New Paper Demonstrates Superiority of Design Model

(Curtis Henderson) #88

I haven’t given it a great deal of thought since I believe God used evolution as his creative tool. The current paradigm works well enough for me to convey to my students the mysteries of life, my awe of the Creator, and the processes that are the most likely explanation for scientific observations of life in the planet today.

But… something that might interest me if I were an ID scientist would be following up on the ENCODE project. ID and YEC scientists alike have stated that this project has overturned our concept of genomes and provides evidence that our understanding of genome evolution is faulty. Perhaps going beyond just stating that much more of the genome is “functional” than currently thought, it would be possible to pursue specific functions for specific parts.

It is my belief that avenues of research are available for ID (and even YEC) research. YEC scientists like to write a lot and some even do computer-based research, but the ID scientists that actually have laboratory facilities could pursue some interesting research that would be publishable in standard scientific journals, not just Bio-Complexity.

(GJDS) #89

I like to think that scientist would ask questions and examine new ideas, and this seems valuable for biological sciences because of the great complexity of the area of research. Unfortunately practicalities mean most (or all) research funding ends up on work within the paradigm, and short of an earthshattering idea, there is little to support effort on new ideas.

From my perception, I am of the opinion that too much effort goes into disagreements - the ID folks also seem to struggle with a nebulous notion of intelligence and somehow tied to nature and design.

(Cornelius_Hunter) #90

Sorry for the shorthand–I’m talking at the scientific level, because that is the claim. Earlier I had explained that in a bit more detail. As I explained, a consensus claim in the literature is that natural law and random chance events are sufficient to explain the origin of the biological world. This claim is not limited to biology though, and it is held in cosmology, geology, etc. So the claim really is that natural law and random chance events are sufficient to explain the origin of the world, period (i.e., a spontaneous process must be adequate to describe origins–naturalism must be sufficient).

Now anyone can say anything about what that all means, and how they interpret it. You can say there is no God, or there is a God, that God uses this process cleverly and simply is working in a way that His divine action is not observable, so that from our perspective it all can be described as merely natural law and random chance events. You can say God is “self-limiting” or whatever. You can interpret Genesis, or any holy book, any way you want. That’s all fine, that doesn’t go against the rules, and most evolutionists will not be too greatly bothered so long as you uphold that naturalism is sufficient.

But you can’t say natural law and random chance events are insufficient; or that any kind of external input is observable. That goes against the rules. You won’t get published, journals won’t accept your papers, you won’t get hired, you won’t get tenure, you won’t pass Go and collect $200, etc., etc.

On top of all this, the overwhelming, consensus position, is not only that naturalism is sufficient to explain the world, but that this is overwhelmingly established. The common terminology is that evolution is a fact, or that it would be irrational to reject it, or that it is beyond all reasonable doubt, etc.

This is, of course, itself an irrational position. But there it is.

I normally don’t go to the trouble to type all that out, and instead merely use the shorthand that if you’re an evolutionist, you must believe that naturalism, or a spontaneous process, is sufficient. You must believe that using the scientific method, and empirical observations, a good (neutral) scientist would be compelled to conclude evolution is “a fact.” The world must have arisen spontaneously, at the scientific level.

Sure you can play around with the word random, you can say God works mysteriously at the quantum level, or whatever. You can hold whatever metaphysics you like. But at the scientific level, you must agree that naturalism is sufficient.

This is a completely irrational view. We can point to all kinds of examples, in the history of science, of mistakes, false conclusions, erroneous hypotheses, non scientific influences, etc., etc. This one is right up there. There is simply no way to establish this claim. It is not supported by the empirical data or the science. It simply isn’t. And yet there it is.

(A.M. Wolfe) #91

Well… sufficient for what? And observable in what context?

Most of us here at BioLogos believe in a bodily resurrection, that can be believed logically and understood as a miracle. This was observable, and naturalism is not sufficient to explain it. Granted, it’s not repeatable, and it’s not a scientific conclusion. But it flies in the face of naturalism and is observable.

Does your model of non-deniers-of-evolution allow us to believe this?

Well, I disagree, but as my degrees are in another field, I’m sure it won’t matter much to you, and that’s fine by me. I will merely say that your presentation here hasn’t convinced me, if you were aiming to convince people… but perhaps that’s less important, I don’t know!

