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EV Ware: Dissection of a Digital Organism

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Can undirected Darwinian evolution create information?

In a celebrated paper titled “Evolution of Biological Information,” a computer program named ev, says yes.  It claims to illustrate the following properties of evolution.

  • “[Ev shows] how life gains information.” Specifically “that biological information… can rapidly appear in genetic control systems subjected to replication, mutation and selection.”
  • Ev illustrate punctuated equilibrium: “The transition [i.e. convergence] is rapid, demonstrating that information gain can occur by punctuated equilibrium.”
  • Ev disprove “Behe’s … definition of ‘irreducible complexity’ … (`a single system composed of several well-matched, interacting parts that contribute to the basic function, wherein the removal of any one of the parts causes the system to effectively cease functioning’. “

In a wonderful friendly GUI (graphic user interface) from the people at EvoInfo.org, it is easy to show that without front loaded programmed information about the search,  ev simply will not work.  These claims therefore ring hollow.

The goal of ev is to identify a given string of bits (ones and zeros).  The reason the ev program works is because of the structure created by the writer of the ev computer program.  A Hamming oracle in ev, for example, tells you how close your guess is to the correct answer.  Contrast this to undirected random search where you either told: No, your guess is wrong or, Yes, your guess is right.  At a trillion trials per second, it would take “about 12 566 000 000 000 000 000 ([over] twelve and a half quintillion) years” to find the ev target using undirected random search.  To identify the target string of bits, the Hamming oracle allows reduction of the number of trials to thousands, hundreds, and even tens.

EvoInfo’s EV Ware GUI  works on your browser and is easy to use.

ALSO: See the GUI autopsy results for Dawkins’s METHINKS*IT*IS*LIKE*A*WEASEL  at EvoInfo.org

Comments
Atom, thanks. This is a nit-picky issue, but at least it's concrete enough for all of us to come to agreement on it, which is pretty rare in this debate. Again, there are bigger issues. Here's another one: Not only is the active info metric a function of the search as whole rather than of the individual components, it's actually a function of a search model, not the modeled process itself. If we want to use the active info metric to draw conclusions about the underlying process, we have to establish some non-arbitrary way of modeling processes as searches. Take ev for example. Schneider did not cast ev as a search -- it was Marks and Dembski who did that. They could have lumped the perceptron into the fitness function, in which case the search space would be of size 4^261 with a good-sized chunk occupied by what they would call the target. This search model would have significantly less active info than their model has. Even worse, they could have defined the target any way they wanted to, as Schneider said nothing about a target. Had they defined it to be the opposite of the message that gets conveyed to the genome, the search would be an guaranteed failure with tons of negative active information. BTW, Schneider needs to be careful with his approach too, as it has the same problem of arbitrary modeling. For instance, what part of a signal constitutes a message, and what part is noise? It depends on how you model it. Given Schneider's approach, I see nothing wrong with his statements about information gain, but his conclusions regarding the underlying biology may not be justified.R0b
January 8, 2009
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R0b, Prof. Marks will include a note on the introduction explicitly stating that the GUI is not an exact one-to-one model of Schneider's simulation. That should make things transparent enough. AtomAtom
January 5, 2009
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Atom, no problem. You're right that this makes no difference to EvoInfo Lab's points of criticism, only to the numbers.R0b
January 5, 2009
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R0b, Re-reading the relevant portions of the paper, I think you may be correct that all 256 bases are ran through the evalutation engine. There is a line on the second page that says:
The weight matrix gene for an organism is translated and then every position of that organism's genome is evaluated by the matrix.
