Nine years ago I made one of the most consequential decisions of my life—I switched to the so-called Paleo Diet (“paleo” is a bit misleading, as I explain in the post). Had I not done so, I would certainly have contributed to the rising obesity statistics for the United States. Within six months of switching to Paleo diet I lost 20 pounds before equilibrating at my current weight. But weight is actually the least important thing. Much more important was a dramatic improvement of my general health I experienced in the months since switching. I feel better today than ten years ago, despite being (obviously enough) ten years older.

The major change was eating much higher on the food chain. The only way to get protein on a purely plant-based diet is to eat grains and pulses, which means wheat and beans. But those are precisely the foods elimination of which resulted in my health improvement. I sometimes unknowingly consume small amounts of wheat, when a restaurant chef uses flour for the sauce (despite explicit entreaties not do so). The next day I know that I had been poisoned.

The other thing is that it’s not just protein deficiency. Your body doesn’t need that much protein. The biggest problem with purely plant diet is that you don’t get enough healthful fat. Instead you end up poisoning yourself with seed oils. See Fatty Foods Are Good for Your Health and More Reasons to Eat Fat

This is why I watch with increasing alarm the current trend to “cancel” meat. Last year the town of Cambridge, home of one of two best universities in UK, banned meat [correction: as several readers pointed out it was not the town of Cambridge, but the University removing beef and lamb from its cafeterias; my apologies for the incorrect statement!]. So Cambridge is now off my list of places to go to (fortunately, I visited it years ago, when it was still safe for carnivores). I am very worried that the veganism tide will continue spreading, leaving us carnivores on reservations (or even driving people following Paleo diet to extinction).

Somebody is sure to immediately accuse me that I don’t care about the environment. Au contraire. Some of the most depressing environments that I’ve seen are giant agricultural fields (e.g. driving through Iowa) .

Source

Compare it to what they looked like before:

Source: Wikimedia

My favorite environment is savanna such as this one in Namibia.

Photograph by the author

This is where I would love to live, if it were practically possible. These are the aesthetically beautiful and ecologically diverse environments that would be replaced with crop monocultures to enable “eating lower on the food chain”.

Actually, recently there has been a quiet trend in America towards replacing crop fields with grasslands. Some of it is done by non-profit organizations, like the American Bison Society.They want more of America to look like this:

Source

And there are individual farmers who switch to ranching. Watch this great movie at Carbon Cowboys.

The case for shortening the food chain is based on bad science. Eating animals (not just muscle, but also fat and organs) is bad for your health? Bullshit. It’s what made us human. It’s bad for environment? Check out this response by James Rebanks to Cambridge University banning meat:

Thanks @Cambridge_Uni for banning my grass-fed beef – how about addressing the primary causes of climate change, divesting your investment portfolio of fossil fuel burning activities, & changing your 17,000 hectares of fairly sterile arable land?
These are my fields
Show me yours pic.twitter.com/Eyf5mh9goM

— James Rebanks (@herdyshepherd1) September 13, 2019

And there is another unintended consequence of switching to plant-based diet, that I haven’t seen much discussion of. After my switch to Paleo diet, I noticed, with surprise, that my cognitive abilities improved. My thinking became clearer and, most strikingly, my social intelligence became more acute. This is, of course, completely “anecdotal” as we say in science. But it turns out that there is a sound scientific basis for this. Several controlled experiments conducted in juvenile prisons (for example) discovered that inmates whose diet was supplemented with Omega-3 were much more socially adequate than those in the control condition. Of course, the best source of Omega-3 is grass-fed ruminants. Or seafood, but we have sadly polluted the world ocean with heavy metals, so eating too much fish can result in arsenic poisoning.

In retrospect, these findings make a lot of sense. After all, humans evolved to become the “smartest animal on Earth” by switching from largely plant-based diet to a diet emphasizing carnivory. This is the only way to evolve (and maintain) large human brains and high social intelligence.

On one level, that of macrosocial dynamics, what happened yesterday, January 6, 2021, is not surprising. After all, my own model indicates that structural pressures for instability in the United States continue to build up. On a more immediate micro-level, watching hundreds of demonstrators break into the Capitol building and rampage through its hallowed halls was shocking. At one point, as I was watching the ABC coverage, George Stephanopoulos exclaimed, “This is not Ukraine!” True, over the past years we have become accustomed to the sight of revolutionary crowds breaking into government buildings in such countries as Ukraine, Armenia, Tajikistan… But something similar happening in Washington D.C., that citadel of democracy and the rule of law? Stunning, indeed.

