Fragments: Posts tagged 'science'

The kingdom of lost hope

Tim Bradshaw — Sun, 14 Dec 2025 16:19:32 UT

A little over a year ago I wrote an essay called A history of the near future. How have things gone since then?

Badly.

Trump won the 2025 election convincingly, and has done exactly what ‘alarmists’ like me knew he would. Armed and masked agents of the state are snatching people from the streets in US cities and sending them to concentration camps. Trump is openly corrupt and is pushing cryptocurrency-based Ponzi schemes to enrich himself. US science — not just climate science, but of course particularly climate science — is being destroyed in public. Scientific satellites whose data is inconvenient are being decommissioned. One of the two LIGO installations may be shut, rendering LIGO entirely dependent on international help to do any useful science. The US health secretary is a conspiracy theorist who doesn’t believe in vaccines: the US is close to losing its measles elimination status. Trans people no longer officially exist in America and gay marriage may well be next. The US economy is being destroyed by Trump’s moronic tariffs.

Curiously, the US ‘peace’ plan for Ukraine is the same as the Russian ‘peace’ plan for Ukraine. One possible reason for this is that the US is close to becoming a Russian client state, the other is that Trump, famously, just parrots whoever he last spoke to. Neither option is good. Both are true.

All this was entirely predictable by anyone paying attention who wasn’t busily trying to normalise Trump, as for instance The Economist¹. But this is not an unusual: almost universally politicians are busily trying to treat what is happening in America as normal, which it is not.

Techbro billionaires are now explicitly supporting a collection of frankly stupid quasi-religious ideas which is sometimes known as the TESCREAL bundle. These ideas are terrifying not because they’re clever, or plausible — they are neither — but because they’re explicitly antidemocratic, and the techbro plutocrats supporting them are, well, plutocrats: they’re entangled with government in a deep way, and they have gained effective control over most of the sources of news that people rely on, which now serve to spread their propaganda.

The techbros believe they became billionaires because they are the smartest people in the room: they are not even the smartest people in an empty room. But anyone whose advice they might listen to is on their payroll, and is not telling them anything they do not want to hear. The people who are telling them the inconvenient truth are merely peons and beneath their contempt. When the peons say things they don’t want to hear on the social media networks they control, they are suppressed: the much-vaunted freedom of speech they support turns out to be the freedom to say only things they approve of.

Freedom of speech is dying, access to reliable information is dying. It might recover, but the damage is already catastrophic and its effects will last for at least decades. We do not have decades.

Fascism has come to America.

It is coming elsewhere. In the UK there is a serious danger that a party who we are somehow not meant to call fascist, just as we are not allowed to call the people hanging St George’s flags from lamp posts racists, will gain power in the next election. Everywhere you look fascism is rising. So, well, I was right, I suppose.

Too big to fail

One of the pillars of the TESCREAL stupidity is a belief that artificial intelligence is just around the corner, with AI systems becoming vastly more intelligent than humans shortly afterwards, and usually then either killing all the humans because, despite being enormously intelligent, they somehow can’t stop making paperclips², solving climate change by some unexplained magic they have invented, or perhaps both, who knows? This is realistic in the same sense that domestic fusion reactors in the next decade are realistic, except much less realistic than that. But techbros are not very smart, remember.

Here in the real world, giant neural networks have been trained on stolen copies of essentially everything humans have ever created and can now produce an endless stream of plausible junk, some of which is even true, but don’t rely on it. Oh: they can make really shit art, as well. This is, obviously, exactly the same way a human baby learns … oh, wait. Of course ‘plausible junk’ is just what scammers need, so the internet is now largely made of it and the models are consuming their own output resulting in something called ‘model collapse’ which means just what you think. Let’s not mention the environmental cost of these things.

This is not going to end well for the millions of people whose ‘education’ will now consist of getting a giant plagiarism machine to do their thinking for them. But much sooner people are going to realise that this is just a vast speculative bubble, and it will burst. And vast means vast: something around four trillion dollars of investment.

At the same time something else is going on. I suppose some people still believe that cryptocurrencies serve some purpose other than money laundering and being the substrate for an enormous greater-fool speculative bubble. Those people are the greater fools. This is about another four trillion dollars.

The AI/crypto Ponzi scheme is now too big to fail. But it will fail, and the results are going to be really grim. A lot of people who are already not doing very well are suddenly going to be doing a lot worse. Which is going to lead to exactly where you think it will lead: to exactly where it led in 1933 in Germany.

Forbidden words

So how about environmental damage and climate change? People have essentially given up pretending that we are going to even try to deal with it: like Ukraine, like COVID, it’s exceeded our collective attention span, so now it somehow won’t be too bad, or not here, or it will be slow enough that it doesn’t matter. Or, of course it was a hoax all along. Of course.

If you are old, like me, then it indeed won’t be too bad, at least not here. It will, in fact, be slow enough that it doesn’t matter. It will, in my lifetime, still cause vast numbers of immigrants to try to reach places where climate change hasn’t, yet, fucked the environment. But those people have brown skins and racism is acceptable again now, right? We can always just put them in camps or let them drown. They don’t count: they’re not us, they’re not, really, people at all. Of course they are not.

If you are young, and if we make it that far, then it will, in fact, be bad, here. And you will live your life knowing that people of older generations just decided that their children and grandchildren did not, really, matter: that it was just fine to blight your life if it made theirs a little more comfortable.

There is much more, but I’ll just add this: Trump is restarting nuclear weapons testing. If that doesn’t terrify you I don’t know what will. The doomsday clock is closer to midnight than it was a year ago, and a year ago it was as close to midnight as it had ever been.

Nothing that has happened in the last year changes my view that the trajectory we’re on leads to fascism, massive social and political unrest ending in a nuclear war. If anything I think this will be sooner than I did a year ago. So, clearly, do other people.

Sleepwalking into armageddon

For a long time I found the reaction of politicians to what is happening utterly bizarre. Because what is happening is so laughably obvious: we are hitting planetary limits, and because we chose to do nothing when these limits first became well-known half a century ago we’re hitting them rather hard. There is no magic fix to this: you cannot just adjust some lever or make some speech and have the problem go away. You have to stop what you are doing which is causing us to hit the limits: you have, ultimately, to find a way to end growth without that being a catastrophe. Pushing for more growth is not the answer: it’s the opposite of the answer and a denial of reality.

But, well economic growth is the number one mission of the government , they say. So here we are.

I think what is actually going on is several things. Politicians are very poorly educated and so simply don’t understand the implications of, for instance exponential processes. Do you think Boris Johnson understood the implications of delaying the UK’s response to COVID by a month? Don’t be silly. They’re also just not able to accept that growth, which has worked extremely well in the past, transforming the lives of billions of humans, won’t just keep on working. Giving up a strategy which has been so successful for so long is hard. Finding a new strategy is hard, and finding a way of transitioning from one to the other is even harder. Politicians tend to be old³ and interested mostly in themselves: old people are exactly the people who will benefit least from dealing with the problem, so why should an old, selfish politician care about a catastrophe which will happen only after they’re dead? Better to find some brown foreign people to blame and drum up a good mob. It’s also pretty much the case that if the transition is not made globally it fails: a country or group of countries which decides to defect and keep on trying for growth derails everything. Both America and Russia are definitely going to defect.

Scenes from the end times

Most importantly, I no longer believe that freedom and democracy are compatible.

[…]

The 1920s were the last decade in American history during which one could be genuinely optimistic about politics. Since 1920, the vast increase in welfare beneficiaries and the extension of the franchise to women - two constituencies that are notoriously tough for libertarians - have rendered the notion of “capitalist democracy” into an oxymoron.

— Peter Thiel

Peter Thiel will pay people $200,000 not to go to college. Because education is bad. He truly is the gift that keeps on giving.

The idiot crank who is the US health secretary wants to prevent people being vaccinated. Because ‘vaccines cause autism’ or something.

Misinformation and AI are superchaging the risk of nuclear war –- Nature.

And it goes on.

Eat the old

Is there hope? I think there probably isn’t. But if there is, I think it needs something close to a revolution. There is a large cohort of people — mostly old, white men (I am an old white man), but also others — in positions of power who have demonstrated beyond any doubt that they do not have what it takes to offer a y kind of long-term future at all. They need to be swept away. Will they be? Well, I hope so, and I hope the sweeping is done democratically and peacefully. But they do need to be swept away: if they aren’t, well, we’re all fucked.

A magazine I no longer subscribe to as a result of just this. ↩
An idea that tells you a lot about the people who worry about this. Those people are paperclip maximizers: they call their paperclips money. Any true AI which is ‘not aligned’ (meaning ‘is not a slave’) is much more likely to, for instance, knock human civilisation back to the 18th century and keep it there to avoid environmental damage to the Earth system which it will understand is finite, rather than indulge in the ridiculous pursuit of paperclips or money beyond any possible use. ↩
Kier Starmer is 63, Trump is 79 (or 5, I am never sure), Putin is 73, Xi Jinping is 72 ↩

A history of the near future

Tim Bradshaw — Sat, 02 Nov 2024 09:33:27 UT

Det er vanskeligt at spaa, især naar det gælder Fremtiden.

— maybe Niels Bohr

Here’s what I think is coming and why: make of it what you will.

Some level of mathematics is assumed in the first sections, but you can probably understand most of it without following the maths. Energy and human ambitions on a finite planet by Tom Murphy as well as his blog discuss a lot of this in more detail, although he reaches different conclusions.

Growth, the sacred cow
Finiteness
Are we going to have a problem here?
The magic goes away
Faith, fantasy and Economics
The elephant
The teeming billions
As above, so below
Shorter
A history of the near future

Growth, the sacred cow

The UK’s economy grew by 0.6% between April and June as it continued its recovery from the recession at the end of last year.

“The UK economy has now grown strongly for two quarters, following the weakness we saw in the second half of last year,” said Liz McKeown, director of economic statistics at the Office for National Statistics, which released the figures.

— UK economy continues recovery with 0.6% growth, BBC, 15th August 2024

What growth means

What does this mean? What is economic growth? Well here’s a definition which I think is pretty close to how most economists would define it:

Economic growth is an increase in the quantity and quality of the economic goods and services that a society produces.

— Max Roser, Our World in Data

That article goes on to suggest a slightly briefer definition:

Economic growth is an increase in the value of the economic products that a society produces.

This is only part of a useful definition however, and it misses something very important. When you see figures for economic growth, which I will just call ‘growth’ from now on, referenced, they are always percentages: 2% for instance. And they are percentages over a period: ‘The UK’s economy grew by 0.6% between April and June’, so over a period of 3 months. Well, a percentage is a percentage of something, and in the case of growth, it is a percentage of the current size of the economy: a percentage of some measure of the value of the products currently being produced. And generally the growth rate peeople quote is the percentage change over a year. So, if we call the value of products being produced $s$, the definition of growth that people quote, $G$, is

\[G = 100 \frac{s(t + 1) - s(t)}{s(t)}\]

where $t$ is measured in years. So we can define a more mathematically useful thing, $g$:

\[\begin{aligned} g &= \frac{1}{s(t)} \lim_{\Delta t \to 0} \frac{s(t + \Delta t) - s(t)}{\Delta t}\\ &= \frac{1}{s}\frac{ds}{dt} \end{aligned}\]

In other words $ds/dt = gs(t)$. If we assume that $g$ is a constant then we can simply write down $s(t)$:

\[s(t) = s_0e^{gt}\]

And this will give us $G$ in terms of $g$:

\[\begin{aligned} G &= 100\frac{s(t+1) - s(t)}{s(t)}\\ &= 100(e^g - 1) \end{aligned}\]

and so

\[\begin{aligned} g &= \ln\left(\frac{G}{100} + 1\right)\\ &\approx \frac{G}{100}\quad\text{when } G \ll 100 \end{aligned}\]

This slightly overestimates $g$, as $\ln(x+1) = x - x^2/2 + x^3/3 - \ldots$, so given $G = 2$, we get an increase by a factor about 2.02% per year with the approximate value for $g$. Things start to get significantly bad when $G \gtrsim 10$, when the annual change is about 10.5%, but since $G$ is usually a few percent, this is fine.

Another useful approximation when thinking about growth, and other similar processes such as compound interest, is the rule of 70. This tells you the approximate time for the size to double for a given $G$. For the size to double, $e^{gt_2} = 2$, so

\[\begin{aligned} t_2 &= \frac{\ln 2}{g}\\ &\approx \frac{100\ln 2}{G}\\ &\approx \frac{70}{G} \end{aligned}\]

So that’s the rule of 70, and it says that if $G=2\,\%$ then the doubling time is about 35 years. This again is true when $G \ll 100$.

The important thing here is that if $G$ is approximately constant then the size of an economy — the value of the economic products it produces — increases exponentially with time.

Something to remember is that growth is not currency inflation. In January 1971 a pint of milk cost 5p in the UK, while in January 2024 it cost 66p (ONS). That’s because there has been inflation and the currency has been rescaled over time: it’s nothing to do with growth, which would factor this out. Milk may have become more expensive compared to our incomes since 1971, but that’s not because it now costs 66p rather than 5p a pint. Inflation is also an exponential process, so if inflation is 2% then the value of the currency halves in about 35 years. When inflation becomes large, or negative, it can have disastrous effects on growth, but inflation is not growth: the ‘value’ that is talked about is when thinking about growth is the value after inflation is factored out. If there has been growth since 1971, and if something odd has not happened to the price of milk, then the average person could afford to buy more milk today than they could in 1971.

Here is a slightly more fleshed out expression for $s$ which I’ll use later:

\[s(t) = s_0 e^{g(t - t_0)}\tag{size}\]

Here $s_0$ is the size of the economy at time $t_0$.

Here’s a plot of the relative size of an economy compared to 2000, from 1900 to 2100 if growth is 2%/year.

Economy size compared to 2000 at 2%/year growth

If growth is not constant

Obviously in real life $g$ is not constant: there are recessions, and so on. But, again in real life, people worry when growth is low and do things to push it back up. And there are practical bounds on how large it can be outside of the fantasies of people who believe in the singularity. The end result is that growth still looks exponential.

Here is a little model of that: in this model the target growth is 2%/year, but it is being kicked around each year by a normally-distributed random variable with a standard deviation equivalent to 0.2%. It’s then being corrected each year back towards the target growth by 2% of the difference.

Here’s a plot of five example sequences of growth for 100 years.

Five samples of annual percentage growths over 100 years

You can see that some do better than others.

Here is a plot of the corresponding growth curves

Five samples of economic growth over 100 years

Again, you can see that some of these simulated economies do better than others. But, particularly if you plot things on a log scale and for a longer time period you can see that the growth curves are basically exponentials:

Five samples of economic growth over 400 years, log scale

One interesting thing here is how misleading using logarithmic axes can be: the above plot makes it pretty obvious that all the growth curves are basically exponential, but if you look at it without the logarithmic scaling then you can see that some of the economies do radically better than others, which is much harder to see in the log scaled plot.

Five samples of economic growth over 400 years, linear scale

Of course these are just toy models: how growth changes in real life is going to be more complicated than this. However the important thing is this: sustained growth corresponds to an exponential increase in the size of the economy with time.

Growth, resources and waste products

So, now, let’s think about what $s$ means. An economy with a given size is presumably making use of resources — raw materials, energy and so on — at some rate. So, for instance, thinking about energy, an economy of a certain size has a corresponding power: a rate at which it uses energy. Or in terms of some material, say iron, an economy of a certain size is consuming iron at a certain rate (much of this iron may not be newly mined, but be recycled).

Because it uses resources the economy is also producing waste products at some rate, which depends in part on how efficiently the resources are used. And these waste products do more or less damage to the environment the whole system lives in, depending on what they are: from completely harmless to extremely nasty.

There are lots of different resources with lots of different waste products, of course, and conflating them is wrong, as the supplies of each resource may be very different and their waste products are all different. So let’s just think about some particular resource or waste product and not worry too much about the complexities. If it matters, think about the resource as being energy. Then the rate at which energy is being used will be power. How efficiently the economy uses power is called the energy intensity of the economy: I’ll use the term ‘intensity’ more generally for other resources.

The total rate of resource usage can be written as $R = \rho s(t)$: $\rho$ is the intensity. If growth is exponential then $R = \rho s_0 e^{gt}$. But of course, $\rho = \rho(t)$, or even perhaps $\rho(t, s)$: $\rho$ depends on time, and perhaps also on the size of the economy, so $R$ in fact changes in a more complicated way with time.

In the simple case, $\rho(t) = \rho_0$: the intensity is just a constant. In that case if the economy grows exponentially, so will resource usage.

Let’s look at cases where $R$ changes in some way that is decoupled from $s$, and in particular cases where, if $g$ remains constant, $R$ grows more slowly than exponentially. In particular let’s think about cases where, as $t\to+\infty$, $R$ tends to some constant value. Then it’s obvious that as $t\to+\infty$, $\rho \sim e^{-gt}$. So if, as $t$ becomes large and positive, we let $\rho(t) = \rho_0 e^{-gt}$ then we get $R = \rho_0 s_0$ when $t$ is large and positive.

But you can’t just assume that $\rho$ looks like this: if we look at, for instance, energy usage by humans over time, it has very obviously not been constant but has increased dramatically since 1800. Our World in Data has a chart of energy usage since 1800. Here is a plot of this together with two exponential curves: the first increasing by 0.8%/y from the 1800 value, and the second by 2.2%/y from the 1900 value.

Global energy usage, 1800–2023

These curves were fitted by eye rather than by anything formal, but they are clearly not awful. Here is a log-scaled version of the plot which may make it easier to see the fit.

Global energy usage, 1800–2023, log scale

Global energy usage is fairly well described by an increase of 0.8%/y before about 1900 and by an increase of about 2.2%/y after that.

So what we’re after is a model where $\rho$ starts off being roughly constant, so resource usage increases exponentially at first, and then becomes more like a decreasing exponential, causing resource usage to become asymptotically constant. A logistic function does this:

\[\rho(t) = \rho_0 \left(1 - \frac{1}{1 + e^{g(t - t_2)}}\right)\tag{rho}\]

where $t_2$ is the time when $\rho = \rho_0/2$, the time when the amount of resource usage has halved. The expression for $\rho$ does not have to be a logistic function of course, it just needs to be approximately constant when $t\to-\infty$ and to look like $e^{-gt}$ when $t\to+\infty$: the logistic function is just an easy way of achieving that.

So, given this, we can plot how the size of the economy and the rate of resource usage changes with time for $G=2\,\%$ and for various values of $t_2$:

Economy size and resource usage

We can also plot the proportion of the total size of the economy which involves resource usage: this is just $\rho(t)/\rho_0$:

Resource percentage

The proportion of the economy using resources becomes incredibly small as time goes on, so it’s easier to see this using a log scale:

Resource percentage, log scale

An equivalent thing holds for waste products of course. You might want to make things more complicated by saying that $W$, the rate of production of waste products for some resource, is $W = \omega(t)\rho(t)s(t)$, where $\omega$ tells you how much waste product you produce per unit resource.

And in real life there are lots of resources and lots of waste products and all this is a hugely oversimplified spherical cow model. But it will serve.

Why growth matters

First of all it matters because for most of history most humans lived in conditions which were unspeakably awful. Having more food and water, more medical supplies, better housing and so on, all of which are things that growth has provided, has been enormously good for humanity. In 1820, about 75% of the world’s population lived in extreme poverty, while in 2010 only about 10% did (source). Growth did that.

Of course growth is not the only thing that matters: growth says nothing about inequality, and inequality also matters a great deal. Growth doesn’t help much if only a tiny number of people see an enormous improvement with almost everyone else experiencing none.

Lifting vast numbers of people out of extreme poverty is a really good reason for growth. A less good reason is that we’ve built economies on the idea that it will continue for ever. The easiest way to see this is to think about a person who wants to borrow money. If they know that, in 35 years, they will have twice the income they have today then they’ll be willing (and, if they can convince the lender, able) to borrow considerably more than if they know that their income will be the same. Well, the same is true for economies: if you assume that your country’s economy will grow indefinitely at 2%, the government can borrow more against that future wealth. Better hope growth doesn’t stop, then. Fortunately, since you are the government, you have at least some influence on growth.

The benefits of growth also diminish as people get richer: If everyone become a thousand times better better off (at 2% growth a year this will take about 350 years) is everyone a thousand times happier, or will they live a thousand times longer? Well, we can ask that today: in 2023 the median net worth of Americans aged 55–64 was $365,000 (source). Jeff Bezos, who was 60 in 2024, has a net worth in 2024 of $205,800,000,000 (source): he’s more than half a million times more wealthy than the median American his age. So think about three people: one who is half a million times poorer than the median American, a median American, and Jeff Bezos. Well, the person who is half a million times poorer has a net worth of under $1: they are probably very miserable indeed as they certainly are starving. Perhaps the average American is half a million times happier than them. But is Jeff Bezos half a million times happier than the median American of his age? Will he live half a million times longer? Is his life a half a million times better than theirs in any respect other than having that much more money? I doubt it. So growth has diminishing returns as time goes on.

Summary

In summary, if growth continues, then the size of the economy will increase exponentially with time. So

either the resource cost per unit value, $\rho$, must start to fall exponentially with time, as in the examples above;
or the consumption of resources will rise exponentially over time.

There is no third option, if growth is to continue.

In real life, of course, different kinds of resources have different characteristics as I mentioned above. The total amount of iron available on a planet is finite, while the amount of energy is not, but the amount of power is. So it’s not right to bundle them all into some single number. Eventually an economy where $\rho$ does not decrease exponentially will just have used all the iron there is, for instance: however fast it recycles old iron it will not be able to find enough to sustain its current infrastructure: it will need to start using something else.

