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For example, yesterday OECD warned Trump and Iran that they have a month to reopen Strait of Hormuz. But what will be the dire consequences, if they don’t? Not a world-wide depression, as you might think, but merely a mild economic slowdown. Well, OECD called it “major slowdown,” but if Hormuz is not opened, in 2027 it forecasts that global economic growth will be 1.8% (about half of 3.4% in 2025).

Other analysts are even more optimistic. Writing today in NYT, Christopher Smart, who was a Treasury official in the Obama administration, suggests that The Strait of Hormuz Is Getting Less Dire by the Day. Although he doesn’t use the word “resilience”, his argument is, essentially, that the global economy turned to be resilient to this particular shock. People are traveling less. (I, personally, took this risk seriously enough to cancel my planned trips this summer.) China is buying more Russian oil (and Trump has eased sanctions on it). Other Asian countries are introducing rationing.

The worst hit is the aviation industry. One company (Spirit Airlines) went bankrupt. Lufthansa canceled 20,000 flights — but mostly on local and less profitable routes.

The prevailing mood, overall, is optimistic. Stocks are higher than ever, while the worst forecasts for oil futures haven’t materialized (so far). Of course, markets have been wrong in the past, so their optimism should be taken with a grain of salt.

Are there historical precedents that can place the current crisis in perspective? The obvious one is the Oil Shocks of the 1970s, but in my previous post I went deeper into the past by remembering the Great Famine of 1315-17. Interestingly, that shock was also based on an energy deficit.

For three summers northwestern Europe found itself under an unusually heavy cloud cover. The sun’s energy couldn’t get through to the crops. Instead, they were inundated by heavy rains.

Wheat Field in Rain by Vincent van Gogh

In these cold and wet conditions crops rotted and domestic animals died from disease. If oil provides the basis of our contemporary civilization, as they say, solar-based agriculture played the same role during the pre-industrial, agrarian period, because grain provided energy that powered both humans and horses.

The magnitude of the 14th century’s energy shock was similar to the one today. Bad weather reduced crop productivity by roughly 15-20%.

About 20% of oil in the world is produced in the Gulf, but some of it is transported by land (pipelines and rail) and some is allowed out by Iran through the Straits. So, the magnitude of impact is about the same. But our civilization is more resilient than the medieval agrarian one. I don’t expect that 10% of population will perish (as happened in England and France in 1315-17). In other words, my personal (inexpert) assessment is that the world is resilient to this shock. There will be a lot of hardship, but we will muddle through.

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I believe China’s more positive trajectory is founded on cheap, imported oil and natural gas supply. The Ukraine and now Iran energy shocks produce a massive vulnerability for China. The global supply chain and trade disruptions from the Iran War will have grave effects on China’s future economic immiseration.

In the ensuing discussion, he referred me to a recent podcast he had on this issue with Nate Hagens, A World On the Precipice: The Last Oil Tanker From the Strait of Hormuz has Arrived – Now What? In this video, and in comments on my post, Berman pointed to the percolating effects of the US/Israel war on Iran on the world energy supply, resulting from the closing of the Straight of Hormuz. The developing oil shortages, in his view, will affect Europe within the next 30 days and the U.S., by July, while China will be severely affected even before the U.S.

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A helpful feature of this prediction is that it clearly sets out the time frame — July is only a couple of months in the future, and surely the severe effects will be obvious by the end of the year. However, Berman is a bit vague on how “severe” the outcome will be — is it going to be a “civilizational decline” that will affect the whole world, or only a temporary, if painful, economic and social disruption akin to the Covid Pandemic, which affected some countries more than others?

Arthur Berman is clearly very knowledgeable energy expert, while I am not. I agree with him that the world is about to experience a huge shock, much worse than the 1970s Oil Crisis.

On the other hand, I know something about societal resilience, ability to withstand, and adapt to such shocks. My colleagues and I are on record arguing that it is societal resilience that determines whether an external shock results in “collapse” or “muddling through” followed by adaptation and recovery (see our publication, CRISES AVERTED. How A Few Past Societies Found Adaptive Reforms in the Face of Structural-Demographic Crises.)

My structural-demographic analysis of today’s China suggests that it enjoys a highly resilient political system, which suggests that muddling through is much more likely than the dire consequences suggested by Berman. We now wait to the end of the year, and see which of the two theories makes a more accurate forecast.

This debate is a special application of a much broader question: what is the relative importance of external shocks against internal resilience in explaining societal collapse? Among “collapsologists” the most popular theories point to severe climate shifts. The oft-used examples are the Classic Maya (where prolonged megadroughts supposedly overwhelmed agricultural systems), the Akkadian Empire (felled by a 200-year arid event), and the Norse in Greenland (starved out by the Little Ice Age).

Main temple at Tulum in 1844

My research team studied many examples of putative collapses driven by climate shocks. Most recently, we developed an empirically-based computational model for the population boom-bust cycles in Neolithic Europe. These were quite severe with whole regions becoming depopulated. But when we estimate the effect of climate worsening on agricultural productivity, we see only 10-15% decline in crops — certainly not enough to result in a complete depopulation of a whole region. The alternative hypothesis, supported by the data and model, is that growing population density results in increasing friction, conflicts, and eventually endemic warfare between farming settlements. This creates a “landscape of fear” inducing people to move to protected areas (such as hills or sea promontories), while large areas, suitable for agriculture, but dangerous to live in, are abandoned. You can read the details in these two publications:

Explaining population booms and busts in Mid-Holocene Europe

Landscape of fear: indirect effects of conflict can account for large-scale population declines in non-state societies

Switching to a historical example, one of the worst environmentally-induced disasters, known to us, was the Great Famine of 1315-17, resulting from a series of cold and wet years. This catastrophe resulted in a loss of perhaps 10% of the population in France and England. But it didn’t precipitate societal collapse. That happened in France starting in 1337 with the start of the Hundred Years War, which, as we explain in Secular Cycles, was a classic structural-demographic (SD) crisis. The huge population collapse came later, with the arrival of the Black Death in 1348. But even that disaster explains neither the start, nor the end of the Late Medieval disintegrative period in France. By 1400 the French population was reduced to less than a half of the 1300 peak, but the internal instability went on until 1453, two generations later. Recurrent warfare created “landscapes of fear” in such provinces as Normandy, which lost three-quarters of its population — and population couldn’t increase because they were all huddling within fortified cities and towns and could hardly work the land due to marauding Englishmen and other bandits.

