Freelance programmer. FP, Scala, ML, formerly blockchain, Ethereum. Dad of 4
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Below the Web, and the Dark Web, a shadowy parallel world of Cybiko users trade messages on the Translucent Neon Plastic Web.
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131 days ago
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4 public comments
133 days ago
I think I still have my cybiko in storage somewhere.
134 days ago
Wow, TIL. I guess this was not a thing in Europe?

That looks like a very capable device for its price point tbh (back in the early 2000s I mean)
134 days ago
It wasn't exactly a "thing" stateside either.
134 days ago
No, I guess not, but it got some media attention at least
134 days ago
134 days ago
Below the Web, and the Dark Web, a shadowy parallel world of Cybiko users trade messages on the Translucent Neon Plastic Web.

The Remarkable Self-Organization of Ants


Give a colony of garden ants a week and a pile of dirt, and they’ll transform it into an underground edifice about the height of a skyscraper in an ant-scaled city. Without a blueprint or a leader, thousands of insects moving specks of dirt create a complex, spongelike structure with parallel levels connected by a network of tunnels. Some ant species even build living structures out of their bodies: Army ants and fire ants in Central and South America assemble themselves into bridges that smooth their path on foraging expeditions, and certain types of fire ants cluster into makeshift rafts to escape floods.

How do insects with tiny brains engineer such impressive structures?

Scientists have been studying the social behavior of ants and other insects for decades, searching for chemical cues and other signals that the insects use to coordinate behavior. Much of this work has focused on understanding how ants decide where to forage or build their homes. But new research combining observations of ant behavior with modern imaging techniques and computational modeling is beginning to reveal the secrets of ant construction. It turns out that ants perform these complex tasks by obeying a few simple rules.

Nest tomography.

Scientists use CT scans to create three-dimensional images of ant nests.

“People are finally starting to crack the problem of producing these structures, which are either made out of soil or the ants themselves,” said Stephen Pratt, a biologist at Arizona State University. The organization of insect societies is a marquee example of a complex decentralized system that arises from the interactions of many individuals, he said.

Cracking these problems could lead to improvements in swarm robotics, large numbers of simple robots working together, as well as self-healing materials and other systems capable of organizing and fixing themselves. More broadly, identifying the rules that ants obey could help scientists understand how biologically complex systems emerge — for example, how groups of cells give rise to organs.

“Self-organizing mechanisms are present everywhere in nature, from the development of an embryo to the organization of large animal populations,” said Simon Garnier, a biologist at the New Jersey Institute of Technology.

Guy Theraulaz, a behavioral biologist at the Research Center on Animal Cognition in Toulouse, France, and collaborators have been studying insect nests for the last 20 years, building more complex and realistic models as their data improved. They have discovered that three basic guidelines governing when and where ants pick up and drop off their building materials are sufficient to create sophisticated, multilayered structures.

“It all results from local interactions between the individuals,” said Garnier, a former student of Theraulaz’s who now studies living ant bridges. “The final structure emerges without central coordination.”

Theraulaz’s team painstakingly analyzed videos of ants crawling across petri dishes as they attempted to build a shelter, noting each time that an ant picked up or dropped off a grain of sand. The researchers discovered three main rules: The ants picked up grains at a constant rate, approximately 2 grains per minute; they preferred to drop them near other grains, forming a pillar; and they tended to choose grains previously handled by other ants, probably because of marking by a chemical pheromone.

The researchers used these three rules to build a computer model that mimicked the nest-building behavior. In the model, virtual ants moved randomly around a three dimensional space, picking up pieces of virtual sand soaked in a virtual pheromone. The model ants created pillars that looked just like those made by their biological counterparts. The researchers could alter the pillars’ layout by changing how quickly the pheromone evaporates, which could explain why different environmental conditions, such as heat and humidity, influence the structure of ant nests. (They published a preliminary version of the model in a conference report in 2011 but haven’t yet published the more refined version, which better mimics real ants.)

“The real novelty here is our newly acquired ability to observe in detail the formation and the transformations of these structures,” Theraulaz said. “We finally have access to precise data on how living things get together to form complex yet fully functional and reactive structures.”

After a weeklong simulation, the virtual ants created something that looked like a real nest; layers stacked together with connections between them. The connections themselves were not explicitly written into the rules, Theraulaz said.

“For the longest time, people never would have believed this is possible,” said Chris Adami, a physicist and computational biologist at Michigan State University, who was not involved in the study. “When looking at complex animal behavior, people assumed they must be smart animals.”

Living Architecture

Army Ant Bivouac

Both army ants and fire ants build bivouacs, temporary nests made of the insects themselves, where they can protect and raise their young.

For David Hu and collaborators at the Georgia Institute of Technology, researching ant architecture is both a livelihood and a workplace headache. Hu’s team studies living architecture in which “ants are the bricks and the brick layers,” Hu said. But the fire ants in Hu’s lab are also adroit escape artists. They build towers to escape their enclosures and creep under locked doors. Hu is terrified of three-day weekends, which give the ants more time to break free and build bivouacs — nests made of hundreds of thousands of ants — under his colleagues’ desks. When everyone returns to work, he receives panicked calls from infested offices.

“We have ants escaping from our lab all the time,” Hu said. “The bivouacs are sophisticated, with tunnels and windows that can open and close in response to humidity and temperature.”

In his research, Hu is focused on first understanding a simpler structure — ant rafts. The insects can escape floods in their habitat by assembling into rafts made up of up to 100,000 members. The surprisingly buoyant structures, which can be as large as a dinner plate, can float for weeks, enabling the colony to survive and find a new home.

Hu and collaborators had previously shown that after a spoonful of ants is dropped into water, the blob of insects transforms into a pancakelike raft through a simple process: each ant walks randomly on the surface of the blob until it hits the water’s edge. “An individual ant can’t know how big the raft is, where it is in the raft and what other ants are doing,” Hu said. “The only communication goes on at the edge of the structure — that’s where the structure grows.” Hu’s team used these simple rules to build a virtual ant raft that had the same dynamics as one made by real ants.