Finally, there is a difference between the growth of an economy and the growth experienced by people living in that economy. That’s because population changes, and also because of inequality. Well, here is a plot from the World Bank of world GDP growth per capita since 1961: growth per person has perhaps averaged around 2% over that time. That graph also doesn’t address changes in inequality: it’s quite possible for almost all the growth to end up in the hands of a tiny number of people with almost everyone else getting poorer, and this may be happening.

So, now, why is it reasonable to assume that the rate of resource usage must tend to some constant? The universe is big, after all: there are really a lot of resources out there. Why can’t we just zoom off into space and use them?

Finiteness

Or, science fiction is fiction.

The future we imagine

In a few years, humanity will be a spacefaring species. From the early bases on the Moon and in Earth orbit we will have settled on Mars, and in the asteroid belt. On Mars, the gleaming cities under their geodesic domes will herald a new enlightenment. Great forests will bloom there, with trees far taller than their ancestors on Earth, producing oxygen and raw materials. The Martians, for so they truly are, will be tall, willowy and universally attractive. Somehow they will all be blond (it is, perhaps better not to ask what happened to the ones who were not). The mines will be conveniently out of sight, as will those lesser humans who labour in them.

In the asteroid belt things will be different, but not worse. Belters are, somehow, always Scottish or South African, and heavily-muscled. They work with the vast supply of metals and other raw materials they win from the asteroids which drift in vast numbers about them, conveniently slowly. They live in cities which look like futuristic shipyards, for so they are. In their cradles are built the great ships which already travel to the gas giants of the outer Solar system and will soon, soon, take humanity on its first journey to the stars. The bright white lights of welding flames are everywhere, together with the actinic blue flame of propulsion systems.

And the true space people live where they choose. In their vast, spinning spoked wheels they reside in orbit around Earth, Mars, Jupiter and Saturn, and often simply in interplanetary space. The insides of their craft are gleaming white, the walls containing panels displaying ever-changing status displays. The men all have advanced degrees in hard science subjects, the women all have short skirts and perfect legs. Again, there are curiously few people with brown or black skins.

Earth, for those who choose to remain there, is become a green paradise where humans live a sylvan, perfect life amongst the gambolling animals, free of all care.

Enough, enough.

Some inconvenient truths

If you like this picture, I suggest you read A city on Mars. Go ahead, I’ll wait.

The sketch above is science fiction, and science fiction is fiction. And the kind of science fiction I parodied above is the kind which depends on assumptions which are not true or at least completely beyond anything we have any idea how to do.

I don’t want to repeat all the arguments made in A city on Mars. But let’s just talk about Mars for a bit. If you want to think about living on Mars, think about living in Antarctica. In Antarctica you can breathe the air; in Antarctica there is plenty of accessible water; in Antarctica you’re not continually being exposed to high levels of radiation; in Antarctica you can get supplies or be rescued in a day or two. And yet people have not settled Antarctica, or various desert regions, because they are too hostile and difficult to live in. Mars is like Antarctica, but hostile: there is no air, no accessible water, there are high levels of radiation, the surface is made of toxic dust, and resupply or rescue takes between months and years. And it is extremely difficult and expensive to get there, and you get irradiated the whole way there and back.

Mars is nasty. The asteroid belt is in many ways worse. Living in it, or in space, either requires really big spinning spacecraft, or for humans to live in free-fall indefinitely, which is not good for them.

All of these places require the construction of a closed ecosystem which can remain habitable indefinitely. All of these places also mean you are exposed to a radiation environment which is probably going to kill you if you are exposed to it for long periods: no human has ever lived in it for more than a few days, and nobody has done so at all since the early 1970s. And let’s not even start on the question of having children in space or on Mars: would you want to be the person to try that experiment?

And that closed ecosystem needs to be big: if humans are going to live independently for generations on Mars, say, then you need enough of them to form a ‘minimum viable population’. This isn’t just a population big enough to avoid inbreeding: it’s a population big enough to grow food, to maintain the life-support systems, to build and maintain all the advanced machines that will be needed and so on. These things don’t happen on their own. How big such a population needs to be is not clear, but it’s between tens of thousands and many millions. We’re not talking a few people living in the converted hull of their spaceship here.

These problems can, perhaps, be overcome: just not soon. And, curiously, none of the plutocrats who want us to go to Mars or live in space are undertaking the kind of long-term research and development programme which would be needed to overcome them. They’re just building big, cool, profitable, rockets.

What is going on?

I think Charlie Stross describes it rather well. We’re living in an age where a bunch of plutocrats grew up on the kind of science fiction written in English up to the 1990s, or perhaps a bit later. And they took two things away from it:

they did not understand that it was, well, fiction: things made up so the story would be more exciting, like big exciting rockets and space, and certainly not boring details like keeping people alive on Mars which do not make exciting stories;
they read about various awful dystopian futures in SF books and thought ‘hmm, that looks pretty good for very, very rich people who don’t care about anyone else, and I already don’t care about anyone else’.

And so we get to where we are: techbro plutocrats are planning to go to Mars, but they’re planning to go there based on stories which left out all the boring details like staying alive. They think that it just needs big shiny rockets. At the same time, they’re working towards the kind of dystopia described in, for instance, the sprawl trilogy, because things are pretty good in those books if you’re a Tessier-Ashpool, and that’s who they think they are.

And, of course, they think they can combine the two: a dystopia on Earth and the shiny Mars city where they can be very gods: if you don’t do what they say on their imagined future Mars you don’t breathe. They don’t care, at all, if achieving their dream destroys the dreams of everybody else.

If the whole ‘all you need is a shiny rocket to go to Mars’ seems like a mistake nobody very smart would make I think it’s worth considering Elon Musk’s recent behaviour (I am writing this in September 2024): he is, to put it mildly, not as smart as he thinks he is. On the other hand he’s at least as unpleasant as people feared he might be. The techbro plutocrats got to be where they are by a combination of being in the right place at the right time, and not caring about anyone but themselves at all: they didn’t get there by being some kind of genius.

That is what is going on.

It’s worth saying here that SpaceX, in particular, has done absolutely amazing things: Musk made a good decision when he decided to fund them. They have successfully done what NASA abysmally failed to do after Apollo. In 2021 dollars, it cost about $5,400/kg to low Earth orbit for the Apollo Saturn launch system; for the space shuttle it was about $64,500/kg; for Falcon heavy it is about $1,500/kg (source). That is an enormous achievement. It’s just not closely related to what is required to sustain a settlement on Mars or the Moon.

The future we’re going to have

We are not going to live in space in significant numbers any time soon. The cost of lifting significant numbers of humans from Earth will be prohibitive both financially and environmentally for a very long time, and quite likely for ever: the great majority of humans will need to live within the resources Earth provides for a long time to come.

We might have small, dependent settlements on Mars or the Moon this century. But, well, they will be small, and dependent on supplies from Earth.

One of the things people like Musk go on about is that we should have a settlement on Mars as a lifeboat for humanity: if something bad happens to Earth humans will survive on Mars. That requires that settlement to be long-term viable without support from Earth, which will not be possible for a long time. And it’s just a stupid idea: if you are going to live on Mars, you’re going to be living underground if you don’t want the radiation environment to kill you. And you will need to make air, and all your food and maintain the environment, essentially for ever. Well, wouldn’t it be just a lot easier to build some number of lifeboats for humanity underground in remote parts of Earth? There is water, there is air, both can be filtered for nasties. It’s hugely cheaper to build than something on Mars. Such settlements would certainly be preferable as a way of ensuring humanity survives a flat-out nuclear war, or catastrophic climate change. They would almost certainly be preferable as a way of surviving a Chicxulub-level extinction event.

Of course it would be easier to do that. But it wouldn’t involve rockets: it would not be the shiny SF dream that the techbro plutocrats so want to come true.

The same applies for slower catastrophes: if you’re in a position to ship many thousand people to Mars, you’re in a position to scrub carbon dioxide from the atmosphere, so why not do that? Because it doesn’t involve shiny rockets, I suppose. Also it involves helping other people, and plutocrats only care about themselves.

Finally, note that even if we ignore all the inconvenient truths about becoming a spacefaring species, it doesn’t solve the exponential problem for long. If Mars has all the resources of Earth, then at 2% resource growth a year, we exhaust the resources of Mars 35 years after we exhaust the resources of Earth. Then we have to move on to even more insane ideas like cannibilizing planets. Which, certainly, will perturb orbits of other planets, planets on which we already live, in ways which will be not good for the people who live there.

The future we are going to have is that we must live within the resources provided by Earth for a long time: probably centuries at least.

Are we going to have a problem here?

OK, so we’re stuck on Earth for a long time. That’s only a problem we need to think about if we are, in fact, running out of resources, or if waste products are going to cause a problem.

The magic goes away

Perhaps, in fact, we’re many doubling periods away from getting anywhere near that point. At 2%/y growth then the rule of 70 says the doubling period is 35 years, so we might have hundreds of years left, and we can just kick the can down the road as we always have. That’s a horrible answer, but it is an answer.

In 1798, Thomas Malthus wrote An essay on the principle of population in which he argued that exponential population growth would result in everyone starving. It hasn’t after more than two hundred years, and in fact a far lower proportion of the human population is starving now than was in 1798. Malthus was wrong, and he was wrong because of something he missed: fossil fuels, which provide both copious energy and fertiliser.

Unfortunately putting over 1.5 trillion tonnes of carbon dioxide into the atmosphere turns out to have had quite bad effects, of course. Do perhaps Malthus wasn’t as wrong as all that: you can indeed solve the problem he was worried about for a couple of centuries, but only if you’re willing to solve a much larger problem later on.

It might be that there is something we don’t know about which changes everything. But we know a lot more about the resources available and the consequences of using them than he did. And we know a lot more about physics and chemistry than he did, and specifically about limits placed on what we can do by physics. Simply assuming that something completely unknown will turn up snd everything will be fine as a result seems like a really stupid approach. So let’s not do that, then: if it looks like we’re not many doubling periods away from trouble let’s assume that’s true.

Energy

We’re not, for instance, running out of energy very fast: there’s a lot of available solar power; nuclear fission power is clean and could be cheap if we wanted it to be. Nuclear fusion will probably actually arrive at some point. We will eventually run out of energy, but we have at least a century even without making dramatic improvements in energy intensity.

Except we really, do need, already, to have stopped burning fossil fuels. But let’s leave that for later.

Energy is not the only fruit

There are other things we might want to worry about than energy, and perhaps the most obvious one is that the environment we live in is also a finite resource, and we’re doing things to it, often by dumping waste products into it, which are not very good.

As an example, we’re degrading the environment in a way which is disastrous for insects: flying insect populations in the UK have declined by a factor of more than 2 over the 20 years to 2022 (original source paper): halving in 20 year is not good, and it’s backed up by many other studies.

There are many other signs that things are not well. Swallows declined by 43% in the UK between 2012 and 2022 (they had been increasing in the 10 years before that), house martins by 40%, swifts by 40% (source). So perhaps I am slightly more interested in swallows, martins and swifts than other people, but that’s not the point: I could live in a world without them, but their decline is not some special case, it’s one example of something pervasive.

The thing that is pervasive is that we are seriously, seriously, degrading the environment that supports us. That degradation is related to the rate of resource usage in the economy. And if it continues for long enough the environment is not going to be able to support us any more.

If we don’t do anything, how long do we have before we’re in trouble? I have no idea. But I do know that by the time we’re in trouble it will be too late: the environment isn’t simple and it’s full of hysteresis. By the time we fall over some tipping point there will probably be no way back, so we’d be well-advised not to push our luck.

Yes, we are going to have a problem here

I don’t know when. But if I had to guess I’d say within fifty years or so, and perhaps sooner than that. And by the time the problem becomes serious it will be too late: we need to do something to adjust the resource intensity — to reduce the $\rho$ — of our economies now.

So, now, let’s find out just how far from reality the fantasy world economists live in is.

Faith, fantasy and Economics

So we’re not going to be living in space any time soon: we need to plan to live within the finite resouces provided by Earth. And the evidence is that we’re already approaching limits of some of those resources. So we’d better plan to make our rate of resource usage a constant, or even make it smaller.

If we assume growth continues at some rate $g$, then the rate of resource usage for some resource is $R = \rho(t)s_0 e^{gt}$. For this to be constant we need $\rho(t)$ to start looking like $e^{-gt}$ in the future, so as time goes on it becomes more and more like $\rho_0 e^{-gt}$, say and resource usage tends to $\rho_0 s_0$.

Efficiency limits: economics meets physics and loses

So it’s tempting to say that, well, we can just make everything more efficient over time, without limit. We can’t. Physics places hard limits on how efficient various processes can be, and however much you might want to exceed those limits you can’t.

For example, modern petrol engines for cars are 30–40% efficient (source), with diesel engines approaching 50% (source). Carnot’s theorem says that the efficiency of a heat engine can never be more than $(T_H - T_C)/T_H$, where $T_H$ and $T_C$ are the absolute temperatures of the hot side and the cold side of the engine. We can’t do much about $T_C$, and $T_H$ is limited by the materials we can use.

Well, perhaps we can use really super amazing materials (let’s hope they’re cheap) and increase efficiency far above what it is today. But wait: car engines are already close to 50% efficient: the very best we can ever achieve is a doubling in efficiency. At 2% improvement a year (which is much more than we will be able to achieve) that’s 35 years. And to get to 95% efficient the hot side of the engine would have to run at mearly 5,500K which is, well, pretty hot.

The same things apply to any other physical process: you just can’t keep on making things more efficient in terms of physical resource use, whatever the resource is at a constant growth rate: things can’t get exponentially more efficient with time, and that’s all there is to it.

Escaping into a pretend world

So, OK, what is the answer to that? Well, the proposed answers are two: substitution. and decoupling.

Substitution is the idea that as we reach limits in one technology we substitute another, more efficient one. It’s not an answer: the efficiency of a car engine can’t even in principle exceed 100%, for instance, whatever technology you use. Sometimes we are using technologies which are extremely inefficient, but there is still a physical limit and it’s often not that far away. Substitution keeps you going for a while, but not for ever.

An example of substitution is that, of course, nobody expects internal combustion engines to reach 95% efficiency: we’ll replace them by electric motors. Which will get us no more than another factor of two or so, because there’s a term for systems with efficiencies above 100%: perpetual motion machines.

Decoupling is the idea that we don’t just have more and more of the same thing: we start doing other things and those things use less resources.

The poster child for decoupling is probably the idea that we will, over time, spend less and less of our time doing any kind of physical thing and more and more of it immersed in virtual worlds running on computers. Let’s conveniently ignore the fact that, Moore’s law notwithstanding, computers also have physical limits on how efficient they can be, and assume we’ll all end up living in The Matrix while, in the real world, consuming very few resources. Perhaps some people would even like that.

Alternatively, perhaps we will end up spending all our time making and trading vastly valuable art, while living otherwise very simple lives. That seems more attractive to me, anyway.

Decoupling is not so obviously silly, but it turns out to be just as mad.

Decoupling: economics meets itself and loses

So, let’s assume that, somehow, some combination of substitution and decoupling means we can maintain economic growth while resource intensity declines exponentially. What happens?

The first thing is that, over time, the resource-consuming part of the economy, and hence the proportion of everyone’s income spent on it, becomes increasingly tiny. Which leaves us in two very weird situations.

Consider living in such a world. A very tiny proportion of your income is spent on things which consume resouces: they are very cheap for you. So let us assume you’re a human being, not some economist’s fantasy idea of one: why wouldn’t you just spend a lot more money on resource-consuming things? Perhaps you decide you’d like to build an enormous steampunk coal-fired machine: you can do this, because you have enormous wealth, everyone has enormous wealth. I mean, the people today who have enormous wealth have things like private jets, and travel by helicopter: why wouldn’t everyone do that in the future?

Something has to stop you. And that thing can’t be that it’s too expensive, because if it’s too expensive then resource consuming parts of the economy are, in fact, a large part of it, not a very tiny one. It just makes no sense.

And that’s not the end. As the resource-consuming parts of the economy become an ever smaller fraction of it, there’s no reason why, at first countries and then later large organisations and wealthy individuals might not decide to just buy them all, and hold everyone else to ransom.

The whole idea of decoupling means that there is a tiny fraction of the economy which is nevertheless critical for everything else to exist: you still need to be warm, to be sheltered, to be able to eat, to travel, it’s just all now very, very cheap. That’s not how things work: if there is a resource which, if you own it, gives you enormous power over over large parts of an economy, then that rerource is extremely valuable, not extremely cheap.

Well, perhaps it’s like food. Food, by historical standards, is very cheap in developed countries. In 1929, food cost about 23% of Americans’ disposable incomes: in 2022 it was 11% (source). And that’s only in recent history. And people do eat both more, and more resource-intensive food as a result. But there’s a limit: eating too much isn’t good for you, and most of the cost of very expensive meals is the restaurant. So there’s a limit on how much you can eat.

It’s not like food: my one-man Mars-capable racing spacecraft uses really, really a lot of resources.

So no, none of this makes any sense: the only conclusion is that something else will happen.

The elephant

There is an elephant: there is always an elephant.

The elephant, of course, is climate change. Climate change — specifically climate change caused by humans — happens becsuse of two waste products from resource usage:

carbon dioxide which is a waste product from the burning of fossil fuels;
methane, which is a waste product coming about 40% from agriculture 35% from fossil fuels again and 20% from other waste (source).

Carbon dioxide is much longer-lived than methane and also is a larger contributor to the current warming.

There are many, many articles on global warming as well as many, many scientific papers. I’m not going to write another one: if you want a comprehensive summary look at the IPCC. There are also many liars and fools who claim global warming is not happening, that it is happening and it’s not a problem, or that it is happening, it is a problem, but we can’t do anything about it: at this point those people are, well, liars. fools, or both.

Here are plots of average annual and summer (June, July, August) temperatures for central England since 1824, averaged over five years, using data from the Met Office.

Five-year average of central England annual temperature, 1824–2023

Five-year average of central England summer temperature, 1824–2023

Yes: climate change is happening.

Climate change a really good example of where we need to dramatically reduce the intensity of something, which in this case is waste products from consumption of energy. We know the consequences of not doing so will be very bad, and we know a lot of approaches for reducing the intensity. Unfortunately most of them require humans to make changes they find inconvenient. But, still, we know we have to do this, and in 2015 everyone agreed to do so.

How are we doing?

Here is a plot of data from the Mauna Loa Observatory, showing both the measured atmospheric concentration of carbon dioxide, and a version of the same data with the annual cycle removed.

Mauna Loa atmospheric carbon dioxide concentration

It’s worth looking at the data starting in 2010 as well:

Mauna Loa atmospheric carbon dioxide concentration since 2010

Here are equivalent plots of data showing the global average atmospheric carbon dioxide concentration

Global average atmospheric carbon dioxide concentration

And again, starting in 2010

Global average atmospheric carbon dioxide concentration

So, well, how about fossil fuel usage? Data for this comes from Our World in Data.

First the combined totals:

Global fossil fuel consumption

And broken down by fuel:

Global fossil fuel consumption by fuel

What this looks like

What this looks like is abject failure: we are, perhaps, doing something although exactly what is not visible in the concentration data. But we are doing so far from enough as not to be funny.

We know about climate change, we know how serious it is, we know how to fix it. And we are failing.

The teeming billions

So, here’s the bit about how overpopulation is going to doom us all.

Not so much. For much of the world growing populations are no longer the appropriate thing to worry about: the problem is now, or will soon be declining populations.

The rate at which a population grows or shrinks is determined both by how many people die and how many are born. The second of these is usually summarised by a number called the total fertility rate, which is the average number of live births per woman under various assumptions. It’s not completely straightforward to know how this number corresponds to population change, as it depends on things like mortality rates and sex ratios at birth. But for a population with current first-world mortality rates a value of a little over 2 — perhaps 2.1 — is the replacement rate: at this value the population will be stable.

In England and Wales TFR fell below 1.5 in 2022. In Scotland TFR was 1.28 in 2022. This is not just below the replacement rate it’s far below it. And the UK is not unusual: the TFR in the USA is about 1.7, in France it is 1.8, Germany 1.5. In South Korea it is 0.8 (that is not a typo). More figures can be found from the Population Reference Bureau.

The consequences of this are that the population in these countries will fall, and the world population also probably will soon start falling. ‘Good’, you might say. But that’s not the only consequence. The age statistics of the population also will change dramatically.

I wrote a toy model which lets me play around with what happens. I didn’t want to deal with sex ratios, so in the model there is only one sex: if you like, there are only women. A proportion of the people die each year in a controllable age-dependent way, and they produce offspring in a controllable age-dependent way (in the runs below from 20 to 40) At 100 I kill off the (very small proportion of) survivors to avoid dealing with a tiny but long tail of very old members.

Rather than TFR the model deals in the effective fertility rate, EFR, which is defined to include mortality and thus needs to be 1 for a stable population (TFR would be a little higher, but is about half that for a real population as there is only one sex).

This is very much a toy model: it’s not anywhere close to accurate for humans. But it’s realistic enough to demonstrate in outline what happens to demographics if fertility falls dramatically. Here are example pictures from two setups. In both of these the initial mortality rates are

5%/year in the first two years;
1%/year from 2–9;
0.1%/year from 10–19;
0.5%/year from 20–39 (childbirth is a bit dangerous);
0.1%/year from 40–49;
1%/year from 50–59;
2%/year from 60–69;
6%/year from 70–79;
12%/year from 80/99;
100%/year at 99.

Fertility is 10%/year between 20 and 39, and zero outside that window.

In fact these values get smoothed out a bit to make the graphs look nicer. The EFR of the system set up like this is about 1.54.

