You guys…all I can say is you’re too young! I think it may very well have started with the pin ball machines. But it morphed into an expression that means, “I’m done for today.” My gin martini made it go away!
Tomorrow is a new day, and I’m coming after you! So get ready.
Elton John? That was The Who from the rock musical Tommy.
Kids…
Elton John’s 1975 recording was way better, and it comes from the Tommy film that same year. And there’s a version with both Elton John and The Who.
But I’m hopelessly biased. Elton John’s Greatest Hits Vol. II ended with that track, and I bought Pioneer headphones with my lawn mowing money so I could listen to that album LOUD without being asked to sleep in the back yard. It was the first album I ever bought, also with that lawn mowing money.
1975 would have been my 1st year in med school. I don’t think I listened to anything but lecture tapes the next 4 years.
While you were all starting med school or blowing your lawn mowing money on Elton, I was playing with tinkertoys and reading about the exploits of Dick and Jane at school.
Geezers.
James post has caused me to reflect at bit on the different perspectives of the two groups, evolution and ID with his good questions. It has prompted me to go back and look at some to the assertions made by the evolutionists and their sources to try and understand the thinking behind their perspective. As you know, I have observed what I see as a significant levels of extrapolation of data to fit the evolution model. It’s concerning and should be seen by all as a risk. I have noted it in this forum and been challenged saying that extrapolation is an intrinsic part of the scientific method. No argument from my quarter. The important question, however, is, “What level of extrapolation is legitimate and what is the threshold where we see extrapolation lose its validity?” An analogy might be helpful. Assume one performs some experiments and plots the data on an X-Y graph to determine the relationship between two variables. If one draws a line through all the data points and observes that every point falls on a straight line, one would be tempted to conclude the relationship between the two variables is linear. But what if the real relationship between the variables was defined by a Nth order polynomial? If that were indeed the case, then clearly the problem is that the extrapolation was based on either plotting too few points because the researcher didn’t have enough data (badly designed experiment) or the researcher tossed out the data points that didn’t fit. Either way, concluding linearity was incorrect for the example. The evidence you have provided me supporting the argument for evolution leaves open this question, viz., do evolutionists have enough data/have they excluded data to justify concluding that evolution is the best explanation?
The ID folks claim that the evolution model is too simplistic to explain the observed data, i.e., relating to the analogy above, the data suggests that the relationship of the variables is not linear, but higher order (inferred from its complexity). As mentioned above there are two questions that must be answered if we’re to find a confident solution. First, we must ask, “Is there enough data?” Second, we must ask, “Are evolutionist excluding data that doesn’t fit their model because of bias, ideology, or other?”
As I thought about this, I thought that it would be appropriate to go back and look at some of the data from the examples pointed to in this discussion which supposedly support macro-evolution. Maybe I’m missing something? (I can see a few of you saying, “No kidding, Sherlock!) So, I went back to some of the early posts in this forum and notice a post (#12) from Scott Buchanan in April 2017 where he was revisiting the issue of nylonase. Apparently, there was some disagreement over whether nylonase arose from a frameshift mutation (evolution) or it was a simple micro-evolutionary change of two amino acids well within the scope of the bacteria’s designed adaptability. Regardless of the cause, the part that struck me as significant from a SE perspective is that the evolution side is assuming that some small mutations (frame shifted or other) resulted in a new protein fold which in turn, resulted in a new function. For a Systems Engineer, that’s a hard pill to swallow! But back to my point. The point is that some extrapolation is going on, and its scope appears to be unjustifiably large in my opinion.
More generally, my observations looking at evidence adduced by evolutionists is that they see linearity (remember the analogy) in their evidence (fossil data, gene maps, etc). All the data they accept is consistent with the evolution model. Ergo, they feel justified that evolution is the best explanation of the data. I find problems in both dimensions of their argument. First, they don’t have sufficient data to rule out that an Nth order polynomial is a better fit, and secondly, they exclude data that doesn’t fit.
Regarding the former (that there’s not enough data), Looking at it from an SE perspective, the nylonase example is revealing. What’s is left out in the Buchanan post and the NewScientist article is an acknowledgement of the details of integration of the two proteins involved in the nylonase capability with other proteins in the bacteria to make the nylonase enzyme capable of digesting nylon. What’s inferred is that these two mutated proteins have sufficient standalone function that no coordinated action from other proteins or protein structures including C4 functions (Command, Control, Communications, and Computing) is necessary for the bacteria to be able to digest nylon which it didn’t have before the mutation. As I said earlier, that’s a hard pill to swallow.
If, however, there is “rich interdependence” of protein function in the cell where many of the proteins cannot work and perform higher level function alone, but must do in synchrony with other proteins and possibly even synergy, it’s unlikely that all the proteins involved in a particular function each mutate to either a new protein (different sequence or more amino acids) or a new fold so that they could collectively perform the new function such as digesting nylon. This is the fundamental issue that the SE perspective identifies as problematic for the evolution narrative. Isolated random changes in one or a few proteins cannot by themselves bring about phenotypic change unless all the proteins necessary to bring about that new function are established in coordination with each other to make that function possible. I stated this at the beginning (post #13), and you all have pushed back, particularly Stephen (see post #14) saying that “we know this to be false ……because of robustness in molecularly biology.”