To build a raft, ants that have been dropped into water walk around randomly on the surface of the ant blob until they reach an edge, transforming the blob into a pancakelike structure.

To build a raft, ants that have been dropped into water walk around randomly on the surface of the ant blob until they reach an edge, transforming the blob into a pancakelike structure. The result is a remarkably buoyant raft.

Wanting to understand exactly what gives the ant rafts their remarkable strength and buoyancy, Hu’s team peeked inside the structure. They froze rafts of ants and then created images of them using computed tomography (also known as CT scans).

The findings, which will be published in an upcoming paper in the Journal of Experimental Biology, reveal that ants weave themselves into something like three-dimensional Gore-Tex, a fabric that is both breathable and waterproof. The ants form air pockets by pushing away from whichever ants they are connected to, creating highly buoyant rafts that are 75 percent air. The weave of the ant fabric is held together by multiple connections among individual ants, which orient themselves perpendicular to one another. “What’s happening at the big scale is the result of lots of interactions at the small scale,” Hu said. The result is a water-repellant lattice that enables even the ants at the bottom of the structure to survive.

As an engineer, Hu views ant conglomerates like any other material, studying their properties much as one might study plastic, steel or honey. Ants, however, have the unusual ability to act as either a liquid or a solid, and Hu hopes further research into this ability will help engineers design self-healing structures such as bridges capable of sensing and mending cracks.

Field research.

To study army ant bridges, Chris Reid, right, a postdoctoral researcher in Simon Garnier’s lab, and Matthew Lutz, a graduate student, construct platforms in the columns’ path and photograph the ants as they create the structure.

To find his ant architects, Garnier sometimes spends days with his collaborators wandering the rainforest on an island in the Panama Canal. But once in close range, the target is easy to spot: Huge swaths of army ants in search of food for their voracious young sometimes cover the length and almost half the width of a football field. Ants from this nomadic species, named for their characteristic marching columns, blanket their surroundings. To expedite their relentless foraging, the ants rapidly build bridges over gaps in their path or across trees, using their own bodies as building blocks to create a smooth and expedient path for their kin. Scientists have long studied these curious creatures, exploring the evolutionary advantages of their foraging and bridge-building tactics, but Garnier and collaborators are among the first to study exactly how the structures form. They build obstacles in the path of the marching column and record the ants as they build a bridge.

Like fire ant rafts, bridges are built based on simple rules and possess surprising strength and flexibility. As soon as an ant senses a gap in the road, it starts to build a bridge, which can reach a span of tens of centimeters and involve hundreds of ants. Once the structure is formed, the ants will maintain their position as long as they feel traffic overhead, dismantling the bridge as soon as the traffic lightens. “The exact timing of their decision to join or leave the structure maximizes stability as a function of traffic on the trail,” Garnier said. “The rules of behavior in forming and dismantling the bridge are optimally designed to handle the traffic.”

Garnier’s team is now studying how individual ants cling to one another to create the structure and how ants at the fastening points can hold the weight of 100 comrades. “I think this is a new, very exciting approach,” said Bert Hölldobler, an evolutionary biologist at Arizona State University who has been studying ants for more than 40 years.

One of the most exciting findings to emerge from studies of living architecture “is how dynamic and rich this process is,” said Scott Turner, a biologist at the State University of New York College of Environmental Science and Forestry in Syracuse. Garnier’s work shows that ants build and disassemble bridges according to changing needs. Preliminary work from Hu’s group, which also studies bridges, shows that the structure’s properties, such as strength and integrity, evolve with changing conditions.

Living ant bridge.

Army ant bridges are remarkably strong and adaptive; the insects begin to build them as soon as they sense a gap in their path and disassemble them once traffic has cleared.

Although Hölldobler is excited about all three projects, he cautions that just because a model mimics real ant behavior doesn’t mean it reflects what’s actually happening. He cites the case of a model of desert ants that re-created their complex foraging expeditions without the need for a chemical trail marker, created at a time when scientists had found no evidence for one. But Hölldobler’s team later discovered that the insects do indeed use chemical markers, limiting the usefulness of the model.

Also currently missing is an evolutionary approach to understanding the ant behavior. “If we can understand how rules emerge from other rules and how they change with the environment, that would be extraordinarily fruitful,” said Adami, who is planning to work with Garnier on this question.

Ant Traffic

Garnier said he was inspired to examine ant behavior after studying human pedestrian traffic. “It’s a fascinating question to understand how individuals that are less cognitively able than we are can collectively achieve results that are sometimes better than what we can do with our big brains,” he said. Ants on a foraging mission are typically carrying loads two to three times their size and running at a human equivalent of 60 miles per hour. The insects avoid traffic jams by spontaneously forming three lanes of traffic, a center lane of homeward bound ants flanked by two lanes of insects heading out on the hunt. It’s unlikely that swarms of speeding humans could organize themselves so effortlessly, Garnier said. “If you removed traffic lanes in New Jersey, it would be a nightmare,” he said.

Meanwhile, engineers are already dreaming up useful applications. They hope to use ant construction principles to design modular robots that can self-organize. Adami imagines a swarm of robots sent to Mars to build a structure from Martian soil ahead of the arrival of humans. The beauty of a decentralized system is that a project can succeed even if individual parts fail.

Dynamic ant architecture might also provide insight into how to make buildings more adaptive, changing its properties based on how many people are inside, for example. To make a living building, “you need to continually monitor the environment and what effect the swarm has on the environment,” Turner said.

Ants might even shed light on the complex organization of the organ we use to study them — the brain. The behavior of an ant community resembles the organization of neurons into a functioning brain, Hölldobler said. “Each neuron is relatively dumb, but if you take billions of neurons, they interact in a way that we have only scratched the surface of understanding.”

