Talk:Efficient Per Query Information Extraction from a Hamming Oracle

Déjà vu
I have to admit that I had all but retired from the - somewhat excruciating -  discussion of  the papers of William Dembski at the end of last year. Of course, most haven't noticed this, but I may even have disappointed one or two by my silence. Sorry.

But the new paper sucked me back into the whole thing: when reading it, I thought, well: proposed that, done that, done that with better pictures, done that better... So, I commented on UncommonDescent:
 * I haven't read the paper in detail yet. And it's a little bit hard to figure out the number of queries needed on average for various combination of parameters from the three-dimensional pics of the Active information per query. I'd like to compare these numbers with mine for the algorithm mutating children with a fixed mutation rate for L=28 and N=27 and L=100 and N=2. }}An of course the numbers for K children each with a single mutation, again for L=28 and N=27! It's good that the exchange of information from oracle to program is looked at now - that got muddled up earlier.


 * at II C:  Results : Figure 2(B) shows the active information per query for the ratchet strategy given different alphabet sizes and message lengths. Increasing the message length does not appear to significantly change the efficiency of active information extraction. However, increasing the alphabet size has a rather noticeable effect.''


 * One should thinks so : As I calculated earlier - when discussing Conservation of Information in Search - Measuring the Cost of Success - the expected number of queries for the One Child Ratchet Algorithm (a more common name would be $$\mathbf{ES}(1+1)$$) with exactly one mutation per child is
 * $$\mathbf{E}(Q) = (N-1) \cdot \mathcal{H}_{(1-\beta) L} \cdot L$$
 * so that the average information per query is
 * $$I_{\oplus}= \frac{\log(N)}{N-1} \frac{1}{\mathcal{H}_{(1-\beta) L}}$$
 * $$\beta\,$$: rate of correct letters to start with
 * $$ \mathcal{H}_{k} $$ k-th harmonic number
 * The effect of $$\frac{\log(N)}{N-1}$$ is noticeable, the effect of of $$\frac{1}{\mathcal{H}_{(1-\beta) L}}$$ less so.

But I'll give an elaborated critique of this paper on rationalwiki. 09:00, 10 March 2010 (UTC)

Also fatigued, glad to see you back
I've written 30 pages criticizing the journal article. I got sick of it, and simply had to back away for a while.

The "search for a search" approach is not new. Here's a link to a paper from a couple years ago. I have read only an expanded version of it, but I'm sure it goes much further than Ewert et al. do.

http://www.springerlink.com/content/a300452035nv0750/

Regarding oracles, Dembski and Marks treat the fitness function as an oracle in the TSMC-A article, but don't use the term: "In evolutionary search, a large number of offspring is often generated, and the more fit offspring are selected for the next generation. When some offspring are correctly announced as more fit than others, external knowledge is being applied to the search, giving rise to active information. As with the child’s game of finding a hidden object, we are being told, with respect to the solution, whether we are getting “colder” or “warmer” to the target."

Hope to get my act together and contact you soon.Tom English (talk) 08:37, 13 March 2010 (UTC)


 * 30 pages? Your stamina is so much greater than mine! I'm sorry that I backed out so nonchalantly, that wasn't my intention. Thanks for the hint to the paper in Applications of Evolutionary Computing, I'll have a look.


 * Again, apologies for my silence


 * yours Dietmar ( 09:42, 13 March 2010 (UTC))

I've never understood this
I won't pretend to understand the higher level math, but I've never understood the ID'ers obssession with the weasel program. I can show mathematically what the probability of increasing, decreasing, and keeping neutral the fitness in every generation, and from those probabilities predict the distribution of the number of generations that the weasel phrase will come out (an decay, if you want). In fact, any college student with a course in probability should be able to do the same with a clear explanation of the algorithm. It's clearly an analogy to show cumulative selection, and Dawkins has been clear as to the limitations of the model. Why are they so obsessed with it? Šţěŗĭļė 14:59, 13 March 2010 (UTC)

Warning
This should maybe contain a warning at the top that it's highly technical and doesn't really seem intended for a layperson. ThunderkatzHo! 06:59, 4 July 2011 (UTC)
 * Yeah, I don't even know what to decap in the headers. 07:51, 4 July 2011 (UTC)
 * What about this as a general template?

 &mdash; Highly Technical &mdash; The contents of this article are highly technical. While those without specialized knowledge are welcome to attempt to read it, please be aware that the it might not necessarily make sense.
 * ThunderkatzHo! 17:21, 4 July 2011 (UTC)