Social life of human beings was utterly transformed during the Holocene. Agriculture, large-scale organized warfare, elites, rulers, bureaucracies, writing, and monumental architecture evolved independently in many world regions at markedly different times. These are truly universal features of complex human societies. Moralizing religion is different.

I recently finished writing a chapter for the Seshat History of Moralizing Religion in which I summarize the statistical patterns from the data that the Seshat project gathered on moralizing supernatural punishment/reward (MSP). You can read more about this project, data, and results in this academic publication. The main thrust of this research was on testing rival theories attempting to explain the evolution of MSP (it is summarized in this blog post). But today I want to write about the historical geography of MSP. I’ve put together this infographic, based on the Seshat data, which depicts the evolution of MSP in time and space.

Our data show that some elements of MSP are found in many different parts of the world and have substantial antiquity. Fully developed MSP, on the other hand, evolved in one particular world region during a period that can reasonably be called the Axial Age.

The Axial Age, of course, is a fairly controversial idea, and there are many different ways to define its temporal boundaries and to localize it in space. However, by focusing on one particular aspect (MSP), defining its dimensions, and gathering systematic data across the world regions and historic eras, the Seshat project provides us with a quantitative, empirically based approach to delineate the spatial and temporal boundaries of this “Age of Moralizing Religion.” Let’s see what the data tell us.

Full MSP first developed in Egypt during the second millennium BCE. The central MSP idea in Ancient Egyptian Religion, Ma’at, presages later developments in West Asian Monotheisms and South Asian Karmic religions, because although Ma’at was primarily conceptualized as a supernatural force or universal principle, it was also personified and depicted as a supernatural (or superhuman) agent.

In the next, first BCE, millennium full MSP became firmly associated with the rise of what Alan Strathern calls “transcendentalisms,” such as Zoroastrianism, Judaism, Buddhism, and Jainism. This evolution took place within the Central Political-Military Network (more on this in Chase-Dunn and Hall, Rise and Demise). The core of this Central PMN was the Achaemenid Persian Empire, which stretched from Egypt and Anatolia to Sogdiana and the Indus. The Central PMN was an “incubator” for religious ideas originating from Egypt, the Steppe, North India, and other geographically adjacent regions. These ideas mixed and recombined in new ways, becoming the progenitors of all currently existing world religions with full MSP.

Source

There were two major flavors of these Axial Age transcendentalisms. The West Asian one emphasized a Big God that monitored human moral behavior and punished the evildoers. The South Asian transcendentalism was based on the notion of karma, a non-agentic force, or universal principle. Despite this difference, these two flavors had a lot in common—beliefs in punishment/reward in the afterlife, internalization of moral norms, and an emphasis on salvation, liberation, or enlightenment.

Interestingly, the East Asian PMN was not part of this religious interaction sphere, although by that point Central and East Asian Prestige Goods Networks have already merged, and religious ideas could travel together with merchants (which, indeed, happened, but a millennium later, when Buddhism arrived in China and Manichaeism in Mongolia).

What was so special about the Central PMN in the Axial Age? Analysis of the Seshat data points to the role of military revolutions and overall intensification of warfare as the primary causal driver of MSP. The invention and spread of cavalry, in particular, is the strongest predictor of the increases in MSP. This statistical result helps us understand why world religions with full MSP evolved in the Central PMN at that particular period in time. Horse-riding was invented in the Great Steppe around 1000 BCE. It spread to Iran by 900 BCE and to North India by 600 BCE. Cavalry revolutionized warfare within the Central PMN, leading to a cascade of other military innovations, followed by rapid cultural evolution of MSP.

All transcendentalisms with full MSP that exist today, thus, are evolutionary offshoots of the religions that developed within the Central PMN in the first millennium BCE. The main mode of evolution of MSP was spread by military conquest, by long-distance traders, and by missionaries. Apart from the initial evolution in the Central PMN, we know of no examples of independent evolution of full MSP, as is imagined by the Big Gods theorists (although world religions often incorporated cultural elements from local religious traditions, once they got to a region). Instead, world religions spread through Afro-Eurasia following the spread of horse-riding and iron weapons and armor. Later another military revolution took place in Europe in the fifteenth and sixteenth centuries. Following it, world religions with full MSP spread to the rest of the world carried by European empires.