Image source: wikimedia

What’s next? The dynamics of political violence at the micro-level and in the short run are difficult to predict. More important is what will happen at the level of deep, structural-demographic trends. Popular immiseration has been increasing for decades. I have written before how shocking it was for me to see such Malthusian indicators of stress as declining life expectancy, which turned down before Covid-19. The epidemic has now delivered a body blow to the well-being of the great majority of the Americans, with life expectancy, employment and incomes, as well as subjective measures of well-being all trending down.

Elite overproduction, and especially overproduction of the youth with advanced degrees, continues unabated. Our institutions of higher education have been churning out law, MBA, and PhD degrees, many more than could be absorbed by the economy. In a Bloomberg View article published just a few days ago Noah Smith provides the numbers for the overproduction of PhDs (America Is Pumping Out Too Many Ph.D.s).

The third structural-demographic force pushing up instability is the state indebtedness. It seems less relevant than the first two, as the U.S., due to its control of the world’s reserve currency, can seemingly print the greenbacks at will (although can this really continue indefinitely?). But the more important level is not the federal one, but that of the states, many of which are getting so cash-strapped that they are forced to reduce their police forces, or unable to hire additional medical personnel that are needed to administer Covid-19 vaccine.

Perhaps the shock of the Storming of the Capitol will spur our political leaders to action that would address these structural pressures. The most important one is reversing the trend of increasing popular immiseration. Now that the US Senate runoffs in Georgia are over, the Democrats control the White House and both chambers of the Congress. The Biden administration has two years to turn the Titanic of the American State around. Will they succeed? The future will show.

The story behind Figuring Out the Past
by Peter Turchin and Daniel Hoyer

Four years ago we got an interesting proposal from Ed Lake, the book acquisition editor at Profile Books. Profile has been publishing The Economist’s Pocket World in Figures series, an annually reissued statistical snapshot of the globe. Ed knew about our involvement with the Seshat project, and he wondered whether we would be interested in putting together a sort of World History in Figures.

Ed stressed that people have some sense of global history – most are probably familiar with their own country’s history, many know ‘key players’ like the Romans or Chinggis Khan’s vast Empire – but that few people outside of dedicated professional historians know much about the full sweep of our collective past. Most have an idea about the rise and fall of famous empires—the Romans, the Persians, the Chinese, the Incas. But how many people know about Jenné-Jenno, one of the oldest cities in Sub-Saharan Africa, located along the Middle Niger river (modern Mali)? And that despite sophisticated iron metallurgy, craft specializations, and high population densities, this ancient society lacks any sign of ruling elite or centralized administration? Or that the Dayak peoples in western Borneo aggressively expanded their territory during the 17th, 18th, and 19th centuries, also without an overarching centralized authority? Such “heterarchical” societies were quite common in the past, and yet they were organized on principles very different from the hierarchical empires in the history books.

The idea had a lot of appeal for us. Although Seshat was initially conceived as a historical data resource for testing scientific theories, we also wanted to make our work useful more broadly. Comparative history is an incredibly fascinating subject, because societies that occupied different regions of the globe in different historical eras were amazingly varied but also shared some general features. Both the huge diversity and a few general common themes are not just a subject of cocktail-party interest, but critical if we want to understand how human societies function and evolve. Historians have collaborated on a number of fascinating comparative studies, such as Rome versus China, or Ancient Egypt and Mesopotamia. But typically such comparisons focus on two, or a few, societies at a time. Is it possible to do comparative history “on steroids,” on a truly grand scale? We thought it would be a great project to try, and so we agreed. Little did we realize how massive such an undertaking would turn out to be, or how much we still had to learn…

As we explain in the volume’s introduction, we are used to thinking about the past in terms of stories of ‘great rulers’ or major battles, but we think of our own world largely through data: the key facts and figures that reveal how modern societies function. Yet history, especially comparative history, if the aim is to do it on a grand scale, must be quantitative as well. We cannot simply present a reader with a bookshelf of narrative histories covering different world regions and eras. To discipline our comparisons, we must seek answers to such questions as when? where? and how much? We also need a list of features that could serve as the common basis of comparing different societies. And then ask the same set of questions for all societies that we cover. We quickly realized through our discussions with Ed that we had a unique opportunity to spread the word on quantitative comparable history.

We thought that our experience with Seshat would make this job a breeze. And it helped, to a certain extent, but we did not appreciate the difficulties we would have to overcome. The first difficulty was coming up with a list of features, or characteristics, of past societies that we would want to cover. The problem, of course, is that unlike modern societies, for which extensive statistics are available on almost any imaginable feature, for past societies we simply don’t know that much. So we agreed with the publisher that “nobody knows” is going to be an acceptable answer; in fact, identifying exactly what we don’t know, and for which societies we don’t know it, is just as important as uncovering what we do know. This exercise has already proven an invaluable spur to our current academic research, helping us come up with a new set of questions more tailored to the available evidence.

