|January 9, 2017||Posted by News under Evolution, Informatics, Intelligent Design, News|
From Progress in Biophysics and Molecular Biology: Abstract: (paywall)The background of this study is that models of the evolution of living systems are based mainly on the evolution of replicators and cannot explain many of the properties of biological systems such as the existence of the sexes, molecular exaptation and others. The purpose of this […]
|December 4, 2016||Posted by kairosfocus under Back to Basics of ID, Cosmology, Design inference, Informatics, Intelligent Design, Science, worldview issues/foundations and society|
. . . that is, the design inference vs. the broader scientific investigation of a world of life and cosmos that are infused with complex, functionally specific information and complex, functional organisation? In the Turing test thread, just now, I raised this issue in responding to GP and SA . . . and I think […]
|December 3, 2016||Posted by News under Informatics, Information, Intelligent Design|
Evo-Info: Some Things Computers Will Never Do: Nonalgorithmic Creativity and Unknowability: See also: Evolutionary Informatics Lab: A look inside Follow UD News at Twitter!
Our Physicist and Computer Scientist from Russia — and each element of that balance is very relevant — is back, with more. MOAR, in fact. This time, he tackles the “terror-fitted depths” of thermodynamics and biosemiotics. (NB: Those needing a backgrounder may find an old UD post here and a more recent one here, helpful.) […]
|November 26, 2016||Posted by johnnyb under Evolutionary biology, Informatics, Information, Intelligent Design, Origin Of Life|
I was sad to recently realize that Hubert Yockey passed earlier (in January) this year. Hubert Yockey, though he personally was against Intelligent Design, made many contributions to science that many of us within the ID community view as pro-ID work. I wanted to take a moment to appreciate and reflect on his contributions as […]
|November 7, 2016||Posted by kairosfocus under Engineering, Informatics, Intelligent Design, UD Guest Posts|
UD has a broad and deep pool of readers and occasional contributors from across the world that have a lot to say, things that are well worth pondering. In this case, I am more than happy to host a guest post in which physicist and computer scientist ES (who hails from Russia) argues the thesis: […]
|June 26, 2016||Posted by johnnyb under Comp. Sci. / Eng., Informatics, Intelligent Design, Video|
This video is from the Alternatives to Methodological Naturalism 2016 conference held earlier this year. It deals with using non-naturalism in order to improve the quality of machine learning programs using a technique called “imagination sampling.” The results of a limited test run are given.
|April 22, 2016||Posted by News under Informatics, Intelligent Design|
From Kirk Durston at Contemplations: There are countless people who use the following rationale to justify why there was no need for an intelligent creator behind life – evolution has had a near-infinite number of trials in which to create the full diversity of life, including its molecular machines, molecular computers, and digitally encoded genomes. […]
|February 23, 2016||Posted by johnnyb under Complex Specified Information, Informatics, Intelligent Design, Probability|
Recently a criticism was leveled against Dembski’s 2005 paper Specification: the pattern that signifies intelligence. As is often the case, if you read the criticism carefully, you will realize that, even though he says Dembski is wrong, it turns out that the more exacting answer would favor Dembski’s conclusion more strongly, not less.
|May 12, 2015||Posted by News under Informatics, Intelligent Design|
From ESSSAT (European Society for the Study of Science and Theology), “a scholarly, non-confessional organization, based in Europe, which aims to promote the study of relationships between the natural sciences and theological views.” By Philippe Gagnon. Here. Warning: ESSSAT uses a retarded system where one can sort of see the article shaded, without getting rid […]
|March 30, 2015||Posted by News under Informatics, Intelligent Design, News|
Readers, do you remember: What great physicists have said about immateriality and consciousness? And Being as Communion? Here’s a new one: It From Bit or Bit From It?: On Physics and Information (The Frontiers Collection) The essays in this book look at the question of whether physics can be based on information, or – as […]
|March 22, 2015||Posted by News under Informatics, News|
Retired British doctor Jon Garvey wrote an interesting review some months back at Hump of the Camel of William Dembski’s Being as Communion: Evolution, then, is legitimately viewed as an algorithmic search, which is agreed at least by those who produce evolutionary algorithms to simulate it. As is well known, Dembski utilized the then recently-proven […]
|March 17, 2015||Posted by johnnyb under Darwinism, Design inference, Evolutionary biology, ID Foundations, Informatics, Information, Intelligent Design, Irreducible Complexity|
There are many ID’ers who complain about the AVIDA simulation, and I for the life of me can’t figure out why this is so.
