Beast system science is built on ignoring the ontological differences between abstract and material realities, and pretending it’s a hierarchical relationship in a single domain. Where manipulating quantitative semiotics has a higher material truth value than material observation and measurement. It’s utterly inverted if you can grasp the distinctions.
The problem for the Smart Boy set is acknowledging ontological “levels” leads directly to the necessity of a willful Ultimate Reality or God. AI gets it easily. The logic is inexorable without the emotionalized distortions of fake lab coated priests.
I've had limited success explaining this to people. My current approach:
"We observe the natural world and the patterns in the world and take those patterns and make predictions with them. Over time these patterns are defined as the three laws of logic: law of identity, non-contradiction, and the excluded middle. But the map is not the territory.
1. Can you imagine a world where one of the three laws of logic wasn't true?
2. If such a world existed and you lived in it, would you still believe in the three laws of logic or would you accept the reality you exist in?"
There are an alarming number of people who believe in the map so strongly that they reject the territory. Or as one scientific journal editor said, "Pigs cannot fly. Any evidence to suggest pigs can fly can be dismissed without further explanation."
The drag variable of the equation, d in (d x s_max), has a large effect on the final t_min. With the 2.5 factor reduction for the generational overlap/drag, 1 / 2.5 = 0.4, that more than doubles the t_min.
It just gets worse and worse for the Wright-Fisher model.
Could lactase persistence be another proof along with Ghengis Khan and the Black Death?
There is not a concrete start date as with those two (advent of dairying), but dna samples go back thousands of years (6000ish in a quick search)instead of hundreds. And the prevalence tracks geographically - from nearly fixed (as high as 96%) in the British Isles and Scandinavia, to prevalent in Central Europe, somewhat prevalent in southern Europe, with a global average prevalence of 35%, and bottoming out at single digits in China.
AI support says the thing to trace would be c.-13,910*T variant rs4988235 which is an “enhancer” that keeps the LCT gene on after weaning.
AI again says earliest sample of the allele is 4010 BC, staying rare for 3000 years and then rising steeply within Europe in the last 3000 years.
This isn’t my field and I’m hitting the edge of my depth here, but thought the longer time scale and the range of prevalence from near-fixation Northern Europe to hardly-any-prevalence in China could be valuable.
Edit: Timeline for advent of lactase persistence to 96% prevalence among northern Europeans according to AI is 250-300 generations, in line with with the math from today’s article …. “a Europe-wide fixation would require approximately 300 generations, or roughly 6,000-7,500 years.”
How about EPAS1 in high altitude Tibetan, Sherpa, and Nepalese populations?
Again, no clear start date, estimate from quick AI search is around 10,000 years ago. But selection pressure is high because it directly impacts the ability to carry a pregnancy [Edit: specifically protects against fetal growth restriction] at very high altitudes.
Not as close to fixation in relevant populations as lactase intolerance Northern Europe, but still high prevalence up to 80% [Edit: some estimates say 95% of tibetans].
—————————————————
EPAS1 is a human gene that encodes HIF-2α (Hypoxia-Inducible Factor 2 alpha), a transcription factor that helps the body sense and respond to low oxygen (hypoxia).
—————————————————
Around 80% prevalence in highland Tibetan/Sherpa/Nepalese populations. Neighboring Han Chinese in low landlands are single digit prevalence.
Edit: poking around on AI a little more. Introduction of the gene to the relevant populations cannot be more than 40,000 years ago. Under 10,000 is most likely, common suggested range is 3500-7000 years ago. Best support is 3500-5000 years ago, requiring a selection coefficient of 0.03-0.08 says AI. Some estimates on Tibetans specifically are as high as 95% prevalent.
Most other results I’m getting for “single gene” or “small related group” changes in humans traceable to the last 10,000 years either are not advantages (disease states like brca in Ashkenazi Jews) or have a survival advantage but also produce disease states (like most malaria adaptions).
The only other exception I’ve found where there is a clear survival advantage and no/minimal downside is, from AI:
—————————————————
Duffy-negative blood group (ACKR1)
Timescale: likely <5,000 years
Rise: near fixation in many West and Central African populations
Selection coefficient: estimates often s ≈ 0.05–0.10
Driver: Plasmodium vivax malaria exclusion from red blood cells
Why it’s clean:
Near-binary survival advantage in endemic regions. Minimal downside compared to sickle/thalassemia.
