Monday, June 6, 2011

"Ten Reasons Why Evolution is Wrong"

I've run across 10 Reasons Why Evolution is Wrong (http://www.evanwiggs.com/articles/reasons.html). It's an interesting argument, and fairly comprehensive. Interestingly, he takes a fairly multi-discipinary approach, for which I give him credit. But I think he makes some fundamental mistakes that I'd like to look at. Bear in mind, that I'm not an organic chemist, microbiologist, and only an amateur Paleo-anthropoligist. So there are some things I cannot directly speak to.
Before I begin my analysis, there are some general concepts I'd like to mention and discuss.
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Let's cover some basic information theory. Although in informal English, 'data' and 'information' are synonyms, in formal use, the two are distinct. Consider, for instance digital data; these take the form of strings of bits, each of which can be set as either '1' or '0'. A data stream by itself does not represent information; the data do not mean anything. Instead, information is encoded into data; the data then represent the information.
For instance, say we are engaged in the simple activity of flipping a fair coin. The coin can land into one of two distinct states: heads or tails, with an equal chance of either. We intend to flip the coin a number of times and we would like to keep track of the results. We decide to encode those results into a digital data stream: each heads result will be recorded as '1', each tails result will be encoded as '0'. So if we flip the coin five times and get a heads, two tails, another heads, and then a tails. We record that as 10010. The resulting data set is five bits long. It also encodes five bits of information; upon decoding the data, we can tell what result happened at a particular throw.
Without getting too involved in information theory, it is possible to encode more information into a particular data size. Take for example, an unfair coin; this coin can again turn up either heads or tails, however this coin will turn up heads only once for every one thousand throws. We expect to see 999 tails for every one heads. Of course, we can digitally encode the result of five throws into five bits the same way we did with the fair coin. Say we throw it five times, and it comes up tails each time. That would be encoded as 00000.
However, we can increase the information density encoded into the data. We expect, when flipping the unfair coin to see many more tails results than heads results. So we could simply count the number of tails results between heads results. Say we throw the unfair coin ten thousand times, counting the number of tails results. We get 655 tails in a row, then a heads, then 1431 tails, then two heads results, then 1213 tails then a heads, then 2211 tails, then one heads, then 955 tails, then a heads, then 1239 tails, then one heads, then 314 tails, then 777 tails, one heads, and then the last 944 tosses are tails. If we tried to use our digital encoding scheme, the data set will be 10,000 bits.
However, if we translate the number of tails results between each heads result into a series of twelve-digit binary numbers, we generate a data set:
001010001111 010110010111 000000000000 010010111101 100010100011 001110111011 010011010111 010011101010 001100001001 011101100000
120 digital bits of data, encoding ten thousand bits of information. That's about 833 bits of information per bit of data.
It's important to note that it is entirely possible to increase the amount of information stored in a the size of a given data segment without increasing the size of the data segment. For example, a computer hard-drive might have 5,124 kilobytes of data. If the data is filled with random zeros and ones contains no information. However, that amount of data is sufficient to encode approximately 10 minutes of audio in an .mp3 format. That's one segment of a talk radio show or about two standard pop songs. We've gone from an information density of zero to much higher. If instead of mp3 audio, we instead transcribe the talk show as plain text, we can encode many thousands of words. By increasing the information density, we have increased the amount of information encoded within a set data size.
Information theory also encompasses a concept called Shannon entropy. Although mathematically Shannon entropy looks and behaves like thermodynamic entropy, the two are quite distinct. Shannon entropy is essentially a measure of the compressability of a given message. As we saw above, there are multiple ways to encode a given message. The Shannon entropy tells us the theoretical minimum of how small we can make an encoding of a message before we begin losing information. I'll spare us all a lot of math, and won't go into how the Shannon entropy of a message is calculated.
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Next, we have the concept of a species. There's a tendency to throw this word around like everyone knows what it means, but the concept is a little vague. Basically, a species is supposed to be a type of animal or plant or other living thing that makes it what it is and not any other type of animal. Lions are lions and not tigers. Tigers have stripes and are found in Asia, lions have manes and live in Africa. Humans are humans and not chimpanzees; roses are roses and not daffodils; crows are crows and not robins. This is harder then it first appears, though. Are great danes a different species than chihuahuas?