|January 26, 2015||Posted by niwrad under Biology, Cybernetics and Mechatronics, Informatics, Intelligent Design|
Sorry if this post is a bit for computer programmers, anyway I trust that also the others can grasp the overall picture. Evolutionists claim that what it takes to evolution to work is simply “a populations of replicators, random variations on them, and a competition for survival or resources”. Today we will try to partially […]
|December 28, 2014||Posted by News under Informatics, Information, Intelligent Design, News|
In June we began to think seriously about William Dembski’s then upcoming Being as Communion, a more philosophical look at design in nature.
|December 26, 2014||Posted by News under Darwinism, Informatics, Information, Intelligent Design|
If information theory is right, Darwinian evolution isn’t even possible
|November 18, 2014||Posted by andyjones under Informatics, Self-Org. Theory, The Design of Life|
(it’s designed to) These are some thoughts prompted by the recent article Arrival of the Fittest: Robustness and flexibility are basic design principles. We design modules so that they are robust against minor damage, bad inputs and changes in other parts of the code. This aids ‘evolvability’ of the whole by untangling the knots so […]
|November 10, 2014||Posted by News under Informatics, Information, News|
Currently (9:00 am EST) in the top 100 in the Kindle store, despite the sweetheart deals offered this summer, for buying the book.
|October 27, 2014||Posted by News under Informatics, Intelligent Design, News|
An Information-Theoretic Formalism for Multiscale Structure in Complex Systems (Open access)
|October 13, 2014||Posted by News under Conservation of Information, Informatics, Intelligent Design|
One hopes that further critical review of Marks and Dembski’s papers focuses on the issues at hand.
Okay, so what of the accusations against their approach to the Law of Conservation of Information (COI)?
Here is English’s 1996 paper, Evaluation of Evolutionary and Genetic Optimizers: No Free Lunch, published in Evolutionary Programming.  At the time, he was at the Computer Science Department at Texas Tech University (Lubbock, TX).
Abstract—The recent “no free lunch” theorems of Wolpert and Macready indicate the need to reassess empirical methods for evaluation of evolutionary and genetic optimizers. Their main theorem states, loosely, that the average performance of all optimizers is identical if the distribution of functions is average. The present work generalizes the result to an uncountable set of distributions. The focus is upon the conservation of information as an optimizer evaluates points. It is shown that the information an optimizer gains about unobserved values is ultimately due to its prior information of value distributions. Inasmuch as information about one distribution is misinformation about another, there is no generally superior function optimizer. Empirical studies are best regarded as attempts to infer the prior information optimizers have about distributions–i.e., to determine which tools are good for which tasks.
The paper was updated as late as 2004. Readers are told to see http://www.TomEnglishProject.com for current information, but the site does not seem to be currently online; the information, as noted above, is now here.
English appears to have more or less retracted his own paper since 1996, as this amended version at his Bounded Theoretics site shows. The Abstract is now heavily edited. A number of pages feature crossouts of the text (explanation appended at ).
That perhaps is the context for his comment at Salvo, “conservation of information” turns out to be nothing but obfuscation of statistical independence—a concept that undergraduates encounter early in introductory courses on probability and statistics.
But, whatever the fate of English’s paper, the sense in which Marks and Dembski have used the phrase conservation of information (COI)  is well supported by the work of others in the literature.
Here is a similar statement from Harvard mathematician Yu-Chi Ho about the related No Free Lunch theorem (NFLT):
… unless you can make prior assumptions about the … [problems] you are working on, then no search strategy, no matter how sophisticated, can be expected to perform better than any other. 
The term “No Free Lunch” itself was coined by Wolpert and MacReady (1997)  who write that search can be improved only by “…incorporating problem-specific knowledge into the behavior of the [optimization or search] algorithm.”