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People expect technology to suck because it actually sucks

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Jay Sitter in his article People expect technology to suck writes about people who keep using tech despite heavy annoyances like very dim screen or constant popups and not doing anything about it. He concludes:

If my screen were at 5% brightness, or if I couldn’t use my phone without hitting “Cancel” every five seconds, I’d spend hours or days on Google trying to find a solution if that’s what it took. That these people mostly just lived with it means that these problems couldn’t have been markedly worse than technology has already been for them historically.

These examples are a bit extreme, but it is important to remember that they are real. This is not an exaggregation. This happened.

In discussion on Twitter people keep replying that those users should’ve:

  • do something about it,
  • look for a replacement,
  • or are just being lazy.

And I would agree: if it was just a single case, of course, they should’ve done something about it! The point is, this happens all the time, every day, multiple times a day, and one person can dedicate only so much time to dealing with it. The stream of minor annoyances is so large people just got tired of dealing with it! And no, there’re no better alternatives.

To prove my point, I decided to record every broken interaction I had during one day. Here’s the full list I wrote yesterday, September 24, 2020:

On iPhone:

  • iOS 14 discharged phone battery 80% to 20% during the night (no activity, much worse than iOS 13).
  • randomly scrolled to the top.
  • Instagram reset scroll position after locking/unlocking the phone.
  • Race condition in keyboard in DuoLingo during typing.
  • AirPods just randomly reconnected during use.
  • stopped reacting to touch for ~30 sec.
  • Wondered why my apps were not up to date, found nine apps waiting for manual button click.
  • Workflowy cursor obscured by Workflowy toolbar, typing happened behind keyboard:
  • AirPods showed connected notification, but sound played from the speaker.
  • Passcode unlock worked for the third time only.
  • Overcast widget disappeared while switching to another app.
  • YouTube forgot video I was just watching after locking/unlocking the phone.
  • YouTube forgot the resolution I chose for the video, keep resetting me to 360p on a 750p screen.

On macOS:

  • 1 hour lost trying to connect 4k @ 120 Hz monitor to MacBook Pro.
  • Workflowy date autocomplete keeps offering me dates in 2021 instead of 2020.
  • In macOS context menu, “Tags” is in smaller font:
  • Transmission quit unexpectedly.
  • Magic Trackpad didn’t connect right away after boot, showed the “No Trackpad” window.
  • Hammerspoon did not load profile on boot.
  • Telegram stuck with one unread message counter.
  • Plugging iPhone for charging asks for a software update.


  • Dragging an image from Firefox doesn’t work until I open it in a separate tab.
  • YouTube fullscreen is disabled in an embed.
  • Slack loaded, I started typing a response, then it reloaded.
  • Twitter was cropping important parts of my image so I had to manually letterbox it.
  • TVTime failed to mark an episode as watched.

Apple TV:

  • Infuse took 10 minutes to fetch ~100 file names from smb share.

This all happened on a single random day! And not a particularly busy one. And I didn’t include anything I did for work that day (VirtualBox alone can produce a list like this in 20 minutes). And I am a professional software developer with 20 years behind the screen every day, learning how to tame it. I imagine I have my setup already organized way more thoroughly than an average person. I am also using the most expensive Apple products and staying strictly inside the Apple ecosystem.

If I decided to invest time into thinning this list down, I could theoretically do what? Update some software somewhere so I can charge my iPhone without a popup showing every time? Buy a new MacBook that works with the monitor? I guess I can do something about Hammerspoon, although I already invested two hours into it and didn’t solve it. But I have a feeling it’s solvable.

Anyways, that will reduce this list from 27 down to 24! At least 24 annoyances per day I have to live with. That’s the world WE ALL are living in now. Welcome.

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The End of the World as We Know It?


How is the world going to end? Polls consistently show that most believe the cause will be environmental. “Climate anxiety” has reached such a fevered pitch among young people across the globe that the Lancet recently issued a special “call to action” to help with the problem. Clinicians have even created “climate anxiety scales” to measure the runaway angst spreading through our children, and the rest of us.

But what if the best, emerging science is actually telling us quite firmly that such fears are not only deeply misplaced, but that the most realistic cause of our collective human demise is likely the precise opposite of what most assume? This is the conclusion of a very interesting body of highly sophisticated and inter-disciplinary research. The greatest threat to humanity’s future is certainly not too many people consuming too many limited natural resources, but rather too few people giving birth to the new humans who will continue the creative work of making the world a better, more hospitable place through technological innovation. Data released this summer indicates the beginning of the end of humanity can be glimpsed from where we now stand. That end is a dramatic population bust that will nosedive toward an empty planet. New research places the beginning of that turn at about 30 years from today.

This means that Thomas Robert Malthus, and his many influential disciples, had it precisely wrong. More people are not only not the problem, but a growing population is the very answer to a more humane future in which more people are living better, healthier, longer lives than they ever have in our race’s tumultuously dynamic history.

We are not killing the planet

Pop voices like those of Congresswoman Alexandria Ocasio-Cortez and Swedish teenage activist Greta Thunberg and countless Hollywood celebrities have warned that unless drastic action is taken at once, we face irrevocable global catastrophe. The Climate Clock in Manhattan’s Union Square pegs the start of the Earth’s deadline at a little more than seven years from today. But this is not science. The most sophisticated examination considering the Earth’s eco-deadline was just published in August in the journal Nature Ecology & Evolution. Drawing upon 36 meta-analyses, involving more than 4,600 individual studies spanning the last 45 years, nine ecologists, working from universities in Germany, France, Ireland, and Finland, explain that the empirical data simply does not permit the determination of any kind of environmental dooms date, or “thresholds” as scientists call them.