(emphasis mine) So I apologize for that and stand corrected. My version of the GUI, therefore, is still a mini-version of Schneider's version: less possible binding sites, only 125 in total vs. 256 (or 131), but this doesn't harm the algorithms involved in any way (and actually helps them.) It would be possible to modify my javascript objects (if you're interested), since I set a constant to control the beginning of the evaluated region. It shouldn't make a difference in the general results, but the option is there if you care to use it. Thanks for the find. AtomAtom
January 5, 2009
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R0b, I have not run Schneider's version, but have the paper with me. Here is a screenshot from the relevant section, with the line you pointed out highlighted: Screenshot The graphic and explanation clearly shows the first 125 positions being used for the weight matrix and threshold. I think the reference to "256" nucleotides is, in context, compared to the 266 nucleotides total. (The next line seems to confirm this.) Also, 256 would exclude any nucleotides after the last ev binding site, which occurs at position 256. This also lends weight to my interpretation. Also, it would seem odd to run the weight matrix nucleotides through the algorithm to see if any binding sites exist in them (which they never will) since the method outlined in the text of the paper seems to indicate that the second portion represent the binding area. (From the Ev paper: "The organism has two parts, a weight matrix gene and a binding site region." emphasis mine) However, I have not run Schneider's version, so if I am in error on this point, I apologize. Again, if you are correct, I actually help the Ev case by providing a smaller target. So while the criticism may be valid (though I think it isn't, for the reasons outline above), it would actually help Schneider's case, and so is irrelevant for polemical purposes.Atom
January 5, 2009
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Hi again, Atom. Schneider's paper states: "Evaluations to determine mistakes are for the first 256 positions on the genome." So in Schneider's version of ev, the number of mistakes ranges from 0 to 256, while in the EvoInfo Lab's version it ranges from 0 to 131. Any binding site in the first 125 positions would constitute a mistake for Schneider's ev. That Schneider's ev allows for more than 131 mistakes is obvious if you you run the java GUI version for a few iterations with selection turned off. I hope that clears it up. Yes, a mismatched oracle and search algorithm would constitute negative active information. My point is that it doesn't make sense to attribute this active information to either the oracle or the search algorithm individually, since it's the mismatch that's the problem. Likewise for positive active information. I realize that the match between the perceptron logic and the small number of 1's in the oracle's target helps the search immensely given either Schneider's evolutionary algorithm or random sampling. But I could come up with a search algorithm for which the perceptron/oracle match does more harm than good. That's the larger issue I referred to above. And we're still left with the smaller issue, which is that Dembski & Marks's original logic for attributing negative active information to the evolutionary algorithm attributes a boatload of positive active information to it once the MATLAB script bugs are fixed. As far as the accompanying paper referenced several times in the info, I've tried many times in the past to get it and it's never been available from the website. I would certainly welcome an explanation of the connection between Shannon info and active info, as the concepts appear to be conflated in the EvoInfo Lab's objections to Schneider's statements. It seems to me that if you cast ev as a classical information system, complete with a message, a noisy channel, and a receiver, all of Schneider's statements about information WRT ev make sense. He's certainly not talking about active information or CSI.R0b
January 5, 2009
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Addendum: Forgot to specify, Schneider's version evaluates positions 126-261, while the last 5 positions are used for the sliding window. My position "1" on the GUI corresponds to position 132 of the 256 nucleotides.Atom
January 5, 2009
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Hey R0b, You wrote:
The difference I noticed between your version of ev, Marks and Dembski’s version, and Schneider’s version is the number of potential binding sites. Yours seems to have 125, while Marks and Dembski’s has 131 and Schneider’s has 256
In Schneider's original paper, he explains that the first 125 nucleotides of the 256 encode the weight matrix and threshold value (as they do in Mark's version and mine), while the rest represent the possible binding site locations. The last positions (256-266) are not evaluated in any version of the algorithm (Schneider's, Marks' or mine), and are just used for allowing the sliding window to operate correctly. Mine rounds the binding site locations to 125, since it makes for a nice grid, and would actually help the Ev simulation succeed, rather than hinder it (since there are less sites to match/mismatch.) You point about the output from the Hamming Oracle possibly being misused to hinder a search actually bolster's Marks'/Dembski's point: you would be applying negative Active Information. As for the cross section of Shannon Information and Active Information, that is covered in the papers co-authored by Marks and Dembski that introduces the metric. They link to the paper from the introduction, but it looks like they removed the link temporarily. Perhaps you can ask one of them for a copy if interested. AtomAtom
January 5, 2009
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Hi Atom. The difference I noticed between your version of ev, Marks and Dembski's version, and Schneider's version is the number of potential binding sites. Yours seems to have 125, while Marks and Dembski's has 131 and Schneider's has 256. This discrepancy doesn't affect the point that the EvoInfo Lab is making with regards to ev, but it does affect the reported numbers. My objection to the claim that ev's efficiency "is not due to the evolutionary program" is this: Marks said in a presentation, with regards to ev, "Some recently proposed evolutionary models are shown, surprisingly, to offer negative added information to the design process and therefore perform worse than random sampling." But it turns out that the evolutionary algorithm actually performs many, many orders of magnitude better than random sampling, so why do we not now conclude that it offers positive active information? But the larger issue is this: Since search efficiency depends on the components being well matched, why do some components get credit and others not? Not even the Hamming oracle is good or bad in and of itself. If the search algorithm interprets the oracle's output as Hamming closeness rather than distance, the search will move directly away from the target, and we'd be better off with a yes/no fitness function. Another general problem is the fact that Schneider's statements, and his entire analysis, are in strictly classical information terms, but the EvoInfo Lab has responded in terms of active information without explaining how the two frameworks are connected. Just because Schneider, the EvoInfo Lab, and Brillouin all use the word "information" doesn't mean that they're talking about the same thing. On a positive note, it is certainly true that the target sequence is well-matched with the perceptron. That's a good find on the part of the EvoInfo Lab.R0b
January 5, 2009
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Mark Dembski's metric of CSI is a mathematical model of great generality and technical depth, more for his technical peers than for anyone else. Summing it up, I have excerpted as follows, From pp 17 - 24 or so, and using X for chi and f for phi:
define fS as . . . the number of patterns for which [agent] S’s semiotic description of them is at least as simple as S’s semiotic description of [a pattern or target zone] T. [26] . . . . where M is the number of semiotic agents [S] that within a context of inquiry might also be witnessing events and N is the number of opportunities for such events to happen . . . . [where also] computer scientist Seth Lloyd has shown that 10^120 constitutes the maximal number of bit [issue or state resolvable to yes/no, hi/lo etc] operations that the known, observable universe could have performed throughout its entire multi-billion year history.[31] . . . [Then] for any context of inquiry in which S might be endeavoring to determine whether an event that conforms to a pattern T happened by chance, M·N will be bounded above by 10^120. We thus define the specified complexity of T given [chance hypothesis] H [in bits] . . . as X = –log2[10^120 ·fS(T)·P(T|H)].