Carnegie-Mellon computer scientist Tom Mitchell seems to have originated the COI model in 1980, though he did not call it that. He noted that, in order for computer programs to learn, programmers must insert their own bias:
If consistency with the training instances is taken as the sole determiner of appropriate generalizations, then a program can never make the inductive leap necessary to classify instances beyond those it has observed. Only if the program has other sources of information, or biases for choosing one generalization over the other, can it non-arbitrarily classify instances beyond those in the training set. 
A similar observation was later made by Cullen Schaffer (1994), a principle he called a “conservation law for generalization performance,” comparing a learning program that learns well regardless of circumstances to a perpetual motion machine (a machine that is impossible under the law of conservation of energy): “… a learner [without prior knowledge] … that achieves at least mildly better-than-chance performance … is like a perpetual motion machine.” 
What Marks & Dembski did in “Conservation of Information in Search”  is to quantify the degree of information infused into a search algorithm when “problem-specific knowledge” (Wolpert and MacReady, 1997) is used. To take a simple example, a kid at an Easter egg hunt has a better chance of finding an egg if an adult is shouting “Warmer. You’re getting warmer!” than if the adult were silent. The probabilities of success with problem-specific knowledge and no problem-specific knowledge are combined into a measure Dembski & Marks call active information. The more problem-specific knowledge that is successfully applied, the greater the active information. And if someone imposes faulty knowledge (yelling “Warmer!” when the kid is getting colder), the active information is negative. This, basically, is how they use the law of conservation of information  when assessing evolutionary search programs.
With respect to Dr. English’s P.S., don’t Lenski et al. use a search model in the computer program AVIDA (see their Nature article “The evolutionary origin of complex features”)? What about computer evolution simulation EV? I am not sure the word choices have so much to do with the power of language (rhetoric?) as the conventional use of terminology.
But now, about that Wikipedia entry…
Now I must raise a sensitive topic: It’s not clear that all the animus here is scholarly. Apart from the tone of the blog intro noted above, two other issues are worth noting:
Dr. English wrote a chapter with G. W. Greenwood, “Intelligent Design and Evolutionary Computation,” the first chapter in Design by Evolution: Advances in Evolutionary Design, edited by Philip F. Hingston, Luigi C. Barone, and Zbigniew Michalewicz (2008). There, his evolutionary computation arguments are introduced by a frankly political argument. Here’s the first paragraph:
In the United States, a succession of lost legal battles forced opponents of public education in evolution to downgrade their goals repeatedly. By the 1980’s, evolution was ensconced in the biology curricula of public schools, and references to the creator of life were illegal. The question of the day was whether instruction in creation, without reference to the creator, as an alternative explanation of life violated the constitutional separation of church and state. In 1987, the U.S. Supreme Court decided that it did, and intelligent design (ID) rose from the ashes of creation science. ID may be seen as a downgraded form of creation. While the creation science movement sought to have biology students introduced to the notion that creation is evident in the complexity of living things, the ID movement sought to have students introduced to the notion that design, intelligence, and purpose are evident. ID preserves everything in the notion of creation but the making.
Suppose all of this is true. It is nonetheless irrelevant to the question of whether Marks & Dembski are correct about the limits COI imposes on Darwinian evolution.
While the intent may have been to apprise readers why the subject of the chapter is important to them, the net effect is to create a question whether the material will be handled in an intellectually responsible way. For that reason, most scholars avoid mingling their political opposition to a social movement with computational reasoning as to why some of its assertions are incorrect.
The second issue is that Dr. English has been subject to a number of disciplinary actions at Wikipedia for attempted edits to the bio entry for Marks.
In this context, it is perhaps relevant that he says in one post at his blog, “I’ve come to see Marks as the quintessential late-career jerk,” also admitting (July 29, 2010),
The reason I come off as a nasty bastard on this blog is that I harbor quite a bit of anger toward the creationist bastards who duped me as a teenager. The earliest stage of overcoming my upbringing was the worst time of my life. I wanted to die. Consequently, I am deadly serious in my opposition to “science-done-right proves the Bible true” mythology. William A. Dembski provokes me especially with his prevarication and manipulation. He evidently believes that such behavior is moral if it serves higher ends in the “culture war.” My take is, shall we say, more traditional.