Apparently, my point is still not clear. Maybe that can be remedied if I explain the assumptions behind the statement, i.e., that the function involved results from coordinated activity of many proteins working together as a coordinated whole. If this is not true, as Stephen claims because of robustness, then you must admit that one of two conditions must exist in the cell: 1) The proteins are not interdependent, i.e., they (thinking of a bacterial cell) can each perform phenotypic function apart from dependence on any of the other proteins in the cell, or 2) if they are dependent on other proteins in the cell, they have (via evolution) built in fault tolerance so sophisticated that any protein in the cell can mutate and all the cell functions that depend on that protein will tolerate any aberrant function caused by that mutation. Said another way, in the cell (a “protein ecosystem”) where most cell functions require significant levels of protein coordinated action to perform a function, mutation(s) of a single protein may not bring about a new function unless all the other proteins that depend on the one mutated also mutate with serendipity if a new function is to be realized. Actually, it’s even more challenging if you think that a new function may arise from a single mutation when that new function is likely to require coordinated action (physically & temporally) of many proteins. Can we get a new function (F2) from a group of 500 proteins previously working together to produce an old function (F1) by randomly changing only one of the 500? If you say yes, are you not implying that there are a very large number (500 factorial – my computer will only calculate up to 170 factorial which gives a number over 1 x 10^306) of useful functions in the cell each the result of a single nucleotide mutation? If functional amino acid sequences are truly rare, doesn’t this suggest that a single nucleotide mutation giving rise to a new functional sequence is unlikely?
An example of the required coordinated action of proteins is found in one of articles in the New Scientist titled “Five Classic Examples of Gene Evolution.” (24Mar2009) where it suggested that our three color-sensitive retina pigments, or opsins, evolved from the two-colored version in other animals such as dogs. Here is the author’s logic:
So why do we have three? In the ancestors of apes and some monkeys, the gene MWS/LWS – which codes for one of the two pigments found in most mammal eyes – was duplicated. Spare gene copies usually degenerate quickly as they acquire mutations, but in this case mutations in one copy resulted in an opsin that could detect a different spectrum of light. In this way, we gained better, trichromatic color vision. (emphasis mine)
What I find remarkable in this example is that the author is assuming that a mutation in one copy of a gene resulted in an opsin that could detect a different spectrum of light. Think about that for a second. What does it take for the entire vision-brain system of systems combination of many proteins and functions to see a new spectrum (new range of light frequencies)? The engineering required to change the entire chain of hardware (proteins) from the eye to the brain to perceive and make use of new range of light frequencies is huge. In the NewScientist article the author suggests that a few mutated genes can make that happen and that that is evidence of evolution. That reflects in my engineering mind a stunning level of naivete. A huge amount of data is missing/ignored that suggests evolution is not a good model. To say that that our three color-sensitive retina pigments, or opsins, evolved from the two-colored version in other animals such as dogs from a mutated gene MWS/LWS is amazing.
This article illustrates well the problem of extrapolation. Biologists know well how vision occurs. The biochemistry of vision is astoundingly complex especially with the way it processes the light signal from initial detection of the photon into an image in the brain that results in an actionable abstraction at the phenotype level? (See Behe’s book “Darwin’s Black Box” for good overview of it.) If a mutation occurs only in the gene MWS/LWS and other very specific mutations don’t occur in all the other proteins and higher-level structures used in processing this new frequency of light, how could that single mutation possibly provide the functional advantage that would allow natural selection to enable it to endure? Hopefully, this example illustrates missing data problem, and how evolutionists draw sweeping conclusions when not enough data is available.
The other dimension to the problem regards excluding data that doesn’t fit their model. The best illustration of that is the book “Contested Bones.” It provides a large number of important scientific facts supported by original sources to confirm the data about hominids and also about radiometric dating. Yet you guys rejected it because it didn’t agree with your sources. That’s the point. There are other legitimate sources that disagree, and your response is to throw it out. If there were a major theme of that book, it would be “There’s data out there that evolutionists are ignoring.”
Don’t you see the risks to your model in tossing out data because it doesn’t fit your narrative? Is that scientific?
To sum up, after reading James’ post, I felt it appropriate to do some reflection and review our discussion over the past month. It seems that the differences we have are pretty much centered on the evidence/data we are using to support our arguments. In that regard, two issues arise: 1) is the data sufficient to infer a best explanation (evolution or ID), and 2) are we forcing the data to fit our model by excluding outliers? I offered an analogy that showed the problem of not enough data so that a linear relationship could be inferred rather than a Nth order polynomial. Failing to acknowledge there’s insufficient data is a legitimate concern. How is this manifesting in the evolution argument presented by the evolutionists? The NewScientist article illustrates well the problem of interpreting without having all the data. The second issue regards exclusion by evolutionists of legitimate data that doesn’t fit their narrative. The reaction to the book “Contested Bones” illustrates that well.