To return to what I started this blog with, the evolution of MSP is distinct in its mode from other aspects of the Holocene transformation, which, unlike full MSP, arose repeatedly and independently in different parts of the world. Instead, full MSP evolved in a particular world region at a particular time, and then spread from there. Furthermore, full MSP is not a necessary condition for effective functioning of large-scale societies. Large bureaucratic empires in China, for example, functioned well enough without other-worldly supernatural punishments, instead relying on this-world state-administered punishments and rewards. The Big Brother may be as effective (if not more so) than the Big God. A sequence of large states in the Peru region, culminating in the Inca Empire, also attests that MSP is not a necessary institution. Well-functioning secular modern states, such as Denmark or Austria, in which religion plays a minor (at best) role is more evidence for this idea. Finally, other aspects of world religions may be of greater importance for sustaining large-scale societies: their ability to symbolically unify large ethnically heterogeneous populations, their emphasis on doctrinal rituals, and their literate clergies that often served in government bureaucracies.

The evolution of moralizing religion is an interesting puzzle that is amenable to an investigation using the tools of cultural evolution and historical databases. Much progress has been made, and we now have a much better understanding of causal process involved. However, and perhaps disappointingly to some, these insights suggest that moralizing religion was not a particularly important force in the evolution of large-scale complex societies.

This post is based on a chapter titled The Evolution of Moralizing Supernatural Punishment: Empirical Patterns, forthcoming in Seshat History of Moralizing Religion (2023), edited by Larson et al.

Why do large-scale complex societies, within which >99% of humanity now lives, recurrently experience periods of social and political breakdown? This question is morbidly fascinating, especially since after we’ve entered the “Turbulent Twenties.” Books on collapse are now a cottage industry and there is a whole new science called collapsology. The classics, such as Joseph Tainter’s The Collapse of Complex Societies, enjoy enduring popularity. “Societal Collapse” has been surging on the GoogleBooks Ngram Viewer.

Tainter argued that as societies become more complex, they become more fragile and susceptible to collapse. More recently, this theory has received a name, “Complexity Overhang.”

A negative view of social complexity as a source of instability fits well with another current in political thought, anarchism, which sees the state as the source of all evil—oppression, violence, and legalized theft (aka taxation). In my view, political anarchism is complete bunk, as I’ve argued in these blog posts:

An Anarchist View of Human Social Evolution

The Pipe Dream of Anarcho-Populism

But returning to the question of the relationship between complexity and stability, I need to confess that until recently I also thought that “simple” societies—small-scale societies of farmers without deep social inequalities and no political organization beyond a local settlement (village)—were quite resilient/not susceptible to collapse. In such societies there is no state or nobles to rebel against and, in any case, what’s there to “collapse”?

Life in a simple society—prosperous, peaceful, and free of oppression? Source

But perhaps “collapse” is not such a useful way of thinking about societal dynamics — it is a problematic concept because it can mean so many things. This is why I am not a collapsologist—I prefer to work with well-defined concepts (which, ideally, are amenable to quantification). So let’s approach this question from the point of view of quantitative history.

The most basic property of a society is its size—how many people interact together and (hopefully) cooperate. Population numbers (or density – numbers divided by area) is, thus, a fundamental variable for cliodynamics. Previous work by my colleagues and me on state-level societies shows that waves of sociopolitical instability are often associated with substantial population declines—“population busts.” The current tabulation of past crises in CrisisDB (note: work in progress), indicates that in roughly half the cases sociopolitical crisis in “complex” societies results in a substantial population decline. What about pre-state, non-centralized societies?

There is a classic study by Ammerman and Cavalli-Sforza on the rate of spread of early farming in Europe, published in 1971. I studied it in graduate school because it was one of the first applications of the diffusion equation to population spread (population dynamics in space and time was my disciplinary specialty before I switched to quantitative history). These distinguished authors started with the observation that farming increases the “carrying capacity”—how many people can be supported per unit of land (e.g., a squared kilometer). A population of farmers, after spreading into a new area, would grow until it reached this carrying capacity and then stabilize at that level (with some fluctuations due to variable climate and other random factors). This dynamic process was captured by Ammerman and Cavalli-Sforza with the logistic equation.