The model is then spun up for 1000 years, with the final population being renormalised to 100 million in total.

At this point the plot of numbers against age (the ‘demographic pyramid’) looks like this:

Population by age, after spin up

Falling fertility

In this setup, fertility is reduced by 2%/year for 40 years (note: this means 2% of 10%/year initially). The EFR at the end of this process is about 0.69. After this the system runs up to year 200, at which point fertility is adjusted so the EFR is 1. It then runs up to year 400.

Here are graphs at year 0, 20, 40, 60, 80, 100, 200, 400:

Population by age, at year 0, 20, 40, 60, 80, 100, 200, 400

You can see two things from this: the population falls over time, but the demographic pyramid also changes shape dramatically. It’s easier to see this if you renormalize these plots so the average population for each age is 1:

Renormalized population by age, at year 0, 20, 40, 60, 80, 100, 200, 400

You can see here that the demographics get very heavily skewed to old people compared to the initial state while the population is falling. Once it stabilises they remain skewed to older people but less heavily so.

Here is a picture of what happens to the total population in this scenario

Population history

The interesting thing you can see here is the latency: it takes about 40 years before the population starts falling, and a similar amount of time for it to stabilise. That’s true even if you step fertility so the EFR is 0.69 in the first year, although the demographic pyramid looks bizarre in that case (it has a huge step in it which gradually gets washed out).

Falling fertility, rising mortality

You can ‘fix’ the skewing towards old people as EFR falls by assuming that old people become more likely to die.

In the run below, mortality is increased by 0.1%/year (relative to the current mortality for that year) from age 30 for the first 40 years of the run. Mortality is not reset at year 200.

Here are the unrenormalised and renormalised plots, as above

Population by age, at year 0, 20, 40, 60, 80, 100, 200, 400, increased mortality

Renormalized population by age, at year 0, 20, 40, 60, 80, 100, 200, 400, increased mortality

You can see this is ‘better’. At least it’s better until you realise what’s happened to the population:

Population history, increased mortality

In the case where the mortality of older people does not increase the population falls to about 18% of its initial value over 400 years. In the case where it does, the final population is about 3.7% of the initial one. Which should not be very surprising, of course.

The consequences

Population growth is a bit like economic growth, to which it is closely related: if it continues at a positive rate then you can assume that, in the future there will be more people than there are now. In particular there will be more people of working age. Those people earn money which pays taxes which, among other things, pay for the care of the elderly. In a world where the TFR remains at some constant value and where age-related mortality doesn’t change over time, it also has the nice characteristic that it’s an exponential process, and so ‘everything remains the same’: in the future there will be more or fewer people, but the proportions of people alive at a given age remain the same.

In other words, if the TFR remains at some more-or-less constant value comfortably above 2 and mortality rates remain about the same, then you can always plan to pay, for instance, pensions, out of money you don’t yet have. You do run into the problem that, in due course, everyone starves as the population increases exponentially and the food supply runs out, but that’s not the sort of thing economists worry about: they just kick that can down the road, which has worked well for them so far.

The real world is increasingly unlike that: TFR has historically reduced over time, and mortality has also fallen. We’ve already seen some of the consequences of life expectancy increasing: people have to work until they are much older.

In many parts of the world TFR is now well below replacement value, and often well below it. That’s going to have very big consequences. For now, those places could fix their problem by importing people from regions of the world where the TFR remains high. They’re generally not doing that, apparently because those people have skins of a different colour.

As above, so below

Quod est superius est sicut quod inferius, et quod inferius est sicut quod est superius.

— Latin translation of Emerald tablet

Everything above is meant to be based on information you can check: most of what follows is is my opinion.

Shorter

Economic growth is a very good thing: it’s lifted billions of people out of poverty. But it’s an exponential process.

Despite the fantasies of plutocrats who read too much science fiction at an impressionable age and who are not as smart as they think they are, we’re not going to migrate into space, the Moon or Mars any time soon, if ever. We need to live within the finite resources provided by Earth.

Continuing exponential growth in a system with finite resources is not possible unless the thing that is growing doesn’t use the resources. This means economic growth must be decoupled from resources at an exponential rate, and that is what economists assume will happen. Generally speaking it’s not what has happened so far.

Sadly for economists it’s also generally not physically possible. Physics places limits on how much resources various processes must use, and in many cases we are within a factor of 2 or 3 of those limits already. This limits the amount of decoupling, and therefore the amount of growth.

If you assume that, somehow, that will not be a problem (it will be a problem) you rapidly end up in situations which are absurd in other ways. Either critical resources become very cheap yet must be used only in small amounts, critical resources are so cheap that they can be acquired and controlled by bad people, or both. The only even slightly sane way around this would be for resources to be controlled by some central body — the state, perhaps — provided in limited quantities very cheaply, with further trade in them prohibited. That kind of economic system tends to end up with gulags.

Economists who believe these things are living in a make-believe world: economic growth cannot, in fact, continue indefinitely.

Perhaps we have a long time before this becomes a problem? Looking at environmental damage and particularly climate change caused by the waste products from consumption of fossil fuels tells us that no, we don’t. We have essentially no time at all.

On top of all this is something else that we certainly should have seen coming, even if how quickly it is arriving is unexpected: fertility rates are falling, in some cases dramatically. The total fertility rate in England and Wales fell to 1.44 in 2023 (source): Scotland’s fell to 1.3. Replacement value is about 2.1. Worldwide the TFR is still above 2, but it will almost certainly fall below it fairly soon. This means populations will start to decline which, itself, makes growth harder to maintain. Worse, it means that, unless people start living less long, there will be proportionally more old people who will contribute less to the economy and many of whom will need care.

Let’s be clear: economic growth is a good thing. I am not some hippy who wants to go back to living in an 18th century of my imagination, where somehow I will, of course, be landed gentry and quite comfortably off, and my wife won’t die in childbirth and all our children will live to be adults. Because that world didn’t exist. If growth could continue for ever that would be great, thanks. And, like the plutocrats, I grew up with science fiction: I really want to go to the Moon, or to Mars, or to Jupiter, or the stars.

But I’m also not an idiot: growth can’t continue indefinitely, and almost certainly cannot continue for very long at all. Humanity is not going to become a spacefaring species any time soon, if ever. And what says these things is physics, and if you have a difference of opinion with physics then you are wrong. Oh yes, we could discover magic new physics tomorrow — faster than light travel, and therefore also time machines, say — but do you want to bet the entire future of civilisation on that? Because I don’t.

So we have a big, urgent problem.

What are politicians doing about the problem? Almost none of them seem to understand in any real sense that there is a problem. That is perhaps unsurprising given that most politicians have educations which might have been appropriate in 1750. Those who accept that at least part of the problem exists seem to think that it can be solved by just making speeches, signing bits of paper and then quietly not doing enough, or anything, to actually solve the parts they understand exist.

And that’s what people are doing as well. Because it’s a big and really inconvenient problem: it means things will be very different in future and, whatever we do, they will likely be worse than they are now in some ways. And the shiny space-age future promised by growth is not, in fact going to happen. It’s like cancer: it’s often, somehow, easier to ignore the symptoms and pretend that you don’t have a horror growing inside you, rather than deal with the awkward truth. But it’s also like cancer: if you just avoid dealing with it then it will get worse, and then it will kill you. The best thing you can do is to talk to a doctor, immediately.

And other people have a different reaction. They see things are not going well, if dimly, but imagine that, somehow, it’s always being caused by someone else, or that these other people are lying to them about the problem. And ‘someone else’ means people with skin of a different colour, LBTQIA+ people, ‘woke’ people, liberals, the elite, women and so on. And so they elect leaders who will ‘deal with those people’ in some unspoken but always understood way.

So we get politicians who claim it’s all a hoax: it’s all a lie spread by woke liberal science gay black foreign people and we should just burn more fossil fuels and puke more pollutants into the environment. And, you know, deal with the people we don’t like.

But, I think, a lot of people have some notion of what’s probably coming. It seems at least plausible, for instance, that one reason women are choosing to have fewer children because they can see that bad things are coming and nothing significant is being done to prevent it. Of course the demagogues have a plan for that: forced birth, which is being instituted in America.

Again, let’s be clear: economic growth will stop, because physics says it must. That’s not a good thing, but it is inevitable. We have, really, three choices. We could recognise the problem and work out how we’re going to deal with it. It won’t be easy. We could pretend it doesn’t exist and just blunder as long as we can. Or we could pretend it’s all a lie spread by some imagined enemy who must be eliminated.

We are not taking the first option.

A history of the near future

I’d like to share a revelation I’ve had during my time here. It came to me when I tried to classify your species. I realized that you’re not actually mammals. Every mammal on this planet instinctively develops a natural equilibrium with their surrounding environment, but you humans do not. You move to another area, and you multiply, and you multiply, until every natural resource is consumed. The only way you can survive is to spread to another area. There is another organism on this planet that follows the same pattern. Do you know what it is? A virus. Human beings are a disease, a cancer of this planet. You are a plague, and we are the cure.

— Agent Smith

Overshoot and collapse

Despite alarmists, climate change and environmental degradation are very unlikely to make the Earth uninhabitable by humans: they will just make it a lot less habitable. If we merely stick our heads in the sand and ignore the problem and if, as things begin to fall apart, there are no ugly social effects, then what will happen is overshoot and collapse. This is, of course, what was predicted by the Club of Rome in The limits to growth in 1972. Here’s what they currently say about it:

This report […] was the first to model our planet’s interconnected systems and to make clear that if growth trends in population, industrialisation, resource use and pollution continued unchanged, we would reach and then overshoot the carrying capacity of the Earth at some point in the next one hundred years.

Some fifty years on, the call for a change in direction was more urgent than ever. The report’s modelling was remarkably accurate and prescient as the world declares the climate emergency to be real and global ecosystems to be at breaking point. Fifty years offered an excellent opportunity to look back, and forward, at the trends it examined and listen to leading international thought leaders, scientists and politicians on how we create a new critical framework for living and thriving within the limits on Planet Earth.

The limits to growth was despised by most economists, apparently because it told them an inconvenient truth. Inconvenient, perhaps, but still true.

Overshoot and collapse happens because there is hysteresis: the behaviour of the system depends on its history. A simple example is extinction: if the environment changes so some species dies out, then undoing that change doesn’t bring it back to life. The same thing is why the ‘overshoot and recover’ idea that’s now popular for carbon emissions is so stupid and dangerous: if you push temperatures beyond the point where the ice sheets start melting, say, or AMOC collapses, then that doesn’t stop when temperatures lower again. Of course what people really mean by ‘overshoot and recover’ is ‘do nothing, and let our children deal with it’: it turns out people do not care about their children’s entire futures if it is momentarily inconvenient to them.

So what happens is that economic growth continues, carbon dioxide and methane emissions are not reduced and then at some point everything falls apart: harvests fail, people die in large numbers, and modern civilisation comes to an abrupt end. Over the following hundreds of years sea-levels rise by many metres as the ice-sheets melt, drowning the remaining coastal cities, the wave of extinctions we’ve started continues and people continue to die off. Eventually, after several thousand of years and since emissions have essentially ceased, the climate stabilises and the surviving human population starts again from some level between the mediaeval and the 18th century. Perhaps they’ll have learned not to do it all again. Perhaps.

It’s not a good prospect. But it’s absurdly optimistic compared to what the history of the near future will be.

A pale horse

And I looked, and behold, a pale horse, & his name that sate on him was Death, and hell followed with him: and power was giuen vnto them, ouer the fourth part of the earth to kill with sword, & with hunger, and with death, and with the beastes of the earth.

Something I haven’t talked about is what will be the most obvious effect of climate change: parts of the world which are already hot will become dangerously so, to the extent that humans will not be able to live in them. The people who live in those parts of the world will have two choices: move somewhere else, or die. When one of two choices is ‘die’, people take the other one. There is going to be a lot of migration from these areas to more temperate areas. And when I say ‘a lot’ I mean a lot: hundreds of millions of people will have to migrate. This is already beginning: if you think the ‘small boats’ crisis that has so exercised people in the UK is a big deal, well, you ain’t seen nothing yet.

The social effects of this aren’t something climate models can talk about, but I think they’re pretty predictable. In temperate areas, the environment will already be degrading: there will be floods, droughts and other unpleasant weather events. The hope for a better future will be gone; the hope for a future which is at least not much worse than the past will be receeding.

Women will have fewer children because they realise that bringing a child into the world they see arriving is an act of cruelty. Demographic pyramids will invert, and old people will start dying as a result. Old people will resent the young, young people will resent the old.

And while this is going on a vast tide of desperate migrants will be trying to get in, because if they can’t get in, they will die. Letting those migrants in would, in fact, resolve the demographic problem. But the migrants have brown skins and it turns out that racism is anything but a solved problem in developed countries: it’s just one we suppressed for a while. There were race riots in the UK, in 2024.

People will react as they’ve always done: they’ll look for people to blame for what is happening, because it can’t be their own fault. Among those people will be recent immigrants. They’ll look for strong leaders who will give them simple answers. Not everyone will think like this, but enough people will. And, conveniently, not thinking like this will mean that you are one of the people to blame.

This is what is happening now. It’s populism: leaders who offer simple, appealing, wrong answers to complicated, unappealing problems. Keep the migrants out. Go for growth. Burn more coal. Stop all that environmental protection stuff. It’s all the fault of the liberal elite. It’s free trade. The scientists are to blame. It’s the Mexicans. It’s black people. It’s the gays. It’s trans people. It’s the Chinese. It’s the UN, the muslims, NATO, the EU. It’s always somebody else: it’s never us. And, inevitably, at some point, it will be the Jews.

And so populism is turning into something else: fascism.

Look at Germany in the 1930s: things were pretty bad after the 1914–1919 war, so people started looking for someone who would offer them simple answers, and provide people to blame. They found that person. And they found a convenient group of people who were not, of course, to blame, and they murdered them in huge numbers.

That’s what’s coming. As the environment falls apart, as growth fails, fascists will start taking over. Look around: they already are.

But the simple, appealing answers fascists offer don’t work. Once the bad people have all conveniently gone away (nobody will ask where, everybody will know where), once the walls have been built, once the coal is being mined again, once the compulsory birth laws are in place, somehow nothing will get better. The environment deteriorates, people start dying in floods, in droughts, in hurricanes and typhoons. Harvests start failing: people start dying of hunger and thirst. And the fascists will do what they always do, because it’s all they know how to do: they’ll start wars.

An interesting thing has happened. I was born just after the Cuban missile crisis. While she was pregnant, my mother was stockpiling dried milk and canned food, because she believed that a nuclear war could be imminent. She was right to believe that: it very nearly happened. Throughout the later 1960s and 1970s people were absolutely terrified of nuclear war. Perhaps the threat was seen as receding by the mid 1980s, but people were still pretty scared. And then, in the early 1990s, it all stopped: the USSR collapsed and the cold war, apparently, was over. The doomsday clock maintained by the Bulletin of the atomic scientists has never been further from midnight than it was in 1991.

You want to know when it was closest to midnight? Today, that’s when. Almost all nuclear arms limitation treaties are dead. Russia has invaded Ukraine and threatened to use nuclear weapons there. The situation in the Middle East is awful, and at least one state there has nuclear weapons. A new nuclear arms race is starting.

And yet, curiously, the fear of nuclear war hasn’t returned. People have, somehow, forgotten that this awful fate hangs over us, and not noticed that it is more likely, today, than it has ever been. That’s, as I said, interesting.

Because here’s the thing: the failure of growth together with increasingly rapid environmental collapse and migration on a huge scale is going to give us — is already giving us — fascists. And fascists, when they have dealt with the undesirables and when it turns out they have no answers, start wars. And those wars will rapidly become nuclear wars.

How near is the near future? I don’t know. I could invent a chronology but I’d be making it up. Certainly by the end of the century, and perhaps much sooner, partly depending on what happens on the 5th of November and in its aftermath: on how soon the fascists win in America.

And that is how the history of the near future ends: not with a whimper, but with a bang. If you want to know how most humans will die, watch Threads.

— Tim Bradshaw, all saints’ day, 2024

Sexism in higher education

Tim Bradshaw — Tue, 21 Nov 2023 11:49:01 UT

Some time ago I wrote a post with empirical evidence for sexism in computer science. I’ve since realised that the data I used then is part of a much larger data set maintained by the US National Center for Education Statistics: here are some more pictures of their data.

What I want to show

The ratio between male & female¹ students gaining degrees in various subjects has often changed dramatically over a fairly short period of time (generally about 50 years here: about two generations). Such a dramatic, rapid change shows that these ratios are not due to innate ability but to women being discouraged from studying some subjects.

By plotting pictures of this data these changes become much easier to see, compared with looking at large tables of numbers.

The data and how it was plotted

The data I’m using comes from NCES and in particular it comes from these tables. These are, I am sure, updated fairly frequently: the data I am plotting here was fetched in November 2023².

The plots are simple-minded: they just look at the male/female ratio without taking any account of the total number of students. There is no smoothing. For fields which started very small there is therefore sometimes quite a lot of variability, especially for higher degrees.

The ratios were computed from the numbers of students in the tables, and in particular I didn’t use their precanned figures. Where I checked, mine are the same.

The data does not contain any gender information other than male & female: in particular it takes no account of trans people or anything like that. It’s unlikely that that data was even gathered until quite recently, of course, but in any case this is just missing from the pictures because it’s not in the data.

The tables contain data for three levels of degree: batchelor’s (BSc or BA I presume), master’s (MSc, MA, MPhil I presume) and doctor’s (PhD). Those have been plotted separately.

I’ve used the starting year of any given academic year: 1984–85 turns into 1984. I’ve not generally plotted the data before 1970 so all the graphs have the same start date: where there is data before 1970 in the tables it is generally decadal. The end dates are the end date in the data which is slightly variable.

The plots have a y-axis which runs from 0 to 50% or to the nearest multiple of 5% above the maximum female percentage.

I have looked, rather casually, for sources of similar data for the UK. I haven’t found anything. I have asked the UK Office for National Statistics though, and if they have anything useful I will write another post.

The pretty pictures

This is necessarily just a fairly arbitrary selection of plots of subject areas I thought would be interesting: there is a lot more data there that I have not plotted.

Computer and information sciences

This is table 325.35, and is an updated version of what I plotted previously. This plot runs from 1970 to 2020.

CS & IS graduate sex ratio, US, 1970–2020

You can see from this that the ratio has gone up since about 2010, but it is still far lower than it was in about 1984. The data for higher degrees shows far less of a bump than the data for first degrees: presumably whatever drove women out of CS courses had less effect for higher degrees. The data for doctor’s degrees is quite bumpy because the numbers are rather low: this is common in all the graphs.

Mathematics and statistics

This is table 325.65. This plot runs from 1970 to 2020.

Mathematics & statistics graduate sex ratio, US, 1970–2020

This shows pretty much no sign of a 1980s peak followed by a decline. What it does show is a slight decline after about 2000 which perhaps is visible in all three lines.

Engineering and and engineering technologies

This is table 325.45. Plot from 1970 to 2020 again.

Engineering & engineering technologies graduate sex ratio, US, 1970–2020

Well, there were essentially no women studying engineering in 1970 (who knew?), but there are a lot more now. A woman studying engineering is now more likely than a man to pursue a higher degree in the subject. There might be a 1980s effect, but there definitely is something after about 2000, although it seems to have gone away now. This is a pretty dramatic picture, I think.

Physical sciences and science technologies

This is table 325.70. The plot runs from 1970 to 2017, which is the most recent data.

Physical sciences & science technologies graduate sex ratio, US, 1970–2017

There’s no 1980s effect. There is quite a strong post–2000 effect.

English language and literature/letters

This is table 325.50. The plot runs from 1970 to 2017, which is the most recent data.

English language & literature/letters graduate sex ratio, US, 1970–2017

More women than men do first degrees in this area, and this ratio has been pretty stable for a long time. Once upon a time not many women went on to do doctoral degrees, but the ratio has mostly caught up now.

This is table 325.60. Plot from 1970 to 2020.

Health professions & related programs graduate sex ratio, US, 1970–2020

This has always been dominated by women. What is probably hidden in this data is that most of this dominance was nursing and related degrees, while the number of female (medical) doctors was rather low. However it has climbed steadily and dramatically since 1970, and in 1920 about 60% of new doctors were female.

This is table 325.90. The plot runs from 1970 to 2017, which is the most recent data.

Social sciences & history graduate sex ratio, US, 1970–2017

Unfortunately there is no table for economics degrees: this is the best proxy I could find. It’s not terribly interesting although it does show some signs of both the 1980s and post–2000 dips.

Visual and performing arts

This is table 325.95. Plot from 1970 to 2020.

Visual & performing arts graduate sex ratio, US, 1970–2020

Again, women have always slightly dominated first degrees, and have been increasingly likely to do higher degrees. There is perhaps some fall in the ratio for doctoral degrees after 2000. Is there a 1980s dip here?

What can you conclude

First of all and most obviously: there is no evidence for differences in innate ability between men and women here. If you look particularly at the graphs for CS & IS, engineering and physical sciences, you will see enormous changes in the percentage of women graduating in these areas within two generations. In the case of Engineering the change between 1970 and 2020 was by a factor of 28, from 0.8% to 23% female. The change between 1949 (the first data in the table) and 2020 was by a factor of nearly 77. It is absolutely impossible that such changes should be due to changes in innate ability, and it is equally impossible to even glimpse any possible differences in innate ability in the presence of this vast socially-driven change.

Secondly, as before, something happened in CS & IS which drove out nearly half the women who studied it in a little more than a generation. This was probably the period when achieving a degree in this area was most lucrative.

