As readers of this blog know well, I don’t claim to be a prophet and I think that prophecy is, in any case, overrated. But I make predictions. A scientific prediction, unlike a prophecy, is not about a future, but about a theory — it’s a way to find out how good is our understanding of the way the world works. I explain more in my 2013 post Scientific Prediction ≠ Prophecy.

As an example of this general philosophy, here’s what I wrote in the final paragraph of an article published in 2008:

Are there any lessons from this history for the current globalization through which we now live? I think there may be, but with two very important caveats. First, as I emphasized repeatedly throughout this chapter, we still have very sketchy understanding of the causes underlying previous world-system pulsations. Much more modeling and empirical research is needed before we could determine just what the history’s lessons are. Second, the world has changed dramatically over the last two centuries. Thus, our understanding of pre-industrial globalizations cannot be mechanically transferred to make predictions about the current one. Our models will have to be greatly modified in order to apply to the modern world. Still, several of the empirical trends associated with the globalization of the twentieth century bear an uncanny resemblance to what has come before. Most obviously, the second half of the twentieth century was a period of massive population growth that has slowed down in the last decade, suggesting that we may be approaching the peak of global population. On the epidemiological front, human emerging infectious diseases have dramatically increased in incidence during the twentieth century, reaching the peak during the 1980s (Jones et al. 2006). The cholera incidence has been on the rise (Figure 11). The AIDS pandemic (Figure 11), as terrifying as it has been, may be the harbinger of even worse diseases to come. These and other trends (for example, the growth of the global inequality of wealth distribution during the last two decades) raise the possibility that studying previous globalizations may not be a purely academic exercise.

For better or worse, these predictions I’ve made have a tendency to eventually become realized (the biggest one is, of course, A Quantitative Prediction for Political Violence in the 2020s). When I wrote of “even worse diseases to come” twelve years ago I, of course, had no idea of Covid-19, or that it would coincidentally hit in 2020, just as other pressures for a structural-demographic crisis are building up to a peak. Rather, this prediction was based on a strong macrohistorical pattern: major pandemics tend to happen during Ages of Discord. For details, see the 2008 article; here I will summarize the main ideas in a nontechnical way.

There are several general trends during the pre-crisis phase that make the rise and spread of pandemics more likely. At the most basic level, sustained population growth results in greater population density, which increases the basic reproduction number of nearly all diseases. Even more importantly, labor oversupply, resulting from overpopulation, depresses wages and incomes for most. Immiseration, especially its biological aspects, makes people less capable of fighting off pathogens. People in search of jobs move more and increasingly concentrate in the cities, which become breeding grounds for disease. Because of greater movement between regions, it is easy for disease to jump between cities.

Elites, who enjoy growing incomes resulting from low worker wages, spend them on luxuries, including exotic ones. This drives long-distance trade, which more tightly connects distant world regions. My 2008 article is primarily about this process, which we call “pre-modern globalizations.” As a result, a particularly aggressive pathogen arising in, for example, China, can rapidly jump to Europe.

Finally, when the crisis breaks out, it brings about a wave on internal warfare. Marauding armies of soldiers, rebels, and brigands, themselves become incubators of disease that they spread widely as they travel through the landscape.

This description is tailored to pre-modern (and early modern) Ages of Discord. Today, in 2020, details are different. But the main drivers — globalization and popular immiseration — are the same.

In my 2008 article I discuss previous waves of globalization (although the early ones are better called “continentalizations” as they primarily affected Afro-Eurasia, rather than the whole world). There is a very strong (although not perfect) statistical association between these globalizations, general crises, and pandemics, from the Bronze Age to the Late Medieval Crisis. The famous previous pandemics such as the Antonine Plagues, the Plagues of Justinian, and the Black Death all coincided (and, typically, helped trigger) prolonged secular crises.

The last two complete crisis periods, the Crisis of the Seventeenth Century and the Age of Revolutions, were truly global in nature. As our data become better for the Early Modern period, we can trace the two pandemics more quantitatively:

The first cycle is traced by resurgent plague, but it should be supplemented by the devastation of the Americas due to such diseases as measles. The second cycle reflects the recurring pandemics of cholera. According to the Encyclopedia of Plague and Pestilence, the great cholera epidemic of 1849 carried away up to 10 percent of the American population. And we shouldn’t forget the Spanish Flu Pandemic, which hit in 1919.

And now it looks like our Age of Discord got its own pandemic.

Last week my analysis of the Covid-19 epidemic in Italy yielded a very depressing result: despite all the measures taken by the government, there was no sign that they were making a difference. The epidemic was still growing exponentially with dire implications for the overall number of deaths it could cause.