Usually, we then like to talk about fertilization. Two animals are members of the same species if they can mate and produce viable, fertile offspring. Wolves and huskies can mate and produce pups. The pups can then go on to mate with either huskies, wolves, or crossbreeds and produce pups of their own. So wolves and huskies are the same species. Horses and burros can mate, but their offspring are sterile. Horses and burros are different species.
But lions and tigers can mate (liger; tiglon). Their female cubs can be fertile, but the male cubs aren't. Are tigers and lions the same species? Grizzly bears and polar bears can mate (prizzly), so can wolves and coyotes (coywolf); are grizzles and polar bears the same species? Are wolves and coyotes the same species? Then there's the phenomena of ring species.
The entire first chapter of Darwin's The Origin of Species is filled with this problem. The problem of defining exactly what a species is appears intractable. The entire modern system of taxonomy is predicated on the idea that there are similar species sharing similar traits. The conclusion, then, is that similar species must be related somehow, and modern taxonomists enjoy metaphorical fistfights about which organisms are different species, and which are subspecies or varieties.
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An element in the philosophy of science is the anthropic principle. There are a number of formal statements of this idea, but they all basically boil down to this: “We observe the universe to be as it is, because if it were otherwise, we would not exist to observe it.” To the uninitiated, this sounds like circular reasoning, but practiced philosophers like circular reasoning. Circular reasoning gives us the assurance that we're minimizing the number of independent premises that would have to be asserted and supported.
As a practical example, consider the fact that we observe ourselves as living in a universe with three spatial dimensions. This is important: in our universe gravity decreases as the inverse square of distance. The apparent intensity of light reflected off of an object decreases as the inverse fourth power. If we lived in a four-dimensional universe, gravity would be the inverse cube of distance, and light the inverse sixth power. We'd all be blind and fly into space. Life as we know it could not exist. So we assert that there are three spatial dimensions to the universe because if there weren't, we would not exist (as we understand it) to observe the universe as otherwise.
An important corollary to the anthropic principle is harder to express. Basically, it states that “things are named what they are because if things were different, they would still have the same name.” One example of this is the discovery of anti-particles. According to modern particle physics, there are many more naturally particles in the observable universe than anti-particles. Why then, aren't there more anti-particles than particles? Nothing in physics says there can't be. But if we humans had discovered first that we were made of anti-particles and only then discovered particles, we would have named the anti-particles 'particles', and the particles 'anti-particles'.
The question will arise later, “why would life evolve on this planet around this star and not another?” The answer is that had human life evolved on a different planet, then that planet would be “our” planet and that star “our” star.
It's important to remember, though, that the anthropic principle is not science, but a philosophy of science. Like Occam's razor, it can't be verified by empirical evidence or falsified by observation of phenomena. Instead, it is a principle meant to guide what kinds of questions science can meaningfully ask and what kind of answers it can expect. It also explains why some questions don't appear to have scientific answers.
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Also as a preface to this discussion is a little chaos theory. The foundation of chaos theory is the unpredictability of complex mathematical models. Chaos theory suggests that x\complex systems, like the weather, are dramatically perturbed by small variations on their inputs. A small variation – as little as a tenth of a degree at one observation point – can change the formation and behavior of weather systems for days to come in unpredictable ways. Such a system can be said to be non-deterministic. The outputs cannot be reliably predicted from the inputs, due to the feedback cycles and interactions of all the variables involved.
Related is the concept of power laws. Essentially, these are mathematical functions that do not change complexity as they change in scale. The mathematics that govern the movements and interactions of particles in a drop of water are as complex as those of an ocean, for instance.
One of the consequences of chaos mathematics is that simple functions can have large-scale, unpredictable consequences on a large scale. Water molecules tend to bond in a hexagonal pattern in a fairly simple geometric way. It is the large scale consequences of this bonding that causes snowflakes to have their overall hexagonal shape. However, innumerable random factors in the environment of the snowflake during formation lead to each to have different detailed forms. It is the non-deterministic nature of the system that leads a simple function governing structure to have complicated ultimate shape.
Another idea of chaos theory is an emergent quality. This is the tendency for certain complex systems to have self-organizing principles. The chaotic motions of water vapor and water particles in the atmosphere self-organize themselves into clouds. The ups and downs of the stock market are an emergent quality of the combined decisions of thousands of investors across hundreds of corporations. There need not be any outside sentient interference governing the system; the self-organizing principles will emerge from the system itself.