I can multiply these examples (we have a huge databases of historical data), but let’s cut to the chase. Yes, environmental shocks, like severe climate events or pandemics, can deliver massive blows to political systems. But their effects, collapse or adaptation, depend on internal characteristics of the system. The Black Death killed as high proportion of the population of England, as in France. Yet it didn’t result in the collapse of the English state. Instead, the king and nobility adapted — by invading France, gaining peasants there to compensate for the ones they lost in England to the plague, and by generally exporting surplus elites and thus instability across the Channel. It worked well for a century (but then the French put their house in order and kicked out the English in 1453, after which England immediately collapsed into its own disintegrative crisis, the Wars of the Roses).

I am not suggesting that we today should follow this example of “predatory” adaptation. But the general principle rules: when states are in integrative SD phases they tend to be much more resilient to external shocks, because populations are reasonably well-off, elites are relatively unified, and the state is tolerably functional. During the disintegrative phases all those positive factors are not operating, and the result is that an external shock often serves as a triggering event that brings the system down.

My prediction that China will weather the Oil Shock of 2026 reasonably well is based on the SD analysis that strongly suggests that PRC is still well in its integrative phase. This is reflected, among other things, in their pragmatic approach to the global geopolitics. In particular, China is allied with Russia and Iran. China has been already profiting from the Ukraine war by importing cheap Russian oil and gas and that connection is secure.

The big question is what will happen to the oil China imports from the Persian Gulf countries. A possible scenario, which seems increasingly likely, is that Iran will be able to maintain its control of Hormuz, and will allow tankers headed to China to pass through. Whether the US Navy would be able to stop this traffic (or even attempt it), we’ll see in the months ahead. And don’t forget that over the months preceding the conflict, China was aggressively buying, and storing, surplus oil.

Of the other geopolitical players, Russia, of course, will be the main winner. In contrast, the European Union will probably be one of the worst losers. Both Germany and France, the core EU states, are already in a SD crisis. Their states are increasingly dysfunctional. Europe is fighting a proxy war with Russia, and they took the American side in the war against Iran. Given a choice where to send its fossil fuels, Russia and Iran are more likely to favor China than Europe.

The energy policy of Germany provides an excellent illustration of their political elites’ dysfunction. They enter the conflict with Russia, lose the supply of Russian cheap gas, and what do they do next? Abolish the remnants of their nuclear power. Then they restart coal mines (burning coals pumps much more CO2 into atmosphere per unit of energy, compared to methane). All such actions do not inspire confidence in their ability to deal with the Oil Shock.

Finally, I’ve been focusing on oil in this post, but that may not even be the most traumatic shock to the world system. In the view of many analysts (which I share) the loss of fertilizer supplies is much more serious. After all, people can drive less and stay home for summer vacation. But eating less means starvation, which I don’t recommend.

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One comment asked, but what about falling birth rates? China’s total fertility rate of around 1.0 is one of the lowest in the world. I was planning to address this issue in the context of a next post, on elite overproduction, but we might as well engage with this issue now.

My main point is that what destabilizes societies is not low birth rates, but quite the opposite. When birth rates peak, 20 years later a country experiences a “youth bulge” — an unusually large cohort of 20-somethings. A youth bulge is an important driver of instability in structural-demographic theory (and is well-accepted as such generally by the scholars of revolutions and civil wars).

One striking example that I discuss in End Times (Chapter 5) is the 2011 Egyptian Revolution, which was driven, to a large degree, by a 60% increase of people in their 20s between 1995 and 2010. Similar demographic dynamics contributed, more generally, to other countries affected by the “Arab Spring.” A structural-demographic analysis of these cases is in an article by our Russian cliodynamic colleagues, A Trap at the Escape from the Trap?

What China is currently experiencing is the opposite development, a baby bust leading to a “negative youth bulge.” As can be seen in the graphic below, for the next 20 years (and probably more, as the trend to low birth rates is not going to be reversed soon), the cohort of 20-somethings will be shrinking:

Population pyramid China in 2026

There will simply not be enough angry young Chinese to fill up Tiananmen Square.

Tiananmen Square, Beijing

More importantly, a shrinking youth cohort means that young people experience less competition from their peers — getting into a top university, securing a good first job, and so on. As a result, they are much more likely to be busy with that, rather than wanting to rebel.

Last, but not least, low TFRs eventually translate into increased wealth of the small cohorts. For TFR = 1, most families will have one child. When two young people form a new household, they will have two sets of loving parents helping them to get set up in life and, eventually, bequeathing their wealth to them. Simply — mechanically — as a result of shrinking population average wealth per individual will double with every generation.

From the point of view of individuals, then, falling birth rates have a substantial silver lining. Now, it is understood that this dynamic also creates losers. One is the state, which wishes to have a growing population that would pay more taxes, contribute to pension systems, and provide recruits for the military. Another is the economic elites who prefer oversupply of labor, so that they can keep wages down. These are all interesting topics, which I am happy to discuss in comments and future posts, but for now I’ll hit the “send” button.