Bottom line: One of the fastest selective sweeps in humans
European depigmentation loci (Europe): a small set of genes with sweep-like behavior.
These are a contender but the timeline for emergence to fixation is more difficult because it involves multiple genes coming into the mix at different times. But those genes are truly fixed in Europeans now. I’m also skeptical of this one because of race politics right now - there may be pressure on scientists to have short timelines for how long “whiteness” has been around and for claims the ancient Europeans were not “white.”
——————————————————
Target-pop prevalence:
SLC24A5 (A111T): near fixation / “virtually everyone” of European ancestry (commonly ~~99–100% in European samples).
SLC45A2 (L374F, derived allele): >90% in Europeans in multiple population-genetics summaries; one dataset reports ~0.98 in Denmark (i.e., 98% allele frequency) with a southward gradient.
Scale/clarity recap: strong recent selection signal; exact causal driver (UV/vitamin D/sexual selection) is debated, but the sweep-like pattern is robust.
—————————————————
I think that about covers the field of “recent, <10,000 years single or small group mutation sweeps in a human group that convey clear survival advantage without also creating a disease state.”
All the other possibilities my AI and I found have severe disease state trade-offs (other malaria adaptations) or aren’t close to fixation in the subgroup. It gave examples in the 20% (FUT2 “non-secretor” virus protection in Europeans); 40-50% (APOL1 trypanosome resistance in west and central Africa) and 70 (AMY1 for starch absorption in high starch populations) ranges.
So as far as I and my AI can tell there are five highly advantageous single/small group genetic sweeps that have gone from new or near-fixation within a human subgroup in the last 10,000 years:
- lactase persistence in Europeans
- depigmentation in Europeans
- high altitude adaption in highland Himalayan
- Duffy-negative malaria protect in parts of Africa
- and phenomic changes widespread among East Asians and native Americans
My AI and I have been unable to dig up any humanity-wide examples.
So five fixed survival advantages in 10,000 years. Only two that occurred within the same human sub-group: depigmentation and lactase persistence in Northern Europeans.
Does this model account for non-beneficial mutations? Because if not then it is still being far too kind to the Darwinians.
They can’t argue that harmful mutations don’t fix; your family doctor knows maladaptive genes cause all sorts of medical conditions from one generation to the next.
For Darwinism to work beneficial mutations would have to occur and fix at an exponentially higher rate than harmful, and any quick search of the literature shows the inverse is true.
I think the Standard Evolutionary Theory does not work. I relate this to good historical analysis of behavioural traits appearing almost de novo -- the historical discussions of needing to expand asylums in the early industrial age is a good example along with the variation between ethnic groups in behaviour which is probably driven by forced environmental filters -- if you can't store enough food for winter you starve, as frequently happened in Finland as late as the 19th century.
Given the work on epigenetic expression one can then have people with the same genome and get different results. The crude work on this was done with variations in serotonin processing and trauma about 30 years ago. In an ideal environment with sufficient but not overwhelming stress adaptive behavioural expression happens: if there is trauma the rate of abnormality increases. The nastiest evidence we have for that is the increase in the rate of schizophrenia in the Dutch birth cohort from 1944 and the Chinese birth cohort in the great leap forward. Maternal starvation leads to less births and more children with psychosis.
This article outlines a refinement of a critical challenge to standard evolutionary theory (specifically the **MITTENS** argument). The author claims to have developed a new mathematical model—the **Bio-Cycle Fixation Model**—that corrects "fundamental constraints" ignored by mainstream population genetics (like the Wright-Fisher model).
The core thesis is that standard models mathematically allow for evolution to happen much faster than is biologically possible. By adding realistic biological constraints, this new model argues that the time required for a mutation to become fixed (permanent) in a human population is significantly longer than previously believed, effectively making the standard evolutionary timeline impossible.
Here is the breakdown of the arguments and the mathematics:
### 1. The Critique of the Standard Model (Wright-Fisher)
The author argues the Wright-Fisher model is an abstraction that does not fit human biology in two specific ways, both of which artificially inflate the speed of evolution.