But as archaeologists developed better methods for estimating past populations and accumulated lots more data, it turned out that this assumption was wrong. In several regions into which first European farmers spread (this is known as the LBK culture) we see, as expected, an initial population boom, resulting from a more productive economy. But instead of leveling off, the boom was abruptly followed by a bust.

Population densities of the early LBK in the lower Rhine basin. Figure 6 in Zimmermann, A., J. Hilpert, and K. P. Wendt. 2009. Estimations of Population Density for Selected Periods Between the Neolithic and AD 1800. Human Biology 81:357-380.

And the same pattern is observed not only in Europe, but also in other world regions.

Last week Daniel Kondor and I organized a workshop at the Complexity Science Hub in Vienna, in which we delved into this issue. While we know that population busts in early farming societies were a frequent occurrence, we don’t yet know whether such cycles were universal, or whether there were periods and places when population levels stabilized for long periods of time. For the list of participants and the questions that we discussed, see the CSH press release:

BUSTS AFTER BOOMS, BUT WHY?

We also discussed the possible causes of population busts in prehistory. But that is a topic for another post.

Six months ago I posted a “progress report”, What I am working on. Since then a lot of things happened.

First, I finished writing my trade book, previously titled A History of the Near Future. As expected, the publisher didn’t like it, so the new title is The Wealth Pump: Ruling Elites and the Path of Political Disintegration. I finished the complete draft in June (right on schedule), but then there was a bunch of lose ends to take care of. As a result, the book went into production in August. The tentative publication date is June of next year. There is still a lot of work to do on it, but it will be largely done by other people. The biggest job for me will be to go over the copy-editor’s suggestions, and then approve the galleys, which will not take a lot of time. This leaves me time to switch to other projects, on which below.

Second, I am now an emeritus at the University of Connecticut, as of July 1. This means I am done with teaching college students, but I will continue teaching a regular Winter School in Austria, similar to what I did this year. Well, because of Covid, a winter school evolved into a spring school: CSH Spring School on the Evolution of Social Complexity 2022. Next year, 2023, Winter School will take place in a retreat in the Alps in January. There will be an announcement about it during the second half of September. But the main upshot is that now I have only one regular job, as a project leader at CSH, which simplifies my life a lot.

Third, I had a vacation-adventure, the first one in three years. I went to several national parks in the Zambezi-Okawango region. I’ll probably write more about it on this blog, but here are two pictures, taken by a friend:

At a water hole in Hwange National Park

Stretching after a long early morning game drive

Fourth, the Seshat project has published most of the papers analyzing what we started calling the Classic Seshat, which focused on data that enable us to test a variety of theories attempting to explain the evolution of complex human societies during the Holocene (roughly, past 10,000 years). I consider it a huge accomplishment, a result of more than a decade of work by a large research network associated with the Seshat Databank. I’ll write more on this in a future post. I am also now in a good position to finish my book, The Great Holocene Transformation, which I hope to do by this winter.

This Fall I am collaborating on a bunch of analyses and articles, led by my colleagues in Vienna and elsewhere. But the major thrust is on CrisisDB. We have gathered data on more than 100 past societies sliding into a crisis, and then emerging from it. A lot of data-cleaning remains, but we are now shifting the effort into analyzing these data. As our societies continue sliding into the ongoing “polycrisis” this work seems to be more relavant than ever.

The question of how we can learn useful lessons from history, which would help us navigate the troubled waters ahead has been much on my mind. You can find some of my thoughts on this subject in my review of Walter Scheidel’s Escape from Rome. It’s also an important theme in the “Big Book” I am working on (there will be an update on it soon). And then yesterday I saw a Twitter thread by Balaji Srinivasan in which he asks the same question. Balaji mentions my work and that of Ray Dalio, but is somewhat critical of us. He says:

The main knock on Turchin & Dalio is that their quantification is imprecise. What’s the y-axis here? Seems like a weighted sum of many variables.

and gives an example from Dalio’s site. This critique may be correct when directed at Dalio (as I remember I similarly tried to get at his data sources some time ago, but couldn’t find it). But this charge is certainly not true when directed at my work. It is all explained in Ages of Discord, which is an academic book and has references for all data sources that I used in it (and that’s a lot). I also started a webpage on which all these data will eventually be posted, then had to set this project aside for a while. Now my research assistant is working on not only posting the data, but extending them to the present. Stay tuned.