My approach to tracking Covid-19 dynamics in various countries is explained in a technical publication and, less technically, in my previous blog post, How Effective Are Public Health Measures in Stopping Covid-19?

Rerunning the analysis on the more recent data (up to March 31) results in a much more optimistic conclusion. There are now clear signs that Italy has turned the corner. Here are the results:

The most visible sign of change is the decline of New Cases. Less visibly, other curves also have started to bend down. The change in dynamics is driven primarily by declining transmission rate of the disease:

At the beginning of the epidemic (in early February) the transmission rate, beta, was almost 0.4, which means that the number of infected grew at the rate of nearly 40% per day (but this explosive rate of growth was invisible to the public or the authorities, see below). The decline in beta was very slow and late (the inflection point of the curve is at March 13). This suggests that it took a while for the exhortation of the Italian government to change the behavior of its citizens. We know this also anecdotally. As late as on March 21, the Chinese Red Cross vice president, Sun Shuopeng, was reported saying “Here in Milan … public transportation is still working and people are still moving around, you’re still having dinners and parties in the hotels and you’re not wearing masks. I don’t know what everyone is thinking.” More amusingly (and we need some humor in these trying times), the latest run-away hit on YouTube has been videos of small-town mayors in Italy raging at people flouting the lock-down. But clearly the gravity of the situation — over 13,000 deaths as of today, has finally impressed itself on the Italians. The transmission rate has been declining — slowly, but is heading in the right direction.

Note the second graph (on the right). It shows quantitatively what we all know: the initial detection rate (the probability that an infected person would become known) started low and then increased. What this means is that we cannot simply use the reported numbers to accurately trace the dynamics of the epidemic. As the detection rate increases (first due to the general awareness that we are in an epidemic, and later as a result of massive testing of asymptomatic people) it would inflate the number of new cases, artificially increasing the transmission rate. To see what is actually happening we need to factor out this change in the detection rate, which is what my model does.

I’ll continue reporting my analysis results as more data come in. I was actually, hoping to run the data from New York, but yesterday they were not available as the team at Johns Hopkins (many thanks to them for putting together and updating the data) changed the way they report USA data.

As many of my readers know, I have accepted the position of research group leader at the Complexity Science Hub (CSH) in Vienna (and I continue as University of Connecticut professor, so right now I am in Connecticut).

A week ago the Austrian government asked CSH to conduct research that would help formulate better policies for dealing with the Covid-19 epidemic. As an aside, I find it incredibly refreshing that a national government would actually ask scientists for help (and have a research institute ready to provide such assistance). In any case, CSH decided to put other research on hold and redirect all of its scientific power to deal with the challenges that the Corona crisis poses to our society.

As a result, last week I have been contributing to a working group that asked the following question: how effective are various public health measures in slowing down, or even stopping the spread of the Covid-19 epidemic? There is quite a lot of variation in how different countries have decided to deal with Corona, ranging from highly Draconian measures implemented by China to (at least, initially) laissez-faire approach of United Kingdom. Of particular interest could be pairwise comparisons between such similar countries as Denmark and Sweden, which have adopted very different Covid policies.

The goal of my research last week, thus, was to estimate the effects of various measures implemented by national governments to slow down and reverse the spread of the Covid-19 epidemic. A direct approach to answering this question is to track the growth rate of epidemic (with the severity of epidemic estimated by the number of “Active Cases”, that is, the number of people known to be currently infected) and then observe how various interventions affect this growth rate. For example, one could use the “basic reproductive number” (R₀) — the average number of cases directly infected by a sick individual. The goal of an intervention is to bring down the reproductive number below 1, which will result in the epidemic dying out.

However, one potential problem with such direct approaches is that a large proportion (at least 50%) of people infected with Corona are “asymptomatic”, meaning that they themselves don’t know they are carrying the disease. As a result, they are not included in the disease statistics. Even worse, the proportion of known infected individuals tends to increase with time, as people become more aware of the epidemic and governments may decide to massively test asymptomatic individuals. Changes in the disease detection rate will, then, tend to mask changes in the epidemic growth rate.

So what can be done? My idea is that we should model both the dynamic process of how disease grows (and eventually declines) and how the numbers of actual infections are translated into official statistics. If we have a good process model (and for epidemics, we do) then an analysis of data based on such a mechanistic model will work better than using a purely data-driven approach. The reason is that we can build into the model what is known which enables us to efficiently use the precious data to estimate what is unknown.