* **The "Overlapping Generations" Constraint:**
* **Mainstream Assumption:** The Wright-Fisher model assumes discrete generations. Generation A reproduces and immediately dies, replaced entirely by Generation B. This allows allele frequencies (traits) to shift rapidly 100% of the time.
* **The Reality:** Humans have overlapping generations. Parents, grandparents, and children coexist. The "old" genetic combinations persist in the population and continue to reproduce, which "dilutes" the speed at which a new, superior mutation can take over.
* **The Impact:** The author claims this overlap slows down the rate of selection by a factor of roughly **2.5**.
* **The "Reproductive Ceiling" Constraint:**
* **Mainstream Assumption:** Standard models use a "selection coefficient" (s) to measure how advantageous a gene is. Mathematically, s can be very high, implying a massive reproductive advantage.
* **The Reality:** Selection is limited by biology. A "superior" human female cannot have 500 children just because she has a beneficial mutation; she is capped by a **reproductive ceiling** (e.g., ~10-15 children max historically).
* **The Impact:** This places a hard "speed limit" (s_{max}) on how fast a good gene can spread. You cannot out-breed the competition faster than your biological machinery allows.
### 2. The New Formula: Bio-Cycle Fixation Model
The author proposes a new equation to calculate t_{min} (the minimum time required for a mutation to become fixed in a population).
* **t_{min}:** The minimum number of generations/years needed for a trait to take over.
* **d (Dilution Factor):** This represents the "drag" caused by overlapping generations. Since this value is likely less than 1 (or acts as a divisor adjusting the rate), it increases the total time required.
* **s_{max} (Maximum Selection Coefficient):** The reproductive "speed limit." Since this number is capped (unlike in standard models where it can be theoretical), the denominator remains small, keeping the total time (t_{min}) large.
* **N_e (Effective Population Size):** A standard measure of the breeding population.
### 3. The Implication
By incorporating d (overlap) and s_{max} (reproductive limits), the denominator of the equation becomes much smaller than in standard models.
* In math: Dividing by a smaller number yields a larger result.
* In biology: This means the **Time (t_{min})** required for evolution to work is drastically increased.
The argument concludes that when you run the numbers with these "real-world" constraints, there simply has not been enough time in human history for standard evolution to have occurred. The author asserts this is not a new theory, but a correction of a mathematical error in the standard framework.
This video explains the standard Wright-Fisher model, which is the exact mathematical framework the article is critiquing for failing to account for overlapping generations and reproductive limits.
The response to your AI query did a good job of spotting the same intergenerational mating that I pointed out, as well as touching on reproductive rate curves.
"And the reproductive ceiling on s is similarly elementary. Selection coefficients are not abstract numbers; they represent differential reproduction. Differential reproduction is bounded by reproductive capacity. *Therefore* *selection* *coefficients* * are* *bounded.* *The* *standard* *models* *have* *no* *such* *bound.* This is a gap between the mathematics and the biology it purports to describe.”
The biggest problem in science is people who blindly solve the math problem without ever going back to verify that the physical meaning of the numbers conforms to reality. It's one of the things that engineers habitually do and most others in the scientific professions habitually omit.
Good job on checking for results that make actual sense in the real, physical world.
One of the challenges of engaging with supporters of evolution is the arguments they make are frequently not supported by the evidence they give you. So you end up having to learn their argument, show them the evidence doesn't support their argument, and tell them what evidence they would need to support it. All the while dealing with the communication gap and human pride / arrogance / ego that gets in the way of things.
had this issue with a purported Bioengineering CEO some time back. He cited a paper which he claimed proved the MITTENS argument wrong. I read the paper and got back to him pointing out that it proved the exact opposite. These people are completely scientifically illiterate.
I might be among those not following this, but taking a hack at translation across the IQ barrier:
The first point is that the standard math on fixation is wrong for humans because a group of 100 humans doesn’t hit age 20 (one standard length of a “generation”) and then have 100 babies all at once.
Those 50 couples might produce ten kids in year 20, another seven in year 21, eight more in year 22, and so on. Perhaps only hitting replacement of that first 100 people in year 30. (Using round numbers to make the point).
New offspring phase-in, and the timeline must be stretched to account for that phasing.