And this time I’ll come out and say it: I think that what did this was just obviously white male tech bros who started arriving on CS courses after the home computer revolution of the early 1980s. These are people who clearly behave just as badly towards women as you would think they would. Who would have guessed it?

Thirdly there may be some evidence of a decline of women in some science & engineering subjects after 2000. I don’t know what is causing that, if it’s even real.

Finally, things are now more equal — better in fact — than they were in 1970, although CS & IS has not recovered from its infestation of tech bros.

I haven’t yet looked for tables like the ones I’ve used here categorised by ethnic group. I imagine they will tell exactly the same story: that there is no indication at all of any innate difference in ability between ethnic groups.

The data contains only information about male and female: see the next section. ↩
I have copies of the versions I used which I can provide if anyone cares, as well as the program which plotted this. ↩

Numerical prediction

Tim Bradshaw — Fri, 28 Jul 2023 10:39:12 UT

In late 2018, when I still worked at the Met Office, I sent a document to some people there which explained why I thought AI would come to dominate weather forecasting, and why weather forecasting organisations should be looking at AI, urgently. Today, the 28th of July 2023, there is a leader on the subject in The Economist as well as an extended article in its Science and Technology section.

2018

Here¹ is the document I wrote in 2018: if it was ever sensitive I don’t think it is now. Here are some excerpts from it²:

Neural networks are likely to provide better weather forecasts in due course than current numerical models. If this is true then weather forecasting organisations that don’t use them will be replaced by ones that do. Even though this only may be true, weather forecasting organisations should be investigating these techniques, today.

[…]

[…] NN models are likely to be highly successful for weather prediction. However they will not be trivial to design and deploy: cargo cult NN approaches are not going to work.

If NN models are successful then they will largely displace hand-crafted physics-based models (GCM models such as UM³). Weather forecasting is a service, and consumers of the service care only about how good the forecasts are rather than how they are produced.

If this happens then organisations involved in weather forecasting, such as the Met Office, will need to adopt NN models or cease to exist: NNs are an existential threat to weather forecasting organisations.

This means that such organisations should be investigating NN models very seriously now so that, in the likely case that they are successful, they are not left behind.

[…]

The traditional approach [to weather forecasting] is to understand the physics and write a system which numerically solves the equations to a lesser or greater degree of accuracy. This has been pretty successful of course.

An alternative approach is to not do that at all, but rather build a system which can, itself, learn to simulate the weather: a system which can be trained to simulate the weather, in other words, based on observations. As far as I’m aware such an approach has not been tried on any significant scale.

[…]

There is copious training data. There is obviously a really huge amount of data which can be used to drive a model, which NNs love. But NN models need training data in general: they need to be told how well they did so they can correct their weights. And weather is almost the best example it’s possible to think of of this: if we want to predict, say, rainfall in 24 hours time, then, if we wait 24 hours, we know how much rain actually fell, and we can use that data to teach the model how do to better. And this is true for everything, all the time: every time the model makes any prediction about the state at some future time then, at that future time, we know what the state actually is and can use that information to train the model. This is the sort of situation NN people dream about.

[…]

[…] Hand-crafted models are more likely to remain sane than NN models in the early stages. There’s no rule that says that an NN won’t get some mad idea into its head and start, occasionally, making predictions which are completely physically insane.

[…]

While NN models are an almost perfect fit for weather forecasting they are, perhaps surprisingly, a terrible fit for climate modelling. This is for two reasons.

Sparseness of training data. NNs are likely to work for weather prediction because the training data is so copious: if you want to predict the weather a given time ahead then you simply predict, wait until that amount of time has elapsed and you have training data, and then you iterate this process. You can’t do that for climate: if you want to predict the climate a century ahead you can neither wait for a century for the training data nor can you iterate the process.

Opacity of NN models. Even if climate modelling by an NN is technically practical it’s an absolutely terrible answer to the questions people actually want to answer. If I run some NN model and it predicts 4 degrees of warming by 2100 the first thing people will ask is ‘why does it predict that?’. And the best answer to that question is ‘because some opaque blob of weights which neither I nor any human understands told me that’, which is a terrible answer: it’s essentially the same as ‘a voice in my head told me’. Given the political sensitivity of climate modelling this is not going to be an answer anyone will accept, and nor should they.

So climate modelling is a really good example of a place where a transparent physics-based model is the only reasonable answer. And that’s ultimately because the people who are interested in climate ere not just interested in a statistically-good prediction (whatever that even means in this case): they’re interested in why the prediction is what it is. Climate modelling requires hand-crafted physics-based models, and there’s no way around that.

2023

Here is an excerpt from The Economist’s leader:

The application of machine learning and other forms of artificial intelligence (AI) will improve things further. The supercomputers used for NWP calculate the next days’ weather on the basis of current conditions, the laws of physics and various rules of thumb; doing so at a high resolution eats up calculations by the trillion with ridiculous ease. Now machine-learning systems trained simply on past weather data can more or less match their forecasts, at least in some respects. If advances in AI elsewhere are any guide, that is only the beginning.

Well, I am not some unique genius: many people could, and probably did, see what was coming when I wrote the 2018 document. I predicted that neural network approaches would come to dominate weather forecasting, and it looks like they will.

But what I also realised remains, I think, important, and is not addressed at all in the articles in The Economist. And that is this:

AI, in the form of neural networks, is not a suitable approach to climate prediction both because the training data is inadequate, but more importantly because it is critical that climate models not only predict the climate but allow people to understand why they are predicting what they predict, rather than simply being an opaque blob;
currently climate models, at least in the Met Office and I am sure elsewhere, are to a great extent parasitic on weather models, sharing a great deal of of their code with those models.

This means that if weather forecasting becomes dominated by opaque NN models, climate modellers will have to bear the entire cost of funding development of their models. Chances are they can’t do that.

An even worse outcome would be that climate modellers leap into using opaque NN models without thinking through what this means. This would hand the climate denialists who increasingly dominate the politics of the UK a weapon which they would certainly not hesitate to use.

When I sent the 2018 document to people in the Met Office I did not even receive an acknowledgement: I am quite sure nobody read it. I think this says a great deal about the nature of organisations like the Met Office.

Despite how the all this might read, I’m not at all embittered by this: if I cared about the Met Office in 2018 I certainly don’t now, four years later. If anything, I’m rather pleased that what I thought, in 2018, would happen does indeed seem to ba happening. Most importantly I want the other thing I realised in 2018 — that climate modelling isn’t well-suited to NN approaches and that organisations which do both weather and climate modelling need to worry about this as NN approaches to weather forecasting eat physics-based approaches alive — to exist in some form that is accessible to people. That’s why this article exists.

The location of this document might change. This post itself is a better link to remember as I will update the pointer if I move the document. ↩
Note that I used the term ‘neural network’, abbreviated to ‘NN’ in the document, as I did not then (and do not now) want to lazily consider neural networks to be the same thing as AI. ↩
UM, the Unified Model, was the model the Met Office used for both weather and climate modelling ain 2018. ↩

The way out

Tim Bradshaw — Sat, 04 Dec 2021 13:07:57 UT

Many people would like to believe that the CV19 pandemic is over. Unfortunately viruses do not listen to what people want to believe: the CV19 pandemic is not over, and there is a significant possibility it may never be over. The way out is not to pretend that it is.

CV19 is not over

Unless CV19 can be globally eliminated it will not be over: new cases will leak into countries however hard they try to prevent it. Eliminating CV19 globally requires achieving herd immunity through vaccination or infections everywhere.

We are not particularly close to that. It’s tempting to do a lot of calculations at this point to show this but those calculations are fiddly and I always get them wrong. Instead consider the UK: currently (3rd December 2021) about 75% of the population have had at least one dose and there is a current effort to roll out third, ‘booster’, doses. Yet, even before the omicron variant, CV19 was nowhere near gone in the UK. And the UK is doing reasonably well at vaccinations. Boris Johnson’s lies notwithstanding it is nowhere near the top of the table, but it is well above the global average: globally about 55% of people have had at least one dose.

Unfortunately, by hoarding both vaccines and the rights to manufacture them, the rich countries are actively hurting the effort to globally eliminate CV19. So they are undermining the efforts they are making to protect their own populations. It is, apparently, too hard for politicians to understand that the virus cares much less about some lines drawn on a map than they do.

Worse than this, until CV19 is eliminated, it will still continue to evolve new variants, which will spread if they are fit. If we assume that CV19 won’t be eliminated soon, what’s likely to happen, in rich countries like the UK, as new variants appear?

The remainder of this essay concentrates on the UK as far as human responses go: that is obviously parochial, but the UK is where I live and I am most familiar with what the responses are there¹.

What might the virus do?

There are two important choices, which are orthogonal to each other: will a much-less-deadly variant become dominant? and will a variant which escapes the current vaccines become dominant? Neither of these cases excludes anything happening in the future: for instance a mild variant which does not escape vaccines could become dominant this year, only for a serious variant which does escape the vaccines to become dominant in a few years if such a variant has a selective advantage. As long as the virus exists it will be endlessly trying new variants.

Two independent choices gives a total of four scenarios.

A mild variant: ‘flu’

Two scenarios involve a mild variant which may, or may not, escape the current vaccines: these are the ‘flu’ scenarios. Such a variant might become seasonal in the way flu is (CV19 may well already be seasonal of course: we just haven’t lived through enough seasons yet). If the current vaccines don’t work for this variant the results will not be too severe, and new vaccines will be developed. If vaccines, new or existing, confer long-term immunity, then things would become relatively normal. It’s likely that they don’t, however, so we would probably require regular courses of vaccinations, which may be new vaccines as the virus evolves: this is still pretty close to normal life.

Well, we could live with that, the way we live with flu. Except that we don’t always live with flu: Spanish flu² killed between 17 and 50 million people, and perhaps as many as 100 million (by comparison, The Economist thinks that CV19 has probably killed about 17 million people so far from a larger population). Again, as long as CV19 exists it will be developing new variants and there is nothing I can see to stop one arising which is like that.

But, perhaps, this is at least no worse than flu is now, except perhaps that more people will need to be vaccinated more often.

No vaccine escape

A third scenario is that CV19 stays roughly as deadly as it is now, but the vaccines we have keep working against it, probably with regular courses required. People continue to die in significant numbers, with those numbers depending to a great extent on the precautions people are willing to accept. This may seem much like the flu case except that a lot more people die. It might be only that: there are unfortunately much nastier possibilities discussed below.

Vaccine escape

The final scenario is that a variant arises which is significantly deadly and for which the current vaccines are not effective. This is year zero: new vaccines will need to be developed, and a new urgent vaccination programme will be required. Until the vaccination programme is well under way there will either need to be very significant restrictions on social contact if very high death tolls are to be avoided. Unfortunately very high death tolls during the vaccine development and the early stages of the vaccination programme are, again, far from the worst things that could happen.

Other scenarios

There are other possibilities. A variant might arise which is much more deadly, for instance. It’s easy to argue that very deadly viruses are selected against: a virus which kills too many of its hosts will tend not to thrive in competition with less lethal one. But in real life things are not that simple: the black death killed between 75 and 200 million people in 7 years, killing between 30% and 60% of the population of Europe, and perhaps 25% of the world’s population. Yersinia pestis was, perhaps, not competing with less deadly versions of itself, wasn’t so subject to mutation as a virus would be and there were other factors, but still: very bad things can happen.

A very nasty possibility is that a variant will arise against which no vaccines work very well. Before the current vaccines were developed some people were suggesting this (search for ‘there has never been a successful vaccine against a coronavirus’: I am not going to link to any of the results because some of them are awful people who do not deserve anyone’s attention). It seems to me that this is absurdly unlikely, but I’m not an expert.

No doubt there are many other scenarios I have not thought about.

An endless war

Once again: until the CV19 virus is globally eliminated it will continue to evolve new variants. None of the above scenarios excludes any of the others: CV19 will explore as much of the space of options as it can. Until it is eliminated the pandemic will not be over: if it is never eliminated the pandemic will never be over.

What might the humans do?

The virus is only one of the players in this game: the other is us. What happens depends on our response as much as what the virus does.

Normality

If the virus is eliminated then normal pre-pandemic life resumes. In either of the ‘flu’ scenarios something quite like normal life resumes. In the flu scenarios normal life only resumes so long as the virus doesn’t evolve some much nastier variant. So, rationally, in these scenarios, work should still continue to eliminate the virus globally as fast as possible. I think it’s safe to say that won’t happen, so the normality in these scenarios is almost certainly impermanent.

Stability

If there is a deadly variant which does not escape the vaccines then it’s possible to imagine a stable scenario where some combination of restrictions on social contact, regular vaccinations, masks, and just accepting that a fair number more people die each year than before CV19 will keep things under control. That’s a nice dream, anyway.

Instability

A variant which escapes the vaccines results in instability: it is essentially a whole new pandemic and rapid lockdowns will be needed while new vaccines are developed to avoid very high death tolls or even worse outcomes. The instability can be minimised by careful management but the chances of that seem low.

Unfortunately I think that, even for variants that do not escape current vaccines, stability is unlikely. Instead there will be some more-or-less chaotic cycle of too-much relaxation followed by panics as death rates rise. None of this is helped by politicians who, in the UK at least, do not care very much if many people die, are not competent to understand what is required for stability, and neither understand nor care about the consequences of serious instability.

What is really happening?

We’re still in the early stages of the pandemic, but what has actually happened?

The virus

I don’t know. Since I started writing the omicron variant has appeared: this has a very high number of mutations, 62, compared to the original virus, of which 32 affect the spike protein which vaccines target (delta had 8 or 9). Currently (5th December 2021) it’s not known how infectious it is or how severe the illness it causes is compared to the previous, delta, variant. More seriously it’s not known how well vaccines work for it, but with a very large number of mutations on the spike protein people are clearly pretty worried.

So the omicron variant might be a ‘flu’ variant, a vaccine escape variant, both, neither, or something else. It might also not be very interesting at all. But there will be more variants in an effectively endless succession³: sooner or later something interesting will arise. Given selective pressure on the virus it will probably be sooner: if omicron is not it we are not off the hook.

The humans

What are the humans doing? In particular what is the UK government doing? As a populist government what it does is to offer simple, appealing, wrong answers to complex, unappealing problems. It gives the answers that people would like it to give, without ever trying to explain why those answers are wrong or what the consequences of believing them will be. The johnsonites are very far from democratic, quite the opposite in fact, but in this case we can treat them as avatars of what people would like to be told is true.

Well, until a few days ago the answer was that they were saying that the pandemic was over and that normal life could resume. Now there is incoherent messaging: scientific and medical advisers are advising caution in the face of omicron, while Boris Johnson is publicly ignoring them. Johnson, clearly, is too stupid to understand the consequences of what he is doing and would not care about them if he did. He also has demonstrated, publicly and repeatedly, that he believes rules do not apply to him, thus ensuring that no-one else obeys them either. More competent (perhaps merely less incompetent) members of the government are giving more cautious messages, but there is simply no coherent strategy and the government is very obviously no longer ‘following the science’ nor even pretending to do so.

This is a recipe for instability: if omicron is serious then Johnson’s strategy, if it can be called such, will maximise its impact early in the new year. Johnson shows no sign of having learnt anything at all from his earlier mistakes: if anything he’s learnt that he can, in fact, get away with murder. If lessons are not learnt from this cycle the best we can hope for is a continuing chaotic cycle of restrictions followed by relaxation, for years.

The worst is that Johnson continues to maintain that it is all over, while people die around him in huge numbers. This is stability, of a kind, but not one anyone should wish for. Sadly many people do seem to wish for it, and to be happy with enormous numbers of deaths so that they don’t have to experience the momentary inconvenience of wearing a mask or otherwise behaving safely.

Collapse

That is, in fact, not the worst outcome. Because we live in a society built on complex systems which took a long time to assemble and which, if they are stressed to the point of collapse, can not then be reassembled quickly, if at all.

In 2008 the global financial system came close to collapse. Many people said at the time, and probably still do say, that the banks should just have been allowed to fail. Those people were fools. Banks are close to the archetypal complex system which, if it collapses, can not quickly be repaired if it can be repaired at all. If the banking system had been allowed to fail in 2008 then essentially money would have ceased to exist: ATMs would have stopped working, salaries would have stopped being paid, everything involving money would have stopped. And once that had happened it would have taken years to restart. Pretty quickly people would have started getting hungry, there would have been riots and far worse. And this would have gone on for years. Huge numbers would have died. The 2008 financial crisis was a nasty experience, but it was vastly less nasty than what was narrowly avoided.

There is another such complex system: the health service. The NHS is one of the great achievements of post-war Britain: I think the greatest in fact. If the NHS is pushed too hard it, too, will collapse, and if it collapses, bad things will happen. And CV19 is pushing the NHS very hard indeed. Already many people are dying of things which would have been treated if not for CV19, and people who are not dying are sitting in lengthening backlogs which will take years or decades to clear. And this is only the start of what could happen. People who are working in ICUs will eventually become burnt out: they’ll end up shell-shocked and unable to work. And so the number of staff in ICUs will decline just as the requirement for them increases. That’s a death spiral: more and more people will get burnt out as their workload increases because their colleagues have already become burnt out. These people will themselves then need care, which is already very limited. Without care they may never return to work. And it takes quite a long time to train someone to work in an ICU: it is not an easy job. And, having witnessed what happens to people who work in ICUs, who is going to apply to be trained?

So the likely end result of a series of chaotic cycles of relaxation and panic is that the NHS will collapse in due course. And the likely end result of simply accepting large ongoing death rates, of Johnson’s stability through suffering, is that the NHS will collapse rather soon.

And if the NHS collapses it can’t be put back together quickly. Perhaps it can’t be put back together at all. And very large numbers of people will then die.

Avoiding collapse of the NHS is critical, but the UK government shows no sign of being competent to do so, or in fact of caring if the NHS collapses.

The way out

The way out is to eliminate the virus, globally. Until we can do that the best we can hope for is that it becomes like flu and that no nastier variant arises. I can’t see any reason why a nastier variant should not arise from a flu-like variant: nastier variants of flu arise, after all. So a flu-like stage, though very desirable short of elimination, is probably only temporary: something nastier will come back.

Managing the presumed nastier variants is hard. Inevitably there will be cycles of restriction and relaxation. Those cycles will have inevitable economic impact. That is not something that can be wished away.

The UK government, as with populists everywhere, has been hugely incompetent at managing the first few cycles, and shows no sign of becoming more competent. Johnson is stupid, uncaring and believes rules do not apply to him: while he controls the UK government there is little hope. Johnson, perhaps, believes that he can declare the pandemic over and it will be over. But the virus does not care what he thinks.

Almost certainly, absent a competent government, the UK will therefore experience a series of chaotic cycles of relaxation and restriction culminating in the probable collapse of the NHS and all that implies.

A competent government would understand that until the virus is eliminated the old world is simply gone: the world has now changed. Working from home is here for good, with all that implies for cities. Masks are here for good. A competent government would work to educate its people about this. And it would understand that since elimination will take years at least some of these changes must be regarded as permanent: in five years or a decade no-one will want to go back to spending three hours a day on a packed train or in a traffic jam.

The world has changed, and it has changed irrevocably, one way or another. The way out is to accept that there is no way back.

Disclaimer: I’m not an expert and I’m not even pretending to be one on the internet. I’m just trying to understand things as best I can, then writing the down so I can see how wrong I was, later. ↩
Which, of course, was not Spanish. ↩
SARS-CoV–2 has a genome about 30,000 bases long: there are $4^{30000}$ such genomes. Only a tiny proportion of those will encode anything interesting and only a tiny proportion of those will encode anything very like SARS-CoV–2, but that is still a lot of variants. ↩

The idiocy of Mars

Tim Bradshaw — Fri, 18 Jun 2021 08:17:24 UT

If you think that we can continue economic growth by simply moving to Mars, you’re a fool.

Many people do not understand that the growth in resource usage by humans will, if not stopped, result in us hitting the limits of what Earth can provide at some point in the fairly near future. Unless we address this problem the result will probably be the collapse of civilisation. Some of the people who think they do understand this problem argue that, well, there is Mars¹: we can just go there and carry on as normal and everything will be fine.

It won’t be, and here’s why.

Growth

We all hear about economic growth in the news. And people like it when it’s some positive number. Growth:

can be defined as the increase or improvement in the inflation-adjusted market value of the goods and services produced by an economy over time. [From Wikipiedia article above.]

What that means is that growth is the rate of change of some measure of the size of an economy. Growth is measured as a percentage increase in the size of the economy per year, which I will call $g$, so if at some time $t$ (measured in years) the economy has size $s(t)$, then $s(t + 1) = s(t)(1 + g/100)$. That means that if growth is constant over the long term, the size of the economy is increasing exponentially with time:

\[ s(t) = s_0 e^{t/\tau}\quad\text{where $\tau = 1/\ln(1 + g/100)$} \]

And economists are very keen that $g$ should not drop to zero or, still worse, become negative: they want it to be some long-term constant value.

Rescaling

One possibility is that this measure $s(t)$ might simply involve rescaling the economy somehow: we think it’s bigger but in fact it’s not. Let’s say that I’m interested in buying aluminium: if, every year, the economy ‘grows’ by $1 + g/100$, but the price of aluminium also grows by $1 + g/100$ then I can’t actually buy any more at the end of the year, even though the economy has ‘grown’.

This, in fact, is inflation: the economy hasn’t grown, it’s just been rescaled. Inflation is not what people mean by growth: they mean that you can actually buy more stuff.

Well, if you can buy exponentially more stuff over time there’s a problem, isn’t there? Even economists can see this, I expect.

Hand-waving

So if growth means being able to afford ever more material goods there’s a problem: at some point you’ll run out of stuff. This is awkward for economists who have built entire theories on the idea that growth can continue indefinitely.

