Unfortunately I’ve not found this point. Could you please restate roughly what the argument is?

Unfortunately, the deleted comment is not in google cache either… The impression I got was that S. believed that by its very nature that algorithms that are carried out by naturally occurring processes should perform better than software-based programs. I find this assertion odd since to my mind the constraints of nature either are too wide or too narrow and not at all balanced like a well-designed GA. It should be a rather rare event that an environment provides a balance and the variation provides functionally positive mutations. So on balance I would expect nature to perform worse than even a poorly designed GA. I was hoping to ask for his justification for his assertion.

First, any thoughts on using pi-250 as a usefgul model complete with hill-climbing?

A possible criticism could point on the choice of the precision according to which the first hit could yield the start of hillclimbing. Somebody could say: ok we cannot add to the algorithm a formula for Pi but here we are in the mathematical world and we aren’t constrained by measure precision (as it’s the case in real world example; so, we have a fittness function that does tell us how much the hit is “good”. Why don’t start from a whichever point and use hillclimbing without any constraint on the precision? So the example is OK if we state explicitly that that precision is due to the use of “rel world” fittness functions, for example the direct measure on a circle.
With this additio it’s an interesting example that is somewhat similar to what I meant (for real world examples). I would suggest that the example could be made less bound to our specific mathematical notation by using directly the binary representation of Pi instead of the BCD one. this would mean to search in a solution space with S={0,1}. It’s also a good example because , as you have already stated, computation of Pi can be expressed in a very short way (i.e. with very high specificity), for example by providing the code for computing the Gregory-Leibniz series: Pi=4*(1 – 1/3 + 1/5 – 1/7 + …).

GEM are my initials, the … TKI is the short form of my consultancy personality and organisation — I am involved in a loose regional network. The Kairos Initiative.

I beg your pardon; I didn’t understood.

First, any thoughts on using pi-250 as a usefgul model complete with hill-climbing?

[BTW, the fact that the coin can go to binary codes that BCD does not use both brings in points that are very binarily close to functional points that are not, and brings in a non-uniformity on the bit patterns, i.e not all of the set from 0000 to 1111 is used. That means that the 1's and 0's will not express the same amount of information!]

Thanks

GEM of TKI

PS: Kairos, GEM are my initials, the meaning of which is easily enough accessed through the always linked (and links to you above that Semiotic unfortunately decided to abuse); indeed, in hand-drawn stylised form it is a form of my initials-style signature. TKI is the short form of my consultancy personality and organisation — I am involved in a loose regional network. The Kairos Initiative.

Now Semiotic made an “interesting” claim that no one jumped on (unfortunately, it appears it was deleted since it was part of an offending comment). He briefly mentioned how the (presumably) software-based programs that generate information would exceed the UPB (duh) then he claimed that an algorithm furthered by natural processes should be expected to perform better (or something to that effect).

Unfortunately I’ve not found this point. Could you please restate roughly what the argument is?
I know that in the past some critics did claim that code generation (having in mind gene duplication obviously) would be an easy way to increase CSI. Was this S. argument? In this case this would simply show a very typical misunderstanding of what CSI concept really means.

PS for Kairosfocus. Please excume my ignorance, but what does GEM of TKI stand for?

I too am sorry to see the conversation end as it did. I wish it had not — and despite having had to complain of tort, and before that having had to point out through the hostile witness, Wiki, that there was more to the story than we were being given by the ones tightly focussed on whether NFLT strictly holds in relevant real-world contexts.

To put a similar case, at a very crude level, pi is not strictly speaking equal to 22/7, but that is often “good enough for government work.”

Similarly, NFLT probably does not hold strictly in the sort of situation we were facing, but it is probably true that no “blind” algorithm will do significantly better than an arbitrary “pick a config at random” in finding the FUNCTIONALLY SPECIFIED DNA configs of life [much less the other components and organisation of a cell], starting from any plausible or generous pre-biotic soup. That is why I said in 184 above that PaV put his finger on the money in his comment in 175:

If we mentally try to visualize what’s going on, we can look down on a sea of two-dimensional space. At each location, that is, each point[I would say cell -this is a discrete space!] , of this two-dimensional space we find a permutation of a 3,000,000,000 long genome. As we look down onto this 2D space, these 100 trillion “high fitness” genomes, along with each of their trillion “high fitness” permutations, are randomly dispersed on this plane. What we’re going to do is to “pull together” all of these trillion of “high fitness” permutations to form a cluster. (After all, they’re ‘independent’ of one another) We end up with 100 million clusters, consisting of one trillion permutations. We could have, admittedly, “clustered” all 10^25 (100 trillion x one trillion) together. But, if we were to do a blind search for just that one cluster, it would be much harder to find than having 100 trillion “clusters” (of a trillion permutations) throughout the space of all possible genomes.