For the technically minded I posted a document that describes exactly what I have done (GitHub:pturchin/Covid19). But in this blog, I will simply illustrate the approach with one specific example using non-technical language, which (hopefully) should be understandable to all readers (ask me questions in comments, if anything is unclear).

Important disclaimer: these results are quite preliminary and should be taken with a grain of salt. I often use my blog to air new ideas in order to find out any problems with them at an earlier, rather than later stage. And I certainly don’t speak for any organization (including CSH) or a government.

The illustrating example I use is the Covid-19 epidemic in South Korea. First, let’s look at the data. The chart below shows the progression of the disease, as measured by the number of “Active Cases” (people known to be infected).

Next, let’s see how fitting a model to these data can clarify the internal mechanics of the epidemic. I use a variant of a standard epidemiological model, known as SIRD (so named for the first letters of the variables it tracks: the numbers of Susceptible, Infected, Recovered, and Dead). We want to make sure that the model does a good job approximating a variety of different angles from which an epidemic can be viewed. The next series of charts show whether the model succeeds in this. Points are the actual data, while curves depict model predictions.

In fact, the model does a very good job. This increases our confidence that it has captured the essential mechanisms driving the epidemic. And we only need to add two additional features to the basic SIRD model to do this.

The key parameter in the model is the transmission rate, which determines how fast the disease spreads from the infected population to that of susceptibles. The second important parameter is the detection rate. Both of these parameters changed during the epidemic. As is well known, once the South Korean officials realized that they have an epidemic to deal with, they massively expanded their testing program and imposed vigorous quarantine measures. These measures should have increased the detection rate and decreased the transmission rate. Building these changes into the model, we can estimate when and how much these two rates changed. Here’s what I got:

Panel (a) shows how the transmission rate (beta) changed with time. Initially the infection rate was very high, with the exponential rate of increase of around 0.4 day^–1 (in other words, every day the number of infected increased by 40%). This parameter began declining after day 25 (mid-February), but reached low levels only close to day 50 (early March).

Panel (b) shows the detection rate. It is estimated as 0 until day 30, which suggests that initially, and for quite a while, the epidemic was growing “below the radar screen”. People were getting sick in growing numbers, but the society as whole was not yet aware of it.

South Korean authorities started testing for Covid-19 in early February, and the scale of testing was massively expanded after Feb. 20, which closely corresponds to day 30 when model-predicted detection rate began increasing. Eventually it reached a very high level of nearly 70%, suggesting that aggressive testing of asymptomatic people is bearing fruit.

Overall, then, this analysis of South Korean data makes a lot of sense in light of what we know about the course of the epidemic there. There are some caveats, which I discuss in the technical document, but the model fits exceedingly well and provides us with numerical estimates of the effectiveness of the measures taken by the SK government. The intervention was highly effective.

A future post will report on the analysis I’ve done for China. The situation there was more complex, and the model fit was not as excellent as for the SK epidemic. But it still yields very interesting and instructive insights. Stay tuned.

Added (21:00 23.III.2020): I have posted the document providing technical details and the R-script on my GitHub directory

My reading of the month is Unearthly Powers by an Oxford historian Alan Strathern. It’s a very interesting and thought-provoking book. Highly recommended.

There is much that I like in the book. Strathern avoids the ideological extremes that preoccupy today’s humanities, such as an aversion to essentialism and teleology, and the prioritization of the emic over the etic (if you have no idea what these mean, no worries, it doesn’t affect what comes below). I like his defense and practice of the comparative method in history, and his willingness to engage with questions of large-scale causation. But the most interesting and thought-provoking, to me, was the core argument of his book, stemming from the distinction that he makes between two forms of religion, which he calls “immanentism” and “transcendentalism”.

As a true scholar, Strathern defines these two forms using very precise technical language, listing ten characteristics of the first and fifteen characteristics of the second. But let me try translating the main ideas into human language.

Immanentism is really about the supernatural side of religion. It’s about gods, angels and demons, spirits, and departed ancestors. Its focus is on how these “metapersons” can be induced to avoid harming one, or harnessed to advance one’s interests. Thus, as Strathern argues, it’s primarily about power: ability to avoid bad outcomes and to achieve good ones in the here and now. Morality is local and unsystematized, or even not an important part of religion. The focus is on ritual, propitiation, and sacrifice, including human sacrifice. As a vivid example of the latter, and a great illustration of what immanentism is about, recollect how following a dire military defeat Romans during the Republican Period on several occasions buried alive two pairs of foreigners in the Forum Boiarum to propitiate the gods and to protect the city from invaders.