The article is a brief news piece, in a magazine, about how genes evolve. It’s 10 years old.
The author knows that the opsin can detect particular wavelengths, and knows that phylogenetic analysis points to gene duplication. The author may also know that gene duplication is common (it happens in humans very regularly) and that the genomic evidence for this particular duplication is specific and strong. He made no particularly remarkable assumptions.
It would be interesting to see analysis and evidence to support these claims. There is a lot we don’t know about color perception, but the most recent work suggests that it involves comparison (in the brain) of signals coming from different photoreceptors. The new pathways required to add comparison of an additional photoreceptor would definitely not require vast (or even remarkable) new proteins or cellular systems. Don’t get me wrong: rewiring brains to add new capacities is a very interesting research problem, and I don’t mean to suggest that such changes in evolution are trivial. But there is no scientific reason to propose the vast new systems that you claim are needed.
The first part (“can make that happen”) is to me an unreasonable misreading of what the author wrote in a few paragraphs in a news blurb. The second part “that that is evidence of evolution” is false.
Think carefully about what you’re saying here. The authors of Contested Bones obtained their scientific facts about hominids from original sources. Who produced the original sources? “Evolutionists,” in your parlance. Yet, the authors of Contested Bones claim that the evolutionists who produced the data are ignoring the data? This strains credulity.
We don’t reject the sources because our sources disagree. We are looking at exactly the same sources as your authors and challenging their conclusions, because their conclusions don’t fit the very data that they claim to have studied for four years. Worse, they twist the data to make it fit their conclusion, which is neither scientific nor honest. That’s the point.
@Raymond_Isbell Did you watch any of the multi-hour critique of CB on the YouTube link I provided?
I watched about half of it, and was very unimpressed.
I think your eisegesis core is overheating. My point is clear enough, and book is a good illustration even if you don’t agree with it. As we continue with the discussion I’ll provide more examples.
Why doctors should not be computer (or systems) engineers.
I’m probably too sensitive on this issue since part of my background involved designing the next generation of classified imaging satellites for out national systems. I know how hard it is to build an image chain from photon detection in orbit to display of the image on a computer. it makes your head hurt. There are so many linking subsystems in the image chain that thinking that the human visual systems and its image chain analysis can be deciphered from looking at gene duplication is a bridge too far.
My point in this comment is that I think evolutionist are not truly understanding the complexity and the role it plays in mapping the observed data to their model. A simple linear overlay of the data won’t get the job done. We’re dealing with data that is better represented with a Nth order polynomial, and that points me to design, not evolution.
You keep saying things like “design not evolution” and that you are choosing between “design or evolution” but you have never responded to the fact (at least not that I read) that several of the prominent ID writers accept common descent and evolutionary mechanisms as a given. It’s not this false choice, either-or, you keep presenting it as. Why do you keep framing it as if design is mutually exclusive with evolution and if design is compelling therefore evolutionary explanations must be entirely wrong (as opposed to insufficient)?
So much for considering both sides.
Since I don’t have access to the book would you care to summarize the argument that radiometric dating is completely wrong?
Regarding ID writers who accept common descent is something I’m researching now. I didn’t realize they did. My understanding coming into this was just that…common descent is the result of evolution. I’m going back and rereading the ID literature to see how I misunderstood. Logically, they seem to be incompatible to me. I’m learning, however.
If however, the YEC position is true, then common descent doesn’t have enough time.
I also don’t read or listen to flat earth stuff. If a horse stumbles out of the starting gate, it’s out of the race.
Maybe later when we get into that aspect of the problem. You can see the review I wrote of the book on Amazon. Amazon.com I didn’t go into any detail in the review, but noted that it was compelling. Here’s what I said about the chapter on Radiometric dating:
Of particular interest to me was the Chapter on Dating Methods. Evolution requires time for the Neo-Darwinian mechanism to work. Can the earth be shown to be 4.5 billion years old? Rupe examines carefully the argument behind the most popular dating techniques (e.g., potassium-argon, argon-argon, Uranium-Thorium, and Uranium-Lead.) The depth and clarity of this chapter convinced me that the dating methods are not reliable. Rupe’s analysis is impressive and critics will strain to find fault. He supports his case with many citations from the scientific literature. This is the only book I’ve found that treats the subject with sufficient rigor to be convincing.
Are you familiar with the RATE project from ICR? They went to great lengths to come up with a way to explain away the radiometric dates and were unable to do so. But Rupe was able to do so using papers that support the method? Neat trick.
Evolution explains common descent. The fossil record, which was the first evidence for common descent, was established before Darwin. In fact, if my memory is correct, the fossil record was one of the reasons Darwin started to think about evolution.
“Let your conversation be always full of grace, seasoned with salt, so that you may know how to answer everyone.” -Colossians 4:6
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