To reiterate: all data on which my graphs are based are sourced in the academic pubs and the methods used for generating various curves from these data, such as the one below, are properly described and can be reproduced by anybody. When the webpage I mentioned is fully operational, this will be even easier.

As to the substance of Balaji’s proposal, I obviously agree with the basic idea that we need to construct and analyze historical databases, since that is what I’ve been doing since 2010 (see here the latest example). But there are good reasons to believe that the specific approach that he proposes for analyzing historical data will not work. What he describes is an atheoretical, entirely data-based analysis. Such a “brute-force” approach has the following problems:

1. There are thousands of variables that characterize societal dynamics. In the absence of theory, how do we choose the relevant ones? If we don’t select, the curse of dimensionality will defeat any attempt at trajectory-matching, which is essentially what he proposes.

2. Trajectories of specific societies are constantly perturbed by exogenous shocks (climate, external wars, epidemics…) and endogenous sources of variation (free-willed people behaving in unpredictable ways). Also, because social system are characterized by complex nonlinear feedbacks, they operate in a chaotic regime (in technical terms they are characterized by sensitive dependence on initial conditions). Thus, the trajectories of two identical dynamical systems starting from identical initial conditions will rapidly diverge (because sensitive dependence will amplify even small shocks within each system) and lose any resemblance of each other. This will defeat any attempt at trajectory matching.

So what can we do? First, stop treating societies as black boxes. We have pretty good ideas about the mechanisms that generate social dynamics. So we need to use explicit mechanism-based models. In fact, the problem is the opposite: there are too many ideas about what are the important mechanisms. Thus, the first use of a historical database is to empirically test different mechanistic theories against each other. Next, we can use the data to estimate such things as the signal/noise ratio and fine-tune parameter estimates (initial estimates should come from other sources than time-series as much as possible).

I can write more about this, but it’s too big a theme for a blog post, so I’ll stop here. As a final thought, the way I envision the eventual product, coming from this work, is a multi-path forecasting engine, the prototype of which is described in this publication.

I want to emphasize that these critical comments are “tactical” in nature. I completely agree with Balaji on the need to construct comprehensive historical databases that avoid the problems of cherry picking and the bed of Procrustes. In fact, our CrisisDB project is doing something very similar to what he wrote in the tweets, collecting time-series data on economic, political, social, and cultural variables of societies as they slide into a crisis, and then emerge from it. Perhaps there is a potential for collaboration here. In any case, the hard part is collecting the data, once there is a good database, different teams can (and will) use different analytical approaches to extracting insights from the data.

Eleven years ago we launched the Seshat project with the goal of collecting data that would enable us to test the many theories aiming to explain the evolution of large-scale complex human societies over the past 10,000 years. We now have a bunch of papers, some already published, others accepted and soon to be published, that report on the results of these tests. A major article is coming out next month in Science Advances, in which we tested 17 specific hypotheses from five general classes of theories, and I will talk about it here when it is published.

We are also (finally!) coming to a closure on the controversy about the role of moralizing religion in the rise of complex societies. I wrote about it in these posts:

Do “Big Societies” Need “Big Gods”?

What Came First: Big Gods or Big Societies? Round Two

This debate is the subject of a special feature in the journal Religion, Brain, & Behavior. The Feature has been finalized and should be soon published on line. It starts with the “target” article written by our team (see here the SocArxiv preprint); then there are six commentaries on our article, followed by our response and, finally, a “retake” article on the history of the controversy.

What are the main conclusions? Our analysis confirms that there is a strong and positive correlation between socio-political complexity (SPC) and moralizing supernatural punishment (MSP). However, this correlation arises not because there are causal effects in any direction (that is, either SPC is an evolutionary driver for MSP, or MSP drives SPC, or both), but because both variables respond to a similar set of other evolutionary forces (namely, agriculture and warfare). Thus, the correlation between SPC and MSP is not causal, but “spurious.” This amounts to a decisive rejection of the Big Gods theory (in all its variants).

A summary of causal influences affecting the rise of MSP, suggested by the dynamic regression analysis of the Seshat data (Figure 3 in the Target Article)

Of the six commentaries that were written, all but one are quite positive. They are at times critical of various aspects of theory, data, and methodologies in our article, but such constructive critique is welcome. We do not claim that our article is the final word; clearly, there is a lot of work ahead before we can claim that we really understand the role of religion in the evolution of complex societies. I look forward to this future research.