Transcendentalism is about salvation, liberation, or enlightenment—“escape from the mundane reality” as Strathern puts it. Variants include entrance into the paradise or escaping the endless rebirth cycle. Transcendental religions are profoundly moralizing. Ethical norms are codified and arranged into lists of prohibitions or injunctions, as in the Ten Commandments of Christianity or the Five Precepts of Buddhism. Religious specialists are highly organized and gain great power and potential autonomy from the state institutions. Interestingly, all transcendental religions repudiate blood sacrifice.

Of course, these are “ideal types” and there are many gradations in between. Immanentist religions can have transcendental elements. Furthermore, the switch to a transcendental religion may be rapid (often happening as a result of conquest or ruler conversion), but usually not complete, and the result is often a synthesis between a transcendental religion and the local varieties of immanentism. In fact, a central thread running through Strathern’s book is the uneasy coexistence between transcendentalism and immanentism, with transcendentalism periodically “back-sliding” and needing a revival or reform movement to purify it of creeping immanentism.

Because I have become involved in the debate about the Big Gods theory (see What Came First: Big Gods or Big Societies? Round Two), I was particularly interested in the profoundly different approaches to morality in immanentist versus transcendental religions. Strathern’s book made me look at the whole question of the role of supernatural beings in sustaining cooperation in large-scale societies from an entirely different angle. So here’s how my current thinking goes.

Morality in small-scale societies is sustained by face-to-face interactions. Everybody watches each other and imposes sanctions on non-cooperators ranging from mild ones, like gossip and ridicule, to severe ones, like expulsion and capital punishment. Morality is not systematized—there is no explicit list of rules, because everybody learns them the way children do. Religion is immanentist. Some spirits and deities may care about morality and even punish the bad and reward the good, but the main focus is on manipulating reality to avoid negative outcomes and to achieve positive ones. For example, the spirit of the hunted deer needs to be propitiated so that this hunt and the next one are successful.

With the rise of centralized societies, chiefdoms and archaic states, at first things don’t change dramatically. Morality is still local, which creates problems for integrating these larger-scale societies, because people coming from different local groups don’t cooperate well with each other. The nature of supernatural agents change in that they become more hierarchical, reflecting the social arrangements in the real world. But the main focus of religion is still on power, not goodness.

And then there is an abrupt (on an evolutionary time scale) rise of transcendental religions, more commonly known as World Religions or Axial Religions, because they appeared during the Axial Age. The proponents of the Big Gods theory emphasize the supernatural aspect of world religions. Ara Norenzayan argued that because “watched people are nice people” in large-scale societies the role of watchers is taken over by gods (see Do “Big Societies” Need “Big Gods”?).

The Watcher by Kurt Huggins.  Source

But after reading Unearthly Powers I now think that this supernatural part is really a side issue.

First, the supernatural aspect varies quite a lot between Axial religions. It’s quite prominent in the Middle Eastern monotheisms (Zoroastrianism, Judaism, Christianity, Islam, etc.), but not so in the South Asian religions (Buddhism, Hinduism) in which the main moralizing force is karmic retribution. As many scholars of Buddhism emphasize, karma is not really a supernatural thing. It’s simply the operation of cause and effect. You kick a ball and it rolls away. You do really bad things, and you get reborn as a frog. No supernatural watchers or punishers are needed. And the supernatural content is almost entirely absent in Confucianism, which many scholars don’t even consider to be religion.

Second, the main watchers and punishers are not supernatural beings but very human people. They include neighbors, agents of the state, and, especially, the clerics. An amusing illustration of how the real-life “Eye in the Sky” operates is provided by the viral video of the Chinese drone operator chiding an elderly woman who failed to wear a mask during the coronavirus epidemic.

A very non-supernatural Eye in the Sky

Third, people who grow up in societies with fully moralized organized religion internalize the rules of morality. Many behave morally even when not watched and there is no possibility of punishment.

To conclude, when I first read Ara Norenzayan’s book, I was quite impressed by its main argument, and wrote a positive review (see From Big Gods to the Big Brother). But the more I learn about the evolution of religion in past human societies, the more skeptical I become. It’s a really neat hypothesis, but, as happens in science, beautiful theories are often slayed by ugly facts.

Question: What was the word for “two” used by people living in the Pontic-Caspian steppes (modern Ukraine and Southern Russia) 5,000 years ago?

Answer: *dwóH₁

This is how historical linguists reconstruct “two” in the Proto-Indo-European (PIE) language (see Indo-European vocabulary on Wikipedia). And now evidence accumulates that PIE speakers belonged to what archaeologists call the Yamnaya culture. Sure, there is still a bit of a controversy lingering about some aspects of this reconstruction, but recent aDNA evidence, in my opinion, has quite decisively put a wooden stake into the heart of  the alternative Anatolian hypothesis. Of course, nothing in science is 100% certain (if you want certainty, your best recourse is Divine Revelation). But to me, 99% certainty is good enough.