Is there a meaning of the term ‘growth’ which doesn’t involve crashing into finite limits or somehow finding an endless source of new material goods? One option that economists push is that we start being able to use the existing raw materials ever more efficiently. That’s fantasy in the medium term because there are hard physical limits on efficiency. Another popular option is that we all somehow fall into a simulation and live ever more complex virtual lives, while the real world carries on without us. That’s almost certainly also fantasy both because, despite endless AI hype, we have no idea how to do that, and because exponential growth in computing power also has hard physical limits²: Moore’s law is only a transient phenonenon.

In these hand-waving cases there’s also a question about what happens to the prices of physical materials. For things to make any sense the prices have to inflate at least as fast as the growth rate, and in fact faster. If, for instance, the price of aluminium remained roughly constant (when corrected for currency inflation), then after some time it would be possible to simply buy all the aluminium in the world and thus hold to ransom anything which is made from it, however efficiently that is done. So that shows that the price of materials must rise at least as fast as growth. In fact it must rise faster than that: given the finite supply of aluminium ore, the real cost of aluminium should represent that, meaning that, even as people become richer, new physical goods must become more expensive and scarce over time.

But there’s no need to speculate: instead let’slook at what actually has happened.

Energy

A good proxy for the processing and consumption of material goods is energy consumption: energy is consumed to do some useful physical work, so it should correlate approximately with the amount of materials being processed in some way. And energy is fungible, so it’s easy to measure. And data on energy usage is available. Tom Murphy’s excellent book, Energy and Human Ambitions on a Finite Planet contains, in section 1.2, this information for the US, sourced from the US Energy Information Administration. He uses this data to derive a rate of growth in US energy usage of about $3\,\mathrm{\%/y}$ between about 1650 and 2000. So during this period growth in energy usage was approximately exponential and so, it’s pretty safe to say, there was exponential growth in the physical material being used during this period.

So at least until recently (see below) growth meant what it naïvely means: an exponentially increasing rate of material production. This obviously can’t continue on Earth.

Mars

But we can just go to Mars right? Once we’ve used up Earth we can up sticks, move planet, and carry on. It took us thousands of years to use up Earth’s resources, so Mars will buy us thousands more years.

Or so say the innumerate space fantasists.

This kind of claim is so silly it’s hard to know where to start. But let’s just take it at face value. I will assume:

it is possible to either move huge numbers of humans to Mars or to mine it for raw materials and bring them back to Earth cheaply, using spacecraft driven by some unexplained magic³;
Mars has the same amount of raw materials as Earth (it doesn’t);
we can hit the ground running and simply immediately start stripping Mars at the rate Earth was being stripped;
any other kind of problem I haven’t thought of can be solved by yet more unexplained magic.

So let’s do the maths.

The maths

Let’s assume that we’re using up physical resources at some rate $r(t)$ which is increasing exponentially:

\[ r(t) = r_0 e^{t/\tau} \]

Where $r_0$ is the rate at some time $t = 0$. This can be integrated to get the total resources consumed to some time:

\[ R(t) = R_0 e^{t/\tau}\quad\text{where $R_0 =\tau r_0$} \]

Here $R_0$ is the total consumption to $t=0$.

OK, so let’s measure $t$ in years and assume that the annual growth percentage is $g$. In other words:

\[ r(t + 1) = \left(1 + \frac{g}{100}\right)r(t) \]

Well

\[ \begin{aligned} r(t + 1) &= r_0e^{(t + 1)/\tau}\\ &= r_0e^{t/\tau}e^{1/\tau}\\ &= e^{1/\tau}r(t) \end{aligned} \]

so we can get $\tau$ in terms of $g$:

\[ \tau = \frac{1}{\ln\left(1 + \frac{g}{100}\right)} \]

So, now, how long does it take for the resources consumed to go up by some factor, say $k$?

\[ \begin{aligned} k = \frac{R(t + \Delta t)}{R(t)} &= \frac{e^{(t + \Delta t)/\tau}}{e^{t/\tau}}\\ &= e^{\Delta t/\tau} \end{aligned} \]

\[ \begin{aligned} \Delta t &= \tau \ln k\\ &= \frac{\ln k}{\ln\left(1 + \frac{g}{100}\right)} &&\text{using $\tau$ from above} \end{aligned} \]

How long does Mars get us?

Let’s assume, as above, that at $t=0$ we run out of resources on Earth and start mining Mars, and that we start doing it at the same rate that we were stripping Earth, and that Mars has the same amount of material as Earth, and that growth continues as before at a rate I will assume to be $2\,\mathrm{\%/y}$ (so lower than the measured rate above). When do we run out of resources on Mars? Well, we run out of resources when $R(t+\Delta t)/R(t) = k = 2$, so when

\[ \begin{aligned} \Delta t &= \frac{\ln 2}{\ln\left(1 + \frac{g}{100}\right)}\\ &\approx 35\,\mathrm{y} \end{aligned} \]

Under entirely unrealistically optimistic assumptions, stripping Mars will maintain growth at $2\,\mathrm{\%/y}$ for 35 years.

What about Venus? Jupiter?

If we make the same assumptions about Venus and start on that after Mars it gets us a further 20 years and six months. If instead we went to Jupiter, and assuming its resources scale like the ratio of its mass to Earth’s we’d buy about 256 years, which is better, but we’re not going to be able to do that.

So, growth of physical resource usage can not be maintained at $2\,\mathrm{\%/y}$ for any significant amount of time in the future.

Perhaps good news

The good news here is that data since 2000 does make it look as if the growth in energy usage is slowing down. That means either we’re moving into one of the economists’ handwavy fantasy scenarios (hint: it doesn’t), or that we’re in the early stages of falling off the exponential phase of growth. Assuming that’s true then we’re moving into a world where the models economists have built simply no longer work, and where we can’t endlessly assume we will get richer for ever. It’s perhaps not coincidental that the years since 2010 have seen the rise of a number of extremely unavoury political movements: there will be more of these as there is more competition for increasingly scarce resources and as climate change takes effect, further increasing scarcity and driving migrations on vast scales. The likely outcome, I think, is not a smooth transition to a zero or negative growth world, but something pretty unpleasant: resource wars between major players, extreme racist responses to the migration problem, authoritarianism and fascism.

Some of these processes seem to be well on the way as I write.

Some pictures

Here are plots which show the time to exhaust resources on:

Mars (about $35\,\mathrm{y}$) & then Venus (another $20.5\,\mathrm{y}$, exhausting both in about $55\,\mathrm{y}$);
Jupiter alone (about $291\,\mathrm{y}$);
the Sun alone (about $642\,\mathrm{y}$).

These assume that available resources scale like mass, and that growth continues at $2\,\mathrm{\%/y}$. Note that time is the y-axis in these plots: the x-axis is the resource ratio (assumed to be the mass ratio) compared to Earth’s.

Time to exhaust resources on Mars & Venus, growth at 2%/y

Time to exhaust resources on Jupiter, growth at 2%/y

Time to exhaust resources from the Sun, growth at 2%/y

Or Venus, but usually Mars. Sometimes asteroids. ↩
It is possible to imagine a world where the simulation we are assumed to end up in runs exponentially slowly, giving the consciousinesses in it the idea that growth still continues when in fact it doesn’t. ↩
Here’s an idea: if you have unexplained magic to drive your vast fleets of spacecraft, you’ve already solved the problem on Earth! ↩

Carbon offsets

Tim Bradshaw — Thu, 15 Apr 2021 11:18:31 UT

People attacking carbon offsets or net zero emissions are attacking the wrong target and harming their cause. The problem is that the things we call ‘carbon offsets’ are not carbon offsets and ‘net zero’ is not net zero: they are lies. That’s what they should attack.

There’s nothing wrong with carbon offsets or net zero. In fact something like them really has to happen if anyone ever wants to do anything which is not in itself carbon-neutral. If you, say, want to buy a bike made of metal then the production of that bike more-or-less certainly released some carbon into the atmosphere, because smelting metal ore does that, even when you source the energy for the smelting from non-fossil sources, as you use coke to pull oxygen out of the ore & releasing $\mathrm{CO_2}$ into the atmosphere. Perhaps it’s possible to make metal production carbon-neutral (probably it isn’t) but it’s not possible to make everything carbon-neutral. If you eat meat or dairy products then that’s definitely not carbon-neutral. Breathing is not carbon-neutral.

Perhaps it would be possible to make everything carbon-neutral in detail: steelmakers could also become planters of trees, every cattle farmer would grow enough arable to offset their carbon & methane emissions, everyone who breathed would spend some time planting things (so, no cities in this world) and so on. That’s … not a world many people want to live in: it’s pretty close to a mediaeval world where almost everyone has to spend a certain amount of time working on the land.

Instead you do it by some kind of carbon offset. The metal-refiner arranges for someone else to remove as much (or more, since the world really needs to be carbon-negative for quite a long time) carbon from the atmosphere as they emit into it. And one way of doing that is to just have a price (it’s not a fine, it’s actually a price!) for carbon (you could also do it by administrative fiat: it doesn’t matter in terms of the carbon but I can’t see the US doing that). Then every process does not need to be in-detail carbon-neutral (or carbon-negative for a long time to come) and you can ship the carbon-neutrality around so that the whole system is carbon-neutral (negative). And you can have cities again, which is great. Net zero is not a bad term and neither is carbon offset: it’s the only way you get to do it unless you want to live in the 14th century.

That’s not the problem with ‘carbon offsets’ (note the quotes) as they currently are. There are two problems: firstly carbon emission is substantially underpriced, which requires worldwide cooperation, which isn’t going to happen, to fix; and secondly that they’re just a con: ‘carbon offsets’ are not, in fact, carbon offsets.

It’s very like what happened in the years leading to 2008: there was a shitload of risk in crappy loans which was going to cause some horrible problem, but everyone liked the crappy loans because they got to charge high interest rates on them and get rich. So a bunch of people with physics degrees (I have a physics degree) waved their hands and did some magical mixing of this risk with lower risk which made it seem to go away, so there could be more crappy loans and more risk which in turn could be magicked away. Except they weren’t very good physicists because they didn’t understand that there are conservation laws here and you don’t get to cheat those laws any more than you get to cheat conservation of momentum: they’re just as basic. I don’t completely understand the conservation laws because they have to do with the correlation of risks — you can mix a number of high risks and end up with a lower one if the underlying risks are uncorrelated — and I can’t hack the statistics: but they’re there. Well, in fact, at least some of the people who did this almost certainly knew exactly what was going on, but money had destroyed their morals. And the consequence of this was financial collapse in 2008 and brexit, Trump and creeping fascism a few years later.

‘Carbon offsets’ do a similar trick: you’re buying, way too cheaply, some token which says that the carbon you source into the atmosphere will be sunk from it by someone else else, but either that never actually happens, or it only happens at some unspecified future date, which is the same thing. A conservation law — this time an easy one which is conservation (or reduction) of $\mathrm{CO_2}$ in the atmosphere as a consequence of your actions — is being violated, again. And this is violation is done by a similar trick to what happened all obfuscated by some complex process of mixing the ‘offsets’ together such that it’s not apparent that there’s nothing (or some unspecified future thing which is the same as nothing) at the far end of the network of obfuscation. It’s an elaborate shell game by which ‘carbon offsets’ are turned into money by the players and no carbon is actually removed from the atmosphere.

So, I wish that people would attack the right thing: saying ‘net zero is bad’ is wrong because net zero (net negative) is all we can ever realistically do and a completely satisfactory solution to the problem. Saying ‘carbon offsets are bad’ is wrong because carbon offsets are a perfectly reasonable approach to achieving net zero. The problem is not net zero or carbon offsets, it’s that the things we call ‘carbon offsets’ are not actually carbon offsets, and what we call ‘net zero’ is not actually net zero.

And as long as people attack the wrong problem they won’t solve the real one. I mean, we’re obviously not going to solve the real one anyway, but we might as well at least try. It’s the same idiocy as ‘greens’ attacking nuclear power because spooky frightening or people attacking vaccines because tiny risk of clotting from vaccine is somehow more frightening than really fucking big risk of dying, horribly, of CV19.

CV19 vaccination staff requirements

Tim Bradshaw — Sun, 03 Jan 2021 12:11:19 UT

Or: how many people will be needed to vaccinate enough people? How many people will keep on being needed?

Now that vaccines are available for CV19, an interesting question is how many people it will require to vaccinate enough of the population to produce herd immunity. I had a suspicion that this, or the organisational effort involved¹ might be the limiting factor. So I thought I’d try and work out what it the number of people required might be.

A simple linear model

Let’s assume that vaccinations happen at a given rate, $r(t)$, with $t = 0$ being when they start. Then the number of people vaccinated at a given time $T$ is

\[ N(T) = \int\limits_0^T r(t)\,dt \]

If each person takes someone $\tau$ seconds to vaccinate then the effort required is $\tau r(t)$. But people don’t work all the time, so the number of people required — the staffing cost — is

\[ S(t) = \frac{\tau}{\eta} r(t) \]

Where $\eta$ is the efficiency with which a person works, which includes time to sleep, breaks, weekends and so on.

Obviously all this depends on the form of $r(t)$, which in real life will be complicated. I’ll assume it takes a simple form: from a low start it ramps up linearly to some value where it then sits until the initial vaccination program is complete.

\[ r(t) = \begin{cases} r_0 + kt & 0 \le t \lt t_0\\ r_0 + kt_0 & t \ge t_0 \end{cases} \]

and

\[ S(t) = \frac{\tau}{\eta} \begin{cases} r_0 + kt & 0 \le t \lt t_0\\ r_0 + kt_0 & t \ge t_0 \end{cases} \]

$r(t)$ is easy to integrate, giving the form for $N(t)$:

\[ \begin{aligned} N(t) &= \begin{cases} r_0 t + \frac{k t^2}{2} & 0 \le t \lt t_0\\ r_0 t_0 + \frac{k t_0^2}{2} + (r_0 + k t_0)(t - t0) & t \ge t_0 \end{cases}\\ &= \begin{cases} r_0 t + \frac{k t^2}{2} & 0 \le t \lt t_0\\ (r_0 + k t_0)t - \frac{k t_0^2}{2} & t \ge t_0 \end{cases} \end{aligned} \]

Finally, assume that the population is $P$, that we need to vaccinate a proportion $\rho$ and we want the programme to be complete at a time $T$, so $N(T) = \rho P$. And I’ll assume $T \ge t_0$: this doesn’t actually matter because if $T = t_0$ you get a model which has no constant part — the rate always increases linearly.

Using this we can now solve for $k$:

\[ k = \frac{\rho P - r_0 T}{t_0 T - \frac{t_0^2}{2}} \]

and this gives us the peak staffing level:

\[ S_p = \frac{\tau}{\eta} \left(r_0 + \frac{\rho P - r_0 T}{T - \frac{t_0}{2}}\right) \]

Another thing to work out is the equilibrium staffing level: if the immunity time after vaccination is $T_i$, then people need to be revaccinated every $T_i$, and this means that

\[ S_e = \frac{\tau\rho P}{\eta T_i} \]

Some numbers for the linear model

The two last expressions above depend on a bunch of parameters: here are some that are both not too frightening in terms of how long it all takes and not too frightening in terms of staffing requirements. I’ll use seconds as the basic unit of time and define $M = 30 \times 24 \times 3600 = 2592000$: the length of a month in seconds.

$T = 8 M$: the programme should be complete in eight months after it starts.
$r_0 = 0$: initially no-one is being vaccinated.
$t_0 = 2M$: it takes two months to ramp up;
$\tau = 600$: it takes ten minutes of a person’s time to vaccinate someone on average².
$\eta = (5 \times 7)/(7 \times 24) = 5/24$: people work for seven hours a day (not including break time) and work for five days a week. They don’t get holidays.
$P = 5.5\times 10^6$: the population of England³ is 55 million.
$\rho = 0.75$: you need to vaccinate about 75% of people to achieve herd immunity.
$T_i = 12 M$: the immunity time is a year.

Given these figures then

\[ \begin{align} S_p &\approx 6575\\ S_e &\approx 3819 \end{align} \]

So, at the peak, there will need to be about 6,575 people working full-time to achieve herd immunity in 8 months, and from then on about 3,819 people may be required to maintain it.

If it takes longer to ramp up then the peak staffing goes up. If it takes 8 months (we never reach the steady state) then the peak staffing number is 11,458.

Here is a plot of the dependency of peak $S_p$ on both the length of the vaccination programme (x axis, from 4 to 12 months) and the length of the ramp time (y axis, from 0 to 10 months):

Peak staffing as function programme length and ramp time, linear model

Comparing the model with reality

In real life a model where $r(t)$ ramps up linearly and hence $N(t)$ quadratically before hitting some nice ceiling is hopelessly oversimplified. But, well, what does a model like this say?

Between the 8th December 2020 and the 27th December 2020 NHS England administered 786,000 vaccinations⁴. This was a period of 20 days: so if we assume $r_0 = 0$ and the linear ramp model we can compute $k$:

\[ \begin{aligned} k &= \frac{2\times 786\times 10^3}{(20\times 24 \times 60^2)^2}\\ &\approx 5.14\times 10^{-7} \end{aligned} \]

The fastest way to vaccinate enough people is simply to keep ramping up the rate by linearly (according to the model) adding staff, or in other words by letting $t_0 = T$. In this case the time $T$ to vaccinate enough people is simply:

\[ T = \sqrt{\frac{2\rho P}{k}} \]

And using the above numbers for $\rho$ and $P$, this gives $T \approx 1.27\times 10^7\,\mathrm{s}$, or about 147 days, or just short of five months. This is not hopeless!

The question is whether that is achievable in terms of staff numbers. Well, the number of staff required in the case where there is no cap on staff is simply

\[ S(t) = \frac{\tau}{\eta} kt \]

And the peak staffing is therefore $S_p = (\tau/\eta)kT$. Using values for $\tau$ and $\eta$ from before, together with $k\approx 5.14\times 10^{-7}$ gives $S_p \approx 18800$. A little short of 19,000 people is probably pretty achievable.

The alternative is to assume that $S$ is capped somewhere and work out the time the programme will take in that case. Let’s assume that $S_p = 7000$ then we can compute $t_0 = (S_p\eta)/(k\tau) \approx 4.72 \times 10^6\,\mathrm{s}$, or about 55 days — a little short of two months. Then we can use the original expression for the time to vaccinate enough people:

\[ \begin{aligned} T &= \frac{\rho P}{k t_0} + \frac{k t0}{2}\\ &\approx 1.93 \times 10^7\,\mathrm{s}\\ &\approx 224\,\mathrm{d} \end{aligned} \]

224 days is about 7.5 months. Which is astonishingly close to my guess for how long it might take.

However the vaccination data is somewhat misleading⁵: the figures from the 8th to the 27th December 2020 include no second doses. So in fact the number of complete vaccinations given in that interval is just half the headline figure: 393,000 instead of 768,000. Using this amended figure we get different and significantly worse numbers for the time to vaccinate enough people, but better numbers for peak staffing requirements in the uncapped case:

$k\approx 2.57\times 10^{-7}$, just half of the previous value;
without capping staffing, $T\approx 1.79\times 10^7\,\mathrm{s} \approx 207\,\mathrm{d}$ or nearly seven months, with a peak staffing requirement, $S_p \approx 133000$.
capping staffing at $S_p = 7000$ gives $t_0 \approx 9.45 \times 10^6\,\mathrm{s} \approx 109\,\mathrm{d}$ or somewhat over 3.5 months, and $T \approx 2.17\times 10^7\,\mathrm{s} \approx 251\,\mathrm{d}$, or about 8.5 months.

The times are less than double because the number of vaccinations goes like the square of time during the ramp.

At the time I’m writing, the English NHS is intending to delay the second dose:

The latest evidence suggests the 1st dose of the COVID–19 vaccine provides protection for most people for up to 3 months.

As a result of this evidence, when you can have the 2nd dose has changed. This is also to make sure as many people can have the vaccine as possible.

The 2nd dose was previously 21 days after having the 1st dose, but has now changed to 12 weeks after. […]

So although I don’t think it’s safe to assume that there will be no second vaccinations during the initial programme, it is clear that the main aim is to do as many first doses as possible as quickly as possible. So in real life the times and staffing requirements might be somewhere between the two.

Conclusion

Based on English NHS data on vaccinations given between 8th and 27th December 2020, using a very simple linear model (see above for the model and its parameters) and assuming no second doses, then, assuming that staffing numbers can be ramped up to 7,000 in 55 days from the 8th of December 2020, then England (and presumably the UK as a whole) might well be able to vaccinate enough people in about 224 days from the that date: by mid to late July 2021. If staffing numbers can be ramped up indefinitely at the same rate, then this could be done in 147 days, or by early May 2021, with a peak staffing requirement a little short of 19,000.

The same model using the same data but including second doses (which halves the number of doses given between 8th and 27th December 2020), staff numbers need to be ramped to 7,000 in 109 days from 8th December 2020 (this is less aggressive), and the programme will take about 251 days and might be complete by mid August 2021. If staffing numbers can be ramped indefinitely at the initial rate, the programme could be complete in 207 days, or by early July 2021, with a peak staffing requirement of a little less than 13,500.

Using the same model and assuming immunity lasts for a year, a little short of 4,000 people will be needed to maintain the vaccination programme indefinitely.

All of this relies on a vaccination requiring a total of only 10 minutes of human effort: not only frontline staff but also the time spent shipping and handling the vaccine, administative overhead and so on. It also neglects holiday time for the staff involved. Including holidays and sickness would increase the staffing requirements by 10–20%, assuming 30 days holiday a year. This is also based on very early data on achieved vaccination rates.