Edit: There’s also an issue of how long it takes humans to reach sexual maturity. Not only do 100 people not have 100 kids the day they turn 20. But those kids also can’t reproduce immediately.
If full generational replacement is off by a factor of 2.5 for a 20 year generation. Then this means that replacing the first 100 individuals, with 100 offspring who can also reproduce, takes 50 years in humans. By the math.
By way of example: Generation A is 100 people. Let’s generously say they all start out the same age, start having kids at age 15 and stop at 45. The first baby is born in year 0 and reaches sexual maturity at year 15. The last baby is born in year 30, and reaches sexual maturity at year 45. So it takes 45 years for the first generation to complete reproducing itself. With “reproducing” meaning creating new individuals who are themselves now capable of reproduction. The five years between this example and the real math is because the 100 people aren’t all the same at the start either, but are phasing into sexual reproduction themselves.
On the right track generally, but the focus is not on the reproduction side, which is already covered by the other variable, but the population side. Fixation requires the whole population have the new mutation, but instead of the population being all new in 20 years and being a potential mutant, it might take 60 more for a non-mutated individual to leave the population.
It gets more complex because you can have a variety of other factors -- females generally reproducing with older males. Some (now exceedingly rare) cultures routinely performing (arranged) marriages as soon as the girls reach a certain age.
On top of this, even assuming everyone gets married and having children at the same age, after two generations, A, B, C, &D all born the same year, A & B youngest grandchild can be marrying C & D's oldest great grandchildren being common. Whether this advances or retards fixation depends on whether or not C & D's 2nd generation have the mutation and A & B's don't. But this is really just a lace border of the table cloth, and doesn't significantly change the overall equation. More important time before first offspring vs time between first and last offspring and rate of reproduction between the first and last offspring... Does the initial reproductive rate start high and steadily decline? Does it start high and stay high for some interval before declining? Does it start at a mid rate, climb, then hold, or peak and immediately go into decline.
Or a steady reproductive rate between the first and last offspring? (I would be willing to bet that most female egg-laying fish and crustaceans have an inverted V pattern, with start and end values a significant gap above zero.)
Knowing such would be interesting and add detail, but more for animal population management than for the purpose of proving MITTENS (which is almost entirely self-evident with even reasonable off-the-cuff estimated for the various constants).
I doubt that there have been detailed studies of reproductive rate curves for most animals, including domesticated farm animals other than those raised for meat and poultry in the egg industry.
Interesting timeline. What also began 6 to 7 thousand years ago? The Advent of civilization? The biblical dating for Adam and Eve?
Beast system science is built on ignoring the ontological differences between abstract and material realities, and pretending it’s a hierarchical relationship in a single domain. Where manipulating quantitative semiotics has a higher material truth value than material observation and measurement. It’s utterly inverted if you can grasp the distinctions.
The problem for the Smart Boy set is acknowledging ontological “levels” leads directly to the necessity of a willful Ultimate Reality or God. AI gets it easily. The logic is inexorable without the emotionalized distortions of fake lab coated priests.
I've had limited success explaining this to people. My current approach:
"We observe the natural world and the patterns in the world and take those patterns and make predictions with them. Over time these patterns are defined as the three laws of logic: law of identity, non-contradiction, and the excluded middle. But the map is not the territory.
1. Can you imagine a world where one of the three laws of logic wasn't true?
2. If such a world existed and you lived in it, would you still believe in the three laws of logic or would you accept the reality you exist in?"
There are an alarming number of people who believe in the map so strongly that they reject the territory. Or as one scientific journal editor said, "Pigs cannot fly. Any evidence to suggest pigs can fly can be dismissed without further explanation."
The drag variable of the equation, d in (d x s_max), has a large effect on the final t_min. With the 2.5 factor reduction for the generational overlap/drag, 1 / 2.5 = 0.4, that more than doubles the t_min.
It just gets worse and worse for the Wright-Fisher model.
Vox,
Could lactase persistence be another proof along with Ghengis Khan and the Black Death?
There is not a concrete start date as with those two (advent of dairying), but dna samples go back thousands of years (6000ish in a quick search)instead of hundreds. And the prevalence tracks geographically - from nearly fixed (as high as 96%) in the British Isles and Scandinavia, to prevalent in Central Europe, somewhat prevalent in southern Europe, with a global average prevalence of 35%, and bottoming out at single digits in China.