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The text below is based on a massive dataset that was collected by researchers in my team, most notably Samantha Holder (whose position is entirely supported by paid subscribers — many thanks to them! New readers: see About).

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But quantitative models of attritional warfare say otherwise: Russia continues to dominate the battlefield and the eventual outcome, barring a Black Swan event, is inevitable defeat of Ukraine. My readers may know that three years ago I developed a an Attritional Warfare Model, AWM (based on the Lanchester equations) for forecasting this war’s outcome (see links at the end of this post).

Ten realizations generated by the AWM for the dynamics of Ukrainian casualties. The blue band represents the estimated end point (the level of casualties when the war becomes unsustainable). Source

More recently a similar conclusion was reached by Warwick Powell (see Estimating Trajectories in Attritional Warfare: The Russia-Ukrainian Conflict Through a Quantitative Lens). Powell used a similar model, with the most important difference being the choice of the end point. My model assumes that the war ends when the level of casualties, as a percentage of population, exceeds a certain threshold, which I estimated via a sample of past attritional wars from the Correlates of War data (see the SocArxiv preprint below for details).

Powell, alternatively, assumes that the beginning of the end for Ukraine will happen when its army size declines below a certain threshold (0.65-0.73 of the initial size of 550,000). From that point, Ukrainian losses will accelerate and the full collapse will happen once the army size is below 50% of the prior peak. Powell’s model predicts that the tipping point will happen in July-September (updated on May 14).

Naturally, this is only a model-based forecast, not a prophesy. There is a lot of uncertainty about the estimates of various parameters. Furthermore, the threshold at which collapse occurs is only imprecisely estimated. For example, it’s not clear whether the threshold of 0.65-0.73 above which the Ukrainian force can maintain its operational integrity still applies on a battlefield heavily dominated by drones. For example, a smaller force size may be sufficient to continue defending positions given an abundant supply of drones.

My model also doesn’t incorporate any possible effects of the shift to the drone warfare — simply because it hadn’t happen when I published its predictions. Determining how this technological shift affects the AWM’s (Attritional Warfare Model) predictions will have to wait until the post-mortem after the war is over and when estimates would become much more precise. However, I tried a few preliminary explorations and they suggest that the drone effect on the war trajectory is not quite as huge as might be imagined. What’s important is the casualty rate inflicted on the Ukrainian army by the Russians, and it doesn’t matter whether it’s a result of artillery, air bombing, or drones.

Is Ukraine reaching its recruitment limit? This is the key factor in both our models. There are some indications that this is the case. A week ago, Branko Marcetic (using Ukrainian sources) provided some relevant numbers in a Responsible Statecraft article, Ukraine’s conscription crisis is getting increasingly bloody; While outside voices insist the war can still be won on the battlefield, young men in the country are violently resisting recruiters to stay out of it. Here are some numbers supporting this conclusion.

The number of complaints over possible violations committed by enlistment officers, received by Ukraine’s Human Rights Ombudsman, Dmytro Lubinets:

2022 — 18
2023 — 514
2024 — 3312
2025 — 6127

Source: Kyiv Independent

The number of violent attacks against enlistment officers shows the same trend: from 5 in 2022 to 117 in just the first four months of this year.

This resistance translates into lower enlistment numbers. Warwick estimates that (as of May 14) that on the Ukrainian side net daily loss rate is 900–1,700 units (as best as I can determine, by “net” he means the difference between casualty and recruitment rates). And in his estimation the current Ukrainian effective force has already declined to 320,000–380,000 (from the peak of 550,000). In other words, according to his calculations, the Ukrainian army has already entered the downward spiral.

When will these pressures reach the breaking point? Powell thinks by September of this year. But I would be much more cautious, because the nature of such dynamical processes resists precise predictions. Think of a steam engine with broken regulator valve. The pressure grows, but the timing of explosion cannot be predicted, because it depends on the presence or absence of internal flaws in the engine casing. Same with earthquakes: we understand perfectly the physics of them, but it can be years or even decades before they actually strike.

Ukraine is like a steam engine with internal pressures building up. But it is impossible to make an accurate forecast on when things blow up.

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Here are the links to my blog posts on the Ukraine-Russia AWM:

What Osipov and Lanchester Tell Us about the War in Ukraine

War in Ukraine II: The Model

War in Ukraine III: Projections

War in Ukraine VI: Adding Economic Power to the Attrition Model

War in Ukraine V: Alternative Hypotheses

The SocArxiv Preprint:

Empirically Testing Predictions of an Attrition Warfare Model for the War in Ukraine

You can experiment with the model here:

Attrition Warfare Model: Projections for the War in Ukraine

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In their book The Highest Exam, Jia and Li, following standard sociological literature, identify three such factors: merit, connections, and luck. The importance of luck is often underestimated. It’s natural for successful people to claim (and even believe) that their achievements are entirely due to their brilliance and hard work. (On this topic, I recommend reading Success and Luck: Good Fortune and the Myth of Meritocracy by Robert H. Frank.) But it’s hard to quantify the effect of luck, and in statistical analyses we often have to assign the unexplained, residual variance to this factor.

Merit, on the other hand is, in principle, measurable. Different professions use a variety of metrics to rank people by merit. In academia, which I know well, the department head usually has a point system of assigning merit to each faculty, based on their publications (and how many citations they get), getting research proposals funded, serving on committees, and such. In my department this system was known to the faculty and generally agreed to be fair.