Honestly, if I may say so, quite sincerely, I actually sort of admire your level of confidence in what you’re saying. Even as a convinced evolutionary creationist myself, with no interest in the common-descent-denialist literature, I find comfort knowing that there will be people who will feel reassured in their faith in God because of your extreme level of confidence in the paucity of evidence for the evolutionary model. I hope that works for them. These are issues we all struggle with, and I don’t judge those who retain their faith in God by rejecting evolution. (I just hope they won’t attack others who believe differently on the subject…)

(Cornelius_Hunter) #92

To follow up, did you notice how much skepticism and push back there is here at BioLogos to this paper which I posted about? Here we have a paper with a new model that works a lot better than the traditional model, for understanding species classification. I’m getting all kinds of resistance and complaints. Complaints about the journal, about ID people, about the way things are done. All kinds of skepicism of an excellent piece of work, that is using best methods and practices. Of course, the result of the paper is a model that contradicts evolution. Hmmm.

(Cornelius_Hunter) #93

Yes, of course. The context here is origins. The Leibnitzian approach, which seems to hold today, is that miracles for purposes of grace are OK. Otherwise, not OK.

Well academic background doesn’t really matter. You don’t need to be a life scientist, or scientist at all, to see this. Why do you disagree?

Hmm, “denialist” is not too flattering. Am I also a “flat-Earth-denialist”?

(Mervin Bitikofer) #94

Complaints? Or observations and push-back? I might have missed a few things above, and I don’t doubt that IDers do put up with all sort of ad-hominem (which seems notoriously difficult for all parties to agree upon – one party’s “observations of facts” is another party’s “ad-hominem”.) So I don’t think that noting that a certain model or proposal hasn’t been published in any peer-reviewed journal (other than a specialized ID journal) is inherently ad-hominem, for example. But if you are being attacked personally or unfairly, then we moderators aren’t doing our jobs -which is quite possible, and we do need help. Feel free to point out objectionable content, and be specific about the exact sentences if you do. You are indisputably in “hostile territory” to your views, and so to be commended for your courage to stand up here for what you see to be true.

This conversation looks interesting to me as a non-specialist, and I hope to follow more exchanges to the best extent my limited knowledge allows. It also looks to me as if @cwhenderson finally gets what he had been waiting for: a proposed model from you (the “dependency graph model”), though it sounds like you still have some selling to do on that before others knowledgeable in the relevant fields would find it convincing enough to begin to engage in details. Carry on!

(T J Runyon) #95

I suggest you all head over to the peaceful science forum. Winston is actually discussing the paper with swamidass and the conversation is a lot more pleasant than this.

(Cornelius_Hunter) #96

No, I wasn’t referring to this forum.

(Cornelius_Hunter) #97

Any hints on how to find it? Do you have a URL?

(Stephen Matheson) #98

I have only skimmed the paper, but have already seen that the author does not claim, and in fact disclaims, that the paper undermines common descent. He admits that the paper ignores multiple known facts of evolutionary genetics and natural history, but insists that this should not hinder consideration of his model. To some extent, I agree. Here is the text I refer to, from page 18 of the paper:

An obvious objection is that we have not included any of the mechanisms thought to account for nonhierarchical data such as incomplete lineage sorting, gene flow, convergent evolution, or horizontal gene transfer. As such, it might be argued that any of the features of the data interpreted as evidence for the dependency graph may also be explained by these mechanisms. The focus of this paper has not been to critique common descent, but to the test the predictions of the dependency graph hypothesis. The challenge to common descent lies not in the comparison of the tree and dependency graph models but in explaining the successful predictions of the dependency graph hypothesis.

What I found a lot less clear was the reasoning or data behind some of the specific scientific claims. Here is the one I am most interested in, from the beginning of the section “Small examples” on page 12:

We will now consider a few small examples of cases which our model-fitting method inferred to be explained by modules. A striking example can be found in Nematostella vectensis (scarlet sea anemone) and Branchiostoma floridae (Florida lancelet). These are distantly related organisms with the anemone being in the phylum Cnidaria and the lancelet being in the phylum Chordata. Nevertheless, they contain between 25 and 564 (depending on the database consulted) gene families found in both species but in no other metazoan species in the database. In all datasets where both species are present, a module is inferred to exist to explain the genes found in both species.

I strongly suspect that this claim is an uninteresting artifact of how the author is interpreting a database, and specifically how he is building conclusions from the fact that something is “not found” somewhere, but if there is any basis to it at all, it would be very interesting. Does anyone know, or can you tell, where he even got this? I’ll look harder too.