Lastly, refer here for why, even if the existence of some watchmaker could be proven, that would not be proof of John 3:16.
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The following quotes are, of course, from the essay itself.
Introduction:
Science. According to the Oxford Dictionary science is "A branch of study which is concerned either with a connected body of demonstrated truths or with observed facts systematically classified and more or less colligated by being brought under general laws, and which includes trustworthy methods for the discovery of new truth within its own domain."
The process is for a postulate is first formulated and then announced. Then there are three things about this postulate that must be true before it can be considered a theory.
  1. The postulate must be observable.
  2. The postulate must be capable of repeatable experimental verification
  3. The postulate must withstand a fasifiability test, or an experiment conceived which the failure of the experiment would disprove the postulate.
“Theory” as the term is understood by the philosophy of science is a little more complicated than this. A theory may be made of more than one postulate. Individual postulates or hypotheses serve as the atomic constituents of a theory as a whole. The more postulates that are proven, the stronger we believe the theory becomes. Each postulate becomes a brick holding up the whole structure of the theory, and the more supporting postulates we have, the stronger our theory becomes. However, we can discard non-functioning postulates without discarding our theory as a whole. Falsifying one or more of our postulates requires us to modify our theory, but we can modify our theory to accommodate more experimentally verified phenomena without discarding the whole thing and starting from scratch.
For example, consider the evolution of the theory of gravity. The Aristotelian theory of gravity holds that solids and liquids are attracted towards the surface of the Earth at a rate proportional to their weight. Essentially three postulates:
  1. Solids and liquids move towards the Earth
  2. At a steady rate
  3. Proportional to their weight
Wrong, of course. Galileo constructed a number of experiments to test these postulates, and found two and three incorrect. Turns out that solids and liquids fall towards the Earth all right, but not at a steady rate. Instead, they tend to accelerate. Also, they accelerate at nearly the same rate, regardless of their weight.
And what of feathers? Feathers after all don't fall at the same steady acceleration as billiard balls. Do we throw out the theory of gravity all together, and go casting about for a new theory from scratch? Of course not; we observe and experiment and expand our theory, building on what we already strongly suspect to be true. We come up with the idea of air resistance and cross-sectional densities that account for the way feathers fall.
The moon refusing to fall towards the Earth? We don't discard the theory of gravity, we modify it and expand it to account for why the moon doesn't appear to fall despite our predictions that it should. The theory of gravity is constantly being corrected, rewritten, expanded upon, and reconfirmed. We don't throw the whole theory out every time we observe a phenomenon that was different than what we expected. Theories of evolution are similar.
Before we take on the ten reasons evolution is wrong we must first define what we are talking about. Evolutionists will say the word evolution to you and you may think you know what they are saying, but you probably don’t. There are at least five concepts of evolution that the evolutionist speaks of as one. They are:
  1. Cosmic Evolution – Their Cosmology or how the Universe came into being.
  2. Stellar Evolution – How the stars, galaxies etc. formed
  3. Earth’s Evolution – How the Sun and the planets formed in our solar system.
  4. Macroevolution – The postulate that says all life formed from earlier organized non-life and through some form of mutation, natural selection, and enormous amounts of time.
  5. Microevolution – The limited variation that takes place in a species or families complex gene pool or genome.
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When you talk with evolutionists make sure you have these points covered. They will talk circles around you and call you stupid if you don’t know what they are talking about. As Evolutionists have never observed any of the first four supposed evolutions they assume are true, they only talk about the last microevolution and try to define it as all five! The constantly point out microevolution as being the proof of all the other four. The sooner we creationists figure this out the sooner we can win this debate.
True, humans have only seen the briefest of snapshots of cosmic, stellar, planetary, and macro biological evolution. Current theories of these phenomena suggest that they take place on staggeringly long time scales, and we humans have simply not been studying them all that long.
Still, we're not completely at a loss. We look around us, and we think we see scenes that sample what we think might be different stages in the same process. It's not hard to look at an acorn, dig up a germinated seed, find a sapling, then look at a fully grown oak tree and suspect that they might all represent various stages of a continuing process. Similarly, we haven't seen a star coalesce out of the primordial matter of a stellar nursery, ignite, grow, enter the main sequence, get older, grow to a giant stage, shrink to a dwarf, and then eventually grow cold and disappear. Our best guess is that it takes about ten billion years for the process to play out. But we can build telescopes and look about. In doing so, we can see stellar nurseries. We can see an incredible number of main sequence stars. We can see red giants and white dwarfs. We can count them and compare them to one another. We can guess how the process goes.