The one exception is the comment by Purzycki et al. (also published as a SocArxiv preprint). It is a bizarrely intransigent commentary that finds fault with “each step of its [our target article] workflow—from data production and theory to modeling and reporting”. We fully rebut these criticisms in our response, which will be published as part of the special feature, but here I wanted to address the main critique from Purzycki et al, on the causal inference.

If you read their claims carefully, it becomes apparent that this particular team of scientists simply lacks sufficient data-science expertise to evaluate the statistical approaches used in our article. Purzycki et al. are like the proverbial boy with a hammer, to whom every problem looks like a nail. They know one particular approach to causality, which uses directed acyclic graphs (DAG). However, our statistical approach to causality, as explained in the target article, uses a different conceptual framework, dynamic regressions (DR), which is based on the ideas of Norbert Wiener in 1956, later developed by Clive Granger (thus, economists know about this approach as the “Granger causality”).

Possible causal scenarios giving rise to correlation between X and Y

The most important difference between DR and DAG is that the latter does not explicitly include the time dimension. Thus, instead of causal links in DR, such as X_t→ Y_(t+1), in DAG causal connections are denoted without time subscripts, as X → Y. Because of this difference, DAGs have to be acyclic. In other words, scenarios of mutual causation cannot be investigated; only one-directional causality is allowed. Furthermore, the main goal in DAG is estimation of the causal effect. This approach is appropriate if we need to know, for example, by how many years a particular drug would increase life expectancy, when we only have observational data. To answer this question, an analyst must assume a particular DAG—its form is under-determined by (time-unresolved) data. In other words, rather than asking what are causes and what are effects, we assert the direction of causality and then use the data to estimate its strength.

The goal of the DR approach is different. We aim to use data to adjudicate between different theories of social evolution, each proposing a different causal graph. This is generally impossible to do with static (time-unresolved) data, which is why the goal of the Seshat project from its inception was to collect time-series data. Unlike with DAGs, the main goal of the DR approach is model selection, choosing among different models each proposing a different causal graph. We are also interested in estimation, because we want to compare the numerical strengths of different factors, but this goal is secondary to model selection, as we first need to determine which causal graph should be used for coefficient estimation. Thus, the DAG approach, excellent as it is, differs in goals and methods from the DR approach.

The DR approach was designed to resolve questions of causation in evolutionary processes (“descent with modification”) that unfold slowly in time. A model’s ability to predict data is interesting not in itself, but as a tool for adjudicating between different theories. The DR analysis attempts to distinguishes correlation from causation by estimating what influence potential causal factors at a previous time have on the response variable at a later time.

Purzycki et al. did not understand this crucial point, which is why they make statements such as “the direction of causality cannot be determined by data (or regression coefficients) alone.” This statement is correct for an analysis of static data, but not true for time-resolved data. If a theory predicts that a factor X causes Y, but we find that change in Y precedes change in X in time, then clearly the theory cannot be correct. This is how we can use time-resolved data to falsify theories. Of course, there are many complicating factors that require caution in analyzing time-resolved data, but generally a temporal dimension allows us to approach questions of causality much closer than it is possible with static data.

Purzycki et al.’s lack of statistical expertise is particularly glaring in the bizarre simulation model that they use to illustrate their claim that our approach cannot yield accurate causal inferences. They start with this DAG (see their Figure 3, based on the one in McElreath 2020: 515):

Their R script then implements the DAG in a mechanical way that actually does not make sense. Thus, “Missingness” is simply a random variable that is positively correlated with “Writing” (although we would expect that Missingness would decrease in the presence of texts). Worse, Missingness then has positive effects on observed Social Complexity and Moral Gods. In other words, high Missingness increases the value of observed Moral Gods. This is nonsense, because what Missingness actually does is convert some values of observed Moral Gods into missing values. Instead, in the Purzycki et al. model, Missingness grows Moral Gods! Naturally, the “results” from a model with such meaningless assumptions can have no bearing on the validity of the DR approach.