This leaves us connections, which is an important, but not the only component of, more generally, social power. After all, there are four sources of social power. One can advance up the hierarchy by means of coercion, economic power, and persuasion (threatening or intimidating people, paying them off, or talking them over to one’s side). Still, the political or relational form of power — being embedded in a power network — is, of course, most important. The structures and dynamics of social power is a key topic in cliodynamics. I will be talking more in this blog about its role in elite dynamics and overproduction, but for now let’s not worry about this broader context.

One striking feature of the Chinese education system is that merit, or performance, weighs so heavy in university admission. Yes, luck is always present — getting questions on the exam with which one is familiar, avoiding mistakes, etc. And connections (plus wealth) are important in getting into the right schools and securing tutors. But these influences all percolate through the grade you get on the Gaokao.

Having grown up in the Soviet Union and then living in the United States, I can attest that each country has unique features, distinguishing them from China. In the Soviet Union, there were no private schools, all schools were public. But they were not all equal. There was a number of elite schools, with different specializations. For example, after the 7th grade I transferred from the regular neighborhood school to School No. 2, which specialized in math and sciences, and was considered to be the top choice for such schools in Moscow.

For me it was relatively easy. Both my parents had university degrees, and my father was a scientist. I grew up wanting to be a scientist and to use mathematics in my future work. As a result, I simply learned material by voracious reading. I also went to the evening class at the school, which was taught by senior students. I passed the entrance exam without any problems.

My wife went to a school that specialized in languages — one of the top three-four such schools in Moscow. Graduating from an elite school was a huge boost for both of us, helping to get us into the Moscow State University (MSU), the top university in the USSR, as well as contemporary Russia. I studied Biology, my wife was in the Faculty (meaning Division or School) of Philology.

MSU Main Building

Our parents hired tutors for both of us (this is similar to the Chinese case). This was quite a stress on their finances. Thus, wealth, since we didn’t have it, gave us no advantage over other aspirants (of course, we profited from human capital, having a practicing scientist as a parent). Neither did connections.

In fact, in my case connections had, on balance, a negative effect, because by the time I was finishing school and studying to enter the university, my father, who was a dissident, was already fired from his position and was regularly interrogated by the KGB. It was pure luck that this didn’t prevent me getting into the university (unlike many, I readily acknowledge the role of luck in my life and career). Additionally, unlike everybody else, I didn’t join the Communist Youth League (Komsomol), which was thought to be a requirement for the university. As a result, I was the only non-Komsomol member in the Faculty of Biology (and perhaps the whole university).

But my route to the university was not straightforward. The competition was brutal. On my first attempt, I failed to score high enough on the entrance exams. In the USSR there was no general exam, like Gaokao. Instead, in those times your score was the sum of five components: the average grade from the school final exams, plus grades from four University entrance exams.

Having failed entering the university, I had a second chance to try for an “institute” (at that time only one tertiary school in a city could be designated as university, the rest of them were called institutes of this or that). We decided to try for the Second Medical Institute, the Division of Biomathematics. Only later we learned that this was a terrible mistake, because all student places in this division were assigned when children were still in their fifth grade. Children were ranked by the positions of their parents in the Soviet hierarchy, and someone like me, who had an “anti-ranking” had no chance. I actually still don’t understand why this division was so popular among the Soviet elites.

The very first exam was Math, which weirdly took the oral, rather than the usual written form. This mystery was soon dispelled, when the examiners began posing really tough questions to me, got me flustered (I was just a kid), and soon enough they failed me and that was the end of it. Keep in mind that I was always friends with math and I graduated from a school where math was taught at the college level. This is how I got my first personal encounter with the importance of connections, or “blat” as it was known then (and still is now).

To my Soviet friends, my experience in trying to enter this college, where blat ruled all, was an indication of extreme naivete by my parents, who should have known better. Partly this is true, but also partly this was a result of the kind of person my father was. He was a man of honor and integrity (which is how he ended up dissident in the Soviet Union), and actually, now that I am thinking about it in retrospect, a bit of a saint. (To be clear, I don’t blame him at all for this fiasco and have no regrets that the fates assigned me such a wonderful father.)

Next year I managed to score high enough and was admitted to the MSU Faculty of Biology. There was a lot of luck involved — I didn’t get drafted and the KGB clearly never issued explicit instructions to prevent me from being accepted (in which case I would be toast). Somehow I slipped in.

Overall, life in the late Soviet Union wasn’t that bad (despite the “evil empire” propaganda). Connections — blat — were all important. Some institutes were completely closed to anybody “unconnected.” The one I failed to get in was one, but most such “forbidden” institutes were those that trained for professions in Foreign Affairs, which were extremely desirable for the children of the Soviet elites. At the same time, it was quite possible for an unconnected person to get into the Number 1 University (MSU), as happened to my wife and me (eventually). Growing up in Moscow in a scientific family, of course, was a huge advantage.

There are some similarities with the situation in China, in particular, the importance of living in the capital. But China doesn’t have the same system of specialized elite schools like the Soviet Union had. There are functional equivalents, such as “key schools”; as well, many regular high schools run specialized classes. But in China wealth plays a much greater role than in the late Soviet Union. There is a large “private ecosystem” that coaches students for exams and Olympiads, if parents can afford to pay (quite a lot). On the other hand, no amount of wealth or connections can guarantee a high grade on the Gaokao, whereas in the late Soviet Union entrance exams were much easier to manipulate (as I experienced personally).

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Notes on the margin: I am currently in Morocco, attending the Cultural Evolution Conference. Next week I will continue with the Stairway to Heaven series, where we will look into how merit and connections influence careers of the Chinese mandarins.