The second problem was selecting a set of societies to include in this survey. Pocket World in Figures covers 66 modern countries, which is a decent sample of the 193 sovereign states in the United Nations. But historical states number in the thousands, and we couldn’t include every one we have in our Databank (the resulting book would have been too big to carry out of the bookstore!). The book presents ‘deep dives’ into 57 separate societies from the ancient, medieval, early-modern, and modern periods, along with a host of rankings (the largest empire by territory ever, the tallest Medieval building, etc.) and a special section highlighting the adoption of important traits in each world region (like having a full-time bureaucracy, or the first use of paper currency, or the adoption of monogamy as widespread practice) along with some maps showing how key technologies like agriculture and gunpowder spread throughout the world.

We wanted to avoid the twin biases of Eurocentrism and “presentism” as best as we could, but also had to deal with the opposite problem: the farther you travel from a well-studied region, like Europe, and back into time, the less is known. So we had to balance these opposite demands.  We undoubtedly made choices that could be criticized, but our main goal here is to present interesting and important facts about our shared past to those who are not familiar with thinking about these things.

The third problem was that checking the thousands of data that we gathered together turned out to be a monumental effort. We sadly underestimated the amount of labor that this job would require. A crew of our wonderful research assistants worked for months and then years, expert historians and archaeologists were very willing to share their knowledge with us, but with every data cleaning sweep we would find errors or omissions that the previous phase missed. There is no doubt more of such problems are still in the present volume, but eventually we had to stop and present the result of our labor to the world. So please be gentle when you find any mistakes—but also please share them with us. Our intent is that this will be just the first of several iterations, with each getting better and more accurate.

We also hope that the book will sell well. This may sound mercenary, but from the beginning we plowed the advance we got from Profile into supporting research assistants, and our intent is to use the future revenues for the same purpose. Thus, if the book sells well we will be able to, first, make future editions more accurate and complete. Next, we would like to expand the range of past societies we cover. Perhaps we would offer three volumes, on Ancient, Medieval, and Early Modern periods. And we would also like to expand the range of variables we cover.

Finally, we would be remiss if we didn’t thank the various people who have helped in making this volume possible. The first acknowledgment goes to Ed Lake, whose idea it was and who managed the whole process. Thanks are due to the very professional and incredibly patient production crew at Profile. We did stress their good will by sending them multiple corrected versions as we kept finding things to fix, but they came through with accommodating these changes. And they produced a very handsome book—one that would make a great Christmas present (hint, hint).

But our deepest gratitude is to our dedicated crew of research assistants – Jill Levine, Sal Wiltshire, Enrico Cioni, Jenny Reddish, Edward Turner, and Gregory Youmans – who did the real heavy-lifting, gathering and checking all of the data (and putting up with our constantly changing requests!). It is important to thank as well our collaborators who were extremely generous in providing their time and expertise reviewing the figures in this book: Mark Altaweel, Abel Alves, John Baines, Jim Bennett, David Carballo, Metin Cosgel, Alan Covey, Gary Feinman, Patrick Kirch, Andrey Korotayev, Nikolay Kradin, Jennifer Larson, John Miksic, Ruth Mostern, Alessio Palmisano, Peter Peregrine, Johannes Preiser-Kapeller, Katrinka Reinhart and many others.

Figuring Out the Past on Hachette BG

Figuring Out the Past in the Economist BookStore

Figuring Out the Past on Amazon

Figuring Out the Past on Profile Books

Graeme Wood penned a “long read” about cliodynamics, me, and the Age of Discord in which we are currently find ourselves. Graeme is a very intelligent journalist and his explanations of cliodynamics and structural-demographic mechanisms that bring about state breakdown are quite good. The Atlantic went through a thorough fact-checking process (unusual in these times of online media with limited resources to check facts) and I have no argument with the factual foundations of the Graeme’s article.

But Graeme is a journalist and it’s his job to present facts in ways that sell journal copy (or subscription). I am a scientist, and I can’t help but disagree with several angles through which Graeme views what I do; with the “spin” that he gave to the story. Certainly I don’t agree with his portrayal of me as a “prophet” (worse “the mad prophet”). He used this characterization not in the article (thank goodness), but in his Tweet about it.

My profile of Peter Turchin, the mad prophet of Connecticut, is up at @TheAtlantic: https://t.co/L0GUaUoQif

— Graeme Wood (@gcaw) November 12, 2020

But I am not a prophet, never claimed to be one, and in fact I had specifically written about why I eschew prophecy in this blog post. And I am on record criticizing other “prophets”. As I am a scientist, I use scientific prediction as a tool to test theories, in other words to discover which theories are true and which are not (for an example see here).