Why Aren't the Twin Locations of >100k+ ERV's (human vs. chimp) Discussed More?
(Chris Falter) #99

My understanding is that the theory of evolution is mathematically modeled as a stochastic process. Consequently, its predictions are probabilistic.

From a practical perspective, what does this mean? It might be helpful to look at another well-known, stochastic domain for insight. The domain I choose for this example is sports predictions (not that I wager money on anything personally).

Sports: Another Domain of the Survival of the Fittest

Let’s take a look at the 2018 World Cup. At the onset a month ago, some of the teams (Saudi Arabia, Morocco, Peru, Russia) seemed very weak and unlikely to advance even to the knock-out round. Others looked very strong and likely to go far into the knock-outs (France, Germany, Brazil, Belgium). How did these predictions fare?

They were mostly right, but there some notable exceptions. Tiny Croatia advanced all the way to the final, but mighty Germany did not even reach the knock-out stage. Huge exceptions!

Does these exceptions mean that Germany has a weaker team than Croatia? Almost certainly not; if they played a 20-game match over a 4 month period, I would expect (with near 100% certainty) Germany to prevail. Because the competition is not structured that way, exceptions are expected; outcomes can be predicted only probabilistically in a noisy, stochastic process such as a soccer tournament. (For our international readers, that’s football, not soccer.)

Do these exceptions mean that our basic premise–that stronger teams can be identified in advance and they can be expected to perform better–was wrong? Overall, no. Collectively, the stronger teams (France, Germany, Belgium, Brazil) performed far better than the weaker teams (Saudi Arabia, Morocco, Peru, Russia), in spite of the occasional contradictory outcome. The noise did not erase the signal.

Evaluating Models of Biological Origins

(1) Evolution

Evolution, as I mentioned, proposes a stochastic model. From the practical standpoint, this implies quite curiously that evolution predicts confounding observations–noise–just as sports prognosticators expect that occasionally Germany will fall early and Croatia will advance to the final. Biologists have even identified some of the confounding factors in evolution: for example, convergent evolution in phylogenetic trees, and incomplete lineage sorting in genomic-built trees.

At the same time, evolution predicts that that the forces of drift, mutation, flow, recombination, and natural selection will result in

  • transitional fossils;
  • the appearance of more homologous endogenous retroviruses in populations as they are situated more closely on a nested hierarchy;
  • adaptations, exaptations and vestigial structures will appear throughout the domain of biology;
  • pseudo-genes throughout the domain of biology,
  • homologous pseudo-genes being more common among populations that are closer in a nested hierarchy; and so forth.

And indeed these predictions are borne out in observation.

(2) Design Model

I am not able to glean any predictions from Ewert’s paper, since he specifically rules out the validity of using his paper to draw comparisons between evolution and dependency graph models:

The focus of this paper has not been to critique common descent, but to the test the predictions of the . dependency graph hypothesis. (p.18)

In my next post, on the shortcomings of Ewert’s methods, we will see why Ewert recognized that his paper could not be used the way that ID proponents would wish to use it.

This does not prevent our friend @Cornelius_Hunter from critiquing common descent on the basis of this paper.

What I would like to see from the mild-mannered Biola professor (who leaps tall buildings in a single bound?) is what predictions a design model would make about data other than component-based vs. tree-based modeling. What would a design model predict with regard to:

  • Homologous ERVs - Should ERVs be found in homologous or orthologous locations under ID? Should the ratio of homologous to orthologous ERVs vary based on the taxonomic distance of species?
  • Vestigial structures such as the sightless lenses of marsupial moles where eyes are expected?
  • Pseudo-genes (i.e., vestigial genes) such as the vitellogenin and vitamin C genes in H. sapiens.
  • Noise. Dr. Hunter complains incessantly when conventional biologists attribute confounding observations to noise that nevertheless does not erase the evolutionary signal. Given robust predictions by a design model, would the design model nevertheless predict noise in real-world observations ? If so, why?

Dr. Hunter, I would appreciate very much hearing your thoughts on these questions.

Until we can get these predictions formulated, there is no way to compare evolution with a design model. Suppose for the sake of argument I were to grant Dr. Hunter’s assertion that, at the gene family level, dependency graph is 104 bits superior to a tree model. Given predictions by a design model for other classes of data such as ERVs, vestigial structures, and vestigial genes, we could still discover that the tree model is 1010 bits superior to a dependency graph. This would overwhelm the gene family model-building data.