This is hardly the most neutral or, dare we say, productive mindset for an editor of a biography, even at Wikipedia.
One hopes that further critical review of Marks and Dembski’s papers (and Dembski’s Being as Communion) focuses on the issues at hand and takes into account the usual use of terminology in the field.
 Peter Medawar (1915–1987) was a Nobelist (Physiology or Medicine, 1960). Here is a statement of COI from a book by Jan Kahre, The Mathematical Theory of Information, which attributes the concept to Medawar. Medawar was very much a believer in science, as conventionally understood, to judge from his 1988 book, The Limits of Science and in the fact of evolution. He also chaired the mathematics-heavy Wistar Conference in 1966, at which he spoke about mathematical challenges to current Darwinian theory:
“[T]he immediate cause of this conference is a pretty widespread sense of dissatisfaction about what has come to be thought as the accepted evolutionary theory in the English-speaking world, the so-called neo-Darwinian Theory. … There are objections made by fellow scientists who feel that, in the current theory, something is missing … These objections to current neo-Darwinian theory are very widely held among biologists generally; and we must on no account, I think, make light of them. The very fact that we are having this conference is evidence that we are not making light of them.”(Sir Peter Medawar, “Remarks by the Chairman,” in Mathematical Challenges to the Neo-Darwinian Interpretation of Evolution (Wistar Institute Press, 1966, No. 5), pg. xi
 Thomas Milford English “Evaluation of Evolutionary and Genetic Optimizers: No Free Lunch.” In Evolutionary Programming, pp. 163-169. 1996. Available online:
I was not able reproduce it here visually but the bracketed material has replaced material in the Abstract published in the book. So this is how the Abstract now reads:
The recent \no free lunch” theorems of Wolpert and Macready indicate the need to reassess empirical methods for evaluation of evolutionary and genetic optimizers. Their main theorem states, loosely, that the average performance of all optimizers is identical if the distribution of functions is average. [An \optimizer” selects a sample of the values of the objective function. Its \performance” is a statistic of the sample.] The present work generalizes the result to an uncountable set of distributions. The focus is upon the conservation of information as an optimizer evaluates points [statistical independence of the selection process and the selected values]. It is shown that the information an optimizer gains about unobserved values is ultimately due to its prior information of value distributions. [The paper mistakes selection bias for prior information of the objective function.] Inasmuch as information about one distribution is misinformation about another, there is no generally superior function optimizer. Empirical studies are best regarded as attempts to infer the prior information optimizers have about distributions [match selection biases to constrained problems] | i.e., to determine which tools are good for which tasks.
 William A. Dembski and Robert J. Marks II “Conservation of Information in Search: Measuring the Cost of Success” IEEE Transactions on Systems, Man and Cybernetics A, Systems & Humans, vol.5, #5, September 2009, pp.1051-1061.
 Yu-Chi Ho, D.L. Pepyne; “Simple explanation of the no free lunch theorem of optimization,” Proceedings of the 40th IEEE Conference on Decision and Control, 2001. pp.4409 – 4414 ; Yu-Chi Ho, Qian-Chuan Zhao, Pepyne, D.L., “The no free lunch theorems: complexity and security,” IEEE Transactions on Automatic Control, Volume 48, Issue 5, May 2003 pp. 783 – 793.
 David H. Wolpert, William G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evolutionary Computation 1(1): 67-82 (1997).
 T. M. Mitchell. “The need for biases in learning generalizations,” Technical Report CBM-TR-117, Department of Computer Science, Rutgers University (1980). p.59. Reprinted in Readings in Machine Learning edited by J.W. Shavlik and T.G. Dietterich, Morgan Kauffmann Series in Machine Learning, 1990, pp.184-190.
 Cullen Schaffer, “A conservation law for generalization performance,” in Proc. Eleventh International Conference on Machine Learning, H. Willian and W. Cohen. San Francisco: Morgan Kaufmann, 1994, pp.295-265.
See also: William Dembski, Responding to My Talk at the University of Chicago, Joe Felsenstein’s Argument by Misdirection, for a related discussion.