Neither am I a writer of “megahistory”. I enjoy books by Jared Diamond and Yuval Harari, because they generate interesting generalizations that can serve as testable hypotheses. But these authors stop at that. Where I take over is translating their verbal ideas into dynamical models, extracting quantitative predictions from them, and then testing them with historical data. Although I have proposed my own “grand theories,” my main job is slaying theories, not multiplying them.

The other big problem with how Graeme portrays me is that I come through as an arrogant jerk. I cringed in a number of places as I read his article. Yes, I propose a fairly ambitious program of testing theories about historical processes by translating them into explicit models and then testing model predictions with large datasets. But no, I don’t think of myself as a Hari Seldon. In fact, the fictional Hari Seldon had no appreciation of nonlinear dynamics and mathematical chaos (because Asimov wrote the stories before the discovery of chaos). And cliodynamics is not psychohistory.

But the worst misconception that readers will get from reading Graeme’s article is about my views of History. “Terms of surrender,” really! This is entirely on Graeme’s conscience. My view of History and historians (and archaeologists, religion scholars) is appreciative and respectful. I have written on many occasions that cliodynamics needs history. Ten years ago, a group of us launched the Seshat project, whose success critically depends on collaboration with expert historians and archaeologists. Read this introduction to Seshat to see what I and my colleagues really think about history.

Historians who read this Atlantic article will be — rightfully — incensed (thanks, Graeme). But I urge them to read more about the Seshat project to find out about our goals and approaches. Far from abolishing History (or forcing it to “surrender”) we want History to flourish. We need academic historians to use their expertise to continue amassing the knowledge about different aspects of past societies. We rely on historians and archaeologists to interpret complex and nuanced historical evidence, before it can be translated into data for analysis. Our knowledge about past societies also has many gaps and a lot of uncertainty — this uncertainty needs to be reflected in data so our analytical results represent not only what we think we know, but also the limits of our knowledge.

History can exist (and has existed) without Cliodynamics, but Cliodynamics cannot exist without History. And my hope is that Cliodynamics will eventually pay its debt to History by showing that studying past societies is not just an academic endeavor — it can help us understand, among other things, our current Age of Discord, how we got into it, and what we can do to navigate the turbulent waters ahead.

As readers of this blog know, structural-demographic theorists distinguish between two causes of revolutions and civil wars: structural trends, which build slowly and are quite predictable, and much less predictable, or even unpredictable, triggering events. In this view, a revolution is like an earthquake or a forest fire. As Mao once wrote, “a single spark can start a prairie fire.” A fire needs fuel—dead plant material—which accumulates gradually as plants die and fall down. But what it needs to start is a spark—somebody throwing away a careless match, or a lighting strike coming from the sky.

Structural trends undermining social resilience in the United States have been building up for decades. It became clear to me 10 years ago (see my 2010 forecast) and has become obvious to most everybody in the last few years. These structural forces are: increasing popular immiseration (declining incomes, falling life expectancies, growing social pessimism and despair), elite overproduction and intra-elite conflict, and failing state (growing state debt and collapsing trust in state institutions). The Covid-19 pandemic put even more pressure on the system, especially exacerbating immiseration.

What is somewhat unusual is that the triggering event for USA in 2020 is also highly predictable. Every four years America elects president. Even under “normal” circumstances a ruler transition stresses the system, but when it happens under conditions of high social fragility, it can deliver a death blow to it. Last time this happened was in 1860. The result was the American Civil War and what many historians call the “Second American Revolution,” because it overturned the previous social order, dominated by slave-owning southern planters in alliance with northern merchants who shipped their products overseas. This ruling class was replaced with the new governing class, the northern manufacturing, mining, railroad, and agro-business elites. The main fault-line then was between the slave-owning South and the free-labor North.

Today the faultline is between what could be called the Red and Blue Americas. Blue Americans hate and fear Trump and everything he stands for. Red Americans hate and fear what Biden stands for. Either side is united primarily not because they particularly like their candidate, but by their dislike of the opposing party. There is a geographic aspect to this confrontation (the coasts versus the heartland) but it is not as clear-cut as it was in 1860. Also, the Red and Blue parties coincide imperfectly with the Republicans and Democrats, because many Obama voters switched to Trump in 2016, while many republican politicians have endorsed Biden. The division is over the issues.

As a reminder, my analysis in this blog (and elsewhere) is always non-partisan and as even-handed as I can make it. So let me try summarizing how each side feels, as though I were an observer from Alpha Centauri.

Blue Americans cannot bear thought of four more years of Trump, his desecration of the values that made America a beacon to the world, his bullying and lies, his undermining of the norms and institutions that make America work, his contempt for European allies, and his kowtowing to foreign dictators. They fear that Trump will use false claims of election fraud and the post-election social unrest to engineer a military coup, in which he would set himself up as dictator, and abolish free press and American democracy.