Returning to the Special Feature on Moralizing Religion in RBB, what I find strange is that none of the supporters of the Big Gods theory have chosen to write a commentary on our target article. Most of them have been actively criticizing us on Twitter over the past three years, but when the time came to respond in a substantive way—and they were given ample opportunity to do so—they fell strangely silent. Does this mean they now accept our conclusion? Is the theory dead?

Recently there has been a lot of interest in translating my books into non-English languages, a development that I heartily welcome (I touched upon it in my previous post and in this one). Earlier this month, Warsaw Enterprise Institute published a Polish translation of War and Peace and War (WPW). They asked me for something to prime a debate about my book that they are planning to hold on Tuesday.  So I thought I would offer a retrospective on WPW, looking back to when I initially wrote it. So here it goes.

The original cover of the hard-book edition (2005)

Greetings, my Polish readers!

War and Peace and War (WPW) is my first trade—popular science—book, published in 2005. When I started working on a science of history (which later got a name, Cliodynamics) in the late 1990s, I decided right away that I need to combine science writing (articles in scientific journals and academic books) with popular books. Scientific rigor—details of mathematical models and statistical analyses of data—would be relegated to the academic publications, which would free me to focus on the insights from this science unencumbered by the nitty-gritty. I also wanted to indulge my passion for history (which was one of the main reasons I decided to go into Cliodynamics). Of course, writing historical narratives has no place in an academic article.

Thus, when Stephen Morrow, who at the time started an imprint called Pi Press (which later was folded into Penguin Random House), asked me whether I would be interested in writing a trade book, I immediately agreed. Incidentally, Stephen’s offer was either foolishly brave, or showed an amazing degree of judgment (you choose), because at the time Cliodynamics was a two-year old baby, and I never before wrote a popular book. I literally had to retrain myself as a writer to be able to switch between academic writing and what is needed for a popular book. But I enjoyed the experience, and WPW was followed by my second popular book, Ultrasociety, and now I am working on a third one.

WPW has a somewhat unusual structure. It follows in the footsteps of Historical Dynamics, which was my first academic book introducing Cliodynamics. The questions it asks are familiar to any thinking person. Where did our large scale complex societies come from? Why do they periodically break down? Thinkers from Aristotle, Polybius, Ibn Khaldun (to whom I devote a lot of space in WPW) to Oswald Spengler and Arnold Toynbee—all were fascinated by these questions. And so are contemporary historians and social scientists (there is now a whole new scientific discipline called Collapsology!). So what distinguished my approach?

My goal, from the very beginning, was to help start a scientific field, because a scientific discipline, combining formal models with analysis of big data, and advanced by a community of researchers, will always beat in the long run any individual, no matter how brilliant they are. Back in the early 2000s, of course, we were just at the beginnings of such a new field. Nevertheless, my colleagues and I had already proposed a number of plausibly sounding theories and even tested some of them with data. So my job, as I saw it, was to explain these new insights in non-technical language, and illustrate how the processes we identified worked in concrete historical examples.

As a result, WPW is a kind of a necklace. The string is the leitmotif, the theoretical backbone. The beads are historical narratives of how specific societies, which faced various kinds of challenges, surmounted them (or not). Each narrative illustrated a theoretical idea. Thus, there is a lot of historical detail, but it is needed to flesh out the general understanding of how history works.

Although the theoretical skeleton at the time of writing was still in flux, as my colleagues and I continually developed new models, collected new data, and confronted model predictions with data, overall the main explanations aged surprisingly well. New data, such as the ones now published in Seshat: Global History Databank, and in CrisisDB, currently in development, may have shifted the accents and added additional depth here and there. But overall the theories explained in WPW have stood the test of time.

I hope that you will enjoy the book!

Readers of this blog have, no doubt, noticed that over the past year, or more, my posts here have been few and far in between. I have also been turning down 99% of requests for interviews and public lectures. The reason is that I have taken on too many projects—more than I can deal with. No time left for social media and speaking. This situation will change over the next six months, and I expect to become active on social media again, hopefully from the early Fall on. There are a lot of things happening in the US and in the world that Cliodynamics could help us understand.