A bit of commentary:
1 --> As you know information is an inverse probability metric, so to get it in bits we do a negative log to base 2 metric; 2 --> This, working with a modified conditional probability of being in the relevant target zone, on a relevant chance hyp [often a quasi-flat distribution across microstates as is common in stat thermodynamics and as reflects the Laplace indifference criterion.] 3 --> We then in essence multiply the probability by an upper bound that measures the available search resources of the cosmos, and some other adjusting factors. [The idea being that if one has a low probability 1/n, n tries will more or less make bring the odds of seeing the event at least once to an appreciable fraction of 1.] 4 --> fS(T) is a multiple of the base probability that brings out "sufficiently similar" patterns in the target zone, i.e. we are interested in seeing any of a cluster of materially similar outcomes. 5 --> As his technical bottomline: >> . . . if 10^120·fS(T)·P(T|H) L/T. 1/2 or, equivalently, that if X = –log2[10^120·fS(T)·P(T|H)] > 1, then it is less likely than not on the scale of the whole universe, with all replicational and specificational resources factored in, that E should have occurred according to the chance hypothesis H. Consequently, we should think that E occurred by some process other than one characterized by H.>> 6 --> In short if X >> 1, it is highly unlikely that a given entity or event has occurred by chance. And since highly contingent aspects of events -- the focus of the discussion -- are the opposite tot he pattern of regularity we see in cases of lawlike necessity, this leaves intelligent agency as the best explanation.
Now, as you know, I start from a different place, the status of OOL research circa mid 1980's by which time CSI had been recognised conceptually and FSCI had been identified as the key characteristic of life, a characteristic that is well known from intelligently designed systems. Accordingly, I have long intuitively used and have now explicitly identified a simpler and more readily usable metric, functionally specific bits. And once the number of FS Bits approaches 500, design is a probable explanation. As it approaches 1,000, that would allow for islands of functionality that equal the number of quantum states in our universe across its lifespan, and still have them so isolated that they are at 1 in 10^150 of possible configs. So, once we pass the upper limit of the band, it is morally certain -- note my use of a term of epistemic responsibility, not proof beyond all doubt -- that an event is not a matter of chance or necessity but design. (Of course the biologically relevant cases, such as the DNA of the simplest life forms, start at 100's to 1,000 times that upper limit.) A similar metric could be done for K-compressibility specified entities, which would get us to a simple metric for CSI. Now, you wish to work with a hand of 13 cards from 52 in a standard deck that "by chance" or "by design" would have all 13 spades in it:
A deck of 52 cards will have 52C13 [~ 635 * 10^9, I believe, if my freebie software calculator is right] possible 13-card hands, and of these precisely one will be 12 spades, and having 13 hearts, Clubs or diamonds would be materially similar. P(T|H) would I believe be 1/52C13, and fS(T) would be 4 [or a similar number]. X = - log2[10^120 * 4 * 1/52C13] ~ (- 361), i.e. much lower than 1
That is -- as can be inferred from the high value of the odds of being in the target zone [~ 1 in 10^12] relative to the UPB -- the Dembski X-metric rules "not sufficiently likely to be designed to be ruled a clear case of design." I trust that helps. G'day GEM of TKIkairosfocus
January 5, 2009
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Sorry posted that last comment on the wrong blog!Mark Frank
January 5, 2009
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Gpuccio First thanks for participating, without you it would be a very one-sided discussion. My prime interest was in checking my understanding of Dembski’s paper and also how many ID proponents understand it. As you have admitted you don’t fully understand it, I guess the answer so far is zero :-) I will leave comments on your own method of calculating CSI until after I have walked the dog...Mark Frank
January 5, 2009
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Re #47 gpuccio I would be happy to discuss the calculation of CSI on any blog but I only have the ability to make new posts on my own. That's why I am using it. Anyhow thanks for your contributions over there. It is a bit disappointing that no one is able to give a worked example of CSI as defined in "Specification: The Pattern That Signifies Intelligence". I thought it would be possible at least for a bridge hand of 13 spades. However, I note that you are not entirely happy with the definition of CSI in that paper so I can't blame you!Mark Frank
January 4, 2009
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GP (and Mark): I took a look at the thread over at MF's blog on calculating CSI, on seeing your post just above. GP, you have with great patience composed a more than adequate response, and then undertook onward interactions with the set of commenters there; and in far more than a mere two posts! I applaud your effort, and agree with its overall substance, and even moreso with your ever so gracious tone. Having already spent some time at CO, I will simply note here on a few points:
1 --> For those who wish to look, I have updated my online note to highlight metrics of FSCI, first the intuitively obvious functionally specified bit, and also have given an excerpt on Dr Dembski's more sophisticated 2005 model. 2 --> Venus, in the old English Common Law, murder was defined as maliciously causing the death of an innocent within "a year and a day." The line has to be drawn somewhere, and you will note that in the case of interest, (1) the EF is designed to be biased towards false negatives, (2) I in fact use a range from 500 - 1,000 bits to take in reasonable cases of islands of functionality in the wider config space, and (3) the biologically relevant cases are far, far beyond the upper end of the threshold band. 3 --> I also note that I responded to a stated bit string of unspecified functionality of 60 bits length. It is not a natural occurrence observed "in the wild", and alphanumerical characters are contingent. It could be the result of a program that forces each bit, or it could be the result of a [pseudo-]random process. I simply pointed out that as given there is not enough length to be relevant to the FSCI criterion. 4 --> Subsequently, it was stated that this is the product of a definitive process in mathematics, by reference to an example of a pseudorandom string based on an algorithm, by Dr Dembski. In short, per the report taken as credible, it is the result of an algorithm, which is of course in all directly known cases, designed. And indeed, once we bring in the workings of the algorithm to generate the bit string, we immediately will observe that the complexity jumps up hugely; credibly well beyond the threshold, once we take in the statement of the algorithm, the compiling of it, and the physical execution required to implement the stated output.
I trust that these notes will be helpful. GEM of TKIkairosfocus
January 4, 2009
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Mark (and those interested): I have posted a couple of answers on your blog, but just excuse me if I will not necessarily follow up a long discussion there. The main reason (but not the only one) is that my (I hope intelligent) resources to design posts are limited, and I do believe that the important discussions are here. But I have great respect for you and for your blog. And anyway, if I really believe that I have something useful to say there, I will try to do that.gpuccio
January 4, 2009
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Mark: For the record here, I note that I have commented at your blog thread as follows; with onward link to the thread in which your commenter Oleg raised the question of a 60-element bit string as a candidate for CSI status. _______________ Mark: For the record, I observe here that in your previous thread, I used a far simpler but nonetheless effective metric to address Oleg's case. His case was 60 bits long so even if functional and specific in that functionality, not CSI as below the relevant range that on the crude metric begins at 500 - 1,000 functional and specific bits. For the further record, I have contrasted the case of biofunctional DNA, pointing out that even with parasitic lifeforms that start at about 100,000 4-stage elements, we are well within the CSI territory, with 200 k+ functionally specific bits. In short, CSI is not only measurable but can be measured in simple as well as sophisticated ways. The material issue is settled at the outset already, then. And, that is all I need to do here. G'day. GEM of TKI _____________ Onlookers may wish to look at point 5 in 42 above for the simple but I believe useful enough metric I used, and the other points here give its context. I trust that his will help clarify the record there and here. GEM of TKIkairosfocus
January 4, 2009
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Slightly off topic - but related. I would like to invite anyone to do submit simple examples for themselves or others to calculate the CSI. It seems to me this should be informative. I have set up a blog post to do this and submitted a very straightforward example of my own - a hand of 13 spades. Oleg already has one example slightly more complicated example which inspired me to do this. No trick is intended. I just want to see when and how it can be done.Mark Frank
January 3, 2009
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R0b, Thanks. As for the discrepancy's with Marks/Dembski's Matlab version, there were some design decisions that were made and differences in my implementation. You will notice some differences in results, but nothing materially relevant (in my opinion.) If you find a significant difference between my javascript implementation and Schneider's version (other than memory issues - a web browser isn't a C++ box, so I had to limit the GUI to keep browsers from locking up), then please let me know. You wrote:
I can’t reconcile these statements with the following fact: If you replace Schneider’s evolutionary algorithm with random sampling, the performance degradation renders the search unmanageable.