The first step in a structural-demographic (SD) analysis of any country is to understand how its ruling class reproduces itself. Where do aspirants for power positions come from and how do they strive to travel up “the stairway to heaven” — the road to supreme power?

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As readers I am sure noticed, I post regularly a substantive post on the weekend (preferably on Saturday, but sometimes it gets delayed to Sunday). In the middle of the week I usually publish a less substantive, shorter post (such as this one). So far, I posted 87 articles, which works out to 1.4 article/week. And this is on top of hundreds of posts on my previous blog, which you can browse through in this Blog Archive. It’s amazing to see how, if you work at a steady rate, the body of your writings gradually becomes increasingly more substantial.

Incidentally, while I use A.I. during the research phase, I don’t use it for drafting posts. First, I want this blog to convey my unique style and perspective, warts and all. It’s usually quite obvious when authors rely on A.I. too much — from the bland quality of the resulting texts.

Second, using A.I. for writing actually doesn’t save time for me. If I know what I want to say, I can actually type faster than (or, at least, as fast as) I speak. Incidentally, I wasn’t like that at the beginning of my career. Then, I would often sit in front of the typewriter (yes, I remember the times before computers and word processors) for hours without being able to squeeze out a single sentence. For those of you who suffer from this problem (“writer’s block”), take heart — if you persist, you will overcome it. Perspiration is 90 percent of success.

Which of my posts have had the most impact? Substack provides a variety of statistics, for example, “open rate” and “engagement rate.” But these do not seem to be terribly informative, because there is not much variation and it’s not clear what it reflects. Much more useful are numbers of new subscribers, generated by a post. By far the champion in generating paid subscribers is The Structural-Demographic State of America. It is followed by other installments in this series, by the posts on Japan, and my geopolitical posts (e.g., Sick Man Europe). This makes sense to me. These topics are where I can bring non-trivial insights from Cliodynamics to bear on tectonic shifts affecting us all.

On the other hand, my post on lemmings was largely ignored… Oh well, humans are more interesting than rodents; I should have guessed 🙂.

The absolute record in attracting free subscribers is, surprisingly enough, The French Wars of Religion I. This is also heartening — it appears that many readers agree with me that history matters.

Despite a lot of variability in success of different posts (measured in subscriptions), overall I see a steady rate of increase in the numbers of both free and paid subscribers. Both kinds are important to me, which is why I will continue publishing some substantive posts without paywall.

But of course, paid subscribers are most valued. After all, one of the chief goals of this Substack is to generate income to support researchers in my project (see About). I am happy to report that, thanks to the generous contributions from paid and founding subscribers, I now pay wages of two part-time researchers, a historian/anthropologist (whose job is checking the output from LLMs) and a data scientist (who helps with the Seshat DB maintenance and works on expanding functionality). If the current trends continue, I will be able to hire another researcher in Summer.

Many thanks to all of you who are supporting Cliodynamic research!

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But wait, you might ask, can we really trust these sources? After all, recorded history is affected by a variety of biases. Furthermore, subjective decisions must often be made when translating textual evidence into numbers reflecting the extent and intensity of an instability event.

Many doubt that History can be transformed into a quantitative, analytical science, including famous philosophers of science, such as Karl Popper. When I started on the research path that eventually led to Cliodynamics, I also didn’t realize the potential of quantitative history.

It turns out that the solution is to look for, and find, “proxy variables,” quantitative indicators that are highly correlated with the variable of interest. One unexpected place to find such data, indicating the fluctuations of political instability, turned out to be coin hoards. Not just a stash of precious metal ingots, but one containing coins — because coins are datable.

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A bit of prehistory. After publishing my prediction for the coming wave of instability in the US in 2010 (see the inaugural post), I periodically revisited it and every time I was impressed by how closely the trajectory to date had followed the predicted path. This post addresses at the state of elite overproduction in America as I saw it in 2013 — well before Trump and many years before our current crisis. It’s remarkable to me how well this post aged — but you’ll be the judge.

Below the Surface: the Structural-Demographic Roots of the Current Political Crisis

The latest news from Washington is that there is a compromise in the making that would allow the government to raise the debt limit. But even if an eleventh hour agreement is reached tonight, it is clear to everybody that there will be more budget battles to fight in the months to come. As I wrote in a previous blog, our political elites are deeply divided among themselves and unable to reach a lasting compromise. The likelihood of a catastrophic default, sooner or later, is still very high.

The proximate reason for the current political crisis is an unbridgeable ideological divide. Each side (especially the Tea Party and the Democrats, with traditional Republicans caught in between) has drawn a line in the sand that it is unwilling to cross.

Source

The ideological conflict has been “sacralized,” to use the terminology of Scott Atran. Killing ‘Obamacare’ has become a sacred value for the Tea Partiers, while preserving it is an equally sacred value for Obama and the Democrats. As Scott’s research shows, once a dividing issue has been sacralized, a compromise is very difficult and sometimes impossible.

But why have ideological differences been allowed to become so deep and so resistant to compromise? After all, the same basic tension between conservatives and liberals has been a constant feature of the American political landscape for at least a century, if not more. Yet during the 1950s and 1960s ideological differences were not allowed to get in the way of running the government. What has changed?

One observation that I and others (for example, McCarty, Poole, and Rosenthal) have noted is that political fragmentation waned and waxed together with economic inequality. Some popular press accounts of my views treat inequality as the cause of political instability (for example, in an otherwise excellent commentary by Dave Ross on CBS). However, this is not quite right. Inequality is a symptom, not the root cause. Yes, inequality harms cooperation and breeds conflict, but there is a deeper, more fundamental process that drives up both inequality and instability. This factor is intraelite competition, resulting from elite overproduction – too many aspirants for too few power positions.

