A Problem to Solve

Evolution is often presented as problem-solving. Genetic algorithms are often offered as proofs of evolution’s ability to solve problems. Genetic algorithms are as search algorithms.

As one book says:

Fundamentally, all evolutionary algorithms can be viewed as search algorithms which search through a set of possible solutions looking for the best – or “fittest” – solution.

Tom has asked me to specify a problem independently from the evolutionary process. Now I have to admit that I don’t really understand what that means. But I like Tom and I have a lot of respect for him, so I want to give it my best shot and see where it takes us. I’m also hoping this will shed some light on claims about how problem-solving genetic algorithms are designed to solve a particular problem.

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Determining Probability

There’s been some debate here at TSZ recently about probability and the interpretation of probability.

I took some flak (my personal subjective opinion) for attempting to distinguish between calculating probabilities and estimating probabilities.

Yet in recent reading I came across this bit of text:

How do you determine the probability that a given event will occur? There are two ways: You can calculate it theoretically, or you can estimate it experimentally by performing a large number of trials.

– Probability: For the Enthusiastic Beginning. p. 335

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Introduction to Evolutionary Informatics

Introduction to Evolutionary Informatics, by Robert J. Marks II, the “Charles Darwin of Intelligent Design”; William A. Dembski, the “Isaac Newton of Information Theory”; and Winston Ewert, the “Charles Ingram of Active Information.” World Scientific, 332 pages.
Classification: Engineering mathematics. Engineering analysis. (TA347)
Subjects: Evolutionary computation. Information technology–Mathematics.1

Yes, Tom English was right to warn us not to buy the book until the authors establish that their mathematical analysis of search applies to models of evolution.

But some of us have bought (or borrowed) the book nevertheless. As Denyse O’Leary said: It is surprisingly easy to read. I suppose she is right, as long as you do not try to follow their conclusions, but accept it as Gospel truth.

In the thread Who thinks Introduction to Evolutionary Informatics should be on your summer reading list? at Uncommon Descent, there is a list of endorsements – and I have to wonder if everyone who endorsed the book actually read it. “Rigorous and humorous”? Really?

Dembski, Marks, and Ewert will never explain how their work applies to models of evolution. But why not create at list of things which are problematic (or at least strange) with the book itself? Here is a start (partly copied from UD):
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Two planets with life are more miraculous than one

The Sensuous Curmudgeon, who presently cannot post to his weblog, comments:

This Discoveroid article is amazing. Could Atheism Survive the Discovery of Extraterrestrial Life?. I wish I could make a new post about it. They say that if life is found elsewhere, that too is a miracle, so then you gotta believe in the intelligent designer. They say:

“The probability of life spontaneously self-assembling anywhere in this universe is mind-staggeringly unlikely; essentially zero. If you are so unquestioningly naïve as to believe we just got incredibly lucky, then bless your soul.”

Actually, “they” who posted at Evolution News and Views is someone we all love dearly, and see occasionally in the Zone — that master of arguments from improbability, Kirk Durston.

Evolution and Functional Information

Here, one of my brilliant MD PhD students and I study one of the “information” arguments against evolution. What do you think of our study?

I recently put this preprint in biorxiv. To be clear, this study is not yet peer-reviewed, and I do not want anyone to miss this point. This is an “experiment” too. I’m curious to see if these types of studies are publishable. If they are, you might see more from me. Currently it is under review at a very good journal. So it might actually turn the corner and get out there. An a parallel question: do you think this type of work should be published?

 

I’m curious what the community thinks. I hope it is clear enough for non-experts to follow too. We went to great lengths to make the source code for the simulations available in an easy to read and annotated format. My hope is that a college level student could follow the details. And even if you can’t, you can weigh in on if the scientific community should publish this type of work.

Functional Information and Evolution

http://www.biorxiv.org/content/early/2017/03/06/114132

“Functional Information”—estimated from the mutual information of protein sequence alignments—has been proposed as a reliable way of estimating the number of proteins with a specified function and the consequent difficulty of evolving a new function. The fantastic rarity of functional proteins computed by this approach emboldens some to argue that evolution is impossible. Random searches, it seems, would have no hope of finding new functions. Here, we use simulations to demonstrate that sequence alignments are a poor estimate of functional information. The mutual information of sequence alignments fantastically underestimates of the true number of functional proteins. In addition to functional constraints, mutual information is also strongly influenced by a family’s history, mutational bias, and selection. Regardless, even if functional information could be reliably calculated, it tells us nothing about the difficulty of evolving new functions, because it does not estimate the distance between a new function and existing functions. Moreover, the pervasive observation of multifunctional proteins suggests that functions are actually very close to one another and abundant. Multifunctional proteins would be impossible if the FI argument against evolution were true.