Replacing Hamming Search or any other search with random sampling will also make the search intractable. You can replace the Ev strategy with either a Hamming Search or a Ratchet search, and you will still eventually converge on the target. (Hamming will converge quickly always, while Ratchet usually works, but not always.) So the "Ev Strategy" (Schneider's evolutionary search strategy - mutation and selection) isn't the main source of the information. The selection step takes advantage of the information presented to the search by the Hamming Oracle. The Hamming Oracle has access to information about the target space and passes this along to the searches that use it. The second source of Active Information is the skewing of "randomness" towards phenotypes with few ones and many zeros. As you saw, this can allow you to converge on a target using random input, which shouldn't happen if all phenotypes were equally likely. Also, changing the target to random sites will make the Ev search take longer, demonstrating the application of negative Active Information. (the assumption about the target space, many zeros and few ones, is false on average.) AtomAtom
January 2, 2009
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A few points: - Props to Atom for his mad javascripting skillz. Very well done. - I agree that Schneider tends to overstate the implications of his results. In particular, I don't see how his results refute Behe's ideas. Like Dawkins, Schneider gets quite excited about his own code. But I think all coders go through that stage, and some of us never get past it. - If you choose all zeroes and random input, Atom's code takes around 370 queries (averaged from 10,000 trials), while Marks and Dembski's code takes around 440. I can explain one reason for this discrepancy if anyone's interested. - I'm baffled by the following statements: "The ability of ev to find its target in much less that twelve and a half quintillion years is not due the evolutionary program" "the ability of ev to find its target is not due to the evolutionary algorithm used, but is rather due to the active information residing in the digital organism." "It is the active information introduced by the computer programmer and not the evolutionary program that reduced the difficulty of the problem to a manageable level." "The active information from the Hamming oracle and ev's number cruncher are responsible for the rapid convergence - not the evolutionary program." I can't reconcile these statements with the following fact: If you replace Schneider's evolutionary algorithm with random sampling, the performance degradation renders the search unmanageable. Which brings up a question. The above fact indicates a significant amount of active information, but in which component(s) of the search does it reside? The evolutionary algorithm, the number cruncher, or the Hamming oracle? There are even more fundamental issues in play with regards to the EvoInfo Lab's understanding of Schneider's paper, and with the active information concept in general, but I don't want to wear out my welcome.R0b
January 2, 2009
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Mark (and GP et al): A few remarks: 1] [Shannon] Info is . . . tied to probability First, let us note that the basic Shannon-style metric of information is a probabilistic metric, here excerpting my note, Section A at a point where I build on Connor's classic remarks:
. . . let us consider a source that emits symbols from a vocabulary: s1,s2, s3, . . . sn, with probabilities p1, p2, p3, . . . pn. That is, in a "typical" long string of symbols, of size M [say this web page], the average number that are some sj, J, will be such that the ratio J/M --> pj, and in the limit attains equality. We term pj the a priori -- before the fact -- probability of symbol sj. Then, when a receiver detects sj, the question arises as to whether this was sent. [That is, the mixing in of noise means that received messages are prone to misidentification.] If on average, sj will be detected correctly a fraction, dj of the time, the a posteriori -- after the fact -- probability of sj is by a similar calculation, dj. So, we now define the information content of symbol sj as, in effect how much it surprises us on average when it shows up in our receiver: I = log [dj/pj], in bits [if the log is base 2, log2] . . . Eqn 1 This immediately means that the question of receiving information arises AFTER an apparent symbol sj has been detected and decoded. That is, the issue of information inherently implies an inference to having received an intentional signal in the face of the possibility that noise could be present. Second, logs are used in the definition of I, as they give an additive property: for, the amount of information in independent signals, si + sj, using the above definition, is such that: I total = Ii + Ij . . . Eqn 2
In short, a metric based on probabilities is inherent to the generally used concept of information in info theory. And, the issue of distinguishing signal from noise, or message from lucky noise's mimic, is an issue of inference to design. One that is probabilistically based. Coming out the starting gate. 2] Meaningful information: In a letter that should be better known, March 19, 1953, Crick wrote to his son as follows:
"Now we believe that the DNA is a code. That is, the order of bases (the letters) makes one gene different from another gene (just as one page of print is different from another) . . . "
In short, it has long been recognised that DNA stores functional information. (Which is what messaged are about in the end: they do a job when received by a suitable receiver and "sink.") 3] Sequence complexity: Trevors and Abel in this 2005 paper made a distinction between random, ordered and functional sequence complexity, based on a 3-d metric space:
OSC: high algorithmic compressibility, low algorithmic functionality, low complexity RSC: highest complexity, low algorithmic compressibility and functionality. FSC: Fairly high complexity, a bit higher compressibility than RSC, high algorithmic functionality.[cf Fig 4.]