Red Americans fear a Biden administration that will open borders to massive immigration, encourage looting and property destruction by BLM and antifa rioters, take away their guns, increase their taxes, and end the oil and gas industry in America. Many see Biden as the senile figurehead for the global cabal of Wall Street, Silicon Valley, and child-sex predators that include prominent Democrats, Hollywood elites, and deep state allies, whose aim is to establish an unelected “liberal dictatorship” that would abolish freedom of speech and American democracy.

Each side sees the world in Manichean terms and increasingly endorses violence as the necessary means to prevent the other side from staying in, or coming to power. As a result, we are in an extremely fragile state, which in technical terms is known as the revolutionary situation.

What will happen on November 3? One possibility is that one side wins by a landslide and the other accepts it. This is what the generals fervently hope for. This would avoid a civil war, at least for a time. The problem is that neither side has shown any willingness or understanding to solve the structural problems that have brought about the current revolutionary situation. And it takes years to reverse the negative structural-demographic trends, even once the necessary reforms are implemented. So we simply kick the problem forward to 2024.

Moreover, a clean win by either side, while possible, doesn’t seem to be very likely. Let’s face it, we live in a “post-truth” world. The difference between the Red and Blue parties is stark not only in their visions of where America needs to go; they also completely disagree on what is true or false. Each side believes that the other has been lying and suppressing information. The polls reported by the mainstream media, who has decisively taken the Biden side, suggest that Biden leads Trump by 10 percent or more. The Blue party is convinced that they are winning. But if you read Red party-aligned media and social media, they are equally convinced that they are the ones winning. Come election date and the days after, during the messy process of counting ballots and contesting results state by state by the lawyer teams on each side, it is unclear to me how either party could be convinced that they lost the elections.

What comes next—in November and in the months ahead? In dynamic systems terms, we are on the cusp with a highly positive Lyapunov exponent. What it means in English is that, unless there is a clean win, we will be in situation where possible trajectories start diverging dramatically. All kinds of outcomes become possible, even ones that seem outlandish right now, such as American Civil War II.

Many social scientists, who study civil wars and revolutions, don’t believe that a civil war here is likely. They look at the current wave of violence and don’t see how it could escalate to a civil war—the United States has a strong and well-armed police force that can easily put down any popular insurrection. But this view misses an important point: successful revolutions rarely result from the revolt of the masses. The most important factor is the divisions at the top, with dissident elites mobilizing the masses to advance their political agendas.

Using the past as a guide to the future, I can think of a number of possible trajectories after Nov. 3. These are all very speculative, and some may seem outlandish, but others have also been war-gaming various post-election scenarios.

In one demonstrations against Trump turn violent, he uses the military to suppress them, and then sets himself up as dictator (this is what the Blue party fears). In another Trump is arrested by the FBI and is put on trial. Alternatively, Biden is tried and convicted for corruption.

Other possibilities include a regional rebellion, e.g. the West Coast announces independence and the state governors use National Guard to defined themselves against the Trump administration in Washington. Or the Deep South announces independence against the Biden administration in Washington.

A group of colonels seizes the power and establish a junta (this one seems the least likely as the social norm that the military doesn’t interfere in politics is one of the few that has not yet unraveled).

Since we seemingly live in a dystopian novel, I can also imagine a trajectory in which Trump is assassinated by a lone gunman, who is then killed, and the person who killed the killer of Trump’s assassin commits suicide by shooting themselves four times in the head, and then jumping from the twentieth floor window.

Just about the only way in which street violence could directly escalate to a revolution is if revolutionary crowds break into the White House and depose Trump. But even this trajectory requires collaboration from the top (the police and the army standing aside to allow this to happen).

I am sure you can think of other possible trajectories. The main point is that hitting a cusp creates a fan of possible trajectories, many of which couldn’t even be imagined ahead of time.

One final thought is that the timing of any such possibilities is completely unpredictable. Social breakdown can happen in days, but historical comparisons suggest that usually it takes many months. For example, Lincoln was elected on Nov. 6, 1860, but the first real battle of the Civil War took place in July 1861.

And this concludes my “analysis from Alpha Centauri.”

Postscript: 4.XI.2020. A day after the elections, and the possibility space has started collapsing, but there is still a multitude of trajectories ahead. As I expected, a “clean win” by either party has not materialized (even though many from the Blue Party were expecting a Blue Wave, that turned out to be wishful thinking). The next likely phase is when both parties declare victory.

Some years ago I had a discussion with Ian Morris about the approach he took to quantify the social development of East versus West in his book, The Measure of Civilization. So I asked him: Which pre-industrial society was the richest in terms of energy use per capita? I have an answer to this question, which could be quite controversial (and when I offered it to Ian, I had a feeling that I didn’t persuade him).