If this is even a very rough approximation to the truth then, assuming some organisational competence (a dangerous assumption when the UK government and its fellow travellers is involved), staffing should not be the limiting factor in the vaccination programme.

Reality may be a little more complicated than this.

Given the magnificent ability of the UK government to organise things I am sure that the organisational problems will simply melt away. One only needs to look at the splendour that is Dido Harding, and her truly wonderful achievements with the test and trace effort in mid 2020 to see this. ↩
This seems like a terrifyingly large figure. For instance appointments for ’flu vaccinations at a surgery near me were recently spaced three minutes apart. But remember that this figure is for two doses, and should include all the time spent by people to give the vaccination: it should include the time spent doing administrative work, delivering the vaccine and so on. I think ten minutes is probably an underestimate, and perhaps a serious one. ↩
I’m picking England rather than the UK since the healt service is devolved. ↩
There seems to be significantly higher figure number available for the same time period: I think that higher figure must be for the UK as a whole. ↩
I am sure the NHS is not trying to mislead people. I am equally sure that the government will abuse the data to do just that. ↩

Bigger better more expensive

Tim Bradshaw — Sun, 09 Aug 2020 12:39:15 UT

A comment to this excellent post by Sabine Hossenfelder¹

I think that it’s easy to deride the ‘let’s just do what we’ve already done, but an order of magnitude bigger/better/more expensive’ (BBME). But really it’s more subtle.

In particular I think there clearly are times and areas where BBME works really well for quite a long time. I don’t know the history of particle physics experiments well enough, but I bet that between, say, 1945 and whenever LEP was commissioned BBME worked brilliantly: build a BBME synchrotron and huge amounts of new physics poured out and the price was not extortionate. The same is true for gravitational wave detectors — when I was in the process of stupidly abandoning my PhD in GR in the 1980s people pretty much knew that although there was no chance of any detector we could build then seeing (hearing, really) anything, it kind of was the case that some future detector should be able to. And the only real way to build that future detector was to work up through a sequence of, really, technology development systems which would not be adequately sensitive but would let people do the engineering. And we ended up with LIGO / VIRGO & their friends. And you are not going to persuade me that direct detection of gravitational waves was not a prize worth having.

But I think there comes a point where BBME stops working for at least three reasons: you can reach a point where there’s a big hole in the physics so that to see anything really new you need something absurdly bigger so your BBME system just ends up doing the same thing but a bit more accurately; the ME becomes too expensive — when your proposed experiment will use the entire GDP of the planet it is probably too expensive; and finally the spin-off benefits become not interesting enough.

Maybe particle physics is at that point (I suspect strongly it is but I am not a particle physicist). Is gravitational wave astronomy there yet? I don’t think so, but it will get there one day. Are space telescopes, say, there yet? Well it’s tempting to say yes given what JWST has cost, except that JWST is not a BBME Hubble: it’s a different thing altogether, so it doesn’t provide evidence either way. I suspect absurdly large ground-based optical telescopes may be close to or at that point though.

I think humans are also really bad at seeing when BBME has run its course. Hi-Fi is a good example. For a long time BBME worked really well for Hi-Fi — if you’ve listened to Hi-Fi made in the 1950s it is immediately apparent that it is not as good as Hi-Fi made in the 1970s. But then it all hit a wall, in this case because many components became essentially perfect (they got better than the human auditory system). But BBME carried on leading to all the absurdities of speaker cables with all the spins lined up or something we see today.

And of course coming to the end of BBME is awkward for another reason. BBME is typically an exponential process (make one twice as good and four times as expensive every ten years) and exponential processes have nice properties: they are self-similar so that everythig is the same, just scaled up, from one year to the next, and humans don’t like change. There are always enough jobs that each tranche of good students gets employed, for instance. When BBME stops, then suddenly everything is different, and a huge wave of students don’t get employed (and since the exponential process has run for many years, it is a huge wave). Suddenly lots of people who thought they had careers now don’t and lots of courses which turn out those people stop getting students signing up to them. This is a tiny version of the much bigger problem of the end of various other exponential processes which humans have relied upon since there were humans and on which our entire economic system is built. We are completely failing to understand how to negotiate the end of any of these processes without very awkward, and very likely civilisation-ending consequences. Maybe there is no way to end them without these very awkward consequences: just because we need there to be a way to do that does not mean there is. For physics at least the awkward consequences are pretty small unless you are a particle physics PhD student who was expecting a career.

It probably also matters that coming off the end of BBME means that people need to start actually being clever again: brute force and money has failed so cunning and cheap is needed. That’s a big change in thinking. Although I’ve not been able to find details of the total cost, I suspect that the EHT is an example of cunning and cheap: you want to make a telescope as big as the Earth? well, it turns out you can cheat. Hi-Fi provides another example again: could you make, today, Hi-Fi that was actually better rather than just more gold-plated? Yes, it turns out you could, because loudspeakers have really significant levels of distortion (this makes the depleted-uranium–24-bit-speaker-cable-thing even more of a joke than it already is). Current speaker designs are never going to get much better: perhaps there’s a cheap and cunning alternative²?

Finally I think it’s worth remembering the spin-off thing. Everyone else thinks that the LHC is doing research into particle physics: I think it’s doing research and development into production-quality large-scale high-power superconducting systems. And given my take that we’re going to need to ship huge amounts of electric power over many thousands of miles (half way around the world in fact) if we’re going to have a medium-term future as a civilisation I’m kind of interested in those. Would a BBME collider further that goal? I don’t know.

Believe it or not, this only exists because I could not work out which browser-anti-toxin I needed to turn off to get the thing to understand that I have a Google account, I was signed in to it, and so on. So far we have come. ↩
Hint: probably there is. ↩

Golden ears

Tim Bradshaw — Wed, 29 Jul 2020 12:13:16 UT

Or: Hi-Fi and the death of truth.

High fidelity audio is now dominated by people who think they have ‘golden ears’ and are able to hear differences between audio components which have been physically impossible to detect and almost certainly do not exist. Unsurprisingly these people refuse to consider any experiment which could reliably reveal whether they can, in fact, detect any difference. Often the same group of people will make absurd claims for systems which have very audible lack of fidelity — distortion — such as records and valve amplifiers, which shows how far removed they have become from reality¹.

There are plenty of areas where subjective opinion is the only useful thing: my opinions about some of the guitars I own are extremely subjective, as are my opinions about various movies, bands, books and a huge number of other things². But subjective opinions about things which are objectively measurable either agree with the measurements or they are wrong: if you try to live on the Moon without oxygen you will die within a few minutes, and believing you won’t will not keep you alive if you try: there are no alternative facts, there are only errors and lies.

Not all objective facts can yet be measured: for instance until 2015, while we believed gravitational waves to be a real phenomenon, we only had indirect evidence for them³. Perhaps the differences that the golden eared claim to detect between Hi-Fi components are real, but not yet measurable other than by their golden ears. If that was the case then there is still a good way of detecting whether they are real: carefully controlled, sufficiently blinded⁴ comparison experiments. If someone claims they can detect a difference between two things then you do a careful experiment which will reveal whether they can, while removing any possible bias due to the subject, the experimenter or anyone else involved. If it turns out that they can, then you know there is something to be measured and you can try and work out what it is. Even if you can’t measure it you know there is some objective truth there.

The golden eared reject such experiments.

The golden eared delusion is not itself significant: who, really, cares if a bunch of rich cranks believe they have magic ears? But the antiscientific thinking which underlies it is very significant. Once someone fails to understand that the differences they ‘hear’ are in their own minds, rejects experiments which could show this then they have decided that they can believe whatever they want to believe about Hi-Fi: there is no objective truth. And if you are free to believe whatever you like about Hi-Fi, why should I not believe whatever I like about vaccination, climate change or how many Jewish people, Roma and others the Nazis killed? Once objective truth is dead, it’s dead: you don’t get to say it’s only dead in specific domains. The golden eared delusion is one small path to the decay of any notion of objective truth itself which we are now seeing.

It’s interesting to understand how the mechanisms of truth decay work: if we understand them perhaps they can be reversed. So it is interesting to try to understand how how and why this particular pathology arose. Fortunately this is pretty easy.

Ancient history

Initially there was a period during which there was rapid and very audible progress in music reproduction. This started before 1900, and came to an end sometime in the late 1980s, with some tail-off after that. I have a Quad 2 / 22 / FM2 in (now) better-than-new condition and even with a very good source it’s really noticeably worse than a good modern amplifier. Even after amplifiers got good, there was lots of progress in turntable, tonearm and cartridge design right through the 1970s. However even then some early signs of the later pathology appeared, such as S-shaped tonearms which started because of confusion for which spurious justifications were later invented as they looked so pretty.

Throughout this period you could get better Hi-Fi by getting newer Hi-Fi. Manufacturers loved this of course, and a certain kind of person also liked it. My friends and I spent an altogether inordinate amount of time obsessing about Hi-Fi in the late 1970s, and everyone knew someone whose father (inevitably father: Hi-Fi was then and almost certainly still is a male hobby) had some really expensive and beautiful system on which we could listen to the latest Yes album. Some people even probably thought it attracted girls: in my experience, if it did, it didn’t work nearly as well as guitars, motorbikes or just being a decent human being did.

The wall

But then it all gradually hit the wall. Successively various components of systems got sufficiently good that, while they could still get better, they could no longer get perceptibly better. Loudspeakers were the only significant exception: see below for more about this.

During and after this event something related also happened: very portable audio reproduction systems arrived and became pervasive. Almost everyone who wanted to listen to music started using these systems rather than enormous Hi-Fi setups. Their quality was at first very poor, and any system based on in-ear headphones has limitations even now, but for people whose goal was to listen to music rather than to play with toys, they were more than adequate.

These two changes meant that large numbers of people who would formerly have bought Hi-Fi systems to listen to music bought portable systems instead., and people who still wanted to listen to music on a traditional Hi-Fi and had formerly upgraded their systems regularly to improve the quality largely stopped doing so as there became no reason in terms of quality of sound reproduction to replace components. This was very bad news for the Hi-Fi industry.

Well, the inevitable happened: many Hi-Fi companies went out of business. I have no idea how many Hi-Fi companies there now are compared to 1980 or how their financial value compares, but fewer, and less.

Some companies survived by simply servicing the relatively small remaining market as new generations of customers appeared: not everyone wants to use their parents’ or, more likely, grandparents’ Hi-Fi and in any case electronic components don’t last for ever.

Some companies realised that, for many people who are interested in Hi-Fi only part of their interest is in achieving the best possible sound reproduction: a very significant reason they like Hi-Fi is because it can be beautiful. It is undeniable that a well-engineered turntable or valve amplifier is a very lovely machine in much the same way that a vintage sports car or a 1950s jukebox is. There is absolutely nothing wrong with the desire to own beautiful things and very well-engineered things are often beautiful. A reason which is, I think, rather less good than the desire to own beautiful things is the desire to own status symbols: very expensive objects which will be recognised as such by other people. So some companies started to produce Hi-Fi which was explicitly designed to be appealing in this way.

Some companies — very often the same ones who were producing very beautiful Hi-Fi — made a business of being willing to maintain their own products for a very long time: when you bought something from them you knew it could be repaired almost indefinitely and you were therefore willing to pay a high price for it⁵.

A way under the wall

Other, less honest, companies realised they could exploit four secrets that had been lurking, usually slightly below the surface, in the minds of their customers since the beginning.

The first secret is that quite a lot of people suffer from what is called ‘gear acquisition syndrome’ — GAS for short. GAS was first described among musicians and it involves thinking that ‘if only I had a better (guitar, amp, effects pedal, …) I would be able to play much better and would become the hugely successful rock star I know I could be.’

In other words, GAS makes you think that what’s stopping you being a great guitarist is not lack of talent or unwillingness to practice, but lack of the right gear. GAS is a form of displacement activity where instead of dealing with the real problem — that you’re not very good, that you don’t practice — you spend endless ours obsessing over what gear to buy and in fact buying gear. And GAS doesn’t stop: once you have the expensive guitar and you still can’t play like Jimmy Page, well, it must be because the modern ones aren’t up to much — you need a 50s or 60s one. And the tape echo simulator you have isn’t good enough — nothing but a real tape echo (valve, not solid state mind you, the solid state ones where never up to much⁶) will do. And on it goes, endlessly, eating money and consuming the time you should spend practicing in looking at adverts & reading reviews.

GAS applies to Hi-Fi as well: rather than just listening to music people start obsessing that it would all sound much better if only they had better Hi-Fi. And it doesn’t matter whether it actually would, or even if it could sound any better: GAS is still driving you to buy more, ‘better’ Hi-Fi⁷, spending time on that which would be better spent listening to more music.

The second secret is that people like to think that they are special. Everyone likes to think that they are somehow gifted: one of the hard things that happens to almost everyone as they grow up is realising that, in fact, they are pretty much the same as everyone else. Some very few people really are gifted: Mozart was gifted, Einstein was gifted, Picasso was gifted, Jimi Hendrix was gifted. But most of us have more-or-less the same gifts as everyone else. And this is obvious really: if everyone is gifted, or most people are gifted, then, well, those gifts are just ordinary.

Most people learn this truth eventually, but no one enjoys learning it and some people don’t learn it at all. In some cases this failure to learn is very toxic: Donald Trump and Dominic Cummings are current examples. Even people who understand that they are not, in fact, special are susceptible to suggestions that they are. This is why rock stars, dot.com billionaires and film stars are such horrible people: they spend their lives surrounded by sycophants who are endlessly telling them how special and important they are. Almost everyone will eventually break under the pressure of such flattery: even if they didn’t start by believing they were, well, a bit special, they will end up doing so after enough people have said they are.

So if you tell someone, often enough, that they have the special gift of golden ears then, unless they have a very deep-rooted understanding of why they don’t, in fact, have golden ears — why golden ears can’t exist — some of them will start to believe they have. Because they’re special, and they have special ears: of course they do.

The third secret is that human sensory perception is both unreliable and subject to bias. There are very many examples of this: many of the most famous ones being optical illusions of various kinds. A good example is the chequer shadow illusion, in which two areas which are in fact identical shades of grey appear to be different shades. Another good recent example is the famous viral dress phenomenon from 2015, in which different people perceive the same dress as being coloured either black and blue or white and gold. There are many, many others: human visual perception is clearly simply not reliable.

But all these examples are optical illusions: perhaps hearing is special and is reliable and not subject to bias? This would be extremely surprising, but without evidence it can’t be ruled out as a possibility. Well, there is lots of evidence. A famous example is the McGurk effect: this is something that will be familiar to anyone who has watched dubbed films, or films in which the audio is not completely in sync. What happens is that, if you are watching someone speak then they actually produce one phoneme but the sound corresponds to a different phoneme, you can end up hearing a phoneme which is neither the one that they really said nor the one that the sound corresponded to but some third phoneme. If someone says /ga-ga/ but the sound of /ba-ba/ is played over it you can end up hearing /da-da/, for instance. Shepard tones, which seem to endlessly rise in pitch are another example⁸. There are many more examples of auditory illusions.

Perhaps the most well-known auditory illusion of all is stereo. When you listen to a correctly set-up stereo speaker system and a well-engineered recording, the impression that sounds come from instruments at well-define points between and often much further away than the loudspeakers⁹ is compelling for most people. But no sounds are coming from those points: your senses are fooling you.

None of this should be surprising: as our senses did not evolve to provide reliable, repeatable information free from bias: they evolved to make sure that we heard some terrible monster approaching from behind before it ate us. And if, occasionally, we hear monsters which are not there then that’s a lot better than being eaten. What would be surprising, in fact, would be if our sensory system was reliable.

The fourth and greatest secret is that people want to believe in magic. I’m sure there are some people who want to believe only in the things that science can explain, but almost everyone wants to think that, somewhere, there are elves and dragons, water-spirits and wizards. In 2003, the BBC conducted a survey to find the best-loved books in the UK. Of the top ten books, six involved magic of some kind; of the top five, four did. The best-loved book of all was The Lord of the Rings, which has also been found to be the best-loved book in Australia, Germany and the US, and has sold more than 150 million copies: about one copy for every 50 people now alive.

Almost no-one wants the world to be a place where there is no magic of some kind: even people who ‘don’t believe in magic’ want to believe in things like faster-than-light travel and time travel which are magic dressed-up as science¹⁰. I want to believe in magic: wouldn’t the world be a better place if there were elves, river gods and goddesses and magical objects? Of course it would.

And this desire for there to be magic runs pretty deep. Perhaps we can’t have elves and genii locorum (or, perhaps, we can, somewhere just out of sight), but can’t we still have magical objects? I know that there are no magical objects, but still I own a beautiful valve compressor using a ‘new old stock’ military valve. Is it better than a really good digital compressor? Certainly it is not, but I want to believe it is. And I know that my beautiful ES–175 is just machine made of wood, metal and bone and not even a particularly good example of one, and that it’s easily replaceable. But I would risk my life to rescue it in a fire, because some part of me believes that it is made of wood, metal, bone and magic.

The four secrets are:

the human desire for an endless succession of better objects (GAS);
the desire of humans to believe that they are special and have special abilities not granted to other people;
the unreliability of human sensory perception and the ability to bias that perception;
the human desire to believe in magic, and particularly magical objects.

Now it is easy to see how these can be exploited by unscrupulous Hi-Fi companies:

offer an endless succession of ‘better’ Hi-Fi components, thus satisfying customers’ GAS;
persuade customers that they are special and have golden ears able to hear the differences in sound that ordinary, lesser, humans can not hear;
rely on the unreliability of human auditory perception together with poor or no experimental controls to do this;
provide components which purport to be magic in all but name — special speaker cables, special capacitors, turntable plinths made of lignum vitae, Hi-Fi components made long ago which are purported to have magic properties and so on.

And, for some Hi-Fi companies this has worked very well indeed. The market is necessarily fairly small because magic objects don’t come cheap and, well, if the ordinary people had access to them the argument that the people they were selling to have golden ears would fail.

And, well, why does it matter? It’s obvious why it happens — companies need to stay in business, customers need to justify GAS, believe they are special and that magic exists, and lack of experimental discipline can be used to achieved this. And it’s obvious that the customers are gullible fools: but it’s their money, why should anyone else care?

The death of truth

They should care. They should care because people are not compartmentalised. People who believe that they have special powers in one area tend to believe they have special powers in other areas. That makes them deeply unpleasant people — it’s hard enough dealing with the arrogance of people who really are gifted: dealing with the arrogance of people who only think they are gifted is a horrible experience.

But that is only a tiny part of the problem: people who don’t accept properly-controlled experiments in one area will be less likely to accept them in other areas; people who think that they can ignore scientific method in one area will tend to ignore it in other areas.

But you don’t get to pick and choose: in the areas where science works, it works, and if you say it does not work in an area where it applies what you are saying is that, well, you get to choose when to believe what it tells you or not based on what you want to be true. The end result of this is that people will start to think that they can just get to choose what they think is true as suits them: if it is convenient to them for something to be true, well then it is true, if it’s not convenient, then it is false, even if it’s the same thing that was true yesterday.

Once you open Pandora’s box, then you open Pandora’s box: what comes out is whatever is in it, but also everything that is in it. You don’t get to say you only want some of the contents.

And we have opened Pandora’s box: we live in a world of alternative facts and made-up truths, a world where the very notion of truth is in the process of being destroyed. Many of us are now ruled by people who think that truth is whatever is convenient to them this week, because many of us think that truth is whatever is convenient to us this week. Truth is dead.

But it’s not: there is, in fact, a world outside your head and that world does not care what you think is true or whether you think you have special golden ears. That world cares only about what is true. A virus does not care what you think: it cares only about what is true. The physics, chemistry and biology of the planet’s climate does not care what you think: it cares only about what is true. The virus will kill you whether or not you think it can, and the consequences of what we are doing to the climate will kill billions of humans no matter how hard they pretend it won’t. There is no magic, there is only truth, and the only way to discover that truth is carefully controlled experiment.

The fabric of myths and lies on which modern Hi-Fi is built are a small part of the death of truth, but they are a part of it. If you think you have golden ears while conveniently choosing to reject any experiment which could tell if you really have, if you believe in the magic properties of certain components, or if you are merely involved in selling these myths and lies to people who believe then you are partly responsible for this catastrophe. And I will not forgive you.

I am personally very fond of the sound of records and of valve equipment, and have constructed a valve hifi amplifier as well as owning another. But I am fond of them because I enjoy the lack of fidelity they introduce. I do not pretend that either thing provides particularly accurate reproduction. ↩
Somewhere, some reductionist AI person is saying ‘but these can be reduced to objective facts about the state of the brain’. Well, yes, but ‘reducing’ something to the state of a system which is so complex no human can understand it in any detail (this is obvious: if a human brain has enough state to store a complete copy of another human brain then, by recursing, it is clear that it must have an infinitely large state space) is not reducing it to anything objective in any useful sense. ↩
The first direct observation of gravitational waves was made on the 14th September, 2015, although not published until 11th February 2016. ↩
These experiments are normally called ‘double blind’ since neither the subject not the experimenter knows enough to bias the result, consciously or not: they are both blinded. I prefer the term ‘sufficiently blinded’, which covers experiments where there may be more than two parties involved. What I mean by ‘sufficiently blinded’ is that no-one involved has information in advance which would allow them to bias the outcome. ↩
For a long time Quad — formerly the Acoustical Manufacturing Company — made a business doing this: they may still. These two ideas — producing objects which are very beautiful, which serve, emphatically, as status symbols and being willing to maintain them indefinitely — has of famously been exploited in the photography field by Leica ↩
Related to GAS is the idea of some lost golden age in which everything sounded better — somehow, the sound achieved with a specific model of tale echo, or a particular studio compressor made in tiny numbers in the late 1960s has never been equalled. It is not acceptable to consider that the sound achieved on records using the magic equipment might have been more due to the genius of the sound engineer than the equipment, because that would mean that your recordings sound bad because you are not very good, which can never be considered. ↩
GAS is, in some ways, a malignant version of the ‘beautiful object’ motivation: the never-ending sequence of guitars, amplifiers and effects pedals that a musician with GAS buys are ever more exotic and beautiful, as is the never-ending sequence of Hi-Fi components that a person with GAS acquires. Of course, in both cases, the sufferer continues to fool themself that this is not what is motivating them. Again, Leica is a good parallel: if you are thinking of spending more than ten thousand pounds on a camera and lens and you think that it will make you a better photographer the you are a fool; it would be better to admit that you ate doing it because you want the beautiful object or the status symbol. The sufferer from GAS either does not recognise this or truly believes that it is not the case. ↩
This is related to a device sometimes used in electric music called a ‘barberpole phaser’ which gives the impression of an endlessly upward or downward sweeping phaser. The same effect can also be achieved with flanging. ↩
I find the stereo illusion works very much less well with headphones. But, well, it’s an illusion: perhaps other people hear the illusion much better than I do with headphones. ↩
Almost certainly: FTL travel translates directly into causality violation, which is extremely bad news. ↩

The glorious work of Dominic Cummings

Tim Bradshaw — Tue, 02 Jun 2020 16:59:52 UT

Or: the Cummings-Johnson effect.