Consider two facts. First, between 1983 and 2007 the number of American ‘decamillionaires’ (individuals with personal wealth exceeding $10 million) increased from 60 thousand to 460 thousand (that’s a five-fold increase in proportion to the total population!). Many of these newly wealthy people turned their attention to the political arena – either running as candidates themselves, or supporting others.

Second, between 1975 and 2011 the number of lawyers tripled from 400 thousand to 1.2 million (more than doubling in proportion to population). The law degree has always been and still remains the main route to political office in the United States. So the number of potential candidates for political offices increased manifold.

But the supply of offices themselves stayed flat. There are still 100 US senators and 435 US representatives today – same numbers as in 1970. When there are many more candidates vying for a limited number of positions, competition heats up, efforts (and methods) to defeat the opposition become increasingly desperate, and ‘us-versus-them’ mentality sets in.

Note that ‘us-versus-them’ does not need to refer just to Republicans-versus-Democrats. It might as well be true conservatives versus RINOs (Republicans In Name Only).

I am not saying that ideology is used purely cynically by politicians trying to get ahead in the game. For some this may be true, but the majority is clearly quite sincere in their convictions. The question is why politicians today are driven to extreme ideological positions and have lost their ability to compromise.

(As an aside, this is a non-political blog and usually I avoid singling out one or the other political party as being ‘right,’ but in this case it is clear to any disinterested observer that the radical, extremist element in the current crisis is largely, if not entirely, the far political right. This is supported by the quantitative research of McCarty, Poole, and Rosenthal, who showed that since the 1960s the Democrats remained pretty much in the same position on the liberal-conservative spectrum, while the Republicans have moved far to the right.)

Intraelite competition also helps us to understand some of the particular aspects of the current political situation. As I said above, what’s different today is that there are huge numbers of potential donors and an equally large pool of potential candidates – desperate law school graduates earning $50K per year, which is not enough to repay their Law School loans (I will have to devote another blog to tracing out how this stratum of desperate elite aspirants emerged). Such conditions make it very easy for unscrupulous political organizations, focused solely on their narrow agendas, to threaten sitting members of the Congress that they would run a well-funded challenger against any wavering congressman in the primaries, should they ‘cave’. Do I need name any names?

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For those readers who are interested in the sources of the data I use in this blog, take a look at the (rough) draft of my book on structural-demographic analysis of American history, posted on my web site. The book manuscript also explains other aspects of structural-demographic dynamics, such as why the number of elite aspirants exploded between 1980 and now.

Back to the present. The book manuscript that I referred to in the last paragraph was published in 2016 as Ages of Discord.

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When a person dies, it is sometimes necessary to perform an autopsy to determine the cause of death. Even more importantly, dissection of deceased people’s bodies was incredibly important in advancing the state of medical knowledge.

The Anatomy Lesson — Rembrandt Source

The same logic applies to the analysis of past states.

There is no question that human societies have been dramatically transformed during the past two or three centuries. But this change mostly related to science, technology, and economy. Politics — the structure and dynamics of social power — has not changed that much. Ever since the first states appeared c.5,000 years ago, they’ve been subject to the same general principles. In the previous post, Cliodynamics of End Times I: Complex Dynamics of Political Instability, I show that the dynamical pattern of political instability in the US over the past 230 years is eerily similar to the pattern we observe in past states and empires, such as Ancient Rome or Medieval France: secular cycles with shorter father-sons oscillations, superimposed on top.

Furthermore, the drivers of these cycles, resulting in “End Times” have not changed that much. Take population immiseration and discontent. As I explain in detail in End Times, the fundamental cause of increasing immiseration is the operation of the wealth pump, which takes from the common people and gives to the elites. The dynamics of relative wages (typical wage divided by per capita GDP) in America went through two clear secular cycles, despite its economy being completely transformed by the Industrial Revolution at the same time.

Graphics by the author

And declines in relative wages, sustained over several decades, were a contributing factor to outbreaks of political violence; Civil War in the nineteenth century and our own revolution today.

Same story with elite overproduction. Political scientists make a big deal about the distinction between democracies and autocracies (and “anocracies”). But all such regimes are vulnerable to pressures of excessive elite overproduction, which brings in its wake intraelite competition and conflict. An explosion of the numbers of uber-wealthy preceded both the American Civil War and our own Age of Discord (for details, see Ages of Discord). And the shrinking of this class (the Great Compression) was associated with increasing internal peace, social order, and cooperation.

In any case, today America is not a democracy, but a plutocracy (details in End Times, Chapter 6). And it’s not just America. In France and Germany the governing elites routinely go against the wishes of the electorate.

The third structural-demographic driver (in addition to immiseration and elite overproduction), state fiscal health — or, rather, lack of it — also works in similar ways as it did under the Roman Empire.

Again, I am not denying that our contemporary societies are very different from those in the Ancient period or even Early Modern era. But the general structural-demographic principles operate in very similar ways. After all, Jupiter is big and made of gas, Saturn has rings, Venus is very hot. Yet their trajectories around the Sun obey identical forces of Newtonian Mechanics. States vary in many ways among themselves, and some of those differences need to be taken into account when we translate general theory into country-specific models. But our theory gives us not just general principles, but also understanding which differences are important (and need to be reflected in the specific model), and which are not. And all this understanding is a result of dissecting the corpses of defunct states and empires.

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One thing that didn’t go according to the plan was that I didn’t give my talk Cliodynamics of End Times, in which I survey secular cycles of linked political instability and population well-being/immiseration in past societies. Everybody is fixated on today and tomorrow.