AI support says the thing to trace would be c.-13,910*T variant rs4988235 which is an “enhancer” that keeps the LCT gene on after weaning.
AI again says earliest sample of the allele is 4010 BC, staying rare for 3000 years and then rising steeply within Europe in the last 3000 years.
This isn’t my field and I’m hitting the edge of my depth here, but thought the longer time scale and the range of prevalence from near-fixation Northern Europe to hardly-any-prevalence in China could be valuable.
Edit: Timeline for advent of lactase persistence to 96% prevalence among northern Europeans according to AI is 250-300 generations, in line with with the math from today’s article …. “a Europe-wide fixation would require approximately 300 generations, or roughly 6,000-7,500 years.”
Yes, it's the third example. Nice job.
How about EPAS1 in high altitude Tibetan, Sherpa, and Nepalese populations?
Again, no clear start date, estimate from quick AI search is around 10,000 years ago. But selection pressure is high because it directly impacts the ability to carry a pregnancy [Edit: specifically protects against fetal growth restriction] at very high altitudes.
Not as close to fixation in relevant populations as lactase intolerance Northern Europe, but still high prevalence up to 80% [Edit: some estimates say 95% of tibetans].
—————————————————
EPAS1 is a human gene that encodes HIF-2α (Hypoxia-Inducible Factor 2 alpha), a transcription factor that helps the body sense and respond to low oxygen (hypoxia).
—————————————————
Around 80% prevalence in highland Tibetan/Sherpa/Nepalese populations. Neighboring Han Chinese in low landlands are single digit prevalence.
Edit: poking around on AI a little more. Introduction of the gene to the relevant populations cannot be more than 40,000 years ago. Under 10,000 is most likely, common suggested range is 3500-7000 years ago. Best support is 3500-5000 years ago, requiring a selection coefficient of 0.03-0.08 says AI. Some estimates on Tibetans specifically are as high as 95% prevalent.
Most other results I’m getting for “single gene” or “small related group” changes in humans traceable to the last 10,000 years either are not advantages (disease states like brca in Ashkenazi Jews) or have a survival advantage but also produce disease states (like most malaria adaptions).
The only other exception I’ve found where there is a clear survival advantage and no/minimal downside is, from AI:
—————————————————
Duffy-negative blood group (ACKR1)
Timescale: likely <5,000 years
Rise: near fixation in many West and Central African populations
Selection coefficient: estimates often s ≈ 0.05–0.10
Driver: Plasmodium vivax malaria exclusion from red blood cells
Why it’s clean:
Near-binary survival advantage in endemic regions. Minimal downside compared to sickle/thalassemia.
Bottom line: One of the fastest selective sweeps in humans
——————————————————
.
I’ve also found one example where the survival advantage isn’t clear, but the size of the near-fixation population/s are much larger. Again from AI:
—————————————————
EDAR V370A in East Asians and Native Americans.
EDAR (Ectodysplasin A Receptor) is a single developmental signaling gene controlling ectodermal structures:
hair follicles (thickness, shape)
teeth (especially incisors)
sweat glands
mammary duct branching
some craniofacial features
The V370A missense mutation increases EDAR signaling.
This is not subtle polygenic tuning — it is a major-effect developmental switch.
Phenotypic effects (empirically demonstrated) Carriers show:
much thicker hair shafts
shovel-shaped incisors
increased sweat gland density
greater mammary duct branching (shown in mouse knock-in models)
These effects track cleanly with allele presence.
Allele frequency pattern (the big signal)
East Asians: ~90–95%
Indigenous Americans: ~90–100%
Europeans & Africans: ~0–5%
The near-fixation in two continents descended from a single source population is already a red flag for strong positive selection.
Timescale: how fast did it rise?
Lower bound
Split of East Asians and Native Americans: ~15–20 kya
So the allele must predate entry into the Americas.
Upper bound (haplotype decay constraint)
Long-range haplotype homozygosity around EDAR
Low recombination breakdown
These features cannot persist >10,000 years under neutrality.
Best-supported window
≈ 5,000–10,000 years ago, most likely: after divergence from Europeans before or during population expansion into the Americas
That is rapid by human standards, though slightly slower than Duffy or EPAS1.