You will see that compressibility and complexity carry an inverse relationship, and that high order sequences are too rigidly defined to carry much information [they are a repeating block]. Random sequences resist comprtession, as they essentially have to be listed to describe them. Functional sequences of course will have some redundancy in them [for error detection and correction i.e. resistance to corruption], but have low periodicity otehrwise. They of course are constructed to fuction and so have high fucntionality. This brings us back to the flooded fitness landscape issue. 4] Flooded fitness landscapes and local vs broadcasting oracles vs maps and helicopters: Atom, first thanks for sharing the EIL's work with us. I want to play a thot exercise using the landscape analogy. When a fitness landscape has a threshold of functionality, it means that unless you are at least at the shores of an islands of function, you cannot access differential success as a clue to climb towards optimal function. For a sufficiently complex space [the ones relevant to FSCI and CSI), we know that the search resources of the cosmos are vastly inadequate for reasonable chances of success at reaching islands of function from arbitrary initial points. Many GA's -- apart from dealing with comparatively speaking toy-scale complexity -- allow in effect broadcasting oracles so that non functional states that are close enough can get "pointed right" towards function. Worse, non-functional states are rewarded and allowed to continue varying until they become functional. (This might work for micro-evo changes but we need to deal with macro-evo changes, and with getting to first complex function.) Once one then reaches to function, voila, one can hill-climb to optimal function, on the assumption that we have nice ascents -- if there are overly high and steep cliffs, that is another problem. But now, what if you have a map, even a less than perfect one [or an imaginary map . . .] that will often get you into the near neighbourhood of archipelagos of function? That may well allow short range explorations that get you on an island and allow island hopping. So, we see how imaginative creative ideas and suitably restricted trial and error may work as tools of design. Indeed, if general location A does not work after reasonable tests and trials, one may proceed to suspect hot zone B and so on. (Cf here mineral exploration.) In short, design can use constrained random search inthe context of a hot zone. 5] Is CSI observable and/or measurable? I have discussed a simple approach: consider a [pseudo-]vector {L, S, C} --> L: degree of contingency, Low being 0 and high being 1. [We observe such routinely when we identify natural regularities and seek explanatory laws that cover such patterns. Similarly, we see high contingency when we see that under reasonably cisimilar circumstances outcomes scatter sufficiently to give us pause. Cf dice and how people behave for undirected and dirtected contingency) --> S: specificity, especially functional specificity, high being 1 and low 0. (This we observe every time we contrast the words just past with say fhuwyuwg7824rqw3jfgyqw3tg.) --> C: complexity, here being indicated by equivalent storage capacity in bits, with 500 - 1,000 bits being the threshold for sufficient complexity. (E.g. cf DNA.) Next, multiply: L*S*C, and take the result. We then identify for this simple case CSI as the zone in which the product exceeds the range 500 - 1,000 bits, i.e we have a crude metric for FSC in the T-A sense. [If we fail at either of the first two stages, we don't get above 0.) Of course Dr WmAD has done a far more sophisticated form, but his form is conceptually similar enough that we can see what is going on. But, clearly we have an approach that can achieve significant intersubjective consensus, and is premised on items that are in fact objectively and routinely identifiable. It is also a discrete state metric that gives an interval scale with a pass-fail threshold. We use such scales routinely to determine who accesses educational levels, and promotions or even firings. So, to reject or raise a stonewall of objections to the approach in this case is plainly selectivley hyperspkeptical. Indeed, just by reading and taking seriously posts in this thread, we have intuitively used such an approach in deciding that posts are real messages, not mere lucky noise mimics. __________ I trust these points help us set the discussion on a more balanced footing. GEM of TKIkairosfocus
January 2, 2009
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Mark: You say: "The use of the word “information” in the phrase CSI is a different use from the usual English language use. CSI is just the probability of an observed outcome being of a certain type given some assumptions about the outcome was generated. The word “information” gives CSI a certain grativas and links it with the idea that the outcome might be have been deliberately arranged or “designed”." As it often happens, I partially agree with you: in a sense, that is exactly the important point of ID. CSI is an "objective" definition of an objective property of an outcome, which is objectively recognizable and measurable. So, you are right: CSI is not "in itself" the meaning, the useful information. It is an objective property which, according to ID theory, allows us to "infer" correctly that that output is designed, and therefore it represents a meaning, ot, as Seversky says, a form of "semantic information embodied in messages passed between intelligent agents such as ourselves". The aspect of CSI which more directly "corresponds" to the common concept of "meaning" (without being completely equal to it) is specification. The key aspect is that design is "inferred" form the existence of CSI. CSI is not design. CSI is an objective formal property that we in ID believe, on the basis of empirical observations and of theoretical considerations, to be invariably associated with the process of design. So, we could say that CSI (once correctly defined, observed and measured) is a fact (an observable), or if you prefer an observable property of facts, while the design inference is a theory (just to remain epistemologically correct). Obviously, some people may not agree, as we well know, that CSI is objectively observable and measurable, but that is another aspect. In other words, let's say that "if" (as I believe) it is true that CSI is a property which can be objectively observed and measured, than it is an observable, while the design inference is anyway a scientific theory, an inference. So, your point is good, but it is not the word "information" which "gives CSI a certain grativas and links it with the idea that the outcome might be have been deliberately arranged or “designed”" (at least, not in ID): in ID, it is a whole scientific theory which accomplishes that.gpuccio
January 2, 2009
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Seversky #37 Nice comment. The problem is that it seems to be used to mean different things in different contexts. Of course! The informal word "information" has a range of informal meanings and has had several different formal interpretations. It seems to me that information is not so much a property of the message as it is a description of the relationship between the message and the recipient or, more precisely, the change the message causes in the state of the recipient. I absolutely agree. The use of the word "information" in the phrase CSI is a different use from the usual English language use. CSI is just the probability of an observed outcome being of a certain type given some assumptions about the outcome was generated. The word "information" gives CSI a certain grativas and links it with the idea that the outcome might be have been deliberately arranged or "designed".Mark Frank