The first of two major reasons why I’ve been overwhelmed is that I currently have two jobs. I continue as Professor at the University of Connecticut (this semester I teach Cultural Evolution). At the same time I am Project Leader of Social Complexity and Collapse at the Complexity Science Hub, Vienna, where I supervise a great transdisciplinary crew, which includes a humanities scholar, a data scientist, and a modeler. And that’s just the core group in Vienna; I continue coordinating the research network associated with the Seshat Databank, which has grown beyond hundred members. The past year was a particularly busy period for Seshat, because we have published, or are nearing the publication of about half a dozen major articles reporting the results of analyzing the data we have collected over the past 10 years.

And if it wasn’t enough to keep me busy, I’ve also been working on two major books.

The Scribe (Arthur Szyk) Source

The first one is called The Great Holocene Transformation (GHT). This is an academic book with an ambitious goal: a review of major theories about the evolution of large-scale complex human societies during the Holocene and a systematic test of them against each other using data from Seshat and several other recent databases. I finished a rough draft of this book in early Fall of last year, and asked a number of my colleagues for their critiques. Right now it’s on the back-burner, however, because starting in the Fall I’ve been working on another book. Actually, in the retrospect, it was the right decision to wait with finishing the GHT, because, as the major articles I mentioned above have been going through the journal review process, we have been running additional analyses and finding new insights. It will be good for the GHT to reflect all those developments.

The other book is a trade book (meaning it’s popular, not academic). It is tentatively titled A History of the Near Future, HNF (a title that is almost certainly going to change).

The genesis of this book is an interesting story. After I have acquired not a small amount of notoriety in 2020, when my prediction of the “Turbulent Twenties” had, rather disastrously, turned out to be right, I was approached by several publishers and literary agents. This was an interesting and new experience. For the background, 8 or 9 years ago I tried to sign up with an agent to help me with marketing a popular science book that eventually was published as Ultrasociety. I talked with three agents, but they all turned me down in the end. So I decided to start my own Indy publishing house, see here:

My Venture into the World of Indie Publishing

This was one of the best decisions I ever made. So far Beresta Books (the name of my imprint) has published four books (three authored by me, and one by the Seshat team). Two more books are in the development stage. Foreign language rights for books published by Beresta have been acquired by publishers working in the Chinese, Korean, Japanese, Italian, and French markets. In fact, this week the Italian translation of Ultrasociety will be published under the title La Scimmia Armata (“The Armed Ape” — the publisher thought this would be a better title for Italian readers).

So, now I have an agent (not the one who turned me down in 2014) and a publisher. Actually, several publishers, because my agent has sold the rights to the (yet unwritten) book in the US, UK, Spain, China, and Japan. This is playing in the big leagues… The main publisher is Penguin Random House US. Yes, “random penguins,” as they are affectionately known in the Indy publishing community. The advantages of publishing with one of the Big Five are clear. First and foremost, my publisher has been doing a great job as a macro-editor, helping me to shape the book so that it would have the greatest possible impact. And the market power of Penguin RH to promote the book and get it to the broadest possible audience is humongous.

At the same time, I have no plans to abandon my own imprint, Beresta Books. I will continue publishing academic books (including GHT), books developed by the Seshat project, and perhaps even trade books, if no established publisher is interested yet I feel the book is worth publishing. Beresta Books gives me the freedom to do what I think is best. To reiterate, launching it was one of the best decisions I made in my life, and I have no plans to abandon this venture.

So this is why my life is so busy right now. Why do I think I will have more time for social media later this year? First, I decided to retire from UConn and shift my center of gravity to Vienna. Second, the deadline for turning HNF in to Penguin RH is June (I am about 85% done with the first rough draft, so I seem to be on schedule so far). After that I’ll go back to the GHT and will try to publish it by the end of the year (inshallah). In any case, the main crunch should be over by the end of the year. If things go according to the plan, HNF will be published at some point in 2023. And this concludes my progress report. I’ll be back!

P.S. Whatever happens in this world, life must go on – goals need to be achieved, commitments fulfilled.

The brilliant atomic physicist Enrico Fermi was notorious for unnerving PhD candidates during their oral examinations by asking ‘How many piano tuners are there in the city of Chicago?’ The point of the question, besides the psychological effect, was to gauge how well the candidate could estimate (and justify!) an apparently inestimable quantity. This is something experimental physicists routinely do to estimate whether a physical effect can be measured. Even in the absence of a neighborhood particle accelerator, Fermi questions are fun ways to pass the time on a long car trip (‘How many trees are in the state of Washington?’, ‘How many gallons of water are there in the ocean?’).