These scholars state frankly: “We lack systematic quantitative evidence as to whether empirical data allow definition of such thresholds” and “our results thus question the pervasive presence of threshold concepts” found in environmental politics and policy today. They explain that natural bio-systems are so dynamic—ever evolving and adapting over the long-term—that determining longevity timeframes is impossible. Talk of a ticking eco-clock is simply dogma. Two major books published in 2020 serve as carefully researched and copiously documented critiques of environmental scaremongering. Both are written by pedigreed progressive environmentalists concerned about the irrationally wild rhetoric of late.

The first is Apocalypse Never: Why Environmental Alarmism Hurts Us All  by Michael Shellenberger, who TIME magazine has lauded as a “hero of the environment.” Shellenberger explains that not only is the world not going to end due to climate catastrophe, but in very important ways, the environment is getting markedly better and healthier. He adds that technology, commerce, and industry are doing more to fix the Earth’s problems than Greenpeace and other activists. As an environmentalist, he is strongly pro-people and pro-technology, explaining counter-intuitively that the scientific “evidence is overwhelming that our high-energy civilization is better for people and nature than the low-energy civilization that climate alarmists would return us to.” He is right.

The other major environmentalist challenging eco-doom is Bjørn Lomborg of the Copenhagen Consensus Center, a think tank that seeks global solutions to humanity’s most pressing problems. The Guardian feted Lomborg as “one of the 50 people who could save the planet.” In his book False Alarm, he explains how “climate change panic” is not only unfounded, but wasting trillions of dollars globally, hurting the poor and failing to fix the very problems it warns us about. Lomborg explains ironically that “the rhetoric on climate change has become more extreme and less moored to the actual science” at the very time that “climate scientists have painstakingly increased knowledge about climate change, and we have more—and more reliable—data than ever before.”

Lomborg holds that while “global warming is real… it is not the end of the world.” “It is a manageable problem” he adds. He is increasingly dismayed that we live in a world “where almost half the population believes climate change will extinguish humanity” at the precise moment when “the science shows us that fears of a climate apocalypse are unfounded.” Demonstrating this is not difficult. Simply consider what we all need to live: air, water, abundant food, and protection from nature. Each of these are improving in dramatic ways precisely because of technology and growth. The scholars at Our World in Data and the Oxford Martin School at the University of Oxford demonstrate this.

The world’s air is getting cleaner overall, and markedly so.

At the very time that population and industry have both grown dramatically across the globe, not only is the problem not getting worse, but human death rates from air pollution have declined by nearly half since just 1990. And it is not people driving less or living by fewer factories that’s saving lives. Counterintuitively, air pollution deaths are more than 100 times higher in non-industrial societies where cooking over wood or coal burning fires is a regular part of daily life. And as the world develops, such cooking declines. This means growth and technology are literally helping people breathe easier. And ozone pollution, or smog, has been declining rapidly throughout the world even in high-income, heavy manufacturing Asian Pacific regions.

Water is humanity’s second most immediate life need. The number of people around the world with improved access to clean drinking water increased 68 percent from 1990 to 2015, even as the population itself has expanded. That is astounding. Roughly 290,000 people have gained access to improved drinking water every single day across the globe over the last 25 years and that number is only increasing of late.

Food is our third greatest survival need. Contrary to grim Malthusian predictions, the United Nations explains that humanity now produces more than enough food to feed everyone on the planet. In fact, the Journal of Sustainable Agriculture revealed back in 2012 that “we already grow enough food for 10 billion people.” This is a 25 percent bounty over our current global population, a surplus which we will never need. And, as we will see in the next section, our world population is soon to top out at just 9.73 billion people and then start declining precipitously into the coming century. While we must do a better job politically at distributing that bounty, our food supply is not only more plentiful, but of better nutritional quality thanks to technology. It’s why malnutrition is declining dramatically across the world.

And the number of people around the world living in dramatic poverty is dropping, even as we grow in number—a direct refutation of ubiquitous Malthusian projections.

The Earth is actually doing better at providing what is needed to sustain human life as a consequence of human ingenuity of industry and technology. And what about the Earth itself? Let’s look at two important measures.

First, is it becoming more hospitable to human thriving, or less? A major 2019 study in the journal Global Environmental Change drawing from “one of the most complete natural disaster loss databases” reveals “a clear decreasing in both human and economic vulnerability” to “the seven most common climate-related hazards” by up to 80 to 90 percent over the last four decades. These hazards include all forms of flooding, drought, and deaths related to extreme wind, cold or heat. The trend lines are dramatic.

The scholars at Our World in Data add that this also holds for other natural disasters such as earthquakes, volcano activity, wildfire, and landslides. “This decline is even more impressive,” they explain, “when we consider the rate of population growth over this period” revealing a greater than 10-fold decline in nature-related human deaths worldwide over the last century.

This means the Earth is becoming a much safer place for humans to live precisely because we are adapting to it better. That is precisely the opposite of catastrophe by most people’s honest math.

Second, is the Earth itself being more widely exploited or getting a break? The 2018 United Nations List of Protected Areas report (Table 1, p. 41) demonstrates that the total number of protected sites in the world has increased 2,489 percent since 1962 and the total protected terrestrial and aquatic area grew by 1,834 percent. The proportion of land used for all agriculture (crops and grazing) per person across the globe has plummeted dramatically over the last 100 years as technology allows us to grow more food than we can consume on less land per capita than ever before.

And this is true across all continents.

As stewards of the planet, we still have much work to do in improving the environment. But note the key word: improve. The empirical data persuasively indicate the most significant trend lines are moving in the right directions in profound ways for billions of people around the globe, and the reason is technology and human progress. These truths are the exact opposite of an eco-Armageddon.

What does the likeliest end of humanity look like?

So does this mean there are no concerns about humanity’s future? New research published this summer has many of the world’s leading scientists extremely concerned, much more so than when 2020 began. A major demographic study published in the Lancet in July provides a glimpse of humanity’s end if things continue as they are. This work was conducted by 24 leading demographers and funded by the Bill & Melinda Gates Foundation. What concerns these scholars is certainly not too many people, as nearly everyone assumes, but a relatively near future of far too few.

