I need a sanity check and thought y’all may could help.
Designarism defined: oddXian: Challenge the Consensus: Designarism: A Comprehensive Framework for Understanding Reality
Preliminary exploration:
From the first blog bit (it doesn’t qualify as a paper), looks like SOS,DL (Same Old Stuff, Different Labels).
As for the second, Shannon entropy is beyond me, but the recurrent assumption that non-randomness indicates design is based on the straw man assertion that evolution is random. Something from geology comes to mind: rivers flowing through ground composed of relatively small sediments result in patterns that display order, yet no one would argue that those patterns were designed.
So it looks to me like just another attempt to use scientific-sounding jargon to prop up subjective impressions/wishes of how the world appears to or ought to work.
2 Likes
There’s too little information presented there for me to understand what you’re doing. First, on the dataset. You randomly selected viruses from what dataset? All viruses anywhere? Some specific viral taxon? Under what sampling framework were the sequences collected?
Second, on the comparison with random trees: they were randomly generated under what model? Was it a random evolutionary process?
There are probably other serious problems with the analysis, but before trying to understand the application of the algorithms, we need to understand the basics of what you’re doing.
2 Likes
From your website:
Despite being a random sequence, the tree exhibited signs of teleological behavior.
Did the Designer, with malice and intent, purposely design viral killing machines?
Or did the viruses “fall”, and the virulent copies represent corrupted, non-designed sequences?
I think that your teleology meets great difficulties in theodicy.
2 Likes
Let’s break this down point by point, and I’ll explain where I’m coming from.
- “Looks like SOS,DL (Same Old Stuff, Different Labels)”
I understand why you might feel that way. It’s easy to lump design arguments into one category and dismiss them as rehashed versions of Intelligent Design (ID). But here’s what I’m doing differently. I’m not just rebranding old ideas—I’m using quantifiable, rigorous methods like Shannon entropy and Complexity-Specified Information (CSI) to actually measure patterns in biological systems.
When I look at non-random patterns in a phylogenetic tree, I’m not just slapping the “design” label on anything complex. I’m running actual comparisons between real-world data and random models. If I find that a tree’s structure is significantly more ordered than what randomness would produce, I think it’s reasonable to explore why. This isn’t just about saying, “evolution can’t explain this,” but about genuinely probing whether something else—something that might point to design—could be responsible.
What’s new here is the level of rigor. I’m not asking anyone to take the design inference on faith or intuition. I’m using measurable patterns in things like viral evolution, where randomness plays a huge role, and applying methods that help us see if there’s more at work than just stochastic processes. It’s a methodical approach, not SOS,DL.
- “The recurrent assumption that non-randomness indicates design is based on the straw man assertion that evolution is random.”
You’re absolutely right that evolutionary processes aren’t purely random. Mutation introduces randomness, but natural selection is a non-random process that “chooses” advantageous traits. I get that. But here’s what I’m doing: I’m not arguing that evolution is completely random. Instead, I’m measuring the degree of order or optimization in systems like viral genomes.
What I’m trying to figure out is whether that optimization goes beyond what we’d expect from natural selection and random mutation. Is there more order than can be explained purely by natural processes? That’s the key question.
Your analogy about rivers forming patterns in sediment makes sense. Yes, they follow physical laws like gravity and fluid dynamics, and no one would argue those patterns are designed. But in biological systems, we’re dealing with information-rich structures—things like DNA and proteins that go beyond the predictable patterns we see in nature. That’s where the difference lies. Rivers follow physics, but living systems encode functional information, and that’s a whole different ball game.
- “Shannon entropy is beyond me, but…patterns that display order, yet no one would argue that those patterns were designed.”
Let me try to simplify it: Shannon entropy is basically a way to measure how much disorder (or randomness) there is in a system. So, when I talk about low entropy, I’m saying that the system is highly ordered. But I’m not jumping to conclusions and saying that just because something has low entropy, it must be designed.
You’re right to point out that order doesn’t always mean design. A river flows in an orderly path, but that’s not because someone designed it that way. It’s just following natural laws. The thing is, living systems—especially things like viral genomes—are more than just natural forces at work. They carry information, and that information does things—like coding for proteins, guiding cellular processes. So, it’s a bit more involved than a river flowing downhill.
What I’m asking with my analysis is whether the level of complexity and order we’re seeing in things like viral evolution is something that could reasonably emerge just through natural processes, or if it’s pointing to something more, something that might even suggest a design.
- “Looks like just another attempt to use scientific-sounding jargon to prop up subjective impressions/wishes of how the world appears to or ought to work.”
I can see why you might be skeptical. It’s easy to get cynical when people throw around scientific terms without much substance behind them. But in this case, Shannon entropy and CSI aren’t just jargon; they’re actual mathematical tools used in everything from biology to information theory and cryptography. They allow us to measure real properties—things like how much information a system holds or how ordered it is.