Dice Entropy – A Programming Challenge

Given the importance of information theory to some intelligent design arguments I thought it might be nice to have a toolkit of some basic functions related to the sorts of calculations associated with information theory, regardless of which side of the debate one is on.

What would those functions consist of?

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Thorp, Shannon: Inspiration for Alternative Perspectives on the ID vs. Naturalism Debate

The writings and life work of Ed Thorp, professor at MIT, influenced many of my notions of ID (though Thorp and Shannon are not ID proponents). I happened upon a forgotten mathematical paper by Ed Thorp in 1961 in the Proceedings of the National Academy of Sciences that launched his stellar career into Wall Street. If the TSZ regulars are tired of talking and arguing ID, then I offer a link to Thorp’s landmark paper. That 1961 PNAS article consists of a mere three pages. It is terse, and almost shocking in its economy of words and straightforward English. The paper can be downloaded from:

A Favorable Strategy for Twenty One, Proceedings National Academy of Sciences.

Thorp was a colleague of Claude Shannon (founder of information theory, and inventor of the notion of “bit”) at MIT. Thorp managed to publish his theory about blackjack through the sponsorship of Shannon. He was able to scientifically prove his theories in the casinos and Wall Street and went on to make hundreds of millions of dollars through his scientific approach to estimating and profiting from expected value. Thorp was the central figure in the real life stories featured in the book
Fortune’s Formula: The Untold Story of the Scientific Betting System that Beat the Casino’s and Wall Street by William Poundstone.
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The Real EleP(T|H)ant in the Room

TSZ has made much ado about P(T|H), a conditional probability based on a materialistic hypothesis. They don’t seem to realize that H pertains to their position and that H cannot be had means their position is untestable. The only reason the conditional probability exists in the first place is due to the fact that the claims of evolutionists cannot be directly tested in a lab. If their claims could be directly tested then there wouldn’t be any need for a conditional probability.

If P(T|H) cannot be calculated it is due to the failure of evolutionists to provide H and their failure to find experimental evidence to support their claims.

I know what the complaints are going to be- “It is Dembski’s metric”- but yet it is in relation to your position and it wouldn’t exist if you actually had something that could be scientifically tested.

 

 

Wright, Fisher, and the Weasel

Richard Dawkins’s computer simulation algorithm explores how long it takes a 28-letter-long phrase to evolve to become the phrase “Methinks it is like a weasel”. The Weasel program has a single example of the phrase which produces a number of offspring, with each letter subject to mutation, where there are 27 possible letters, the 26 letters A-Z and a space. The offspring that is closest to that target replaces the single parent. The purpose of the program is to show that creationist orators who argue that evolutionary biology explains adaptations by “chance” are misleading their audiences. Pure random mutation without any selection would lead to a random sequence of 28-letter phrases. There are 27^{28} possible 28-letter phrases, so it should take about 10^{40} different phrases before we found the target. That is without arranging that the phrase that replaces the parent is the one closest to the target. Once that highly nonrandom condition is imposed, the number of generations to success drops dramatically, from 10^{40} to mere thousands.

Although Dawkins’s Weasel algorithm is a dramatic success at making clear the difference between pure “chance” and selection, it differs from standard evolutionary models. It has only one haploid adult in each generation, and since the offspring that is most fit is always chosen, the strength of selection is in effect infinite. How does this compare to the standard Wright-Fisher model of theoretical population genetics? Continue reading

Philosophy and Complexity of Rube Goldberg Machines

Michael Behe is best known for coining the phrase Irreducible Complexity, but I think his likening of biological systems to Rube Goldberg machines is a better way to frame the problem of evolving the black boxes and the other extravagances of the biological world.
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Spontaneous Generation

A century later we know that the overwhelming obstacle facing spontaneous generation is probability, or rather improbability, resulting from life’s enormously complex phenotypes. If even a single protein, a single specific sequence of amino acids, could not have emerged spontaneously, how much less so could a bacterium like E. coli with millions of proteins and other complex molecules? Modern biochemistry allows us to estimate the odds, and they demolish the spontaneous creation of complex organisms.

Looks like IDists aren’t the only ones to appeal to probability arguments. How does Wagner know what the probabilities are, or that spontaneous generation is even within the realm of what is possible?

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“Uncommon Descent” and “The Skeptical Zone” in 2015

(crossposted from here and here)

(Edited Feb 2, 2016 to add eight figures)

Since 2005, Uncommon Descent (UD) – founded by William Dembski – has been the place to discuss intelligent design. Unfortunately, the moderation policy has always been one-sided (and quite arbitrary at the same time!) Since 2011, the statement “You don’t have to participate in UD” is not longer answered with gritted teeth only, but with a real alternative: Elizabeth Liddle’s The Skeptical Zone (TSZ). So, how were these two sites doing in 2015?