We can also build models. We can learn about chemistry and physics. We can study nuclear reactions and emission spectra and apply what we've learned to the study of stars. We can build models and run experiments to show us how the things we can't observe might operate. We speculate what phenomena we should be able to see from our experiments and our models, and test if our observations match those predictions. Over time, we can make educated guesses about how stars are formed, grow and change.
We haven't been able to directly observe too many planets, and the ones we have seen are all pretty much in the same stage of their evolution. However, we can observe some of those planets pretty closely. We can suggest models that explain the phenomena we see. As we observe new planets and new things about the planets we know, we can suggest what features they might have. We can check those features against our predictions. Over time, we can build theories of planetary development.
Sure, we have never observed cosmic, stellar, planetary, or biological macroevolution. But we can observe the universe and the world around us, and make guesses as to how it got that way. We can build models that give us predictions, and see if those results match our observations. As we learn more, we can change those models and see if their results are more fruitful. We can't see evolution, but we can see the result and guess at how it works.
Creationists do not see microevolution as being able to drive the massive information gain that needs to occur for evolution to be possible, that is the ameoba to man evolution concept.
The information density of DNA will be discussed a bit more later. Exactly how DNA encodes information is not well understood. Fundamentally, DNA encodes the manufacture of proteins that then govern the structure and operation of larger organisms. However, the relation between a given DNA sequence, the proteins it manufactures, and the traits that are expressed do not necessarily have a one-to-one correspondence. Further, evidence suggests that many structures in organisms are built as a consequence of non-deterministic power-law functions incorporating certain random elements. For instance, the layout of leaves and branches of certain trees seem to express power functions that are fairly easy to encode in a small data space.
Many apparent complexities in living organisms might arise not from increasing information, but in the non-deterministic nature of the power laws governing the creation of proteins, and the use of those proteins to build larger structures. The complex involutions of the primate neo-cortex, for instance, stem from a change in the structure and connectivity of brain neurons. This change in structure itself stems from an alteration of the formula that governs the shape of those neurons, a fairly minor alteration of which leads to macro-scale, non-deterministic complexity. There is no inherent increase in the information density of the genes, just a modification of what they encode.
Microevolution changes mainly occur through the practice of selective breeding. There are no “mutations” in selective breeding or in genome adaptation to the environment. The complex changes that occur are already in the genome and are merely being brought out from human or environmental pressure.
This ignores the possibility that variation has already occurred in the genome, but has not been expressed until environmental pressure forces such expression.
Section 1
“EVOLUTION IS NOT RANDOM, FOR (probably not) THE LAST TIME. Variety is there because evolution causes random mutation, hence the variety.” From a debate on talkorigins.org
Ummm a little double talk. Well it also appears this is perilously close to evolution being an intelligent designer. But it is also a tautology or circular reasoning to say that “evolution causes random mutation” because evolutionists say random mutation causes evolution.
There is presumably a reason why the quoted individual here is an amateur evolutionist. So I, another amateur evolutionist, will take a stab at it: As Darwin liked to say, natural processes cause variation in life forms from one generation to another. Natural selection selects and preserves those variations that are best suited to their environment. Organisms better suited for their conditions are mire likely to pass on their traits to the next generation.
Variation is random, but the process of selection is not. Still, in not being random, it is also not directed by an outside force. Natural selection is an emergent quality of the system of life.
But to be correct, evolution is a religious philosophy that operates with a lot of faith. So evolution isn’t necessarily any more random than the person’s thoughts and it certainly cannot be some kind of force driving the random mutation. Nor can it cause mutations random or otherwise.
Evolution is a feature of a number of different theories of biological science. Science is a practice and a way of thinking about the universe that is itself largely governed by philosophy. Religion implies the inclusion of a divine figure or force; science typically does not make recourse to the divine, as the divine is not typically observable, repeatable, or falsifiable. Faith typically involves the belief in that which cannot be empirically observed; science is based on the principle of empirical observation. Even when a phenomenon cannot be directly observed, science seeks to observe the consequences, and so observe it indirectly. Evolution may be incorrect, but it isn't a religious philosophy or driven by faith.
Mutation and natural selection are the engine of evolution. Creationists believe in natural selection but we doubt the role mutations play in evolution and know if we can show that mutations cannot be part of the engine, then evolution will have lost its power.