I’m not using these terms just to make the argument sound more scientific. I’m applying them to see if we can quantify the order in systems like viral genomes, compare that to random models, and ask: Does this order go beyond what random processes would predict? And if it does, does that point to something else, maybe even design?
It’s not about wishful thinking or propping up subjective impressions. It’s about asking objective, testable questions based on measurable data. When I see a tree structure with significantly more order than what I’d expect from randomness, I think it’s worth exploring whether something else—like design—might be responsible. I’m not rushing to conclusions, just using the data and the tools at hand.
So, while I appreciate the points you’re raising about randomness and natural order, and I get why you’re pushing back on the idea that complexity automatically means design - that’s not what I’m doing here. I’m using these mathematical tools to probe the degree of order and optimization we see in biological systems, and I’m trying to figure out if it can all be chalked up to natural processes or if something more might be going on.
This isn’t about trying to disprove evolution—it’s about seeing how far natural processes can explain the complexity we see and whether the patterns point to additional mechanisms, including design. I’m not simply pushing a philosophical agenda; I’m attempting to follow the data where it leads.
You raise some important points, and I’ll try to explain the rationale behind what I’m doing. I also want to clarify that this is very much a first step—an exploratory analysis to see if the approach has potential. The random selection isn’t arbitrary; it’s part of the hypothesis itself, and I’m looking for feedback at this stage to see if it’s worth digging deeper.
- “You randomly selected viruses from what dataset? All viruses anywhere? Some specific viral taxon? Under what sampling framework were the sequences collected?”
Great question. The dataset I’m using is not just a random grab from “all viruses anywhere”—it’s a specific dataset from a viral directory that includes carefully curated sequences from known viral taxa. These sequences were chosen to reflect evolutionary relationships across viral families, and the “random selection” I refer to is simply from within this curated dataset, not the entire universe of viruses. So, the selection isn’t arbitrary; it’s within the bounds of a structured dataset where the evolutionary context is already established.
The random selection is a critical part of the test. The idea is to avoid any biases that might come from cherry-picking certain viral families or strains. I want to see if, by randomly selecting from this dataset, we can still detect patterns of order or optimization in the tree that go beyond what we’d expect from random processes. If I find something promising, that suggests there might be a deeper principle at work that’s worth exploring further.
- “On the comparison with random trees: they were randomly generated under what model? Was it a random evolutionary process?”
Here’s how the random trees fit in. The trees are generated under a null model—in this case, a simple Yule process model. This model assumes a birth-death process for tree branching, meaning the trees are built without any assumptions of optimization or natural selection shaping the structure. These random trees are generated to act as a baseline for comparison, representing what the tree might look like if no evolutionary forces were at play.
By comparing the real viral tree to these random trees, I can measure whether the actual tree is more optimized or ordered than we’d expect from a random evolutionary process. The goal is to see if there’s a detectable pattern of optimization, such as shorter branch lengths or highly structured clades, that could point to something beyond randomness—something that might suggest deeper principles like design, optimization, or even evolutionary convergence.
- “There are probably other serious problems with the analysis, but before trying to understand the application of the algorithms, we need to understand the basics of what you’re doing.”
You’re right to want a clearer explanation before jumping into the algorithms. Here’s the bottom line: this is an exploratory analysis, not the final word. The random selection of sequences is part of the hypothesis—it helps test whether any patterns of order or optimization emerge, even without deliberate sampling bias. The random trees provide a baseline for comparison, helping us measure whether the observed patterns are likely to result from random processes or something more structured.
I’m not claiming that the current analysis is perfect or exhaustive. This is just the first pass—what I need now is an expert “sniff test” to see if the results hold any water. If the patterns I’m seeing show promise, it would justify further research with a more specific sampling framework, refined models, and deeper statistical analysis.
So, this isn’t me saying I’ve cracked the code on viral evolution or optimization. It’s more of a starting point—a way to see if there’s enough in these initial results to warrant more research. And that’s where I need expertise, like yours, to weigh in. Does this approach seem sound enough to explore further?
ID/Designatarism = a reformed TEist who just cant quite get past the notion the earth isnt millions of years old…they’re almost there, but just not quite 
YECism is so much simpler…we have just a single guiding textbook, the bible. Nothing else needed.
I understand the concern, and I’m not suggesting that the Designer intentionally created viruses as “killing machines.” That’s not the focus of my argument. What I’m exploring is whether we can detect patterns of order and optimization in biological systems, including viruses. Viruses, like other biological entities, show complex behavior, and that’s what I’m looking at—not their moral implications.