Now in this configuration of genome space we have “clustering”; in fact, we have it to a staggering degree: viz., one trillion viable permutations per genome. So, [per model just proposed] if the human genome were to experience a mutation anywhere along its length, the likelihood of it not being viable would be 1 in a trillion.

So, again, we have the space of all possible genomes within which are to be found, randomly (again, giving the best possibility of being found by search), 100 trillion “clusters” of a trillion permutations. Once we’ve pulled all these permutations together and formed 100 trillion “clusters” of a trillion permutations each, then the space, G, of all possible genomes is smaller by roughly 10^25 genomes. But 10^25 represents 1/4,000,000,000 of G, leaving G essentially unaffected in size.

Now, what we have left is a uniform distribution of size 10^1,000,000,000 among which are to be found generously realistic “clusters” of genomes for every living being imaginable. The odds of hitting the target, that is, any one of the 100 trillion “clusters” of genome permutations, through blind search is 10^25/10^1,000,000,000= 1 in 10^4,000,000.

You can’t argue that the “clustering” I propose has in any significant way changed the uniform distribution of G, the space of all possible genomes. Nature must navigate this way using, per Haggstrom, Darwin’s algorithm A (reproduction-mutation-selection) to find its way through this uniform distribution. But since it is a uniform distribution, we know that it’s no better than ‘blind search’, and we know that G is to Vast for blind search to work.

This is where the Explanatory Filter, that Dembski describes, would tell us that since randomness cannot explain the “discovery” of living genomes, then design is involved.

However on generation of “information” in one sense, that is ever so easy: flip a coin 1,000 times and you have a sequence that is unique to one part in 2^1,000. That is it is complex in the sense of very highly contingent — you would be ever so unlikely to match that particular string of coins again on the gamut of the observable universe, over its lifespan.

But, to specify the string of coins, we would have to basically list it out.

But, now, suppose I were to tell you that he string of coins specifies the first 250 digits of pi in binary coded decimal, ignoring the decimal point: 31415926539 . . . and on for 250 digits. That is, in 8421 BCD, with dashes to show the digits: 0011 – 0001 – 0100 – 0001 – 0101 – 1001 . . .

Now, the string is not only unique, but also functionally specified, as just described, i.e plug it into the area calculation for the surface of a sphere and it will give the right answer. That functionality can be simply and briefly described [and replicated through a series for pi, at will].

That is, we see here functionally specified, complex information. We can even specify a cluster of functional near-equivalents, e.g will give pi to within .0001% or whatever is useful. BTW, such a specification will of course preserve a certain part of the pi-string very tightly indeed, and will allow the rest to vary as it wills. For the rest is much less important to the function.

We could even extend this: we can allow hill-climbing to pi-250 if the first hit is close enough to count to a required precision. But, that would not help an arbitrary coin toss get near enough to count in the sea of all possible configs. And, if we rigged the coins so that the first toss will to high probability be within teh target zone, that too will be because we have intelligently intervened to shift the distribution of the random variable sufficiently far away form “uniform” that we cna now say we have fed in an increment of acrtive information.

And tha tis what WD and Marks did intheir recent work on NFLT and evolutionary computing — quantified how much information tha tis functional has been fed into Dawkins’ “Methinks” and Avida and Ev.

It turns out that if you are able to do significantly better than random selection across the whole config space, for a sufficiently rich space to be relevant to say OOL or OOBPLBD, you have committed an act of intelligent design.

That is exactly the sort of thing that TBO pointed out in TMLO — the first technical level ID work — twenty-five years ago when they came up with a metric for investigator interference with the chemistry in pre-biotic scenarios; and again the point is that if you are above the threshold of success, you are outside the credible framework of what unaided blind nature in plausible pre-biotic scenarios will do.

Somebody is trying to tell us something, if we are only listening . . .

GEM of TKI

Gloppy

Thanks for the attention.

I appreciate your removal of the unnecessary reference to me by personal name.

A real pity that Semiotic had to resort to personalities and attempted outing; there could have been a useful discussion.

I wish he could have simply apologised and allowed the discussion to move on from there, with a fdue balance between the issues of mathematical niceties and the real-world considerations of modelling and validation — thence, of what we may call: useful reliability.

GEM of TKI

On page 9, Haggstrom writes: “The basic NFL theorem involves an average over all possible functions f.”

If it is an average, then we should write something like Sigma, i=1 to N of f(sub i)/N; but this, then, implies that we should use f(sub i) rather than a simple f, it would seem. If you’re going to call all your functions f, there should be a way of distinguishing one f from another, right? That said, however, since the cardinality of the sets f is mapping can be so huge, I suppose you just simply drop the (sub i) since you can’t iterate, practically, over that large a number of elements. So, it seems, that the lack of a (sub i) is an indicator of the futility of searching for such an f(sub i), and an harbinger of the NFL. Nonetheless, it takes a little getting used to.