tag:blogger.com,1999:blog-1689592451067041352.post7093755669976509306..comments2023-05-08T07:41:01.071-07:00Comments on DiEbLog: Review of "A General Theory of Information Cost Incurred by Successful Search" - IntroductionDiEbhttp://www.blogger.com/profile/02099109109735165335noreply@blogger.comBlogger4125tag:blogger.com,1999:blog-1689592451067041352.post-58599417571105952392013-06-28T12:04:23.190-07:002013-06-28T12:04:23.190-07:00Hi, DiEb. Just found your blog and from what I'...Hi, DiEb. Just found your blog and from what I've seen so far it's awesome! I'm just wondering, has UD responded to anything you've written? Tried to do a namesearch on you at UD but came out empty.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-1689592451067041352.post-83522594180410391162013-06-26T07:11:56.941-07:002013-06-26T07:11:56.941-07:00Ouch, $1-\sqrt{5/6}$ resp. $\sqrt{5/6}/5$, obvious...Ouch, $1-\sqrt{5/6}$ resp. $\sqrt{5/6}/5$, obviously...DiEbhttps://www.blogger.com/profile/02099109109735165335noreply@blogger.comtag:blogger.com,1999:blog-1689592451067041352.post-53443566394555351242013-06-26T07:09:41.318-07:002013-06-26T07:09:41.318-07:00As usual, you are correct, therefore I want to pre...As usual, you are correct, therefore I want to present a conventional framework a la Macready et. el. at first, and than contrast it with Dembski's & Marks's. There are so many problems with D&M's approach: e.g., the "probability for belonging to the target" - the second row of the "search matrix", which becomes the output of a fitness function later on. Or the third row, which is just superfluous. On the other hand, we have cute little helicopters - that must be counted as a plus, I suppose.<br /><br />Then there is the problem that a search algorithm in D&M's world will only work on one singular target, one singular objective function, and not on a family of those: if you change the target, the search will be "represented" by another measure!<br /><br />But the whole charade is just created to claim that searches can be somehow represented by measures - which ist just wrong.<br /><br />As usual, I try to give a simple example: Look for the element 1 in the set {1,2,3,4,5,6}: <br /><br /><b>First algorithm</b>: a simple guess at random. <br /><br /><b>Second algorithm</b>: two guesses, each time guessing 1 with a probability of $1-\sqrt{1/6}$, and any other number with a probability of $\sqrt{1/6}/5$. <br /><br />The obvious discriminator returns the number 1 if it was identified correctly, another number of your guess (or guesses) randomly, if it wasn't identified.<br /><br />It is obvious that both strategies yield the same representation. But they are very different, especially for other targets...DiEbhttps://www.blogger.com/profile/02099109109735165335noreply@blogger.comtag:blogger.com,1999:blog-1689592451067041352.post-261957657614989762013-06-26T05:27:13.363-07:002013-06-26T05:27:13.363-07:00In the conventional NFL analytic framework, the ob...In the conventional NFL analytic framework, the objective (cost, fitness) function is a component of the problem. Dembski, Ewert, and Marks turn the objective function into an "oracle" that is part of the problem-solver itself. This model is inappropriate to most, if not all, of the evolutionary computations they purport to have analyzed.<br /><br />Back in the 1990's, Dembski committed himself to the misconception that Richard Dawkins' Weasel program used the fitness function <i>in order to</i> "hit the target." Various people have tried, with no apparent success, to explain to him that one of the offspring in each generation survives <i>because</i> it is the most fit. The so-called target is nothing but the fittest individual.<br /><br />To put it simply, the fitness function comes first. The "target" is defined in terms of the fitness function. Dembski gets this backwards. He believes that the target comes first, and that the fitness function is defined in terms of the target.<br /><br />Dembski and Marks carry this to extreme in "Life's Conservation Law." They claim that implicit biological targets exist in nature, and that if Darwinian evolution succeeds in "hitting" them, then fitness functions necessarily guide evolution to the targets. A remarkable aspect of this claim is that they treat the fitness function, which is an abstraction appearing in mathematical <i>models</i> of evolution, as though it really exists.<br /><br />The "search for a search" is another abstraction that they reify. A probability measure on the sample space is a mathematical abstraction. They merely assert that a search practitioner, in selecting a search, searches the uncountably infinite set of probability measures. To that I say, "Give me a physical description of the process."Tom Englishhttps://www.blogger.com/profile/03887540845396409340noreply@blogger.com