At a very fundamental level, historical linguists can reconstruct the PIE vocabulary with a high degree of certainty because language change is an example of cultural evolution. Or, as Darwin could have said, it’s ”descent with modification.” Many aspects of language change slowly and in a remarkably law-like manner. For example, I never studied German and don’t intend to do so formally. But I’d like to learn the language as I will be spending a lot of time in Austria in the next five years. So I listen to announcements on the bus and eavesdrop on other passengers. When walking in the streets of Vienna I play the game “figure out the the meaning of this word” (on a street sign or advertisement). The game goes like this: if the first letter in a German word is “z”, try substituting it with “t”; if “t”, with “d”, “d” with “th” and so on. Replace a “b” in the middle with “v” (see here). In many cases you will recover a word that is remarkably like its English equivalent. For example, the mysterious “Diebe” after substitutions becomes “thieve”–thieves!

Back to the PIE. It is absolutely remarkable that we can reconstruct how a word sounded 5,000 years ago—and remember, the Yamnaya people had no writing!

Historical linguistics is clearly the best developed case-study of phylogenetic reconstruction in cultural evolution. But why stop with language—what about religion? My good friend and colleague David Sloan Wilson has long argued that we should use the methods of evolutionary science in the study of religion (read his great book, The Darwin’s Cathedral). Religions evolve. Early Christianity evolved from Judaism. It then split into different branches: Monophysites, Arians, Chalcedonians, …, with Chalcedonians splitting later into the Eastern Orthodox and Western Catholic branches. Indic religion gave rise to Hinduism and Buddhism, with the latter splitting into Hinayana and Mahayana branches.

Of course, it’s not only descent with modification; different languages and religions also borrow elements from each other. The tree model of linguistic evolution needs to be supplemented by reticulations connecting different branches. And so do trees of religions (reticulations denoted with broken lines):

These ideas have been much on my mind during the past year. With the publication of our Nature article on moralizing gods the Seshat project has broken new ground—we are now testing evolutionary theories of religion. Some critics charged that we are trying to do the impossible. Texts and records become sparse as you go back in time. And once you go to the time before writing, they maintain, you cannot say anything about religion.

But that’s clearly wrong. If historical linguists can reconstruct the sounds of languages that disappeared well before writing was invented, why shouldn’t we able to do the same with religion? In fact, it is already being done.

I just finished reading a remarkable book by one of Seshat contributors, Patrick Kirch, co-authored with Roger Green. In Hawaiki, Ancestral Polynesia, published in 2001, Kirch and Green develop the phylogenetic model and apply it to cultural evolution in the Pacific Ocean before the Europeans arrived there (note: the Polynesians never developed writing!). They use a “triangulation method” in which historical linguistics, archaeology, comparative ethnology, and biological anthropology are integrated for the purpose of historical reconstruction. And I would add that in the eighteen years since they published the book, we have acquired an additional powerful source of information: ancient DNA.

An integrated approach is key. For example, historical linguistics is not great at timing when different languages split (it is now clear that glottochronology is much more difficult than initially thought). But archaeology fills this gap by telling us when people arrived at different islands. And so on. Where one avenue of reconstruction fails, another comes to rescue.

Kirch and Green use the phylogenetic model to yield extraordinary insights into the world of Ancestral Polynesians: which islands they inhabited before colonizing most of the Pacific, what they ate and how they prepared their food, how their material culture and socio-political organization evolved, and how their rituals and beliefs about gods and ancestors changed with time.

From their reconstruction it is clear that such fundamental concepts as mana and tapu were well-established in Ancestral Polynesia, but they also have undergone additional evolution in different branches occupying different archipelagos in the Pacific. The deification and ritual supplication of ancestors was also virtually universal.

Of particular interest is their reconstruction of what they call “an elaboration of the pantheon” which particularly affected the Eastern Polynesian societies. First to be added to the single Proto-Polynesian god, *Taangaloa (* indicates reconstruction) was a god of war, *Tu(q)u. Later four more named gods were added to the pantheon. These innovations were accompanied by an elaboration of the ritual. As an example, Kirch and Green suggest that an important innovation during the Proto Central-Eastern Polynesian phase was *tiki (“carved human image”).