January 1, 2009
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Seversky, I think you are right that information gets used in different ways. But we tend to use it in common language as data that mean something. And that is how I believe most use it here. So information is just a piece of data and each nucleotide is a data point. Search the internet for definitions of the word. For example, go here http://www.onelook.com/?w=information&ls=a Use for information such things as news; intelligence; words facts; data; learning; lore Each nucleotide is a piece of information just as each molecule in a rock is a piece of information. Both DNA and a rock are complex so each is an example of complex information. However, some units of the information in the DNA specify something else, for example a gene specifies a protein and sometimes RNA. And some of these proteins and RNA have functions and some of these proteins and RNA work together as functional units or systems. So the information in the DNA is complex, specified and and the elements specified are functional. Life is the only place this appears in nature. It appears quite frequently with human intelligence. Now it is quite possible that not all the DNA in a genome is specified and functional and may be just junk but a large part is not. Sometimes people invoke Shannon's concepts, which I do not understand, to get at the unlikelihood of a particular combination that has functionality. The unlikelihood of a particular combination of data that specifies a function is usually extremely low. However, I cannot pronounce on the appropriateness of Shannon's concepts to do so but common sense indicates the near zero probabilities of most of the combinations and that is what is at issue. I cannot see how DNA is not information under the every day meaning of the term and I assume it is information also on some of the esoteric uses of the term.jerry
January 1, 2009
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Seversky: I think we should be clear about two very different emanings of the word "information" in scientific discourse. One is Shannon's entropy H. Although that value is often referred to the concept of information, it is just a measure of uncertainty. Indeed, Shannon's theory has nothing to do with meaning. So, you must be aware that a random sequence, with no meaning, has the highest value of H. In other words, it is not compressible, it is not redundant, and if you have to communicate it you need the highest number of bits. But it may well mean nothing. When you say: "What we commonly think of as information seems to be what is called semantic information embodied in messages passed between intelligent agents such as ourselves." You are insteda obviously referring to what is usually called "meaning". The concept which tries to explicitly and objectively formalize "meaning" is the concept of specification, for instance as it is given in Dembski. In other words, any complex sequence which can be "recognized" as special by intelligent agents is "specified". Specification comes in different flavors. As I have already said, the most useful form of specification is functional specification, but there are other forms: for instance, even a ramdom sequence may become specified if it is given in advance before it is found in the supposed random event (pre-specification). But let's go back to functional specification: it is a sequence which can do something specific in a specific context. Human language (these posts) is a form of functional specification. And so a computer program, or the project for a machine. You say: "The problem is that it seems to be used to mean different things in different contexts. It can mean the thoughts and opinions being shared through a blog like this, it can mean what is represented by electrons being shuffled around the circuitry of a computer or it can mean the shapes and arrangements of molecules in the gelatinous blob of a tiny living cell." I don't agree that they are different things. You seem to confound the software with the hardware, the specified information with the hardwrae where it is implemented. In the sense of specified information, of "software", our opinions, the arrangement of electrons in a computer, and the arrangement of molecules in the cell are one and the same thing: software, specified information. They are, obviously, differnet softwares. And they are "written" in different hardwares. But information is essentially am immaterial concept, and is independent from the hardware, as we well know. In other words, you can describe the arrangement of the molecules in a cell through a computer code, or in one of these posts, through human language, but you are still describimg the same information. Finally, I have to really disagree with Wilkins: DNA contains specified information, and a lot of it. That's beyond any boubt. I have read what Wilkins says, but I disagree completely. If you just use the very simple definitions of information above, and not the abstruse and innatural self-definitions given by Wilkins, you will see that there may be no doubt that DNA stores a lot of information, exactly in the same sense that a hard disk stores a lot of functional programs: DNA is a mass memory, and it contains "at least" all the information for the synthesis of the proteins in an organism.gpuccio
January 1, 2009
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My thanks for the previous comments but I find I am still grappling with the concept of information. The problem is that it seems to be used to mean different things in different contexts. It can mean the thoughts and opinions being shared through a blog like this, it can mean what is represented by electrons being shuffled around the circuitry of a computer or it can mean the shapes and arrangements of molecules in the gelatinous blob of a tiny living cell. Are they, in fact, different things or do they have something underlying in common which qualifies as information? What we commonly think of as information seems to be what is called semantic information embodied in messages passed between intelligent agents such as ourselves. That involves intention and the capacity to extract meaning from messages which can also be distinguished from background 'noise'. I found this passage from an article called "The Information Challenge" by Richard Dawkins which was helpful:
Redundancy is any part of a message that is not informative, either because the recipient already knows it (is not surprised by it) or because it duplicates other parts of the message. In the sentence "Rover is a poodle dog", the word "dog" is redundant because "poodle" already tells us that Rover is a dog. An economical telegram would omit it, thereby increasing the informative proportion of the message. "Arr JFK Fri pm pls mt BA Cncrd flt" carries the same information as the much longer, but more redundant, "I'll be arriving at John F Kennedy airport on Friday evening; please meet the British Airways Concorde flight". Obviously the brief, telegraphic message is cheaper to send (although the recipient may have to work harder to decipher it - redundancy has its virtues if we forget economics). Shannon wanted to find a mathematical way to capture the idea that any message could be broken into the information (which is worth paying for), the redundancy (which can, with economic advantage, be deleted from the message because, in effect, it can be reconstructed by the recipient) and the noise (which is just random rubbish).