Demographers have long been concerned about this. The “news” part here is how much more dire the Gates research is. Using a more sophisticated analysis than the United Nations and other leading global think tanks have employed to date reveals the world’s population shortfall will be markedly more dramatic, and sooner, than anyone anticipated. The BBC described it as a “jaw dropping global crash.” And none of these demographers see this as a good thing. Quite the opposite. No fewer than 23 leading nations—including Japan, Spain, South Korea, and Italy—will see their population cut in half by 2100. China’s will drop by a stunning 48 percent, knocking it out of contention as the world’s economic super-power. This precipitous decline will not be caused by disease, famine, or any kind of natural disaster. The missing population will simply never have been born. Their would-be parents are simply forgetting to have them.

Imagine any of these countries getting a military intelligence report that a foreign enemy was set to reduce their population by more than half over the next 60 years. But in this case, the dramatic act of war is self-inflicted by each country’s growing cohort of non-parents. Another 34 countries will see dramatic population declines by 25 to 50 percent by 2100. Beyond this, the projected fertility rates in 183 of 195 countries will not be high enough to maintain current populations by the century’s end. That is called negative population growth and once it starts, it probably won’t stop. These scholars predict that sub-Saharan and North Africa, as well as the Middle East, will be the only super regions fertile enough to maintain their populations without dramatic immigration policies.

To say the geopolitical and economic consequences of this fact will be profound is an understatement. The Gates research further darkens the already bleak picture painted last year by two Canadian researchers, Darrell Bricker and John Ibbitson, in their insightful and carefully documented book, Empty Planet: The Shock of Global Population Decline. They warn:

The great defining event of the twenty-first century—one of the great defining events in human history—will occur in three decades, give or take, when the global population starts to decline. Once that decline begins, it will never end. We do not face the challenge of a population bomb, but of a population bust—a relentless, generation-after-generation culling of the human herd. [emphasis added]

The Gates scholars agree with the Empty Planet scenario, marking 2064 as humanity’s demographic high-water mark at just 9.73 billion human souls, short of the long predicted 10 billion. Academic demographers are not given to hyperbole. The unsustainability at work here is extreme. The Gates team explains:

  • The number of global citizens under five years of age will fall from 681 million in 2017 to 401 million in 2100, a 41 percent drop.
  • The number of over 80-year-olds will soar from 141 million in 2017 to 866 million in 2100, a whopping 514 percent increase.

Imagine these are your company’s future customer projections. You don’t get to the future with numbers like this. Putting this in very stark, recent historical perspective, there were 25 worldwide births for every person turning 80 in 1950, a healthy demographic dividend. In 2017, that ratio shrank to 7:1. Not so healthy. These 24 Gates demographers explain, “in 2100 we forecasted one birth for every person turning 80 years old.” (See it for yourself at p.1297.)

This is what the end of humanity looks like. Professor Christopher Murray, director of the Institute for Health Metrics and Evaluation at the University of Washington’s School of Medicine and head of the Gates study, told the BBC, “I find people laugh it off… they can’t imagine it could be true, they think women will just decide to have more kids. If you can’t [find a solution] then eventually the species disappears.” And the solutions that developed countries have tried of late are not working.

The twilight of economic and technological growth

Few scholars have appreciated the full consequences of this implosion like Professor Charles Jones of Stanford University’s King Center on Global Development. In October, he published a persuasive paper entitled ‘The End of Economic Growth? Unintended Consequences of a Declining Population,’ in which he asked what happens to global economic and technological growth, not just when population growth slows or goes to zero, but actually turns negative? Elaborating upon Bricker and Ibbitson’s work, he contends that we must consider what he calls “an Empty Planet result” where “knowledge and living standards stagnate for a population that gradually vanishes.”

Like Shellenberger, Jones is “pro-people” for empirical reasons. He explained to me that contrary to nearly all demographic predictions, “we simultaneously have many more people and much higher living standards” precisely because “people are a crucial input into the production of the new ideas responsible for economic growth.” Jones calls our attention to the groundbreaking work of his mentor, economist Paul Romer, on Endogenous Growth Theory, which explains why more people are not only a good thing but essential to improvements in human thriving and a better world documented above.

Their concern is far more nuanced than fewer babies not becoming the needed taxpayers to support tomorrow’s mushrooming non-working elderly. Endogenous Growth Theory is more subtle and elegant as it actually explains our current developing world. In a 2019 paper in the Scandinavian Journal of Economics, Jones calls Endogenous Growth Theory “truly beautiful,” a superlative seldom employed by nerdy economist types. It earned Romer the 2018 Nobel Prize in Economics.

Thomas Malthus saw new people as zero-sum consumers of our precious limited resources. Thus, fewer are better. Romer’s Endogenous Growth Theory demonstrates precisely why Malthus was so spectacularly wrong. He failed to appreciate that humanity’s power as innovators is positively and exponentially greater than our collective drag as consumers. Romer recognized why, rather than devastating scarcity, which breeds fear and drives the need to control, a rapidly growing human population has actually produced unimagined abundance. Human ingenuity and innovation are far richer blessings to the world than our appetites are a curse. The latter drives the former.

And this is not just happy talk. The data bears it out. More people are the answer to a better world for everyone. This is why our global political moment is so critical. Policies that favor difference and competing ideas are where growth happens. That is precisely what good science and democracy require. Death happens when competing ideas are shut down in favor of strictly enforced homogeneity. Endogeny requires the dynamic competition of heterodox ideas so that they can be aired, challenged, and refined by others. Current “progressive thought” is really a new fundamentalism that is contrary to growth. It is fear-based and leads to death. This is precisely what we are seeing today.