Central-Eastern Polynesians (CEP) are of great interest to us, because one of the regions that we code in the Seshat World Sample-30 is Hawaii, which belongs to this branch. Although Kirch and Green don’t directly address the moralizing aspects of the Polynesian religion, Patrick Kirch has been very helpful in answering questions about moralizing supernatural punishment (MSP) that members of the Seshat project posed to him.

According to our informal and very tentative reconstruction (which still needs more expert advice!), there are clear MSP elements in Hawaiian religion. In particular, the kapu system (tapu/tabu in other Polynesian languages), which denotes what is sacred or forbidden (for more information, see this article on Wikipedia), included injunctions against deceit, theft, and murder. But these moralizing elements were of secondary concern compared to ritual infractions. Furthermore, offenses against kapu were primarily policed by human agents (chiefs and their retinues), rather than by supernatural agents (spirits and gods).

Our survey of Hawaii’s “sister cultures” (Maori, Rarotonga, Tahiti, Tuamotu, and the Marquesas) suggests that even these, relatively weak, MSP elements were largely absent in other CEP branches, with a possible exception of the Marquesas, where theft and murder also could be subject to supernatural punishments (but with the same limitations as in Hawaii). The idea of punishment/reward in the afterlife appears to be universally absent in all Polynesian cultures.

This survey raises a number of intriguing questions. Are MSP elements, which we see in Hawaii, a relatively recent innovation? Note that Hawaii was settled by colonists from the Marquesas. It is perhaps significant that MSP elements in the CEP are found only in these two cultures.

All of this is quite speculative—remember that I often use my blog as a platform for airing new ideas and soliciting comments and critique. What we need is the application of the phylogenetic model to this question by specialists on Polynesian culture (of which I am, most assuredly, not one). Careful reconstruction using the triangulation method of Kirch and Green could be complemented by more quantitative Bayesian phylogenetic models that have been developed by such cultural evolutionists as Ruth Mace and Russell Gray. In fact, Gray’s group recently (in 2015) published a Bayesian analysis of moralizing religion in Austronesia (a broader linguistic grouping that includes the Polynesians). I am in contact with the first author of the article, Joseph Watts, about the details of their data and analyses.

In conclusion, Polynesia (and, more broadly, Austronesia) is a great “polygon” that has served us well in developing a variety of approaches for reconstructing cultural evolution of prehistoric societies. But it’s not the only one. Take Indo-Europeans. Jean Haudry in 1993 compared oath formulas from a number of Indo-European languages (Old Norse, Russian, Sanskrit, and Persian) and found that they share the image of the perjurer struck by his own weapon. Was this MSP element present in the PIE culture? There is a great potential for employing the phylogenetic model to reconstruct not only past languages, but also elements of past religions. And this potential has hardly been tapped.

As long-time readers of this blog know, I am not only a scientist, but also a scientific publisher. I founded and indie imprint Beresta Books in 2015 to publish academic and popular non-fiction books that do not fit comfortably within traditional disciplinary boundaries. The main, but not exclusive, focus of this imprint is on Cliodynamics, a transdisciplinary area of research after which this blog is named.

My primary motivation in launching Beresta was the changing relationship between academic publishers and scholars, which became increasingly exploitative after 2000. At the same time the rapidly evolving landscape of publishing created an opportunity for small independent publishers to challenge the dominance of the big, traditional publishing houses. Here are some of the posts I wrote on publishing in the past five years:

My Venture into the World of Indie Publishing

My Indie Publishing Venture: the Saga Continues

Ages of Discord: My Third Independently Published Book

Indie Publishing ≠ Self-Publishing

Well, I am happy tor report that on Dec. 8 Beresta has published its fourth book:

Seshat History of the Axial Age

You can read about the prehistory of this book in a blog post by the editors Jenny Reddish and Dan Hoyer.

The first three books published by Beresta were books authored by myself. But as I wrote two years ago, my plan was always to branch out into publishing books written by others. I don’t want to become a commercial publisher. Instead I’d like to continue publishing books in Cliodynamics, Cultural Evolution, and similar disciplines. Think of Beresta as a boutique scientific publisher, focusing, basically, on whatever I think is worthy of publication–what I find interesting and well-written.

Seshat History of the Axial Age (SHAA) is the first book for which my main role is as publisher, not author (I did contribute to the last, concluding chapter, but that’s all).