In the absence of any predictions by a design model with regard to these classes of data, though, it is impossible to make any reasonable comparisons between a design model and evolution, other than to say that a design model is better at predicting one class of data, but evolution is infinitely better at predicting many classes of data.

If this is the choice that scientists must make, I suspect the vast majority would prefer a model that predicts many classes of data well over a model that predicts one class really well but is unable to make any other predictions.

Chris Falter

Signal vs. Noise, Part 2: Hunter Opens the Klassen Study Again
Signal vs. Noise, Part 2: Hunter Opens the Klassen Study Again
(Jon Garvey) #100

Peaceful Science.

Please observe Joshua’s Ts&Cs, guys - we’d like to keep the conversation more pleasant than this, as per @T.j_Runyon

(Matthew Pevarnik) #101

Can you perhaps explain to me what a module is that gives genes to these species that is not any kind of common ancestor?

(Cornelius_Hunter) #102

Well perhaps it is not interesting, but what the paper is pointing out in this section are examples that don’t fit common descent, and the dependency graph models as “modules.” Figure 8 gives a nice illustrtation, though it takes awhile to soak it in.

(sy_garte) #103

Dont you find that odd? Especially considering that the HomoloGene data set gave the best performance for the tree model. A different dataset (Table 4 in Results) gave the result as >500,000 (vs. 10,000 for Homologene). I have no idea what the anti log of that might be, but the enormity of these probability differences worries me a bit. Not to mention the disparity between the data sets, which calls to mind @glipsnort’s comment about missing data in these data sets. So I am curious about the use of other statistical methods comparing these models. Do you know if Ewert tried other statistical tests using p values or confidence levels, before going to the Bayesian method? And what those other tests might have shown? I would not be surprised if they showed some improvement of the DG method over the tree, but I would be surprised if t statistics were in the range of billions in difference.

The reason I wouldnt be surprised, and my main concern about the approach (but I might have missed something since I only read the paper once) is it seems to be comparing the DG model with an evolutionary tree model that ignores convergence and gene flow, by focussing on absence or presence of gene families, and not functional ontologies or gene sequence differences (as @T_aquaticus repeatedly pointed out).

Finally please not that Occam;s razor is not a scientific law of nature and is completely useless in biology, so it isnt surprising that evolutionary theory violates it continuously. So, after all, does basic biochemistry.

(Cornelius_Hunter) #104

Well this paper does not explain the mechanism. Just as geocentrism, heliocentrism, common descent, etc., are models which, by themselves, are not explaining mechanism, but rather are describing relationships.

(Stephen Matheson) #105

I’m not sure I understand the whole question, but here’s my best shot.

I think “module” is a very rough concept he is trying to develop, in which gene families are analogous to objects in OOP (or subroutines for older folks like me). If I’m reading him right, he’s trying to treat gene families as free-floating modules that can be employed in combinations the way objects in OOP or parts in manufacturing can be. That part of the reasoning is basic and unoriginal. For it to have any explanatory potential, it would have to also correlate with function (my opinion, anyway). So for example, if “modules” aren’t well explained by common descent, they ought to be explainable by common function. Otherwise, the whole module rationale is ad hoc.

But then you ask, I think, not sure: “…what…gives genes to these species…” and the answer is a “designer.” (End of page 3.) I assume this is Alanis Morissette, but that’s the deep thinky stuff of theology that is way above my head.

(Cornelius_Hunter) #106

I’m not quite following the concern here. The fact the different datesets give different results would be expected. You wouldn’t expect these results to be similar, because they are based on quite different data.

Well the large numbers, in themselves, are not too surprising. You’ll get those if one model is superior, when you have a large data set. Several folks have expressed this concern with the large numbers. But that is what you will get if one model is clearly better. Regarding other methods, I agree, it would be interesting to try them out, for sure. I have no idea what Ewert attempted in his work.

Well, interesting point; however, the concept of parsimony is really crucial in data analytics. And too often analysts don’t pay attention to it. That’s why there are good model selection methods available these days.

(Cornelius_Hunter) #107

I will say, however, that this isn’t going to make much of a difference. The Bayesian model selection isn’t going to give big differences from, say, AIC.