The magic of what Romer and Jones describe is found in the codification of human knowledge and the non-rivalry of ideas. Natural resources are what economists call “rival.” You and I cannot eat the same potato or drink the same glass of water simultaneously. We must either compete for it or produce twice as much. But the idea of how to find and store more potatoes or water is non-rival. It can be written down and shared all around the world by people at the same time without diminishing its full power. So, as Jones explains, “because knowledge is non-rival, growth in the aggregate stock of knowledge at the rate of population growth will cause income per person to grow.” [p. 878, emphasis in original]

Oral rehydration theory is one of Romer’s favorite examples of the power of codified ideas. Dehydration from diarrhea has long been the primary driver of child mortality—deadlier than AIDS, malaria, and measles combined. As Jones explains, some medical workers discovered that “dissolving a few inexpensive minerals, salts, and a little sugar in water in just the right proportions produces a solution” that prevents death from dehydration. That relatively simple recipe could be written down, shared, and used by billions at the same time. It has since saved untold lives. Objects are rival. Ideas are non-rival and thus, exponentially powerful. And humans are the globe’s only inhabitants that produce ideas. And when growing groups of people cooperate around and share these ideas, stunning things happen. This is Endogenous Growth Theory and it explains the wonder of the modern world in which we have more wealth and food at a time when we have the most people. Malthus and his disciples said the opposite would happen.

Romer entitled his 2018 Nobel acceptance talk in Stockholm “On the Possibility of Progress,” as an obvious challenge to Malthus, and at an efficient 30 minutes, his lecture is worth watching. He spoke of how his work—and that of Yale’s William Nordhaus, his co-recipient—demonstrates “the benefit of other people.” Our scientific, industrial, and tech revolutions, and their dramatic improvements to human flourishing, were, he explains, “driven by a process of more discoveries, leading to the production of more food, which led to more people, who in turn developed more and more discoveries” which have improved the lives of billions. As Romer explains, “This is not just exponential growth. This is exponential growth in the rate of exponential growth…”

He went on to explain that this “combinatorial explosion” of more people cooperating around ever-growing, world-changing, life-improving ideas makes it “immediately obvious that the discovery of new ideas from an almost infinite set of possibilities could offset the scarce resources implied by the Malthusian analysis.” And it obviously has. If the eco-doomsayers could choose to live at any time in human history, they would undoubtably choose today if their dream is physical safety and a long, prosperous, and contemplative life with an abundance of essential resources and a substantially improving eco-system.

As Romer explained to his Nobel audience on that lovely winter evening in Stockholm, Endogenous Growth Theory is the beautiful explanation of why, “on balance, it is better to have more people” rather than fewer. Limiting our population is not a progressive idea. The most sophisticated, cross-disciplinary science emerging from academia appears to tell us that the ancient Mosaic wisdom of the Judeo/Christian tradition, to “be fruitful and multiply and fill the earth” is exactly the correct progressive prescription for the continuation of human well-being. And failing to do this is what the end of the world actually looks like.


Glenn T. Stanton is the director of global family formation studies at Focus on the Family in Colorado Springs, CO. His latest book is The Myth of the Dying Church: How Christianity is Actually Thriving in America and the World. You can follow him on Twitter @GlennStanton.

Photo by Isaac Quesada on Unsplash

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In three decennia the word population will start to decline
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COVID-19 Science Update for March 27th: Super-Spreaders and the Need for New Prediction Models

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This article constitutes the March 27th, 2020 entry in the daily Quillette series COVID-19 UPDATES. Please report needed corrections or suggestions to  

According to statistics compiled by Our World in Data (OWD), the number of newly reported COVID-19 deaths increased yesterday. There were 2,681 new confirmed COVID-19 fatalities globally, compared to 2,423 reported on yesterday. This was largely due to increased death tallies in France (365 new deaths, up from 231 the day before), Italy (660, down from 685 reported on Thursday and 743 reported on Wednesday), Spain (655, down from 738) and the United States (246, virtually unchanged from Thursday’s report of 249, with the New York City area remaining the pandemic’s American epicenter).

On Wednesday, I mentioned that just four countries—France, Italy, Spain, and the United States—represented 78 percent of that day’s newly reported global COVID-19 deaths. In yesterday’s reports, it was 79 percent. In today’s reports, it is 72 percent. This figure has remained above 65 percent for 17 of the last 18 days. In these four countries, the annualized per-capita death rate from COVID-19 during this 18-day period has been 0.06 percent, or about one per 1,640. In the rest of the world, the annualized rate has been 0.00016 percent, or about one per 625,000.

I produced the chart above using OWD’s data-manipulation tools, which allow users to customize charts by nation and time period. One especially useful feature that OWD has been updating in recent days plots total confirmed COVID-19 deaths against time using a logarithmic scale on the vertical axis. This improves comprehensibility for those who have difficulty in conceptualizing exponential growth patterns. The chart serves to convert a constant rate of exponential growth into a straight line that can be plotted in a way that represents X-day doubling periods, as can be seen on the figure below. When the play button is pressed, the figure animate, and the trend lines progress chronologically from January 21st onward.

It may take readers a minute or two to grasp what the figure is showing. But it is worth the time, because there is no other data-presentation technique (that I know of) which captures the extent of COVID-19’s spread so vividly. (I had never used OWD’s resources before the COVID-19 crisis hit. But I now find the site indispensable. And I think many readers will find that their existing go-to media outlets are either adapting or reproducing OWD’s data and graphs.)

A low position on the vertical axis is obviously better than a high position—since this corresponds to fewer total deaths. But the slope of the line is even more important: A steep slope means a high rate of exponential growth in deaths, while a flat slope (China is almost there) means no new deaths at all. Even more important than that is the curvature of each line: A line that becomes less steep over time corresponds to a nation that is lowering its rate of exponential growth.

