huntington’s protein. Or would be, if Huntington’s disease decreased reproductive success, which it may or may not do.
CJYman once posted the following:
Since I don’t know what CSI is or how to calculate it, I will just have to guess what it might meant in this case.
Since we end up with 4^100 as our “maximum fitness” in this case, then perhaps we could take CSI to be the logarithm to base 2 of 4^100 = 2^200. That gives us 200 bits of CSI.
But, as I say, I have no idea what CSI means.
Well, my best product has now reached 8.0828e+58. So it’s creeping up, but ever more slowly! I’ll see where it’s got to in the morning.
But it clearly works. That number is almost certainly high enough for the proportion of sequences that high or higher to be small enough to give a chi >1
So Dembski is falsified, unless someone can tell me how my calculation of chi is wrong. I calculated it as -log2[10160*N/2500] where N was my guesstimate of the number of sequences with products higher than my threshold. Actually N can be far bigger than my estimate, and it still makes it by miles.
CSI- Complex Specified Information.
Information- see Shannon, Claude
(When Shannon developed his information theory he was not concerned about “specific effects”:
And that is what separates mere complexity (Shannon) from specified complexity.)
Specified Information is Shannon Information with meaning/ function
Complex Specified Information is 500 bits or more of specified information
MathGrrl wants a mathematically rigorous definition of CSI and I say that is like asking for a mathematically rigorous definition of a computer program (which contains CSI).
The mathematical rigor went into calculating the probabilities that got us to 500 bits of SI = CSI
In the preceding and proceeding paragraphs William Dembski makes it clear that biological specification is CSI- complex specified information.
In the paper “The origin of biological information and the higher taxonomic categories”, Stephen C. Meyer wrote:
Biological functionality is specified information.
So what do we have to do to see if it contains CSI? Count the bits and figure out the variation tolerance because if any sequence can produce the same result then specified information disappears.
And again, CSI is all about origins…
Well, that’s not Dembski’s definition. His is the opposite. He uses compressibility as a measure of specification. From Dembski’s paper:
All I’m demonstrating is that Dembski is incorrect.