It’s understandable — we live in calamitous times. But there is much to learn from past societies, especially, how we could safely navigate through the troubled waters ahead. So, I decided to convert this talk into a series of posts on this Substack.

Notes on the margin: as this series relies heavily on empirical results generated by researchers in my group, I am putting it behind paywall. Remember that every dollar generously contributed by paid subscribers will go to supporting these researchers.

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I don’t know any systematic large-n research that could answer this question. There is a database of assassinations or attempted assassinations of political leaders (see Hit or Miss? The Effect of Assassinations on Institutions and War), but it focuses on internal actors attempting to overthrow the head of state, whereas we are interested in how successful decapitation attempts are in inter-state war. Another set of studies looks at the effect of taking out a leader of an insurgency (see Does Decapitation Work? Assessing the Effectiveness of Leadership Targeting in Counterinsurgency Campaigns). But this relates to the effects on non-state actors. The problem for this kind of research is that since the establishment of the Westphalian system in 1648, targeting foreign leaders for assassination during wars became strongly discouraged. We simply don’t have enough numbers to do statistics with.

What I am going to do in this post is much more informal. I’ll go through a number of successful decapitation attempts from history that I know about, and consider what they might tell us about the possible outcomes for the US/Israel-Iran war.

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The migration issue is somewhat complicated, and is hard to explain properly in the limited space, but here are the main points. During peak summers, lemmings are well fed. The meager primary production of the tundra is heavily concentrated in the few summer weeks, and during this period there is more than enough to feed lemmings. The problems with food start later and culminate in the post-peak winter - as you showed in your 2001 Ecology paper with George Batzli (Availability of Food and the Population Dynamics of Arvicoline Rodents).

A lemming swimming across Lake Iešjávri on an August night in 2007.

Evolution has taught Norwegian lemmings to anticipate this bleak future awaiting them, if they are born in the peak summer. The young ones, thus, start moving in early August, when they still can look forward for several weeks of favorable conditions. They hardly know where to go, but they’ve figured out that keeping a fixed direction is the best bet (as it gets them out from the lemming outbreak region in a fastest way). So they choose direction at random and try to keep it — and are remarkably good at doing so. They move as individuals, never as groups. When they face the mighty Iešjávri Lake (8 by 10 km of open water) they perceive it as an obstacle to be respected, but not to be shunned.

Iešjávri in Spring (late June), seen from a mountain 10 km away. Numbers refer to islands

They become “dammed up” on big near-shore islands, to which they swim, but then realize that this doesn’t solve the problem of impending famine. They then wait for a calm night and then take their chance. And if they stumble on a small island far in the lake, they rest and feed before continuing. The behavior of lemmings is thus a careful balance between wanderlust and prudence.

Lemmings cannot not see the small islands before they are close, as these islands are low and merge with the opposite shoreline, especially when seen from vantage point of a lemming.

A small island, seen from a 10 m high ridge on a bigger one.

Nor are these islands especially attractive. They are pieces of glacifluvial sand-gravel ridges, covered by lichen-dwarf shrub heath, with just some lush patches, where the land is close to the water table.

A typical island habitat: lichen, dwarf birch heaths. Not optimal for lemmings who prefer grasses and mosses.

The point of the migration is to have a chance to arrive at a different kind of an island: at a big marine island or a tundra island within inland mountains. Such islands offer good lemming habitats, especially if local lemmings have gone extinct (from a previous outbreak and collapse). Of course, it would also be fine for a lemming to move within the continuous tundra and reach far enough to get out of the outbreak area, but that would be quite a feat, as this area can be 300 to 600 km wide.

During the 2011 outbreak, we found a big marine island with no lemmings just 70 km from our main study area and just 3 km offshore. And these islands are tall and easily visible - so are the inland mountains, too. The lemming populations thus founded are normally short-lived. After 4-5 years, they produce a local outbreak and usually go completely extinct. However, during the outbreaks they produce migrants, and some probably reach back to the main tundra-mountain formation. Lemming populations thus come and go — while the lemming metapopulation survives. So do the genes that tell lemmings when it is time to go and to keep moving.

Midnight sun creeping behind Sievju (Seiland) — a big marine island where we did not find any lemmings during the 2011 outbreak.

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Notes on the margin: I am currently in Salt Lake City where I will be giving a talk tomorrow. For my itinerary and talks, see

It is well-known that mechanisms of stellar evolution, determining how stars change with time, result in a very strong statistical pattern, known as the “main sequence.”

The Hertzsprung Russell Diagram by Richard Powell

Around 90% of stars in the universe are in the main sequence, but there are important alternative routes and variations, most notably Giants and White Dwarfs.

What was surprising to me is that our recent analysis of more than a thousand of societies in the Seshat Databank revealed that cultural macroevolution has its own main sequence!

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Skeleton of a woman in Yorkshire Museum Source

Human bones are an incredible source of quantitative data, because they can last for centuries and millennia under the right conditions. And when they are unearthed by archaeologists, they can tell us a lot about people they came from: what was their health and wellbeing, how likely they were to be injured or killed, and more recently what was their ancestry. Today I will focus on one particular indicator of biological well-being: stature or average population height (and in future posts I plan to deal with other insights that skeletal data can yield).

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When people hear about lemmings, they immediately think of the myth that lemmings mindlessly follow each other off cliffs—“mass suicide by herd instinct.” But anybody who has any understanding of evolution would immediately realize that this can’t be right. Any kind of mass suicide instinct would be quickly selected out, and only those mutants lacking it (and there are always mutants) would survive and repopulate the world.

So, what is the actual answer? My story begins with Charles Elton, who is deservedly called the father of animal ecology.