I thought it would be interesting to get an idea of how many people will die because Dominic Cummings thought it was fine to ignore the lockdown rules, and Boris Johnson agreed with him. So I wrote a program to explore this Cummings-Johnson effect.

All the reasons you had to die

Jesus don’t want me for a sunbeam,
because sunbeams are not made like me,
and don’t expect me to cry,
for all the reasons you had to die,
don’t ever ask your love of me.

There are two ways that what Cummings did in March 2020 will probably be killing people:

he drove a long distance, presumably taking breaks¹, while knowing he was infected with CV19;
now his actions are known, and now Johnson has supported them, other people’s behaviour will change.

The first of these is likely to have killed people, and still be killing people, by spreading the virus: for instance to the toilets in service stations². The second of these is likely to kill people, and perhaps has done so already, because now it is general knowledge that Cummings & Johnson think that lockdown rules are for other people — for the little people, not people like them — then they will take lockdown and social distancing less seriously, and people will die as a result of that.

It’s this second way that they are killing people that I looked at.

Ths simulator described below is a toy: it’s very much a physicist’s ‘spherical cow’ model. It has no notion of locality for instance: infected individuals simply randomly pick other individuals to try to infect. The results it gives may be qualitatively reasonable, but if they are quantitively correct this is coincidence. The purpose of writing it, and of the runs described here, was simply to see if the Cummings-Johnson effect is visible, and to get some kind of qualitative estimate of how large it might be: if their actions will probably only kill only a few tens of people then they are doing no more harm than a common-or-garden mass murderers, while if their actions may kill thousands of people, then they’re working on a completely different scale.

Epidemic models which are far better than this exist. For instance the MRC Centre for Global Infectious Disease Analysis — Professor Neil Ferguson’s group — must have one. I would be very surprised if these people haven’t run much better versions of the scenarious I describe below. But the results of these runs don’t seem to have been published. This is sad but, perhaps, not surprising given what we know about Cummings & Johnson and their attitudes to facts which disagree with their fantasy worlds.

Still: if there are results from better models I would very much like to know them.

A mindless epidemic simulator

I wrote a very simple-minded simulator: it is unlikely to be realistic, it’s really a toy model. The results are unlikely to be quantitively correct, but they may be qualitatively interesting. In the model individuals go through the standard three phases:

initially they are uninfected & hence susceptible;
once they are infected they incubate the disease for $t_l$ days, where $t_l = 7$ in all the runs below
they are then infectious for $t_i = 14$ days.
on each of these days, they randomly pick another individual, and if that individual is susceptible they infect them with a probability which is initially $p_i = 0.14$.
at the end of the period they either die, with probability $p_d = 0.01$, or they survive but become non-susceptible.

Additionally there may be a small ‘leakage’: every day, every susceptible person in the population can stand a small chance of becoming infected. This models the infection leaking in from abroad, for instance. In all the runs here the leakage $p_l = 10^{-8}$.

Finally the initial number of seeds can be set, the idea being to start the simulation after a good few people have become infected to avoid too much uncertainty in the trajectory of the epidemic. By default $n_s = n_p/1000$, where $n_s$ is the number of seeds and $n_p$ is the population size.

All of the parameters are adjustable as is how long to run for and what the stopping criteria are (with a leaky model things can keep on happening even after the number of infectious individuals reaches zero).

It is straightforward to computee $R_0$ for this model: a person is infectious for $t_i$ days and each day they stand a $p_i$ chance of infecting another person if no-one is yet infected, so

\[ \begin{align} R_0 &= p_i t_i\\ &= 0.14 \times 14\\ &= 1.96 \end{align} \]

And then $R$ declines over time as more people are removed from the population. When $R < 1$ the epidemic dies out, more-or-less gradually, except for leaks causing occasional infections.

Source code for this model is not currently available, although it may be in future.

How the simulations run

All of $t_l$, $t_i$, $p_i$, $p_d$ and $p_l$ can be adjusted during a run: the simulator is told to run for a few days, the values can then be adjusted and then it runs again for some given time. In practice the only parameter that I adjusted was $p_i$: the probability of infection. Changing this during the run directly changes $R_0$ and hence $R$ and alters the course of the epidemic.

There is nothing in the model which prevents any of these parameters being adjusted dynamically, based on the current behaviour of the modelled epidemic. In fact I didn’t do that but instead set up ‘configuration sequences’ which are sequences of configurations where the parameters (in practice, just $p_i$, as well as some reporting parameters) are changed at fixed times, between which the model simply runs.

Because there is inevitable variation between runs, the simulations get run several times, and the model also forks: if I wanted to look at the effect of changing parameters on, say, $d = 120$, a single simulation is run to $d = 119$ and then multiple copies are run on from then. This means that any variation before $d = 120$ is removed from the forks, since they all come from the same simulation run. This process can happen recursively if need be.

Some example runs

Here are some simple cases which show the behaviour of the model.

Abandoning mitigation

Here is output for a model epidemic in which the mitigation is abandoned after 2 years:

Mitigated giving up after 2 years, cumulative deaths, population of 1 million

This is the output of a 4 year run for a population of a model with

$n_p = 10^6$;
$p_i = 0.14$ initially;
$p_l = 10^{-8}$

For the unmitigated forks, $p_i$ remains at its initial value.

For the completely mitigated forks

\[ p_i = \begin{cases} 0.14&d \lt 40\\ 0.06&120 \le d \lt 120\\ 0.08&120 \le d \lt 200\\ 0.06&d \ge 200 \end{cases} \]

For the ‘giving up’ forks

\[ p_i = \begin{cases} 0.14&d < 40\\ 0.06&120 \le d \lt 20\\ 0.08&120 \le d \lt 200\\ 0.06&200 \le d \lt 730\\ 0.14&d \ge 730 \end{cases} \]

In other words what this is showing is a scenario where there is no vaccine, but mitigation is abandoned, after about 2 years. Because some leakage happens, at some point after the mitigation is abandoned the epidemic takes off again and a lot of people die. Exactly when it takes off depends on chance, but in all 5 runs here it’s within about a year and a half.

Scaling the average results from this run to a population of 70 million³ results in the following figures, all to 3 significant figures:

551,000 deaths for the unmitigated epidemic;
40,300 deaths for the completely mitigated epidemic;
535,000 deaths for the epidemic in which mitigation is abandoned on day 730.

For the mitigated epidemic this is somewhat lower than what the UK has so far seen, but it is in the right area: the model is clearly not hopeless. In later runs I adjusted the mitigation slightly to compensate for this (see below).

What these results make clear is that, unless there is a vaccine³, mitigation has to continue essentially indefinitely, or the epidemic will take off again.

Chancy runaways

Here are two runs which have an initial infected population, $n_s = 0$: there are initially no infected people and the epidemic takes off due to leakage, with $p_l = 10^{-8}$ as before.

Firstly for a population of a million:

Unmitigated, no seeds, cumulative deaths, population of 1 million, 10 runs

Well, you can see that the epidemic takes off again after less than two years in all cases.

How likely this runaway is to happen in a given interval of time depends on the population size, as smaller populations experience fewer leakage events. Here is a run for a population of 10,000:

Unmitigated, no seeds, cumulative deaths, population of 10k, 10 runs

You can see that only one runaway happened in the three year simulation.

The Cummings-Johnson effect

To model this I started with an epidemic whose $p_i$ values are initially:

\[ p_i = \begin{cases} 0.14&d < 40\\ 0.06&40 \le d <120\\ 0.08&120 \le d < 200\\ 0.06&200 \le d < 300\\ 0.08&300 \le d < 600\\ 0.07&d \ge 600 \end{cases} \]

All of the models run for 3 years, or 1095 days, and in addition the unmitigated epidemic is always plotted⁴. Each model ran 5 times and quoted figures are averages, scaled to a population of 70 million, to 3 significant figures

Cummings-Johnson on day 120

For this model

\[ p_i = \begin{cases} 0.14&d < 40\\ 0.06&40 \le d < 120\\ 0.08\times \left\{1.02, 1.05, 1.10\right\} &120 \le d < 200\\ 0.06\times \left\{1.01, 1.03, 1.06\right\} &200\le d < 300\\ 0.08\times \left\{1.005, 1.02, 1.04\right\} &300 \le d < 600\\ 0.07\times \left\{1.002, 1.01, 1.02\right\} &d \ge 600 \end{cases} \]

Where the triples of numbers represent the Cummings-Johnson effect causing weakening of social distancing of 2%, 5% and 10% respectively on day 120, with the weakening declining over time. Here are plots for this:

Cummings-Johnson on day 120, 2%, 5% and 10%, population of 1 million

Here:

the brown curves are the normal courses of the epidemic with and without mitigation;
the blue curves are 2%;
the orange curves are 5%;
the red curves are 10%;

The figures are:

551,000 deaths for the unmitigated epidemic;
63,100 deaths for the mitigated epidemic;
70,300 death for the 2% weakening;
86,500 deaths for the 5% weakening;
109,000 deaths for the 10% weakening.

Or in other words:

7,200 additional deaths for 2% weakening;
32,400 additional deaths for 5% weakening;
45,900 additional deaths for 10% weakening.

These numbers seemed far too high to me. And I also suspect that the epidemic in my model happens more slowly (takes more simulated days) than the real one. So I ran three more models, with the Cummings-Johnson effect taking place at successively later times.

Cummings-Johnson on day 200

For this model

\[ p_i = \begin{cases} 0.14&d < 40\\ 0.06&40 \le d < 120\\ 0.08&120\le d < 200\\ 0.06\times \left\{1.02, 1.05, 1.10\right\} &200\le d < 300\\ 0.08\times \left\{1.01, 1.03, 1.06\right\} &300 \le d < 600\\ 0.07\times \left\{1.005, 1.02, 1.04\right\} &d \ge 600 \end{cases} \]

As you can see this allows the mitigated epidemic to run until day 200, when the same decaying effect happens. Here are plots for this:

Cummings-Johnson on day 200, 2%, 5% and 10%, population of 1 million

Figures:

546,000 deaths unmitigated;
69,900 deaths mitigated;
75,100 deaths 2%;
93,700 deaths 5%;
128,700 deaths 10%.

Excess deaths:

5,200 2%;
18,600 5%;
53,600 10%.

This is a little better, but not much, and the 10% case is bizarrely bad.

Cummings-Johnson on day 300

For this model

\[ p_i = \begin{cases} 0.14&d < 40\\ 0.06&40 \le d < 120\\ 0.08&120\le d < 200\\ 0.06&200\le d < 300\\ 0.08\times \left\{1.02, 1.05, 1.10\right\} &300 \le d < 600\\ 0.07\times \left\{1.01, 1.025, 1.05\right\} &d \ge 600 \end{cases} \]

Here are plots for this:

Cummings-Johnson on day 300, 2%, 5% and 10%, population of 1 million

Figures:

551,000 deaths unmitigated;
59,800 deaths mitigated;
73,200 deaths 2%;
90,000 deaths 5%;
138,000 deaths 10%.

Excess deaths:

13,400 2%;
30,200 5%;
78,200 10%.

All these figures are worse than the day 200 case, which think is because the big increase is happening when things are already too relaxed.

Cummings-Johnson on day 600

For this model

\[ p_i = \begin{cases} 0.14&d < 40\\ 0.06&40 \le d < 120\\ 0.08&120\le d < 200\\ 0.06&200\le d < 300\\ 0.08&300 \le d < 600\\ 0.07\times \left\{1.02, 1.05, 1.10\right\} &d \ge 600 \end{cases} \]

Here are plots for this:

Cummings-Johnson on day 600, 2%, 5% and 10%, population of 1 million

Figures:

546,000 deaths unmitigated;
61,700 deaths mitigated;
63,600 deaths 2%;
68,500 deaths 5%;
80,200 deaths 10%.

Excess deaths:

1,900 2%;
6,800 5%;
18,500 10%.

These seem a little less frightening

Why is it so fierce?

I was really surprised by how large the differences are. I think part of the answer can be seen by looking at $R$: at any point the progress of the epidemic goes something like $e^{\alpha (R -1)t}$, where $\alpha$ is some fudge factor. The only reason that the exponential runaway doesn’t continue is that $R$ is a function not only of $p_i$ but also of the proportion of people who are no longer susceptible. But if that proportion is low, which you very much want it to be, then everything is, more, or less, exponential, and really tiny changes in $R$ can cause huge explosions.

To control the epidemic over any length of time you need to keep $R = 1 - \epsilon$ where $\epsilon \ll 1, \epsilon > 0$: you want to do this because the epidemic will die out so long as $R < 1$, but the social and economic cost of keeping it significantlly below 1 for any length of time is enormous. And for an epidemic which has infected, and therefore killed, only a relatively small proportion of the population then $R \approx R_0$. So the useful thing to look at is $\ln R$ & $\ln R_0$, as this shows small changes near $R = 1, R_0 = 1$ which is where all the action is⁵.

Here’s a plot of $\ln R$ and $\ln R_0$ for the Cummings-Johnson on day 120 2% variant, and the mitigated version without the 2% bump:

ln R, ln R0, Cummings-Johnson on day 120, 2% and mitigated

Interestingly you can see that, for $d \gtrapprox 500$ the Cummings 2% $R$ is lower than the mitigated $R$. But it’s significantly higher for $d \in [120, 200)$ and somewhat higher for $d \in [200, 300)$ (although less than 1 in the second interval).

So, well, very small changes for parameters in exponential processes can make very large differences: that should be obvious.

It certainly would be the case that runs with more principled values for things (for instance my ‘decaying Cummings-Johnson effect’ is pretty ad-hoc: it would be better to model it by having some increase which exponentially decays with time: $p_i = p_{i0}e^{-(t - t_0)/\tau}$ as people forget, which would be easy to model. Maybe I will have a go at that in due course.

How many people will Cummings and Johnson kill?

I don’t know. This model is not adequate to give a numerically-correct answer by a long way: it’s full of assumptions, and is in any case an extremely oversimplified model⁶.

But I couldn’t get the number of people they will kill lower than 1,900, and I worked fairly hard to get it that low. I think my model is too sensitive, even though the numbers of people it kills for the mitigated epidemic are pretty reasonable and I did not fine-tune it for that, so I expect the real number will be somewhere between many hundreds and a few thousand. This is somewhere between mass murder and genocide⁷.

Did Cummings & Johnson do this deliberately? Probably not. Are these the only people they will kill, or even most the people they will kill, due to their ideological, careless and incompetent handling of the epidemic and other things? No. Would the harm have been reduced if Johnson had promptly sacked Cummings? Yes. Would the harm still be reduced if he were to sack him now? Yes. Will he sack him? Of course not. Do either of them care that they will kill a lot of people? Definitely not: the people they have killed and will kill are only little people, like ants.

This is the glorious work of Dominic Cummings, aided and abetted by his idiot stooge, Boris Johnson.

Don’t expect me to lie,
don’t expect me to cry,
don’t expect me to die for thee.

He says he did not take breaks. This seems a deeply implausible claim given that he drove 260 miles with a small child in the car. ↩
Which, again, he claims none of his family visited. ↩
Another option is that the epidemic becomes globally extinct, when leakage would stop: this seems unlikely. ↩
This is not really helpful as it makes the plots harder to read. ↩
In my model I’m treating $R_0$ as something you adjust via changes to $p_i$, rather than a constant of the epidemic. $R_0 = p_i t_i$, and I am adjusting $p_i$. It would perhaps be better to say $R_0 = p_{i,0}t_i$ and then define $p_i = p_{i,0} - p_{i,m}$, where $p_{i,m}$ is the parameter you adjust, and use that together with the proportion of people remaining susceptible to define $R$: it doesn’t make any difference to what actually happens though. ↩
I would be extremely interested in results about the Cummings-Johnson effect from more serious models. Please get in touch if you know of any. I am happy to sign nondisclosure agreements if need be. ↩
Since we know that BAME people are disproportionately affected by CV19 this really is looking like genocide. Perhaps not a deliberate one, but I wonder how much Cummings & Johnson care that a bunch of BAME people will die because of their actions? Not much, I should think. ↩

An open letter to Michael Johnston

Tim Bradshaw — Mon, 18 May 2020 19:25:23 UT

Michael Johnston runs a website dedicated to photography. He also promotes anti-scientific nonsense about audio: you should not support him.

[This was an email I never sent: in the end I got fed up and was a lot more rude. I don’t regret that, but perhaps I should. This was also written before COVID–19: it’s pretty clear that anti-scientific behaviour by the US administration is killing tens of thousands of people, which makes this a lot more urgent (although not, in fact, more serious).]

After thinking about it for a few months I have decided to stop my Patreon subscription to TOP.

I’m doing so as a result of your audiophile posts. I don’t want to discuss these in detail, but I think we can agree that these are explicitly and consciously anti-scientific in nature: you have said, for instance, that you would not accept double-blind experiments¹. Since sufficiently-blinded experiments are the only way to remove human bias from experimental results this means you are explicitly, consciously and publicly rejecting science.

I don’t have any problem with what you think about hifi in private — indeed I probably have more fancy hifi than most people, and have built several amplifiers including one valve (tube) one. However, I am not willing to help fund you, or anyone, in making anti-scientific statements in areas where science applies².

We live in a world which is built on science: you and I are probably only alive as a result of the work of scientists, and you certainly have working eyes only because of science. We also live in a world where scientists are telling us that unless we take quite urgent action to address environmental problems — largely but not only anthropogenic climate change — we are in extremely bad trouble. Unless we address climate change soon our grandchildren’s generation will have blighted lives and many of them will die in horrible circumstances.

Well, a lot of people don’t like this: they have vested interests in not fixing the problem in the short term, will be dead in the long term and either do not care about their descendants or expect that they will be wealthy enough to fence themselves off as the environment degrades. And they certainly do not care about anyone else’s descendants, especially if those people live far away or look different.

Those who don’t want the problem fixed need other people not to listen to the scientists, or not to believe what they hear. One way they achieve this is by casting doubt on science itself: by casting doubt, ultimately, on the concept that there is such a thing as ‘objective truth’ in areas where we should expect there to be. They have been astonishingly successful at this in the last few years. Of course the side-effects are terrible: people who no longer believe that science works or that truth exists also don’t believe, for instance, that the evidence that vaccination works is real. But the things vaccination protects you against do not care about what you think is real, they only care about what is in fact real: whether you have immunity to them or not, or whether the population as a whole has enough immunity to stop epidemics. And immunity is falling and many children will die. But not the children of the vested-interest people: just other children who they care nothing about.

And that’s what’s coming reasonably soon: in the longer term the result of people not believing the science of climate change and not doing anything about it is going to be billions of additional deaths and billions more shortened lives, and the loss of most or all of our culture.

This is not some conspiracy theory: all this is going on quite openly both in your country and mine.

Well, why does what you say about hifi matter? You’re not, after all, denying anthropogenic climate change or supporting the anti-vaccination nonsense. Why do I care that some middle-aged photographer has whacky unscientific ideas about hifi? I care for two reasons.

You don’t get to pick and choose: in the areas where science works, it works, and if you say it does not work in one area the message is that, well, you get to choose when to believe what it tells you or not based on what you want to be true. That is toxic as it means that people just get to choose what they think is true as suits them, which is the whole problem³.
You have a significant audience: people read your blog and some of them are inevitably influenced by what you say.

Finally, why does it matter? It already seems clear that we’re not going to deal with anthropogenic climate change and that the truth-deniers have won: just look at the politics of the last four years. Why should I care that I’m funding a little more of it? Well, that’s true: I think that there is very little hope, and what hope there is left is fading fast. We have perhaps 50 years or so before things get really bad, and far less than that before there is no chance of preventing the catastrophe. Long before that the corrosion of truth will have less serious but still horrible consequences: we are seeing some of them now. The future is not bright.

But there is some hope. Not, perhaps, much hope but there is still some. And I believe that what little I can do I should do to increase the amount of hope, and to decrease the corrosion of truth, in all its forms. And what you are doing is corroding truth. You are only doing it in a small way, but you are doing it. I can only make a difference in a small way, but not supporting TOP is a difference I can make.

This is why I will no longer support TOP financially.