Most theories of evolution rest on the idea of variation. Variation includes both recombination and mutation. Also, it's a little unusual for Creationists to agree to natural selection; though that is often said to be the distinction between theories of creation and those of intelligent design. That creation theories are now conceding natural selection indicates even they are beginning to buy into evolutions major postulates.
Genetics and evolution have been enemies from the beginning. Gregor Mendel and Charles Darwin were contemporaries. Mendel is the father of modern genetics and Darwin is the father of evolution. In Darwin’s day genetics was just starting and Darwin knew really very little about how genetics worked. His idea of change in species was based on erroneous and untested ideas of inheritance. Mendel’s ideas were based on careful experimentation and showed that individual characteristics were surprisingly resilient and constant.
Since Darwin and Mendel were indeed contemporaries (and Mendel's theories would go largely ignored for fifty years), Darwin can hardly be faulted for a lack of in depth understanding of genetics. However, Darwin's emphasis on variation of organisms between generation is essentially sound. It was based on continued observation of artificial selection in animal and plant husbandry.
In reality there are multiple mutation processes that can impact a genome but evolutionists only choose one. I will explain why in a bit. First the types of mutations:
1. Duplication or Amplification of a segment of DNA
2. Inversion of a segment of DNA
3. Deletion of a segment of DNA
4. Insertion of a segment of DNA
5. Transposition of a segment of DNA from one place to another.
6. Point Mutation of a single nucleotide.
The first five are interesting genetic processes. Each is a complex and precise process that has much biochemical signaling and purpose. We don’t really know much about why the genes do this as we are still very weak in our knowledge of how our genome works. But none of these processes can add any data to the genome, they just move data around. I must add another point here: some evolutionists place recombination in this list, but recombination is sexual mixing and once again cannot add any data to the genome. Recombination just takes the genome and mixes what is there. There are tens of maybe hundreds or trillions of combinations in our genome to recombine. We are wonderfully and fearfully made.
Here there is an egregious confusion of data and information. With the high information density and high entropy of DNA., even minor alterations to the data can have unpredictable consequences in the information content encoded in that data. Any of the five processes mentioned can add or subtract information encoded into the DNA. Recombination allows new mutations to express themselves (for the weal or woe of the organism). It is only when mutations are expressed can they be judged to pro-survival or anti-survival by natural selection and either propagated through or eliminated from the genome. Recombination is a vital part of the process of natural selection.
But what about mutations then? What are they and how can they be beneficial? Mutations are mistakes in the genetic copying process. They effect one nucleotide base at a time and are called point mutations. Once in every 10,000 to 100,000 copies there is a mistake made. Our bodies have a compare – correct process that is very efficient. In fact it is 1016 times better than the best computer code, but once in every 1,000,000,000 or 10,000,000,000 copies a mutation “gets out” so to speak. That is equal to a professional typist making a mistake in 50,000,000 pages of typescript. You see mutations are predominately bad and the cell tries to make sure they don’t happen.
The mutations of principle importance here occur when adult cells are dividing into gametes for the purposes of reproduction, a process that has fewer error-correcting processes involved. Also, not all such mutations of errors of copying, some are the damage or rearrangement of the DNA in an existing cell. Others are the deliberate machinations of retroviruses, forcing changes to the host cell's genetic code.
Let us continue our example above with Fisher’s calculations. Our organism with a 0.1% survival factor would have one chance in 500 of surviving. If there were 500 organisms with the mutation their odds would be about 5 out of 8. With 1000 with the same mutation their odds would be about 6 out of 7 and with 2500 organisms with the same mutation the odds are about even. What are the odds of 2500 organisms having the same point mutation (it has to be the same for that particular information to get into the genome) in a population?
I admit that I am not a microbiologist, and so can't really speak all that much to a lot in this section. However, I will point out that there is some emerging evidence (out of the University of Tennessee and other places) that certain bacteria-phage virus strains can force pro-survival traits of the host to be expressed (because the longer the host loves, the more viruses it can be forced to create), and that the virus can in fact carry and transfer such traits from one host to another, in effect infecting the host population with the new traits. This research is in its infancy, and has not yet been shown to occur in macro-biotic host populations. It could, however be a means whereby positive traits are passed laterally throughout a host population.