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Today I am not asking whether the structural-demographic drivers of crisis have already been reversed — that could not happen in a single year (and as my blog series on the Structural-Demographic State of America documented, most long-term trends have continued developing in negative directions). Instead, the purpose of this post is more exploratory: to look at the major policy changes of the administration and consider what their probable consequences might be.

Rather than presenting my own judgments, I outline below two contrasting interpretations for each policy area: one likely to be offered by supporters of the administration and one by its critics. Full disclosure: because, as a human, I have my own biases, I asked ChatGPT for help with the text below. Of course, ChatGPT will have its own biases! Thus, I welcome additional perspectives, as long as the discussion remains civil and factual. Let’s see whether anything resembling truth would emerge from these arguments.

Immigration restriction and deportation push

Effect on popular immiseration

• Pro-Trump interpretation: Restricting immigration reduces labor-market competition for lower-skilled native workers, strengthening their bargaining power and potentially raising wages.

• Anti-Trump interpretation: Deportations and immigration restrictions disrupt industries dependent on immigrant labor (construction, agriculture, services), raising prices and harming overall economic dynamism, which may ultimately worsen living standards.

Effect on elite overproduction

• Pro-Trump interpretation: Tighter immigration limits competition from highly skilled foreign workers in sectors such as technology and academia, easing pressure on domestically trained credential-holders.

• Anti-Trump interpretation: Restrictions reduce the inflow of highly productive talent, slowing innovation and shrinking the number of elite-level opportunities in advanced sectors.

Effect on the state

• Pro-Trump interpretation: Strict enforcement demonstrates state capacity and the rule of law, restoring confidence that the government can control national borders.

• Anti-Trump interpretation: Highly visible deportation campaigns and aggressive enforcement by U.S. Immigration and Customs Enforcement risk damaging the legitimacy of the state among immigrant communities and their allies.

Tariffs, protectionism, and reshoring

Effect on popular immiseration

• Pro-Trump interpretation: Tariffs and industrial policy can revive domestic manufacturing, strengthening wages and employment in industrial regions.

• Anti-Trump interpretation: Protectionism raises import prices and contributes to inflation, reducing purchasing power for consumers.

Effect on elites

• Pro-Trump interpretation: Industrial policy may shift elite opportunities away from finance and globalized sectors toward domestic manufacturing, potentially diversifying elite career paths.

• Anti-Trump interpretation: Tariffs create rent-seeking opportunities for politically connected firms and intensify competition among business elites seeking protection or subsidies.

Effect on the state

• Pro-Trump interpretation: Tariffs increase government revenue and strengthen national economic sovereignty.

• Anti-Trump interpretation: Inconsistent application of tariffs and perceived policy volatility weaken credibility and complicate economic planning for businesses.

Extension of tax cuts and pro-business fiscal policy

Effect on popular immiseration

• Pro-Trump interpretation: Lower taxes stimulate investment and economic growth, which can increase employment and wages across the economy.

• Anti-Trump interpretation: Tax cuts disproportionately benefit high-income groups, increasing inequality and doing little to improve the economic position of ordinary workers.

Effect on elites

• Pro-Trump interpretation: Lower taxes encourage entrepreneurship and business formation, expanding opportunities for upward mobility within the elite.

• Anti-Trump interpretation: Tax policy accelerates the accumulation of wealth among already affluent groups, intensifying elite overproduction and competition within the upper strata.

Effect on the state

• Pro-Trump interpretation: Economic growth generated by tax cuts may broaden the tax base and sustain fiscal capacity over time.

• Anti-Trump interpretation: Reduced tax revenue contributes to persistent deficits and growing national debt, weakening the long-term fiscal position of the state.

Reorganization of the federal bureaucracy

Effect on popular immiseration

• Pro-Trump interpretation: Streamlining federal agencies and reducing bureaucracy may lower regulatory burdens and improve economic efficiency, indirectly benefiting workers.

• Anti-Trump interpretation: Administrative disruption and cuts to public programs may weaken social supports and public services relied upon by many households.

Effect on elites

• Pro-Trump interpretation: Challenging entrenched bureaucratic networks opens opportunities for new elites outside the traditional Washington policy establishment.

• Anti-Trump interpretation: Displacement of established officials can create a growing pool of disaffected counter-elites who mobilize against the administration.

Effect on the state

• Pro-Trump interpretation: Institutional reform may reduce bureaucratic inertia and increase accountability within government agencies.

• Anti-Trump interpretation: Rapid restructuring risks undermining administrative expertise and reducing the state’s effective governing capacity.

Aggressive foreign policy and wars

Effect on popular immiseration

• Pro-Trump interpretation: Demonstrating military strength can deter adversaries and secure global economic conditions favorable to American prosperity.

• Anti-Trump interpretation: Military conflicts can trigger energy price shocks and economic disruptions that lower living standards.

Effect on elites

• Pro-Trump interpretation: Foreign policy activism may unite national elites around strategic competition and national security priorities.

• Anti-Trump interpretation: Military interventions deepen polarization among elites, including divisions within the administration’s own political coalition.

Effect on the state

• Pro-Trump interpretation: Military engagement reinforces geopolitical influence and the credibility of national power.

• Anti-Trump interpretation: Sustained military spending and controversial conflicts can strain public finances and fragment state legitimacy across social groups.

As a final note, there is a pretty good chance that the discussion will deteriorate in a slugfest typical of social media. So, I am completely prepared to shut it down if it happens.

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This assumption turned out to be spectacularly wrong. Two posts in the series The Structural-Demographic State of America (Well-being/Immiseration and Health and Health Insurance) discussed the recent trends in American life expectancy (not good). Today I will talk about the other proxy for biological well-being—average population stature.

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