Now, on the theological side, some people believe that harmful things, like certain viruses, could be a result of corruption or degeneration of what might have originally been something beneficial or neutral. This idea doesn’t mean they were designed to harm but rather that, over time, they’ve become harmful in a way that wasn’t originally intended.
This is where theodicy comes in. Why does a world with a Designer allow for suffering or harmful entities? That’s a deeper question, and it’s separate from the teleology I’m looking at. My focus is on whether systems show signs of purpose or design principles, even in a world where some things have clearly gone wrong.
I’m not claiming that the existence of harmful viruses proves a malevolent Designer. I’m simply testing if there are patterns that suggest a degree of order or optimization that might hint at design—even in cases where those designs have gone off track or become destructive. The question of why that happens is a philosophical and theological one, not something this specific analysis is addressing directly.
Why complicate it…if the bible says sin corrupted “all creation”, that death came as a consequence of sin, why does one need to try to reconcile that viruses and bacteria can kill?
If we read the bible in context, the death through sin would have meant that prior to sin and the fall, viruses and bacteria were not harmful in any way. Whether that meant “within their own habitat or not” becomes irrelevant.
Ignoring the YEC issue for now…
I note in the first article it reasons that the observable evidence bears the hallmarks of design…
“The intricate machinery of life, from the genetic code to complex organs, bears hallmarks of engineering and purposeful design.”
These evidences align with the biblical model…they are not “proof of” when isolated, however, when collated with other evidences…the pieces do begin to align i think.
For me, when making a rational conclusion for the affirmative camp (that intelligence created), its just what makes most sense to me.
I am not convinced of the following conclusion in article 1…
“By providing a framework that more closely aligns with observed reality and our intuitive understanding of the world, these approaches have the potential to drive new insights and discoveries across scientific disciplines”
Both Christians and atheists tend to run away with the above and make it their own argument against the other…“only a true believer can be intuitive enough…”.
Im not sure i agree that it is only the “true” believer if you like who is capable of developing the best method and conclusion. The reformation has clearly shown us that there is no single “true” church or doctrine outside of “love god, love thy neighbour, and believe on the Lord Jesus Christ”.
What i really like about this forum is that despite individual differences, there is a genuine push for sound method thats well tested.
If one is to stay harmonius with the biblical model, we can not necessarily maintain that only Christians (or that even a particular branch of religion) have the best method.
Thank OdXian for posting this.
2 Likes
They must pick a lock into the host cell, and high jack it’s resources to their own benefit. That is enormously complex behavior indeed.
Smallpox
Influenza
Ebola
AIDS
Hepatitis
Zika
Dengue
Rabies
and of course COVID
…just to get started on a few virulent diseases or viral agents, all demonstrating the successful results inherent to their specified complexity. The behavior and moral implications are warp and woof and cannot be split apart, because if it is by design then both are inescapably matters of intent.
And how did that happen? Bad attitude? Of course, viruses are little automatons that execute their genetic code, and the only way they can become harmful in a way that wasn’t originally intended is to alter that code to enable novel patterns of order and optimization designed to infect.
2 Likes
Adam, from his wide ranging website I would surmise that @oddXian is YEC, and so you are both in the fold. Either way though, it doesn’t make much difference to his post here, or the response to it.
1 Like
Then you’re looking at exactly what evolution predicts. That’s what makes your blog post no different than other ID material; you’re just setting up a straw man and claiming a better explanation – SOS,DL.
This is especially true since you include the same old “complex, specified information” criterion that IS uses.
It may be more rigorous than the most simpleminded ID, but that doesn’t take much.
a compelling alternative to naturalism and methodological naturalism.
Compelling how? The post sounds like a thesis in search of evidence, not anything that comes from observation. It certainly isn’t more compelling when it starts out with a straw man.
By providing a framework that more closely aligns with observed reality
Which has not been shown since the post does not address the existing paradigm.
and our intuitive understanding of the world
Intuition has a great habit of being wrong.
the explanatory power and alignment with empirical observations
You have to pick and choose your observations to achieve that; there are too many examples of bad design in nature.
So far all the ID movement has been unable to find a single example of such.
Biological molecules follow physics, too, in the form of chemistry. The only information encoded in living systems that has ever been found can be summed up as a record of what works, with a lot of noise in the system as well.
So do crystals – that is, in fact, how catalysts work; they impose information on a more or less chaotic system and alter it.
3 Likes
Oh, if only that were true!
YEC’s real guide is “I think I’m so wise that I can understand ancient literature without needing to study”.
Science fiction – that’s not in the text.
Sorry, but this still tells me next to nothing about what patterns one should expect from randomly selecting from this dataset. The expected branch length distribution, for example, will depend heavily on how viruses are sampled. What actual dataset are you using?
But that contradicts what you wrote above, that the sequences were initially chosen to reflect evolutionary relationships across viral families – that implies that they were chosen at random.