I understand from the illustration about the Concorde flight how a message can be stripped down to its bare essentials in terms of information and that Shannon expressed this in a mathematical form in which the meaning was irrelevant but what, exactly, is information? The question I asked myself is this, the message about the Concorde flight would have told the recipient something they didn't know before, namely, when and where the traveller's flight was due to arrive. But suppose the sender was uncertain whether the message had been received so sent it again just to be safe, would it still contain information? Suppose the recipient had read the first message, they would no longer be surprised or informed by the second message, yet it was exactly the same as the first, so what is the information it contained? It seems to me that information is not so much a property of the message as it is a description of the relationship between the message and the recipient or, more precisely, the change the message causes in the state of the recipient. In a sense, it's a process rather than an attribute. In the case of the Concorde flight message, the first one changed the state of the recipient by adding new knowledge, the second did nothing because the knowledge was already there. The other problem is that I can see how it would be possible to contruct a broad definition of 'information' that would encompass both semantic information and what happens in a computer or a living cell. On that basis, for example, you could show how an organism acquires new 'information' from the environment as Paul Davies has argued. But I also found a piece on a blog by philosopher John Wilkins which argues that it is misleading to think of DNA and what happens at the genetic level as information at all:
A recent New Scientist article poses the often-posed question in the title. The answer is mine. Forgive me as I rant and rave on a bugbear topic... OK, I know that we live in the "information age" and far be it from me to denigrate the work of Shannon, Turing and von Neumann, but it's gotten out of hand. Information has become the new magical substance of the age, the philosopher's stone. And, well, it just isn't. In the article linked, physicist William Bialek at Princeton University argues that there is a minimum amount of information that organisms need to store in order to be alive."How well we do in life depends on our actions matching the external conditions," he says. "But actions come from 'inside', so they must be based on some internal variables." This is a massive fallacy. Really, really massive. Consider what it relies upon apart from superficial authority and technobabble: it means that organisms must be computers, that they must store data in variables, and that nothing can occur unless it is based on an internal program. For gods' sakes, hasn't Bialek heard of causality? You know, physical properties that cause states of affairs? Or is he going to go the John Wheeler route and claim that everything is information (in which case, why care about the information of living systems)? Calling everything information is massive projection, or even anthropomorphism. It takes something that exists as a semantic or cognitive property and projects it out to all that exists. It makes observers the sole reality. In biology, the concept of information has been abused in just this way, but it's a peculiarly twentieth century phenomenon. And that's not coincidental - in 1948, Shannon and Weiner both presented radical and influential conceptions of information - one based on communication [1], and the other on control [2]. Previously, in the 1930s, Alan Turing had developed the notion of a computer, and in 1950 [3] he started the ongoing interest in computation as a form of cognition. So, three senses of "information" got conflated in popular (and technical) imagination, and shortly afterwards, the term was applied to genes, but (and this is often forgotten) just in terms of causal specificity - genes "coded for" proteins by a physical process of templating. But people have gotten all enthusiastic for "information" (bearing in mind the etymology of enthusiast as "in-godded one"), and as a result lots of rather silly claims has been made - not all by physicists by any means - about information in biology. We need to specify carefully what counts as information and what doesn't. I personally think that DNA is not information - allow me to explain why.
http://scienceblogs.com/evolvingthoughts/2008/01/is_information_essential_for_l.php I'm not sure where I stand on this and he admits it is a minority view but it is provocative.Seversky
January 1, 2009
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Maybe an useful mutation
If as I assume you've used the IUPAC code to write the sequence there is a non-defined character in the sequence. (IIRC correctly I've answered before but the comment didn't show up here. Maybe I forgot to hit the submit button)sparc
December 31, 2008
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sparc: Maybe an useful mutation? :-)gpuccio
December 30, 2008
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gpuccio, there's a mistake in the sequence you've presented.sparc
December 30, 2008
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Going from the simple to the complex is just the way nature works-Sal Gal
Evidence please. There is no reason to impute design to a natural process that goes from simplicity to complexity. Mere complexity does not get one a design inference. And do you have evidence for such a process?Joseph
December 30, 2008
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Patrick The problem is that nature is usually too generalized and cannot efficiently optimize the search within reasonable time constraints. Haldane's DilemmaDaveScot
December 30, 2008
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