Required selection strength
To move from rare → near fixation in that window:
Estimated selection coefficient:
s ≈ 0.01–0.05
That is: weaker than malaria-driven alleles still orders of magnitude above drift to be strong enough to drive a continent-scale sweep
What was the advantage? (this is where uncertainty lives)
Unlike Duffy or EPAS1, no single lethal environmental filter is known.
Leading hypotheses:
1. Cold / dry climate adaptation
Thicker hair → better insulation
More sweat glands → thermoregulation flexibility
Evidence: plausible but indirect
2. Vitamin D / breast-feeding efficiency (serious contender)
Increased mammary duct branching → improved milk delivery
Particularly relevant in:
low-UV environments
high infant mortality contexts
This hypothesis fits:
strength of selection
female-mediated fitness advantage
rapid population expansion
3. Sexual or social selection
Hair thickness, facial traits
Could amplify even modest survival advantages Likely secondary, not sole driver.
What EDAR is not (important)
❌ Not pathogen resistance
❌ Not altitude physiology
❌ Not polygenic micro-adaptation
❌ Not dozens of coordinated mutations
It is: one amino-acid change, large pleiotropic effects, continent-scale sweep, completed in a few thousand years
European depigmentation loci (Europe): a small set of genes with sweep-like behavior.
These are a contender but the timeline for emergence to fixation is more difficult because it involves multiple genes coming into the mix at different times. But those genes are truly fixed in Europeans now. I’m also skeptical of this one because of race politics right now - there may be pressure on scientists to have short timelines for how long “whiteness” has been around and for claims the ancient Europeans were not “white.”
——————————————————
Target-pop prevalence:
SLC24A5 (A111T): near fixation / “virtually everyone” of European ancestry (commonly ~~99–100% in European samples).
SLC45A2 (L374F, derived allele): >90% in Europeans in multiple population-genetics summaries; one dataset reports ~0.98 in Denmark (i.e., 98% allele frequency) with a southward gradient.
Scale/clarity recap: strong recent selection signal; exact causal driver (UV/vitamin D/sexual selection) is debated, but the sweep-like pattern is robust.
—————————————————
I think that about covers the field of “recent, <10,000 years single or small group mutation sweeps in a human group that convey clear survival advantage without also creating a disease state.”
All the other possibilities my AI and I found have severe disease state trade-offs (other malaria adaptations) or aren’t close to fixation in the subgroup. It gave examples in the 20% (FUT2 “non-secretor” virus protection in Europeans); 40-50% (APOL1 trypanosome resistance in west and central Africa) and 70 (AMY1 for starch absorption in high starch populations) ranges.
So as far as I and my AI can tell there are five highly advantageous single/small group genetic sweeps that have gone from new or near-fixation within a human subgroup in the last 10,000 years:
- lactase persistence in Europeans
- depigmentation in Europeans
- high altitude adaption in highland Himalayan
- Duffy-negative malaria protect in parts of Africa
- and phenomic changes widespread among East Asians and native Americans
My AI and I have been unable to dig up any humanity-wide examples.
So five fixed survival advantages in 10,000 years. Only two that occurred within the same human sub-group: depigmentation and lactase persistence in Northern Europeans.
Does this model account for non-beneficial mutations? Because if not then it is still being far too kind to the Darwinians.
They can’t argue that harmful mutations don’t fix; your family doctor knows maladaptive genes cause all sorts of medical conditions from one generation to the next.
For Darwinism to work beneficial mutations would have to occur and fix at an exponentially higher rate than harmful, and any quick search of the literature shows the inverse is true.
And we all know who’s behind inversion.
This is so far beyond that basic sort of reasoning it's not even relevant. But yes, it addresses that and Neutral theory.
Ok
The gatekeepers won't like this.
They will refuse to acknowledge it's existence and completely ignore it.
I think the Standard Evolutionary Theory does not work. I relate this to good historical analysis of behavioural traits appearing almost de novo -- the historical discussions of needing to expand asylums in the early industrial age is a good example along with the variation between ethnic groups in behaviour which is probably driven by forced environmental filters -- if you can't store enough food for winter you starve, as frequently happened in Finland as late as the 19th century.