Any scientific theory, which evolution is purported to be, has to be able to predict to be a good theory. But evolution in its’ need to connect mutation in the genome to the massive change needed for evolution incorrectly predicted the direct gene to morphology connection. Only with this connection can small mutations actually have the ability to make massive morphological changes necessary for evolution to be plausible.
Given the non-deterministic nature of changes in the genetic code of an organism and their expressions in the morphology of the organism, the “massive changes necessary” are a plausible consequence. Consider, for instance, the transition from homo sapiens neanderthalitis to homo sapiens sapiens. Paleontological evidence suggests that the adult Neanderthal had a protruding jaw and recessed, sloping forehead, much like the modern gorilla and chimpanzee. Infant chimpanzees and gorillas are born with much higher, less sloped foreheads, which recess as the infant grows older and the jaw grows longer.
Modern humans, on the other hand, don't have the same head morphology. We lack the activation of some hormone or combination of hormones that causes the change in skull shape as we grow out of infancy. As a consequence, the human brain develops in a different fashion; the region directly behind the forehead is the neo-cortex, responsible for abstract symbol manipulation, language use, visualization of counterfactual conditions (including possible futures), and advanced problem solving. The fact that we are “baby faces” as Larry Gonick puts it means that just a small change in the hormonal mixture leads to a large pro-survival advantage.

Bent proteins have had much interest in science for a couple of decades. Many first heard of them in some rather strange diseases such as the Creutzfeldt-Jakob disease or the Mad Cow disease that was caused by a prion or a badly bent protein. We all wondered how could a bent protein cause morphological change in a brain?
As researchers dove deeper into this issue and looked a past research going back into the 1970s they started seeing that there appeared in cells an incredibly complex dance between the genes and protein and RNA folds to transmit data to assemble extremely complex protein machines in the cell as well as transmit data to assemble cell structures as well as create the macro morphology of an organism. This answered some questions that arose in genetic research where it appeared the genes didn’t always have a one to one correspondence with morphological structure. In fact some genes seemed to be connected to multiple structures and some genes seemed to be unconnected. As it turns out the bent proteins provide another layer of highly organized information in the cell. The appear to be bent in non-random ways based on the molecular structure and the bends are actually a function of physics and not biology. We have discovered around 200 of these protein bends and have seen how they actually provide more information to the cell than the genes themselves.
The folding process has been found to be absolutely necessary for the protein to function in the cell and occurs right out of the ribosome. The folded shape is determined by several factors.
1. Internal covalent bonds such as disulfide bridges between cysteine units in the chains.
2. Hydrogen bonds.
3. Hydrophilic and hydrophobic interaction with the surrounding solvent.
4. The interaction with other with other molecules large and small that help carry on cellular function.
In fact two different proteins can fold into similar shapes and perform the same cellular function. But this is all made possible by a process that is guided. Random folds wouldn’t work. The prions of the Creutzfeldt-Jakob disease prove that. There are protein complexes that provide a chaperone that help the proteins to bend in the proper way, and there are chaperones that help the protein to stay in its proper bend. These chaperones are also responsible for metal ions movement in the cell.
This is something evolutionists may claim as “part of the great universal acid” of their theory, but evolutionary theory actually prevented researchers from discovering these protein machines because of the assumptions built into evolution. Another failure and another nail in the coffin.

One of the consequences of Einstein's theories on relativity was the inevitable collapse of universe into a closed geometry of infinitesimal space. He was dissatisfied with this result, and so added the “cosmological constant” to offset the cumulative attraction of gravity. This aspect of his theory was later discarded, with Einstein himself saying that the cosmological constant was his greatest blunder. New astronomical and astrophysical research has suggested that the cosmological constant may be non-zero and positive. Cosmologists have since modified their theories. Being wrong in science doesn't mean you abandon your theories. Being wrong means you modify them.
I'd note that no one foresaw bent proteins ore the role they play in microbiology. Evolutionary biology didn't; creation biology didn't. Conventional biochemistry and microbiology didn't. It wasn't until in depth research into mad cow disease, hoof-and-mouth disease, and Creutzfeld-Jakob disease showed how important bent proteins and prions might be that anyone in any field started to think they were important.
Physics didn't predict X-rays until Roentgen discovered and described them. But no one threw out physics and started over again.
Section 2
Evolutionist speak of natural selection like it is intelligent or something and can spot a mutation that it needs to save.