Now a few words about what else I learned in producing this book as publisher. Mostly, the process went smoothly, because I have already done this three times before. The great advantage of previous experience is that I have gathered a talented crew of specialists on whom I know I can rely, including a typesetter, an indexer, and a graphic designer. A new team member is an artist who drew the illustration for the cover, inspired by a relief detail from Achaemenid royal tombs at Naqsh-e Rustam, Iran. I think she and the graphic designer, who placed the drawing in the cover design, did a wonderful job:

Visit the artist’s website

Finally, about the future plans for Beresta Books. SHAA is only the first offering in the newly minted Seshat Histories series. Soon to follow are Volumes II and III dealing with the rise and spread of moralizing religion and the practice of human sacrifice. And I started working on my own book, tentatively titled Evolution of Complex Societies: Theories and Data.

About the name: ‘Beresta’ (birch bark in Russian) was used in medieval Russia as paper is used today to scribble notes for sending to a friend or business partner. The Beresta logo is styled after the Russian letter “B” as it was inscribed on a piece of birch bark with a few strokes of the stylus.

In my previous post, Historians and Historical Databases, I discussed how the Seshat Databank would be impossible without a close collaboration with historians and other humanities scholars. Today I want to give a specific example of how this collaboration works.

For those who have not followed the Big Gods controversy closely, last Spring the Seshat project published an article in Nature, Complex Societies Precede Moralizing Gods throughout World History. The article generated a ton of positive press (as is usual for a Nature article), but it also elicited critique from some, including supporters of the Big Gods theory. We have responded to these critiques on the preprint server SocArXiv (https://osf.io/preprints/socarxiv/xjryt; https://osf.io/preprints/socarxiv/t8hgu).

The heart of a departed is weighed against the Feather of Ma’at Source

But the Nature paper was only the first of many planned articles investigating the role of religion, morality, and other factors in the evolution of social complexity. We have recently completed a further wave of analyses using more detailed data on moralizing religion, and testing additional theories explaining the evolution of Big Gods. The new analyses confirm our original headline finding (that Big Gods come after Big Societies) and also explore additional dimensions to this complex topic. Before we submit it a journal, however, we want to get feedback from all interested parties, including our critics.

To make the whole process extra transparent, we have posted all related documents online. The main article is on SocArXiv: https://osf.io/preprints/socarxiv/2v59j/, and it is accompanied by supplementary online materials that include the data and the analysis scripts. Our aim is to achieve maximum transparency and critical engagement—everyone who wants, can critique and comment on each phase and reanalyze the data.

But we are also breaking new ground in this project. In addition to posting data and programs, we have invested a huge amount of effort into clarifying where the data come from. And this is where the collaboration with humanities scholars become critical. Most of the work during the past several months was devoted to building “analytic narratives” underlying the Seshat data.

Analytic Narratives are formalized verbal accounts focusing on several (in our case, many) in-depth case studies. The goal of this methodology is to employ the specialized knowledge possessed by historians, archaeologists, anthropologists, and religious studies scholars, who have the understanding of the particular, for the purpose of testing theories that may apply more generally. General theory (which focuses on moralizing religion in our case) imposes structure on verbal accounts by specifying which aspects of past societies we would like to get information on. But within this framework, scholars are free to explore variability between different societies, different continents, and different eras. The aim is to reflect in the document evolving interpretations and persisting controversies. Such qualitative nuance provides a much needed counterbalance to the “hard” quantitative data, which by their nature strip it away.

For those interested in looking “under the hood”, the current draft of this document is posted here:

http://seshatdatabank.info/wp-content/uploads/2019/11/MSP_Narratives_CurrDraft.pdf

Keep in mind that it is very much work in progress—some chapters and sections contain much more text and references than others. There are several reasons for this, the main being that there are societies for which we haven’t yet been able to recruit experts. Our approach in such cases is to “prime the pump” by including an initial description, based on the reading of available sources and then invite expert feedback and elaboration on this initial text.

As I stressed in my previous post, the success of Seshat critically depends on close collaboration with professional historians and other humanities scholars. By adding Analytic Narratives to the spectrum of products from the Seshat project we aim to deepen this collaboration and, more generally, to contribute to a dialogue between humanities and sciences. The richness and quality of Seshat results will also be enhanced. I am very keen to see how this will develop in the near future.

In recent days there was much discussion by historians on Twitter of the proper and improper uses of historical knowledge in testing social science theories. It was initially prompted by the publication of a Science article last week on historical Church exposure and global psychological variation. Most of it was quite negative (I am still reading the article and its voluminous appendices, so I reserve judgment). Some of this negative reaction spilled over to Seshat as a result of Laura Spinney’s publishing yesterday a “long read” on Cliodynamics and the Seshat Databank in the Guardian. But such criticism can only come from those who know nothing about how Seshat works.