So what’s your answer? (I have also posed this question on my Twitter)

Let’s make this question precise, so that we all use the same units. We want to measure energy use per time per capita.

Energy is measured in joules and calories (and some other more esoteric units). One calorie is the amount of energy needed to raise the temperature of 1 cubic centimeter of water by 1 degree Celsius (centigrade). 1 calorie is roughly 4.2 joules. Joules are a better unit for energy, compared to calories, because there is a confusion between 1 calorie and 1 kilo-calorie = 1000 calories. But here are the basics (taken from Box 1.3 of Vaclav Smil’s Energy and Civilization). A moderately active adult spends between 2 and 2.7 Mcal (1 million calories) per day which is roughly 10MJ (10 million joules) per day. The unit for measuring energy flow per time is called Watt = J/s (joules per second). The power of a human body, thus, works out to be roughly 100 Watts. Here’s the calculation: 10,000,000 J/(24 hours x 3600 seconds) = 115 W.

So let’s take this number as the base. In a foraging population the main energy use is the human body burning food, but let’s not forget that additional energy is needed to cook food on campfire. Smil (Boxes 1.4 and 2.1) estimates that 1 kg of dry wood contains about 20 MJ and cooking requires less than 0.5 kg of wood per day. This works out to roughly another 100 W. We have just doubled human energy use!

Bushmen getting ready to cook a meal. Source

By 1500 CE various human societies around the globe would be using a number of additional energy sources:

1. Burning fuel for cooking and heating houses
2. Plowing with animal power (oxen and horses)
3. Transportation, using animal power and wind power (sailing)
4. Energy-demanding industries: metallurgy, pottery, glass-blowing
5. Wind and water mills to mill grain, pump water, etc.

Anything else I am missing?

Now the trick is to convert all those energy-using activities so that we can express them in per capita terms. For example, let’s do a quick calculation of how much iron metallurgy would add to energy use per capita. A peasant needs a steel axe. Let’s say its head weighs 1 kg and needs to be replaced every 5 years. Consulting Box 1.8 in Smil’s book, we find that smelting iron from ore requires 12-20 MJ/kg, and converting it to steel needs further 20-25 MJ. Let’s round it up to 50 MJ per axe head (to account for iron losses during forging). Replacing an axe every 5 years, then, would require 50 MJ/(5 years x 365 days x 24 hours x 3600 seconds) = 0.3 Watts. Well, this doesn’t seem to add a lot (assuming I did the math right).

So here’s the challenge. It’s not enough to name a particularly advanced society. Give me some numbers to show that its energy use was high.

Matt Yglesias, responding to a tweet by Noah Smith today, wrote:

Seen a lot of Turchin citations lately but I think the bar for buying into cyclical views of history should be *really* high.

There were other, similar comments on Twitter. I respond on my blog, because it can be hard to keep track of multiple threads on Twitter, and a blog post has more staying power, compared to a tweet.

What my colleagues and I do is Cliodynamics, which is very different from typical cyclical views of history. “Cyclical history” suffers from two problems. First, mechanisms producing cycles are either entirely missing, or inadequately specified. There is almost never an explicit mathematical model that would clarify these mechanisms. Second, cyclical theories in history are not subjected to empirical tests with independently gathered data. It’s all retrospective eyeballing together with “Procrustean” forcing of  the historical record to fit the postulated cycle by stretching in some places and cutting off a bit here and there. For a specific critique, looking at the Strauss-Howe cyclical theory, see my post The Prophecy of the Fourth Turning.

Cliodynamics is entirely different. Its roots are in nonlinear dynamical systems. We don’t go out looking for cycles; but we don’t shy away from them when there is robust evidence for them. In Structural-Demographic Theory, in particular, oscillations arise because of nonlinear feedbacks between different interacting components of the social system (state-level society). We model the postulated feedbacks mathematically and determine whether our intuition that they should lead to cycles is correct. See Why Do We Need Mathematical History?

Note these oscillations are not strictly periodic cycles, because there are always exogenous influences that continuously perturb the trajectories. Additionally, nonlinear feedbacks often induce the modeled system to behave chaotically.

Furthermore, human societies evolve. This means that many oscillations we see in history occur around a moving target. They are no less real because of that, but our statistical approaches need to be sophisticated enough to detect and characterize them. For an example on how “detrending” works see the graphs on the average age of first marriage here.