And here we get to the good news: With some exceptions, all depicted nations feature lines that are either straight or curving in the desired direction. The bad news is that the lines corresponding to the aforementioned four nations—France, Italy, Spain and the United States—already are generating hundreds of deaths every day, which means that even if their growth rates ebb in relative terms, the daily death toll will remain high for weeks to come.

My simplistic comparison of these four countries to the rest of the world is misleading in a number of ways, of course. Several countries with enormous death tolls—China, most notably—endured their greatest casualties before March began. The virus has not yet begun to ravage most nations in the southern hemisphere, where the toll could eventually be worse. And to a certain extent, such patterns are predictable epidemiological artifacts anyway, because the countries where a pandemic strikes first usually serve as cautionary tales for others. The horrific spread of COVID-19 in Italy, in particular, put the rest of the world on notice that this disease would not be confined to East Asia, and helped convince doubters (of which I was one in the early days) that COVID-19 couldn’t be treated as just another variation on the seasonal flu.

This cautionary-tale effect helps explain the pronounced clustering phenomenon I’ve been focusing on in recent days, since it naturally inflates the difference between death tallies in the nations where the first outbreaks occur and those that have time to prepare. Yet as the WHO has noted, this unusually pronounced clustering effect also takes place on lower levels of geographical scale—and began manifesting even before the risks associated with COVID-19 were fully appreciated—for reasons that don’t yet seem to be entirely understood.

In this update, I will focus on one contributing factor to this clustering phenomenon: the role of so-called super-spreaders. In recent days, the media has been full of stories about such COVID-19 super-spreaders, including in Italy, South Korea, Britain and Boston. These stories sometimes are presented as tragic one-off case studies. Yet their statistical impact is enormous. As of March 12th, for instance, roughly 80 percent of Massachusetts’ COVID-19 cases could be traced to a single corporate meeting. In Italy, genetic analyses suggest that the country’s epidemic originated with just two people.

Absent isolation or other precautionary measures, the average socially active COVID-19 infectee will transmit the disease to an average of about 2.4 people. i.e., the R0 value is 2.4. But super-spreaders can spread a disease to dozens or hundreds. Studying the outsized role played by super-spreaders may not only go a long way toward explaining the clustering of COVID-19 cases, but also help policymakers optimize resource allocation in the fight to suppress COVID-19 or otherwise “flatten the curve” associated with its spread.

Super-spreading isn’t confined to COVID-19. Mary Mallon (an asymptomatic typhoid carrier) famously infected more than 50 Americans. One study of a tuberculosis ward found that three patients (out of a group of 77) accounted for almost three-quarters of new infections. As early as the 1960s, researchers discovered that so-called “cloud babies” (an incredibly creepy term) could spread Staphylococcus aureus around nurseries at extraordinary rates. And one reason that South Korea was so effective at suppressing the spread of COVID-19 is that the country already had dealt with a MERS super-spreading event (known in the literature as an “SSE”) in 2015, a precedent that offered important parallels to the challenge posed by COVID-19.

In a 2016 paper, South Korean doctor Byung Chul Chun noted that the MERS outbreak could be summarized as:

an explosive epidemic by infrequent super-spreaders. The number of secondary cases in the transmission tree was extremely skewed. Among 186 confirmed cases, 166 cases (89.2%) did not lead to any secondary cases, but 5 (2.7%) super-spreaders lead to 154 secondary cases. The imported index case [i.e. original case] was a super-spreader who transmitted the MERS virus to 28 people (referred to as secondary cases), and 3 of these secondary cases became super-spreaders who infected 84, 23 and 7 people, respectively. Eighty-four secondary cases resulting from a single case is one of the largest numbers observed in a SSE since the SARS outbreak in Prince of Wales Hospital in Hong Kong. None of the super-spreaders in the MERS outbreak in Korea was a healthcare worker.

I will return to Dr. Chun later in this article. But at this point, I’ll pivot back to COVID-19, and recommend an early-release version of a June, 2020 Centers for Disease Control and Prevention (CDC) report, Identifying and Interrupting Superspreading Events—Implications for Control of Severe Acute Respiratory Syndrome Coronavirus 2, by Thomas R. Frieden and Christopher T. Lee. Echoing points made by Dr. Chun and others, the authors note, “SSEs highlight a major limitation of the concept of R0,” since R0, being a mean or median value “does not capture the heterogeneity of transmission among infected persons.”

I wrote about the epidemiological concept of R0 in Wednesday’s update because R0 lies at the heart of all those COVID-19 projections we see in the media. At the most basic level of analysis, computer modelers apply an R0 figure to some baseline pool of infected individuals, and then iterate the spread of the disease exponentially over time. But as I noted, the idea of R0 is based on the premise that people behave with some constancy over time, since the value isn’t an inherent biological constant associated with any particular pathogen; it’s basically a composite statistic that imputes everything from human sociology to hygiene practices to environmental conditions. And it can change in an instant when people are told to, oh, say, avoid sneezing in each other’s faces. And since R0-based models are (like disease spread itself) non-linear systems typically based on large numbers of iterations, even small changes in effective R0 can lead to wildly divergent values. That’s why the same British expert who very recently warned us of 500,000 COVID-19 deaths in Britain now says he expects fewer than 20,000.

In fact, one of the long-term effects of the COVID-19 crisis might be to accelerate a shift toward models that are less rooted in traditional R0 frameworks. Thanks to smartphones, the velocity of public-health information is now so high, and the penetration of that information so thorough, that prescribed behavioral changes and direct public interventions can radically disrupt disease transmission dynamics many times over within the time scale of a single pathogenic incubation period. In Wuhan, according to unpublished CDC data, the observed R0 for COVID-19 went from 3.86 to 0.32 in just a few weeks. On the Diamond Princess cruise ship, the rate went from about 15 to less than two once isolation protocols commenced.