The Seshat Project is a collaboration between historians, archaeologists, and social scientists. Historians play key roles in all phases of building the Databank. Two out of five members of the core group have PhDs in History. Historians are involved in the workshops in which we develop conceptual scheme for translating knowledge about past societies into data; they consult Seshat research assistants and check Seshat data, and they fully participate in writing the resulting articles. Seshat articles typically have dozens of authors (in two cases over 50), most of them historians. Seshat would be impossible without such intense collaboration between scientists and humanities scholars. You can read more about how Seshat operates in “An Introduction to Seshat: Global History Databank,” in press in Journal of Cognitive History (SocArXiv Preprint).

Collaboration between scientists and humanists requires a lot more work than other interdisciplinary projects. The “two cultures” are often motivated by different research interests and goals, and use very different methodologies and languages. This is one of the reasons why Seshat spent such a long time in gestation (the project was launched in 2011, but the first article fully utilizing Seshat data was published only in early 2018). Our multi-authored articles go through innumerable iterations before the co-authors, coming from very different research traditions, can arrive on mutually intelligible and agreeable texts. This is a stiff price, but well-worth paying.

Seshat goals also reflect the diversity of motivations of the project participants. As a scientist I am primarily motivated by using the rich knowledge, possessed by the scholars of the past, in testing scientific theories about how human societies evolve over time. But over the last three decades I read and enjoyed thousands of books and articles by specialist historians and archaeologists, who delve into the intricate inner working of past human societies. Such knowledge is fascinating to me because of its intrinsic worth, irrespective of whether we capture it in the Databank.

Initially I thought that only a small fraction of historians would be interested in the Seshat data. I am happy to report that I was wrong. In fact, most historians we approach get involved. The degree of involvement varies. Some help us with identifying good general sources and answer a few questions. Others write detailed narratives and code datasheets with hundreds of variables. Most find a comfortable level of involvement somewhere in between.

Furthermore, many historians are interested not only in studying a particular society, or a segment of it, at a particular time (which is a very worthwhile and important), but also care about comparative history. Seshat allows one to start in a particular society at a particular time and focus on a particular aspect of it, and then “travel” back and forth in time, or in space, or in the conceptual space of different variables. It’s like “comparative history on steroids.”

Thus, Seshat is not only a vehicle for testing scientific theories, it is also a great informational resource both for the scholars of the past and (eventually) for general public. We are currently working on implementing features in the Databank that would make travel in time, geographical space, and conceptual space effortless. And Seshat publications are not limited to articles in scientific journals. We also produce “Analytic Narratives”, formalized verbal accounts focusing on multiple in-depth case studies. The first such narrative, Seshat History of the Axial Age, will be published in early December, and two more are in the works.

In closing, I want to reiterate that Seshat is far from being a threat to History. Seshat depends on the work of countless professional historians who are contributing to a remarkable store of knowledge about the human past. It also builds on and expands comparative history, which has been what historians have done ever since Polybius and Ibn Khaldun. Seshat also allows us to test various theories about the functioning and dynamics of past societies proposed by historians, archaeologists, anthropologists, and economists. By doing that, the Databank increases our understanding of how present-day societies work. And perhaps it will even enable us to change our societies so that they could deliver better human well-being. The last is not certain, but we will not know whether it is possible until we try.

Last week I visited Centre for Complex Systems Studies (CCSS) in Utrecht, where I gave a talk about my research results and plans for the Ages of Discord project. Several people on Twitter asked to see the slides, and so I am posting them on this blog.

First, here’s an abstract of the talk:

A History of the Near Future: What history tells us about our Age of Discord
Peter Turchin
Complexity Science Hub Vienna, and University of Connecticut

Social and political turbulence in the United States and a number of European countries has been rising in recent years. My research, which combines analysis of historical data with the tools of complexity science, has identified the deep structural forces that work to undermine societal stability and resilience to internal and external shocks. Here I look beneath the surface of day-to-day contentious politics and social unrest, and focus on the negative social and economic trends that explain our current “Age of Discord.”

Second, the slides are posted as PDF here.

Third, you might be interested in two articles that provide more detail on our research plans:

Turchin, Peter, Nina Witoszek, Stefan Thurner, David Garcia, Roger Griffin, Daniel Hoyer, Atle Midttun, James Bennett, Knut Myrum Næss, and Sergey Gavrilets. 2018. History of Possible Futures: Multipath Forecasting of Social Breakdown, Recovery, and Resilience. Cliodynamics 9: 124–139.

Turchin, Peter, Sergey Gavrilets, and Jack A. Goldstone. 2017. “Linking ‘Micro’ to ‘Macro’ Models of State Breakdown to Improve Methods for Political Forecasting.” Cliodynamics 8: 77–99.