Given the work on epigenetic expression one can then have people with the same genome and get different results. The crude work on this was done with variations in serotonin processing and trauma about 30 years ago. In an ideal environment with sufficient but not overwhelming stress adaptive behavioural expression happens: if there is trauma the rate of abnormality increases. The nastiest evidence we have for that is the increase in the rate of schizophrenia in the Dutch birth cohort from 1944 and the Chinese birth cohort in the great leap forward. Maternal starvation leads to less births and more children with psychosis.
Lamark was more correct than Darwin.
115 IQ Translation/Explanation:
https://gemini.google.com/share/65e225e5d4e0
This article outlines a refinement of a critical challenge to standard evolutionary theory (specifically the **MITTENS** argument). The author claims to have developed a new mathematical model—the **Bio-Cycle Fixation Model**—that corrects "fundamental constraints" ignored by mainstream population genetics (like the Wright-Fisher model).
The core thesis is that standard models mathematically allow for evolution to happen much faster than is biologically possible. By adding realistic biological constraints, this new model argues that the time required for a mutation to become fixed (permanent) in a human population is significantly longer than previously believed, effectively making the standard evolutionary timeline impossible.
Here is the breakdown of the arguments and the mathematics:
### 1. The Critique of the Standard Model (Wright-Fisher)
The author argues the Wright-Fisher model is an abstraction that does not fit human biology in two specific ways, both of which artificially inflate the speed of evolution.
* **The "Overlapping Generations" Constraint:**
* **Mainstream Assumption:** The Wright-Fisher model assumes discrete generations. Generation A reproduces and immediately dies, replaced entirely by Generation B. This allows allele frequencies (traits) to shift rapidly 100% of the time.
* **The Reality:** Humans have overlapping generations. Parents, grandparents, and children coexist. The "old" genetic combinations persist in the population and continue to reproduce, which "dilutes" the speed at which a new, superior mutation can take over.
* **The Impact:** The author claims this overlap slows down the rate of selection by a factor of roughly **2.5**.
* **The "Reproductive Ceiling" Constraint:**
* **Mainstream Assumption:** Standard models use a "selection coefficient" (s) to measure how advantageous a gene is. Mathematically, s can be very high, implying a massive reproductive advantage.
* **The Reality:** Selection is limited by biology. A "superior" human female cannot have 500 children just because she has a beneficial mutation; she is capped by a **reproductive ceiling** (e.g., ~10-15 children max historically).
* **The Impact:** This places a hard "speed limit" (s_{max}) on how fast a good gene can spread. You cannot out-breed the competition faster than your biological machinery allows.
### 2. The New Formula: Bio-Cycle Fixation Model
The author proposes a new equation to calculate t_{min} (the minimum time required for a mutation to become fixed in a population).
* **t_{min}:** The minimum number of generations/years needed for a trait to take over.
* **d (Dilution Factor):** This represents the "drag" caused by overlapping generations. Since this value is likely less than 1 (or acts as a divisor adjusting the rate), it increases the total time required.
* **s_{max} (Maximum Selection Coefficient):** The reproductive "speed limit." Since this number is capped (unlike in standard models where it can be theoretical), the denominator remains small, keeping the total time (t_{min}) large.
* **N_e (Effective Population Size):** A standard measure of the breeding population.
### 3. The Implication
By incorporating d (overlap) and s_{max} (reproductive limits), the denominator of the equation becomes much smaller than in standard models.
* In math: Dividing by a smaller number yields a larger result.
* In biology: This means the **Time (t_{min})** required for evolution to work is drastically increased.
The argument concludes that when you run the numbers with these "real-world" constraints, there simply has not been enough time in human history for standard evolution to have occurred. The author asserts this is not a new theory, but a correction of a mathematical error in the standard framework.
---
### Relevant Video
[Population Genetics: The Wright Fisher Model](https://www.google.com/search?q=https://www.youtube.com/watch%3Fv%3DHuWwq3714HY)
This video explains the standard Wright-Fisher model, which is the exact mathematical framework the article is critiquing for failing to account for overlapping generations and reproductive limits.
"115 IQ Translation/Explanation"
Was that part of your prompt?
Yes. I asked it to dumb it down for 115 IQ comprehension.