I really don’t understand any of this. Why should any of the patterns you’re looking for suggest anything about design, optimization, or evolutionary convergence? If you construct a phylogeny of randomly chosen SARS-CoV-2 samples, you’ll find lots of short branch lengths and a highly structured clade (assuming I understand what you mean by the latter), simply because the virus has recently expanded into a new host. If you collect SARS-CoV-2 samples from December 2021 and from February 2022, you’ll find shorter branch lengths in the former than in the latter period, even though the virus had changed hardly at all in that time.
These examples are from one small branch of the coronavirus family, but similar effects can happen at any level of a phylogeny.
Real viral evolution should not follow a simple branching model because real evolution is always more complicated than that. What you need is a test that finds features that are specific to your supposed design process, but you haven’t explained why the features you’re testing for fall into that category.
My concern is that they don’t do that at all, as far as I can tell. What you’re testing is whether the real viruses are the product of the specific random process you simulated, not whether they’re the result of random processes. There are lots of random process that occur in real evolution.
Not really, no. As I said above, you need to be looking for some distinct signature of something, or else looking for a deviation from a genuinely expected pattern. Just showing that real data don’t behave the same as an extremely simple model just tells you that your model was wrong – but you had to know that to start with.
2 Likes
Did you look at the supporting example or interact with the algorithms and data, or did you dismiss the hypothesis out of hand? Just curious…
The objection seems to rest on a misunderstanding of what the patterns you’re looking for actually represent in terms of design, optimization, or evolutionary convergence. The question of whether something is designed or evolved isn’t about the mere existence of structure, short branch lengths, or any other random pattern. It’s about whether the pattern carries markers of purpose or intentionality that go beyond what you’d expect from purely natural processes.
Let’s start with phylogenies. Yes, you’re absolutely right that real viral evolution—like that of SARS-CoV-2—doesn’t follow a simple branching model. Evolution is messy, and we see short branch lengths when viruses expand into new hosts, as you mentioned. However, the existence of patterns like short branch lengths alone isn’t what proponents of design or optimization look for. What’s key is whether these patterns exhibit features that point to something more than random mutation or natural selection—whether there’s evidence of convergence towards highly specified, optimized outcomes that wouldn’t easily arise from random processes.
For example, design frameworks (like what you might find in “Methodological Designarism”) often look for instances of specified complexity—patterns that are highly improbable yet precisely tuned for certain functions. In SARS-CoV-2, if you find repeated instances of mutations that converge on similar solutions—solutions that optimize certain functions, like binding efficiency or immune evasion—across different evolutionary paths, that starts looking more like convergence due to optimization or design rather than random drift or chance.
You mention the need for a test that identifies features specific to a design process, but the very features we’re talking about—like repeated convergence on highly functional, optimized traits—are what we would expect from design. When evolution produces such optimization, it’s often through incredibly inefficient, stepwise processes over long periods. Yet, when patterns emerge that seem too rapid, too efficient, or too directed, that suggests a different causal explanation.
The key here is to differentiate between random branching patterns that can occur naturally (which, I agree, don’t suggest design by themselves) and patterns that reflect optimization, convergence, or specified outcomes that natural processes struggle to explain. Naturalism often attributes these to “just so” stories of selection, but in cases where the processes seem too efficient or directed, it’s more reasonable to consider intelligent input as an explanation.
In short, it’s not the short branch lengths or basic clade structure that suggest design; it’s the deeper, functional patterns that hint at convergence towards optimized solutions that go beyond what unguided natural processes tend to produce. That’s what I mean when I talk about evidence for design in biology.
The point you’re making is that just showing a model is wrong doesn’t prove design, and I agree with that. But design arguments aren’t about proving that simple models fail; they’re about finding patterns in nature that don’t match what we expect from random, unguided processes.
Methodological designists look for patterns like optimization, convergence, and complexity that are highly unlikely under natural conditions but are more likely if some kind of intelligence was involved. The focus isn’t on the model being wrong; it’s on real-world data showing something more purposeful than what we’d expect from purely natural processes.
So, it’s not about tearing down a model—it’s about explaining patterns that the model can’t account for. That’s where design comes into play.
No problem there. Given that Coronaviruses are RNA viruses, there is abundant genetic sampling, with transmission probably the main barrier.
The phylogeny of SARS-CoV-2 is exactly as would be expected from random processes engaged in an arms race with a host population. There are no patterns or features that indicate the slightest influence of design, not that there is a conceivable basis for biotech angels having a stake in such tinkering.
3 Likes
Greetings, @oddXian . To clarify–. Do you mean “Designerism,” as in “Designer” + “ism”?
Thank you!
What are those markers, and why do you propose those markers?
You also seem to ignore natural selection which is non-random.
1 Like