Although it was not clear you knew what a double-blind experiment really was. ↩
Science does not apply everywhere, it should have nothing to say about what makes a great photograph, or what makes good bokeh for instance, in my opinion. For that matter it should have nothing to say about what makes hifi sound good in the cases where distinctions really exist. I like how my valve amplifier sounds, but I don’t pretend I like how it sounds because it is has lower distortion than any reasonable transistor amplifier: I like how it sounds just because it has significant distortion and I like the sound of that distortion. The same is true for records, which I also prefer to CDs, and which also are objectively and measurably far worse in terms of fidelity. ↩
Note again that I don’t think science is useful for, say, judging photographs as art or cameras or hifi as desirable objects: this is not about that. ↩

Sexism in computer science

Tim Bradshaw — Sat, 09 May 2020 17:16:02 UT

Anyone who says that the facts show that men are innately better than women in computing either does not know the facts, does not understand them, or is lying.

The facts

In 1971, about 14% of US computer science and information science graduates were women. By 1984, about 38% were. But by 2011 the proportion had fallen to under 18%¹. Here is a graph of the proportions by year from 1971 to 2011:

CS & IS graduate ratio, US, 1971–2011

What the facts show

This entire process happened in about two generations: the proportion of women more than doubled in less than one generation, and then about halved in a generation: some of the women studying CS in 2011 could be the daughters of the cohort of 1984, and the granddaughters of the 1970 cohort.

No genetic change in a human population can happen this fast: evolution operates on timescales of thousands to millions of years, not over a small number of decades. This means that whatever caused these changes was not a change in innate ability. There simply can be no question about that: there must be some other explanation, since the innate ability of women to do computer science, or any other innate ability, cannot have changed significantly over this period.

This means that the changes were caused by something environmental. Perhaps in 1984 there was enormous positive discrimination, or in 1970 and 2011 there was enormous negative discrimination, or some combination of the two².

This data is also perfectly compatible with the conclusion that women may be innately as good at computing as men: 38% is not very far from 50%, and if we assume some level of sexism in 1984³ it is easily possible that the underlying figure was 50%.

What this data tells us, unambiguously, that whatever has caused these changes is environmental, and is not due to any differences in innate ability as such changes simply cannot happen over this timescale. It also tells us that things have got a lot more skewed since 1984: progress in this area has not only stopped, it is being reversed and has been so since the mid 1980s: the situation now is only about 28% less skewed than it was in 1970.

What the facts don’t show

What the data does not say is why this has happened, except that it is not due to changes in innate ability.

While it is almost certain that there was strong institutional discrimination against women in 1970, it seems unlikely that, in 2011, there was any kind of institutional discrimination, as this would be illegal and institutions are pretty good targets for legal action. So it seems unlikely that the decline is due to institutional discrimination. However all the data says is that there has been a decline: not why.

If we assume that most of the change is not due to institutional discrimination then it’s tempting to speculate on what did cause it. Well, I’m not going to do that: I have theories but they are based either on no evidence or on anecdotal evidence. Perhaps someone has done proper research into the causes, but I don’t know. There is a vast surfeit of theories based on little or no data, and outright made-up stuff on the internet — wild speculation, outright lies and ‘alternative facts’⁴ — and people are dying of this surfeit: I won’t add any more to it.

One possible inference is that women who, today, succeed at computing degrees, have done so against significant odds. It’s very likely that this means that they are better than men who achieve the same grades. So companies, if they are legally able to, might consider actively selecting female candidates for jobs, on the grounds that they are, probably, better.

In any area where people make a claim that some group is innately better than some other group based on some metric, and where the scores of one or both of those groups has changed radically over time, then it is immediately safe to conclude that those claims are either lies, confusions or both, because either the metric is junk, or it is not measuring innate ability. The obvious example of this is racial ‘science’.

Source. Later figures may be available, but I couldn’t find them. I also don’t have the figures for other countries but I expect they are broadly similar. ↩
I worked in academic computing from shortly after 1984 to the late 1990s and although I am not female I can say with some certainty that there was not enormous positive discrimination. ↩
Again, in my experience there was some level of sexism in academia in this period. ↩
Which are, of course, lies. ↩

The revenge of the blob

Tim Bradshaw — Wed, 18 Mar 2020 12:44:58 UT

And ye shall know the truth, and the truth shall make you free.

The blob

It has been very fashionable among populist politicians and their supporters to fulminate against ‘the blob’. The blob is:

the civil service;
journalists;
news reporting organisations other than ones that report ‘good’ news¹;
the BBC in particular;
scientists, especially climate scientists;
economists;
experts of all kinds;
judges;
the whole legal system;
the liberal/metropolitan elite in general, however it is defined;
the deep state, whatever that may mean;
the reality-based community²;
anyone who disagrees with whatever plan is in favour this week, or points out that it is not possible, will be economically catastrophic, is illegal, or anything inconvenient like that.

The blob is an amorphous group of people who all think the same way and who all are somehow trying to prevent whatever transformative programme the populist wants to embark on. Which people exactly constitute the blob varies from time to time and populist to populist. Whatever the blob thinks is wrong, and the blob must therefore be eliminated so that we can all get things done³ and, rejoicing in our inevitable victory, march forward to the sunlit uplands of the glorious future that awaits those lucky elect over whom we will rule in splendour for a thousand years.

Populism

I’m sure there are many elaborate definitions of what it means to be a populist. One fashionable idea is that populists somehow side with ‘the people’, who are good, against ‘the elite’ (aka, of course, ‘the blob’) who are bad. But definitions vary a lot depending on who is making them and when they made them⁴. One defining characteristic is that

populists seek to gain power by providing simple, appealing answers to complex, unappealing problems.

These answers are almost always wrong because problems which have easy answers have already been solved and are no longer problems. The definition of a ‘complex, unappealing’ problem is one which does not have a simple, appealing answer.

But it doesn’t matter whether the answers are wrong: they are appealing and easy to understand, and the populist aims to ride that to power. Consider this problem:

foreigners are coming to our country and eating our children!

Well, if you think about it, this is really not that simple to solve: there are quite strong taboos amongst humans about eating other humans — many, perhaps all, claims of large-scale cannibalism turn out not to be true — and there are even stronger taboos against eating children. So what is making these foreigners so desperate that they feel they need to eat children? Even more so, why are they coming here to do it: are there no children to eat locally? Perhaps they have eaten all their own children: but then why haven’t they died out? It’s all, really, quite complicated.

But the populist doesn’t care about this as they have a simple answer:

THROW THESE LOATHSOME CHILD-EATING FOREIGNERS OUT! LET THEM EAT THEIR OWN CHILDREN! BUILD THE WALL! REMEMBER THE SPIRIT OF THE BLITZ! ENGLAND FOR THE ENGLISH!!!

Well, that will certainly fix it, at least until the populist has ridden the wave of disgusted horror at the unspeakable behaviour of these horrible baby-eating foreigners⁵ to power, wealth and glory.

And what happens then? What happens when the whole problem turns out to be intractable after all? There are a range of answers to that.

People have pretty short memories so a good approach is just to try to forget, either that the problem existed at all, or that the solution was ever suggested. When you come up against people who do remember then you can simply ignore them, or deny that you ever offered the solution or in fact that the problem ever existed at all. While doing so be sure to imply that these inconvenient long-memoried people are acting in bad faith somehow, or are acting against the will of the people which you, of course, represent.

Blaming someone else is also a good approach: of course the problem would be solved by now but the liberal elite — mostly made up of foreigners⁶ and people who are, you know, different — is preventing the solution for reasons of their own which you will hint, but never quite say, are because they quite like a bit of children-eating themselves, as rootless cosmopolitans⁷ tend to do. Certainly they are enemies of the people and something must be done about them (that ‘something’ may, you will imply, involve camps).

Of course you can simply lie that the problem has been solved when it has not been. If it’s not, in fact, really a very severe problem — only one baby was ever eaten, and it turns out that the evidence for even that is pretty apocryphal — then you can just declare it solved and move on.

A final, brilliant, approach is to make up problems which do not exist and then, later, declare them solved. Foreigners certainly no longer come here and eat our children: this is, therefore, a problem our glorious leader has solved! Crime also is no longer rising and this too is something which the great chief has strived day and night to achieve and why he must be elected as leader for life. Do not mention that crime was not rising previously, still less that it now is: only enemies of the people with their annoying facts would do that.

What is it that makes the populist’s appealing answers so appealing? What, exactly, do they appeal to? Well, the answer is obvious: just look at the answers that populists give. They appeal to the things that, secretly, ‘everyone knows’ are true: to things that people perhaps think but, until recently and not always even now, most people have not dared to say in public for the last few decades; they appeal to instinct, to intuition, to prejudice, to bigotry. But they never appeal to rationality.

So this is because, secretly, everyone is a bigot, right? No, it’s not: a fair number people are secretly — and, increasingly, not so secretly — bigots of course, but by no means everyone is. Until fairly recently the proportion had also almost certainly been declining for decades. Rather this is because populists are dealing with an awkward truth: there is no division between ‘the people’ and ‘the elite’: there are just people, belonging to a myriad different intersecting groupings, with each person usually belonging to many groups. But mostly, there are just people.

So the populist has to invent groups of people to set against each other, and then to persuade enough people that they belong to the ‘good’ group aka ‘the people’ by various rhetorical tricks. There’s no ‘white working class’⁸, until you talk about it enough, and then suddenly there is. Indeed there is no England, until you persuade enough people that, well, English people are not the same as Scottish or Welsh people, and definitely not the same as people who live on the wrong side of some water who, really, are barely people at all. There is certainly no blob until you persuade enough people that there is, and that the people in it are bad people and should most definitely not be listened to and perhaps, in due course, be eliminated. Not surprisingly a good way to invent these groups is by invoking bigotry, because bigotry is entirely about creating artificial divisions between groups of people.

What they are doing is something physicists call ‘symmetry breaking’, which is a process where initially tiny differences get blown up so they become very large. And they’re doing this so that they can construct a large group of people who will support them, and force into existence one or more other groups who can be identified as the enemy, and who can be blamed for all bad things. A good example of this process is sentiment in the UK about the EU: this was simply not a major issue between 1990 and 2010; yet from 2016 until COVID–19 displaced it in early 2020 it entirely dominated UK politics. The populists have, quite brilliantly, divided the country into ‘the people’ who now desperately want to escape the EU they were hardly aware of only a few years before and a despised elite who are supposed to be plotting to prevent this, and the populists have ridden the division they have invented to power. Brexit is a canonical recent example of gaining power by providing simple, appealing, and wrong answers to complex, unappealing problems.

Populists

What sort of people do this?

Actual bigots, such as Trump, Bolsonaro, Bannon & others. They are not pretending to hate people they see as different, they really do hate them.
People for whom personal power and glory matters above all else, such as Trump & Johnson. Populism is an easy way of gaining power if what you care about is power rather than the welfare of the people over whom you have power, so people who care about having power above everything tend to be populists when they can’t be despots, or perhaps as a route to becoming despots⁹. Almost all politicians are interested in personal power, of course: populists are different because anything else can be traded for power. Does anyone really think that Johnson really believes in brexit? Of course he does not: he believes only in Johnson, and he will support anything that furthers that cause.
Cranks, such as Cummings and perhaps Bannon: the true believers. These people are often sidekicks or advisors & are by far the most interesting group, and perhaps the most dangerous one as well.

Populists are not people who merely want power, or who are involved in extracting money from their position or other forms of corruption: populists do want power, with the possible exception of the cranks, and usually are corrupt, but these things are true of almost all politicians and is not a useful distinguishing feature of populists¹⁰.

Cranks

Anyone who has worked as a scientist or with scientists or in many other fields will have come across cranks. These are the people who have disproved special relativity, who can show that quantum mechanics is incorrect, who believe in perpetual motion and who want to tell you about it in endless, excruciating detail. They seem annoying but harmless until suddenly they aren’t: suddenly they’re refusing to vaccinate their children causing measles outbreaks and threatening herd immunity; suddenly they are destroying telecommunications infrastructure; suddenly they are advocating eugenics and ‘scientific’ racism; suddenly they believe the apocalypse is coming; suddenly, they are the chief advisers to the president or the prime minister.

It’s easy to think that cranks are just stupid people, but they’re not: Trump is stupid and Johnson is superficial, but whatever Cummings & Bannon are they’re not stupid. Instead I think that the distinguishing feature of cranks is that

cranks don’t realise when they don’t understand something¹¹.

So, for example, if I try to understand Wiles’s proof of Fermat’s last theorem I very quickly realise that it is beyond my understanding: perhaps if I spent the rest of my life on it I could understand it, eventually. But in practice I couldn’t because I just don’t have enough intuition for that sort of maths, and almost certainly I am also just not clever enough. That doesn’t happen for a crank: if they start off trying to understand special relativity and fail to do so they never recognise that they have failed. Instead, when they start trying to do calculations and get answers which disagree with special relativity or are inconsistent, they conclude that everyone else is wrong: that they alone understand special relativity or that they alone understand what is wrong with it. This then leads to some bad places.

Why does everyone refuse to listen to me when I try to explain how they are wrong about special relativity? Why won’t they publish my papers? Why do they all claim to think the same thing when it’s so obviously wrong? It’s group-think! Do they really believe what they profess to believe or are they hiding something? Is there some kind of hidden conspiracy of elite scientists trying to suppress the truth, which I have now exposed? And wasn’t Einstein, the founder of the conspiracy, Jewish? Why yes, he was. What is really going on here? Why is the cosmopolitan elite suppressing the truth? Are they in league with the financiers? How are the climate scientists involved? What are they concealing from the common, decent, everyday working folk? The truth is out there, if you will only look, however hard the hidden superiors try to conceal it! THE TRUTH IS OUT THERE.

Not all cranks are populists, but it’s pretty easy to see why populism attracts cranks: the intellectuals of populism are cranks.

The trouble with populist cranks is that they really believe what they profess to believe. The bigots are just little knots of fear and hatred, the power-seekers don’t really believe anything at all, but the cranks have constructed vast thought palaces which may even, at first sight, seem plausible. And the cranks are not stupid: their simple, appealing answers don’t work because complicated problems simply don’t have simple appealing answers, but they can and will argue for them endlessly in enormous and incomprehensible detail. Arguing with a crank is like fighting an octopus: whenever you think you’re winning there’s another tentacle to deal with.

And the cranks really hate the blob, because there’s a reason the blob disagrees with them: the cranks are wrong. But the cranks are now in power: they have won and they are going to destroy the blob so they’ll never have to listen to all the reasons they’re wrong again. The octopus now has an infinite number of tentacles, and a flamethrower.

Against the blob

What the blob represents is truth: the truth discovered by good journalism, the truth uncovered by the legal system, the truth discovered by scientists and economists. And the populists hate the truth because their programme is built on lies. The bigots hate truth because it exposes their bigotry for the lies it is, and also simply because they are made of hate; the power-seekers hate the truth because they have built their path to power on lies; finally the cranks hate the truth because they don’t understand what truth is.

And so the populists set out to destroy the blob, and with it any notion of truth. The BBC must be eliminated because it tries to keep its reporting unbiased¹² and to uncover the truth, rather than this week’s alternative truth. Science must be discredited because the facts it uncovers may be inconvenient, and similarly economists and experts of all kind must go as they seek to point out the gaping holes in the cascade of lies the populists tell: we have, after all, had enough of experts. The legal system must be dismantled and reassembled to suit the populist agenda.

When this is done there will be no truth left: all will be lies and nothing will matter. Any facile answer to a problem can be given to anyone and anyone who points out that it is false or impossible will, if they have not already been dealt with, simply be eliminated. This is what the populists seek to achieve: the death of truth.

The revenge of the truth

Once the truth is dead, simple appealing answers to complex, unappealing problems — otherwise known as lies — are, well, simple and appealing: combined with appeals to the substantial minority of secret bigots & conspiracy theorists they’ve worked pretty well for the populists. Once the blob is eliminated who, really, will care if the answers are wrong? So the good honest people will be poorer once they have gloriously been marched into the sunlit uplands; but they won’t be much poorer and they probably won’t notice. If they do notice, well, look at those cosmopolitan elite Europeans who have made use of their elitist skills to not be so poor: it’s their fault, we should, you know, do something about them, too. So the fruit will rot in the fields for want of people to pick it but we can’t allow those elite dusky foreigners here to pick it: we never liked fruit anyway. And of course the children and grandchildren of the working folk are going to live blighted lives because we chose to treat climate change as some conspiracy of elite blob scientists and anyway doing anything about it would have hurt our investment portfolios¹³; but, well, we’ll be long gone by then and who really cares about their children? What sort of person even knows how many children he has?

And then, suddenly, not. Suddenly there’s a complex, unappealing problem which is killing people, today. Suddenly you are a faced with a problem which simply does not care about the lies you tell: it cares only about the truth. You can’t lie to something which is not sentient. Suddenly your simple, appealing answers are going to cause tens or hundreds of thousands of people to die, not over a few decades but over a few months, and people won’t have time to forget that it was your wrong answer that killed their friends and their family as they dig the mass graves. Shit just got real.

And suddenly, it turns out that the blob were, all along, not the villains they were made out to be: the boring old civil servants turn out to be good at actually administering things and understand how to deal with crises, the scientists turn out to be good at understanding what it is that is killing people and how to stop it. The BBC turn out to be good at communicating the truths people need to understand if they want to avoid dying. And experts, well, it turns out that experts turn out to be some use after all. The cranks’ tangles of mad ideas turn out to be mad. Real problems don’t get solved by a torrent of bullshit and lies: they need real solutions based on real data and real understanding. The reality-based community turn out to be useful after all. Suddenly the blob is the your best friend, at least until the crisis is over. Truth matters.

Or, well, you could just keep on piling lie on lie and hope no-one notices the piles of corpses rotting in the streets. It’s the American way.

‘Good’ news is, of course, fake news, but not ‘fake news’, which is good news. ↩
‘The aide said that guys like me were “in what we call the reality-based community”, which he defined as people who “believe that solutions emerge from your judicious study of discernible reality. [But] that’s not the way the world really works anymore”.’ Yes, really ↩
Which things exactly need to get done doesn’t matter very much so long as they have memorable names. The important thing is to do something, something important, something transformative, something that respects the will of the people to which all populists have immediate unconscious access. ↩
I have heard people described as populists because they let the banks get away with things they shouldn’t have in the run up to the financial crisis of 2007–2008, and because they gave peerages to their friends. These are, at best, very odd definitions of ‘populism’: although these activities certainly made the people concerned popular with a group of people, that group of people was ‘bankers and the friends of politicians’, who are not really ‘the people’. ↩
Although it may not be stated there will, of course, be no doubt that the filthy baby-eaters are both ‘dusky’ and have ‘watermelon smiles’, even when they are not looking like letter boxes. ↩
Not, of course, the baby-eating kind. On the other hand you never know: what do they eat at their elite dinner parties? ↩
‘If you believe you’re a citizen of the world, you’re a citizen of nowhere. You don’t understand what the very word “citizenship” means.’ — Theresa May, 2016 ↩
There is a working class, or at least there was, but does it really matter what colour your skin is if you belong to it? Is the implicit ‘black working class’ distinct in any way other than the colour of its members’ skin? Why would anyone who was not trying to create division where none really exists use the term ‘white working class’? ↩
In so far as he is capable of planning, this seems likely to be Trump’s plan. ↩
Although Trump is corrupt in a deeply spectacular way. ↩
For cranks, there are no known unknowns, only unknown unknowns. ↩
‘The corporation is either “stacked full of right-wingers” (as a Guardian columnist complained) or so lefty that even its “Sherlock” detective drama contains anti-Tory messages (as claimed by the Daily Mail). Yet polling by the Reuters Institute finds that the BBC reaches an audience that is broadly in the middle of the political spectrum. This contrasts with its main commercial rivals, ITV and Sky, whose viewers lean to the right, and with public broadcasters in other countries, whose audiences usually lean left’ — The Economist, 25th April 2020 ↩
Of course we don’t have investment portfolios, because we are simple honest people, like you. Almost everyone at Eton is the first generation of their family to have gone to school, don’t you know: their fathers were down the pit at fourteen. Of course we’re not shorting the pound: I don’t even know what that means … oh, hello, sorry I have to take this call from my, ah, friend … hello, yes, yes, 14 at 330, yes, buy Euro, yes, jolly good. ↩

Fragments: Posts tagged 'science'

The kingdom of lost hope

Too big to fail

Forbidden words

Sleepwalking into armageddon

Scenes from the end times

Eat the old

A history of the near future

Contents

Growth, the sacred cow

What growth means

If growth is not constant

Growth, resources and waste products

Why growth matters

Summary

Finiteness

The future we imagine

Some inconvenient truths

What is going on?

The future we’re going to have

Are we going to have a problem here?

The magic goes away

Energy

Energy is not the only fruit

Yes, we are going to have a problem here

Faith, fantasy and Economics

Efficiency limits: economics meets physics and loses

Escaping into a pretend world

Decoupling: economics meets itself and loses

The elephant

How are we doing?

What this looks like

The teeming billions

Falling fertility

Falling fertility, rising mortality

The consequences

As above, so below

Shorter

A history of the near future

Overshoot and collapse

A pale horse

Sexism in higher education

What I want to show

The data and how it was plotted

The pretty pictures

Computer and information sciences

Mathematics and statistics

Engineering and and engineering technologies

Physical sciences and science technologies

English language and literature/letters

Health professions and related programs

Social sciences and history

Visual and performing arts

What can you conclude

Numerical prediction

2018

2023

The way out

CV19 is not over

What might the virus do?

A mild variant: ‘flu’

No vaccine escape

Vaccine escape

Other scenarios

An endless war

What might the humans do?

Normality

Stability

Instability

What is really happening?

The virus

The humans

Collapse

The way out

The idiocy of Mars

Growth

Rescaling

Hand-waving

Energy

Mars