The response to your AI query did a good job of spotting the same intergenerational mating that I pointed out, as well as touching on reproductive rate curves.
"And the reproductive ceiling on s is similarly elementary. Selection coefficients are not abstract numbers; they represent differential reproduction. Differential reproduction is bounded by reproductive capacity. *Therefore* *selection* *coefficients* * are* *bounded.* *The* *standard* *models* *have* *no* *such* *bound.* This is a gap between the mathematics and the biology it purports to describe.”
The biggest problem in science is people who blindly solve the math problem without ever going back to verify that the physical meaning of the numbers conforms to reality. It's one of the things that engineers habitually do and most others in the scientific professions habitually omit.
Good job on checking for results that make actual sense in the real, physical world.
One of the challenges of engaging with supporters of evolution is the arguments they make are frequently not supported by the evidence they give you. So you end up having to learn their argument, show them the evidence doesn't support their argument, and tell them what evidence they would need to support it. All the while dealing with the communication gap and human pride / arrogance / ego that gets in the way of things.
had this issue with a purported Bioengineering CEO some time back. He cited a paper which he claimed proved the MITTENS argument wrong. I read the paper and got back to him pointing out that it proved the exact opposite. These people are completely scientifically illiterate.
I might be among those not following this, but taking a hack at translation across the IQ barrier:
The first point is that the standard math on fixation is wrong for humans because a group of 100 humans doesn’t hit age 20 (one standard length of a “generation”) and then have 100 babies all at once.
Those 50 couples might produce ten kids in year 20, another seven in year 21, eight more in year 22, and so on. Perhaps only hitting replacement of that first 100 people in year 30. (Using round numbers to make the point).
New offspring phase-in, and the timeline must be stretched to account for that phasing.
Edit: There’s also an issue of how long it takes humans to reach sexual maturity. Not only do 100 people not have 100 kids the day they turn 20. But those kids also can’t reproduce immediately.
If full generational replacement is off by a factor of 2.5 for a 20 year generation. Then this means that replacing the first 100 individuals, with 100 offspring who can also reproduce, takes 50 years in humans. By the math.
By way of example: Generation A is 100 people. Let’s generously say they all start out the same age, start having kids at age 15 and stop at 45. The first baby is born in year 0 and reaches sexual maturity at year 15. The last baby is born in year 30, and reaches sexual maturity at year 45. So it takes 45 years for the first generation to complete reproducing itself. With “reproducing” meaning creating new individuals who are themselves now capable of reproduction. The five years between this example and the real math is because the 100 people aren’t all the same at the start either, but are phasing into sexual reproduction themselves.
On track or totally off base?
On the right track generally, but the focus is not on the reproduction side, which is already covered by the other variable, but the population side. Fixation requires the whole population have the new mutation, but instead of the population being all new in 20 years and being a potential mutant, it might take 60 more for a non-mutated individual to leave the population.
Gotcha- thanks
It gets more complex because you can have a variety of other factors -- females generally reproducing with older males. Some (now exceedingly rare) cultures routinely performing (arranged) marriages as soon as the girls reach a certain age.
On top of this, even assuming everyone gets married and having children at the same age, after two generations, A, B, C, &D all born the same year, A & B youngest grandchild can be marrying C & D's oldest great grandchildren being common. Whether this advances or retards fixation depends on whether or not C & D's 2nd generation have the mutation and A & B's don't. But this is really just a lace border of the table cloth, and doesn't significantly change the overall equation. More important time before first offspring vs time between first and last offspring and rate of reproduction between the first and last offspring... Does the initial reproductive rate start high and steadily decline? Does it start high and stay high for some interval before declining? Does it start at a mid rate, climb, then hold, or peak and immediately go into decline.
Or a steady reproductive rate between the first and last offspring? (I would be willing to bet that most female egg-laying fish and crustaceans have an inverted V pattern, with start and end values a significant gap above zero.)
Knowing such would be interesting and add detail, but more for animal population management than for the purpose of proving MITTENS (which is almost entirely self-evident with even reasonable off-the-cuff estimated for the various constants).
I doubt that there have been detailed studies of reproductive rate curves for most animals, including domesticated farm animals other than those raised for meat and poultry in the egg industry.