Most Fermi questions typically estimate a static quantity but some Fermi questions ask about whether and how a complex system might reach some state. We employ these sort of exercises every day when we ask what the weather will be like in a few hours, whether a stock might do well in a year, or if it is mate in ten. Answering these questions often requires thinking about events that might impact the main underlying processes and about when they might occur. But depending on the complexity of the processes and the odds of the events, people just aren’t very good at keeping track of the alternative paths and hence estimating the likely outcomes.

This is why computers have become indispensable for answering such dynamical questions with any time depth. Of course you have to have a pretty logical, often quantitative understanding of the processes to be able to run such simulations. A lot of work in weather forecasting, climate change modeling, and chess playing involves encoding the ‘rules’ of the process and ensuring the steps are modeled at sufficient resolution to not miss important impacts.

What about Fermi questions applied to history? There have been many well-known questions about events and the dynamics of history, including wondering about the fall of states and the rise of subsequent or invading states. A recent, excellent example of one such investigation is Walter Scheidel’s The Escape from Rome, in which he asks why another Roman empire never arose in Europe after the fall of the original in 476 CE while, for example, vast Chinese empires consistently reformed in spite of various dynastic collapses. His far-ranging investigations asked what other societies might plausibly have taken Rome’s place in the centuries following the fall and the factors that checked each in turn. Could a computer model help illuminate such long-standing historical quandaries?

In a paper published in PLOS ONE last week I did just this, building a sophisticated computer model of agrarian states and nomadic confederations that substantially accounts for the dynamics of societies in Europe and Asia across three thousand years of history, from 1500 BCE to 1500 CE. Originally inspired by work of Turchin et al. 2013, my model combines elements of different popular theories to predict not only the number of states, their location and size but also their population, which drives their military might. The model predicts how a state’s military expansion would be checked logistically and hence when warriors would turn from interstate to civil war, causing new states to appear and spread. Using historically accurate timing, locations and estimates of the severity of the threat from nomadic groups (who used military means to extort tribute from states), the model can, for the first time, account for the rapidly increased size and proper timing of different mega-empires. These include the Achaemenid, Roman, Mauryan, and Han Empires, along with various large ‘mirror’ nomadic confederations such as the Scythians and the Xiongnu, as suggested by Turchin’s 2009 conjecture about an arms race along the agrarian/steppe meta-ethnic boundary.

But the modeling framework goes further. It allowed me to identify a host of key events and developments that are required to account for observed history. For instance, had it not been for the invention of large, fast, and reliable ships throughout the Mediterranean in the first millennium BCE and the use of camels as transport animals in the deserts of Egypt and southwest Asia, our world would look radically different today. By eliminating, delaying or changing the scale of these contingent events, the model can pump our intuitions about what might have occurred ‘but for’ these events. In one experiment, for example, if agrarian states had arisen in 1500 BCE in the Mekong and Ganges river valleys, rather than in the Nile, Mesopotamian, and Yellow River valleys, the time before the nomadic pressure enabled the rise of very large European empires west of Persia would have been delayed by ~600 years. Interestingly, in this case the first Chinese empire would have arisen from the south as the associated military technology would have ‘boomeranged’ back from Persia through India and then via the Mekong states, allowing a very large southern Chinese state to expand north into the remaining hinterland and finally engage with the nomads there a millennium later than observed. The model also suggests that no Persian, Mauryan or Cambodian empire would have arisen in those regions because they were already well-saturated with many smaller, militarily-balanced states, none of which could effectively exploit the improved military technology before their neighbors also adopted it.

Along the way, I was able to answer some other important Fermi questions. How much more agriculturally productive were large-scale Eurasian states compared with non-states? Roughly three times, then doubling to six times starting in the late Middle Ages. How much more logistically effective did the militaries of ancient states become because of the threat of nomadic confederations? Roughly twice. How much longer did the very large mega-empires survive before internal collapse than smaller empires? Roughly not at all – as the figure below shows, the historical data and model predictions agree that increased size appears to be no cure for the threat of internal instability and civil war!

This type of computer model is a crucial step in consolidating and testing causal theories about the pulse of societies in the past as they rose, spread, and, ultimately, fell. Models built to scrutinize the collected insights of historians will continue to pump our intuitions about why and how history unfolded.