Speaking of natural selection as if it were a conscious willing force is a convenient shorthand. What is meant is, “if the mutation is pro-survival, natural selection will preserve it.”
Let’s talk briefly about probability which is a subset of Statistics. What is the chance if you toss a coin you get heads? Assuming the coin is equally weighted, and not a trick coin, it is 1/2. On a die the probability of rolling a six is 1/6. The probability of tossing a coin and getting heads and rolling a die and getting a six is 1/2 x 1/6 = 1/12 Now this doesn’t mean that in twelve tosses and throws you will get simultaneously a head and a six, it means that if you throw long enough 1/12 of all throws will have both a head and a six.
Now let us get a little more complicated. Let’s figure the odds or probability of randomly spelling the phrase “the theory of evolution”. There are 26 letters and one space possible adding to 27 possible selections. There are 20 letters in the phrase and 3 spaces. Therefore the odds, on the average, spell out the phrase correctly only once in 2723 outcomes! That is only one success in 8.3 quadrillion, quadrillion attempts or 8.3 x 1032. Now suppose ‘chance’ uses a machine which removes, records and replaces all the letters randomly at the fantastic speed of one billion per microsecond (one quadrillion per second). On the average the phrase would happen once in 25 billion years by this method.
Whoops! We ran out of time just trying to randomly recombine correctly a 23 letter and space phrase. You see the probability multiplication rule is not so kind to the randomness of evolution thought.
This is true, as far as it goes. However, the author is dismissing some crucial ideas.
Firstly, both artificial selection and natural selection are more effectively modeled as sorting algorithms. The author's example here is what is known in computational science as a “bogo-sort”. It is the equivalent of throwing all 52 cards of a standard deck, picking them up, and then looking through them to see if they are in order. If any of the cards is out of order, the whole deck is thrown up in the air again and started over. This is mathematically provable as the worst way to sort any array.
A smarter way to go about finding the phrase 'the theory of evolution' is to pick 23 random letters. Then, if any letter matches one that you're looking for, you keep it and randomly select for the remaining letters. For example, on the first attempt, there is a one in 27 chance that you'll get an “t” for your first letter, a one in 27 chance that you'll get “h” for the second letter, and so on.
So, there's a 26 in 27 chance that you won't get the 't' right, a 26 in 27 chance you won't get the 'h' right, a 26 in 27 chance that you wont get the 'e' right, und so weiter. Ultimately, the probability that you won't get any of the characters right the first try is (26/27)23. That looks like an intimidating number, but it works out to about .42. That's 42 times out of 100 that you won't get any characters correct the first time. Which means there's about a 58 in 100 chance that you'll get at least one right.
Once you've gotten the first character in place, then you keep that and throw the others out. Now the chance that you wont get any additional characters right on the next throw is (26/27)22, or about .44. Now you've got a 56 in 100 chance to get the second character. The process will continue for each successive character. Yes, I know it looks fishy, that the probability of getting at least one letter right goes down as you've gotten more of them right in the past, but trust me on this one. Think about rolling 2D6, and trying to get at least one six. For each individual die, the chance that you don't roll a six is 5/6. When calculating cumulative probability, you multiply the individual probabilities together. So your chance of not getting a six on either die is (5/6)*(5/6) or 25/36. 25/36 ~ .57/6. .57/6 < 1/6 which is the chance of getting at least one six on one die.
If we multiply the probabilities of getting another character correct each time we try, we can get the overall probability of getting what we want. The whole process works out to our getting what we want in about 2.7 in a thousand. Ultimately, the average number of tries our chance machine will have to go through to reach 'the theory of evolution' is actually about 371.38. That's a heck of a lot better than 8.3 x 1023.
If we're really smart information theorists, we can increase our chances still further. We know that in English, some letters occur more often then others. For example 'E' is more common than 'X'. So instead of weighting each possible letter the same, we instead randomly choose our letters out of a regulation Scrabble (TM) set. I haven't done the math on this, but we'll reach our goal much, much faster.
This is how we believe both artificial and natural selection function. Minor variations occur in offspring, because of environmental factors, recombination, and genetic mutation. Useful variations atr kept and “stored” in the DNA. Eventually, minor variations add up to cause significant genetic drift and speciation.

Section 3-10 coming Real Soon Now.

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Googlebombing for a cause: www.minnesotangos.org

1 comment:

Doc Anne said...

Where did you get your brains?