Number of Comments 2005 – 2015

year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
UD  8,400 23,000 22,400 23,100 41,100 24,800 41,400 28,400 42,500 53,700 53,100
TSZ  2,200 15,100 16,900 20,400 45,200

In 2015, there were still 17% more comments at UD than at TSZ – 53,100 to 45,200.

UD-2015-02

Though UD is still going strong, there is a slight downwards trend (yellow line) in the daily number of comments.
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The Reasonableness of Atheism and Black Swans

As an ID proponent and creationist, the irony is that at the time in my life where I have the greatest level of faith in ID and creation, it is also the time in my life at some level I wish it were not true. I have concluded if the Christian God is the Intelligent Designer then he also makes the world a miserable place by design, that He has cursed this world because of Adam’s sin. See Malicious Intelligent Design.
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Intention, Intelligence and Teleology

On the left is a photograph of a real snowflake.  Most people would agree that it was not created intentionally, except possibly in the rather esoteric sense of being the foreseen result of the properties of water atoms in an intentionally designed universe in which water atoms were designed to have those properties.  But I think most people here, ID proponents and ID critics alike, would consider that the “design” (in the sense of “pattern”) of this snowflake is neither random nor teleological.  Nor, however, is it predictable in detail.  Famously “no two snowflakes are alike”, yet all snowflakes have six-fold rotational symmetry.  They are, to put it another way, the products of both “law” (the natural law that governs the crystalisation of water molecules) and “chance” (stochastic variation in humidity and temperature that affect the rate of growth of each arm of the crystal as it grows). We need not, to continue in Dembski’s “Explanatory Filter” framework, infer “Design”.

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Those Weasely IDCists!

A couple recent comments here at TSZ by Patrick caught my eye.

First was the claim that arguments for the existence of God had been refuted.

I don’t agree that heaping a bunch of poor and refuted arguments together results in a strong argument.

here

Second was the claim that IDCists do not understand cumulative selection and Dawkins’ Weasel program.

The first time I think I was expecting more confusion on Ewert’s part about the power of cumulative selection (most IDCists still don’t understand Dawkin’s Weasel).

here

In this OP I’d like to concentrate on the second of these claims.

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Answer to Barry Part 1 (and, inadvertently, 2)

Barry seems to have noticed TSZ again, and so I will take this opportunity of inviting him over here, where he can post freely, and will not be banned unless he posts porn or malware or outs someone, which I expect he can manage not to do.

And he responds to my post, Lawyers and Scientists.  He does so in two parts, so I will devote two posts to them.  Here is my response to his first part.  Barry writes:

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More statistical confusion…

At UD I noticed, while I was checking the Moran-Arrington score, I couldn’t help noticing a news item entitled, provocatively (for me) Psychology does not speak the language of statistics very well.

So being a psychologist who teaches statistical methods to psychology students, I had to click, and found that it was a report of a blog piece here called Statistics Shows Psychology Is Not Science

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Lawyers and Scientists

There’s been a skirmish between Larry Moran and Barry Arrington about whether Barry understands the Theory of Evolution, and the latest salvo is a piece at UD, entitled, Can a Lowly Lawyer Make a Useful Contribution? Maybe.

Well, in a sense, Barry makes a useful contribution in that post, as he gives a very nice illustration of a common misunderstanding about the process of hypothesis testing, in this case, basic model-fitting and null hypothesis testing, the workhorse (with all its faults) of scientific research.  Barry writes:

[Philip]Johnson is saying that attorneys are trained to detect baloney.  And that training is very helpful in the evolution debate, because that debate is chock-full of faulty logic (especially circular reasoning), abuse of language (especially equivocations), assumptions masquerading as facts, unexamined premises, etc. etc.

Consider, to take one example of many, cladistics.  It does not take a genius to know that cladistic techniques do not establish common descent; rather they assume it.  But I bet if one asked, 9 out of 10 materialist evolutionists, even the trained scientists among them, would tell you that cladistics is powerful evidence for common descent.  As Johnson argues, a lawyer’s training may help him understand when faulty arguments are being made, sometimes even better than those with a far superior grasp of the technical aspects of the field.  This is not to say that common descent is necessarily false; only cladistics does not establish the matter one way or the other.

In summary, I am trained to evaluate arguments by stripping them down to examine the meaning of the terms used, exposing the underlying assumptions, and following the logic (or, as is often the case, exposing the lack of logic).  And I think I do a pretty fair job of that, both in my legal practice and here at UD.

Barry has made two common errors here.  First he has confused the assumption of common descent with the conclusion of common descent, and thus detected circular reasoning where there is none.  Secondly he has confused the process of fitting a model with the broader concept of a hypothesised model.

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