When we test predictions of cliodynamic models that generate cycles, we not only use standard statistical methods for detecting periodicity (such as spectral and time-series analysis). Even more importantly, we want to see whether the observed dynamics of different variables in the model change in the way model predicts. For example, when we see that a variety of proxies for population well-being (a key variable in structural-demographic theory) all wax and wane together, our degree of belief in the theory is strengthened. When we see that well-being proxies and elite overproduction proxies oscillate in almost perfect anti-phase, our confidence in the theory is increased further:

For details, see here. Note that this cyclic pattern may look “too good to be true”, but it is true! You can trace all steps of the analysis down to raw data to ascertain this. Our societies, including the US, are social systems, and they behave in a systemic fashion, meaning that when one component changes, this influences other components in the system. When we see such strong patterns in the data, and especially when we have theory translated into explicit mathematical models that explain these patterns, we must conclude that the theory is capturing something important about how our societies function and change.

As a final note, this is just one example. In our book Secular Cycles we applied structural-demographic theory to a number of historical societies. Other colleagues have extended such empirical investigations to more regions and historical periods, so currently we have good data on about 30 secular cycles. And we are building the Crisis DB to add to this number. In fact, all large-scale societies organized as states, for which we have good data, go through these oscillations. It’s like a law of history, or something.

As the readers of my blog know, the opinions I express here are strictly non-partisan and non-ideological. My main interest is to go where science leads. Ideological thinking is different from science in that in science data triumphs over theories. Ideologues, on the other hand, can ignore or twist facts to suit their theoretical predispositions (see, for example, An Anarchist View of Human Social Evolution).

But it doesn’t mean that everything coming from an ideological camp is wrong. Take Marxism. I realize that it is now used in certain quarters as a label for “bad people”, but here I mean by it just the philosophical ideas of Karl Marx, Friedrich Engels, and their followers.

My own attitudes towards it went through quite wild swings over my life time. I grew up in the Soviet Union, where I was force-fed Marxism in school, so I took a very dim view of the ideas of Marx and Engels. But when I switched from studying ecology to human societies, I realized that there were interesting and valid ideas in Marx’s theory. The main problem for Marx and Engels, I now tend to think, was that they worked with a very limited empirical material (for example, they didn’t have the Seshat Databank!). I now acknowledge Marxian contributions to the structural-demographic theory (together with other important thinkers, such as Malthus, Durkheim, Weber). Furthermore, I found ideas from a number of contemporary Marxian thinkers to be useful in illuminating various aspects of how our complex societies function. As an example, see my use of Kitty Calavita’s “structural model of the capitalist state” in Chapter 10 of Ages of Discord.

A more recent example is Angela Nagle’s The Left Case against Open Borders. This title seems to be a self-contradictory “oxymoron”. As Nagle notes,

In the heightened emotions of America’s public debate on migration, a simple moral and political dichotomy prevails. It is “right-wing” to be “against immigration” and “left-wing” to be “for immigration.” But the economics of migration tell a different story.

Of course, economics is only one of the considerations that should inform public policy on immigration. It has become a hugely emotional issue. As Nagle writes,

With obscene images of low-wage migrants being chased down as criminals by ICE, others drowning in the Mediterranean, and the worrying growth of anti-immigrant sentiment across the world, it is easy to see why the Left wants to defend illegal migrants against being targeted and victimized. And it should. But acting on the correct moral impulse to defend the human dignity of migrants, the Left has ended up pulling the front line too far back, effectively defending the exploitative system of migration itself.

What I want to do, as I often do in this blog, is to follow Nagle and look below the surface to structural issues—economics, but even more deeply, power.

The economic argument is very clear. Massive immigration increases the supply of labor, which in turn depresses its cost—in other words, worker wages. Clearly, such development benefits the consumers of labor (employers, or “capitalists”) and disadvantages the workers.

Of course, immigration is only one of the many forces affecting wages. I explore this issue in a blog series, Why Real Wages Stopped Growing, with the summary in the fourth post, Putting It All Together (Why Real Wages Stopped Growing IV). My conclusion is that immigration was a significant contributor to the stagnation/decline of the wages in the USA over the past several decades, although not the only one. Unless there are strong institutions protecting workers’ wages, an oversupply of labor is going to depress them—it is simply the law of supply and demand in action.

As Nagle points out, this was clear to Karl Marx, who

expressed a highly critical view of the effects of the migration that occurred in the nineteenth century. In a letter to two of his American fellow-travelers, Marx argued that the importation of low-paid Irish immigrants to England forced them into hostile competition with English workers. He saw it as part of a system of exploitation, which divided the working class and which represented an extension of the colonial system.

It was also clear to those, who were negatively affected—the workers and their organizations:

From the first law restricting immigration in 1882 to Cesar Chavez and the famously multiethnic United Farm Workers protesting against employers’ use and encouragement of illegal migration in 1969, trade unions have often opposed mass migration. They saw the deliberate importation of illegal, low-wage workers as weakening labor’s bargaining power and as a form of exploitation. There is no getting around the fact that the power of unions relies by definition on their ability to restrict and withdraw the supply of labor, which becomes impossible if an entire workforce can be easily and cheaply replaced. Open borders and mass immigration are a victory for the bosses.