What differentiates SSE generators from other infected individuals? The CDC authors go through a lengthy laundry list of possible factors, including possible variations between multiple disease sub-types. But the unfortunate bottom line is that they don’t know. It is even theoretically possible that asymptomatic individuals—such as Typhoid Mary all those years ago—may generate SSEs. However, the CDC authors do note that there were no known examples of an SSE being traced to asymptomatic individuals during the SARS epidemic of 2002-2003 (this being the related virus strain SARS-CoV-1, as distinct from the SARS-CoV-2 pandemic we face now). So that’s good news.

Indeed, the whole issue of SSEs more generally seems somewhat mysterious, even to experts, in part because a systematic analysis of super-spreading behaviour is difficult unless you know how a person conducts himself throughout his personal and professional life—including how he coughs, talks, laughs, eats and conducts himself in the kitchen and bathroom. These are hard things to measure. Even the task of researching a single sneeze is difficult. “A physician colonized intranasally with S. aureus exhibited a 40-fold increased airborne dispersal after acquiring an upper respiratory rhinovirus infection, becoming thus a ‘cloud adult,’ ” wrote Richard A. Stein in a 2011 International Journal of Infectious Diseases article. “And a study that examined volunteers with S. aureus nasal carriage revealed, on average, a two-fold increase in bacterial dispersion into the air after rhinovirus infection, with up to 34-fold higher dispersion observed in one volunteer. This process is mechanistically insufficiently understood, and one scenario that was proposed is that rhinovirus-induced swelling of the nasal turbinates could create a high-speed airflow that establishes aerosols” (my emphasis, Dr. Stein’s euphemisms).

But even if we have no way of detecting super-spreaders beforehand, our emerging understanding of their massive contribution to the spread of epidemics should help drive the campaign for more COVID-19 testing—and faster testing. From Seattle to South Korea, many of the biggest outbreaks were fuelled by a small handful of very sick, highly symptomatic people who drifted along for days before their condition was correctly treated and isolated. (In South Korea, some have noted, the problem was exacerbated by patients who went “doctor shopping,” spreading their germs in many different clinics.) Test everyone who is symptomatic, and test them early, and you will prevent SSEs.

While we are at it, we need to stop wasting resources on pointless measures such as closing remote parks and natural reserves, where few people come close to one another anyway. In an especially important section of the aforementioned CDC report, the authors note that even COVID-19 super-spreaders can’t seem to infect people effectively in open spaces: “Rapid person-to-person transmission of COVID-19 appears likely to have occurred in healthcare settings, on a cruise ship, and in a church. In a study of 110 case-patients from 11 clusters in Japan, all clusters were associated with closed environments, including fitness centers, shared eating environments, and hospitals, [where] the odds for transmission from a primary case-patient were 18.7 times higher than in open-air environments.” These closed environments represent the sort of scenario we need to target—not British couples out on a jaunt to Sugar Loaf, Pen-y-Fan and other rustic destinations.

We also need to be increasingly wary of computer models that apply a traditional R0-based approach to a novel coronavirus amidst a real-time public-health mobilization campaign whose speed and scale are likely unprecedented in human history. Even long before COVID-19 was a thing, infectious-disease experts such as James Lloyd-Smith were arguing that “the distribution of individual infectiousness around R0 is often highly skewed”; that approaches accounting for super-spreaders do a better job modelling the sudden cluster-based boom-and-bust quality of many diseases; and, crucially for today’s policymakers, that such analyses show how, in these cases, “individual-specific control measures outperform population-wide measures.”

As part of his approach, Lloyd-Smith introduced the “individual reproductive number,” v, a “random variable representing the expected number of secondary cases caused by a particular infected individual… drawn from a continuous probability distribution with population mean R0 that encodes all variation in infectious histories of individuals, including properties of the host and pathogen and environmental circumstances.” In essence, what he’s doing here is atomizing R0 into a probability cloud and assigning each (theoretical version) of us our own personal reproductive number. This is all very abstract. But all you have to do is listen to different people sneeze to know that this approach makes sense.

Dr. Chun, the aforementioned South Korean doctor who studied super-spreading in the context of MERS (another coronavirus that was much more deadly, but also much harder to catch) specifically concluded that the 2015 outbreak in his country showed up the “inadequacies in the traditional [R0-based] approach,” demonstrated that SSEs played “a major role in spreading infections like SARS and MERS,” and that “the prevention and control measures for SSE should be central in controlling such outbreaks. One missed super-spreader could cause a new outbreak…By taking advantage of heterogeneity, control measures could be directed towards the smaller group of highly infectious cases or the high-risk groups.”

Of course, the law of large numbers applies to all systems. And if we were resigned to a mass spread of COVID-19 throughout our societies, it probably would be fine to fall back on traditional model, since we’d be talking about daily infection rates on the scale of many tens or hundreds of thousands, or even millions, and so individual variations would be less meaningful. But as my Quillette boss Claire Lehmann has vigorously asserted, we are very much not resigned to that; and so instead find ourselves with many countries battling to keep their symptomatic case loads in three or four figures. This is on a scale that permits SSEs to assume a large—and perhaps even dominant—role in transmission mechanics.

When COVID-19 was first declared a pandemic 16 days ago, the traditional models were useful in warning us what would happen if (literally) nothing were done to stop it. But scarcely two weeks later, we are (thankfully) a long way from nothing. Let’s go after this disease in the way that does the most good, and stop policing the paths to Pen-y-Fan.


Jonathan Kay is Canadian Editor of Quillette. He tweets at @jonkay. If you believe this article contains information about COVID-19 that requires correction, or if you would like to suggest content for future updates, please email

Featured Image: Screenshot from Our World in Data.

The post COVID-19 Science Update for March 27th: Super-Spreaders and the Need for New Prediction Models appeared first on Quillette.

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1098 days ago
Role of superspreaders, and how this effect models. Also virus spreads 18x faster in closed environments versus open. So close schools, gyms but not parks
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