In fact, popular opposition to unrestricted immigration goes farther back in the American history. In 1854 the anti-immigrant Native American Party (“Know-Nothings”) achieved a stunning victory in several states that were most affected by the arrival of immigrants from Europe, carrying 63 percent of the vote in Massachusetts, 40 percent in Pennsylvania, and 25 percent in New York.

This 1888 cartoon in Puck attacks businessmen for welcoming large numbers of low-paid immigrants, leaving the American workingman unemployed

And, not surprisingly, the American economic elites also were very well aware that a continuing influx of immigrants allowed them to depress worker wages and increase the returns on capital. Andrew Carnegie in 1886 compared immigration to “a golden stream which flows into the country each year”. During the nineteenth century the corporate community often used the American state to ensure that this “golden stream” would continue to flow. For example, in 1864 (during the Lincoln administration) Congress passed the Act to Encourage Immigration. One of its provisions was the establishment of the Federal Bureau of Immigration, whose explicit intent was “the development of a surplus labor force” (italics are mine).

The business leaders today are much more circumspect about these issues. But one wonders, how many of them think in the same terms, even if they don’t speak publicly about it, instead choosing to emphasize the humanitarian aspects of migration.

To strip Nagle’s main argument to its essence, globalization is wielded by the governing elites to increase their power at the expense of the non-elites. It redistributes wealth from workers to the “bosses”. Some of that extra wealth is then converted into greater political power for big business. Furthermore, antagonism between native and immigrant workers corrodes their ability to organize. As a result, Nagle argues,

Today’s well-intentioned activists have become the useful idiots of big business. With their adoption of “open borders” advocacy—and a fierce moral absolutism that regards any limit to migration as an unspeakable evil—any criticism of the exploitative system of mass migration is effectively dismissed as blasphemy.

Quite a number of people, here and on Twitter, asked me about how I came up with my forecast for 2020. Here’s the story.

By the early 2000s I had already delved into structural-demographic theory and its implications for historical societies. These results went into Historical Dynamics (2003) and in much greater detail into Secular Cycles (2009). But every time I gave a talk about this research, someone in the audience was sure to ask, where are we now in the cycle? So around 2006-2007 I started gathering data on the USA. I remember giving a talk about this research to Santa Fe Institute colleagues in 2008, when I was there for a sabbatical year.

In early 2010 Nature asked a number of scientists about their forecasts for the next decade. By that point I already had a fully developed computational model for forecasting structural-demographic pressures for instability. Frankly, the results for the USA scared me. So I sent them my rather pessimistic forecast, which, somewhat surprisingly, they published. The Nature article had to be very short, so I later published the details of the approach, model, and data in a much longer article, Modeling Social Pressures Toward Political Instability. I’ve been publishing such scientific predictions (see my blog post on how this differs from “prophecy”) from the beginning of my scientific career, because this is the main way we can really test our theories.

In the years since 2010 a number of journalists interviewed me about this prediction, but the quality of resulting pieces were quite variable, from very good to rather bad. If you want to get the story right, better read what I wrote. In particular, here are two popular articles that I published in 2013:

Return of the Oppressed. From the Roman Empire to our own Gilded Age, inequality moves in cycles. The future looks like a rough ride. Aeon Magazine

Blame Rich, Overeducated Elites as Our Society Frays. Bloomberg View Op-Eds

Then, in January of this year I teamed up with another structural-demographic theorist, Andrey Korotayev, to revisit the forecast made in 2010. So far, this article has gone through one round of review at a scientific journal, and we have just resubmitted a revised version. Yesterday I posted the revised version on a preprint server; it also has supplementary materials with the data file and an R script that implements the model. If you want to delve into the details, here’s the link:

Turchin, Peter and Andrey Korotayev. 2020. “The 2010 Structural-Demographic Forecast for the 2010–2020 Decade: A Retrospective Assessment.” SocArXiv Preprint. 

Abstract

This article revisits the prediction, made in 2010, that the 2010–2020 decade would likely be a period of growing instability in the United States and Western Europe [1]. This prediction was based on a computational model that quantified in the USA such structural-demographic forces for instability as popular immiseration, intraelite competition, and state weakness prior to 2010. Using these trends as inputs, the model calculated and projected forward in time the Political Stress Indicator, which in the past was strongly correlated with socio-political instability. Ortmans et al. [2] conducted a similar structural-demographic study for the United Kingdom. Here we use the Cross-National Time-Series Data Archive for the US, UK, and several major Western European countries to assess these structural-demographic predictions. We find that such measures of socio-political instability as anti-government demonstrations and riots increased dramatically during the 2010–2020 decade in all of these countries.