The situation: Beginning in May 2020, after the police killing of George Floyd, a Black American man, ‘Black Lives Matter’ demonstrations and riots engulfed the United States, the United Kingdom, and several European countries. Though Mr. Floyd’s killing served as the immediate catalyst for the unrest, many scholars suggest that the COVID-19 pandemic and the resulting economic crisis played a deeper, more pivotal role in creating conditions that led to the protests.
In fact: There has been a steady increase in protests in the United States and Great Britain since 2011, which, as Peter Turchin and other scientists suggest, is the result of a predictable 50-year cycle of socio-political dynamics that has culminated with a surge of violence. This cycle was identified by Russian experts in cliodynamics and structural-demographic theory. Back in 2010, they predicted the current course of events. And now they have been able to verify their mathematical models.
In 2010, the Russian-American scientist Peter Turchin used structural-demographic theory (SDT) to predict the dynamics of socio-political conditions in the United States and Western Europe until 2020. His model predicted that, over the next decade, political instability and an increase in social conflicts would occur in Western democracies. In a new article, Turchin, together with Andrey Korotayev, another leading specialist in SDT at HSE University, conducted a retrospective assessment of the forecasts made in 2010-2012 and confirmed the accuracy of the conclusions. The paper was published in PLoS ONE journal.
In an interview with Vice on October 30, 2012, Peter Turchin said that violence and unrest in the United States can be characterized by 50-year cycles. Bursts of violence are observed in the 1870s, 1920s, and 1970s, and that an intensification of social conflicts, which will eventually spill out into the streets, should be expected in 2020. Moreover, the period of instability can last from 10 to 15 years. The forecast put forth by Turchin, a specialist in cliodynamics (mathematical modelling of socio-historical processes), complex systems, and structural-demographic theory turned out to be surprisingly accurate. This year, most American cities were engulfed in riots, clashes with police, looting, and mass brawls. Rarely have social scientists been so right, as most sociologists, political scientists, and economists are more known for their ability to explain why the scenarios they predict do not come true than to actually predict events.
The conclusions Turchin outlined in the 2012 interview were based on a study conducted back in 2010 (the results of which were published in Nature) as well as a paper published two years later, ‘Dynamics of Political Instability in the United States: 1780-2010’. The scientist was able to do what had once been described only in science fiction novels.
The idea of predicting future historical events using statistical analysis and mathematical modelling was first described in detail in the 1951 novel Foundation by the chemist and science fiction writer Isaac Asimov. In his book, this is a whole separate science called psychohistory.
According to Asimov, modelling history is similar to describing the behaviour of a gas in physics using molecular kinetic theory: it is almost impossible to predict the movement of one gas molecule, but it is quite possible to predict some of its volume. Likewise, in psychohistory it is impossible to predict the actions of a particular person. However, the laws of statistics applied to large groups of people can map the course of future events. The only limitations of the method are that the population must be large enough (in Asimov’s novels it is a quintillion creatures), and most importantly, the society must remain in the dark about the results of the psychohistorical forecast; otherwise, it will change its behaviour and the predictions will not come true.
Subsequently, similar ideas were developed by other science fiction writers like Dan Simmons in his novel Hyperion, where artificial intelligence predicts future events quite accurately based on big data analysis. And in Michael Flynn’s novel In the Country of the Blind,a secret society uses a special science to control large masses of people—Cliology, which also allows one to know the course of history in advance.
As is often the case, ideas quickly migrated from fiction to science. Only instead of cliology, cliodynamics appeared on the scene. In an interview (in Russian) Peter Turchin briefly explains its essence: ‘Historians have 200 hypotheses about the reasons for the collapse of the Roman Empire. Moreover, this number only increases from year to year. At the same time, in the natural sciences, bad theories die off—they are replaced by good ones. No one is seriously considering phlogiston in physics or Lamarckism in biology. But in case of history, there are no selection tools for choosing the most plausible hypotheses. However, using large amounts of empirical data, the best theories can still be selected. The problem is that society is a complex nonlinear system, which means that for this kind of test we must use a mathematical apparatus.’
For this, the following approach is applied: the postulated historical hypothesis is turned into a mathematical model. It is then calculated. A specific prediction is extracted from the model. This forecast is then tested on real historical events. Thus, mathematical models can be tweaked, fine-tuned and, as a result, provide fairly accurate predictive analytics.
Historians are helped by the theory of complex systems, originally developed by physicists to describe nonlinear, chaotic processes, which can be used for climate modelling and weather prediction, for example. The American sociologist and historian Jack Goldstone was the first scholar to apply a mathematical apparatus from the theory of complex systems to historical processes. He developed the structural-demographic theory (SDT), which made it possible to take into account the many forces interacting in society that put pressure on it and lead to riots, revolutions, and civil wars.
Using the SDT, Goldstone established that every major coup or revolution is preceded by a surge in fertility. As a result, the size of the population exceeds its economic possibilities for self-sufficiency. A crisis comes, the population’s standard of living the drops sharply, and unrest begins. At the same time, the state loses political flexibility and the elites split, with some of them siding with the protesters against the current system. A coup takes place, usually accompanied by an explosion of violence and a civil war.
Later, Goldstone’s ideas were picked up and developed by Russian scientists and scholars, including not only Peter Turchin but also Sergei Nefyodov, Leonid Grinin and HSE Professor Andrei Korotayev. They applied their developments to predict socio-historical dynamics in the United States and Great Britain, as well as other Western European countries.
Structural demographic theory consists of four main components:
the state (size, income, expenses, debts, the legitimacy of power, etc.);
population (size, age structure, urbanization, wage level, social optimism, etc.);
elites (number and structure, sources of their income and current welfare, conspicuous consumption, internal competition, social norms);
factors of instability (radical ideologies, terrorist and revolutionary movements, acts of violence, riots, and revolutions).
Goldstone himself also proposed methods to operationalize and measure them, as well as a general integral indicator that allows future unrest to be predicted—an indicator of political stress Ψ (PSI, or the political stress indicator). Retrospective studies have shown that Ψ was off the charts before the French Revolution, the English Civil War, and the crisis of the Ottoman Empire. Therefore, if the mathematical model shows the growth of Ψ curve at any time intervals in the future, then we can confidently speak about coming socio-political instability at this time in this region.
In general terms, the equation for calculating Ψ looks like this:
Ψ = MMP * EMP * SFD
Here, MMP stands for Mass Mobilization Potential, EMP stands for Elite Mobilization Potential, and SFD represents the level of State Fiscal Distress in the state. Each of the equation indicators is calculated separately using many other socio-demographic variables and various mathematical tools, including differential equations.
For example, the level of fiscal distress involves calculating the ratio of national debt to gross domestic product, multiplied by the indicator of the legitimacy of government. The first two values are easy to obtain from official economic statistics. The indicator of legitimacy is calculated as the difference 1 - T, where T is the expressed trust or distrust in the authorities. The latter is defined as the proportion between the number of responses ‘Some of the time’ and ‘Never’ to the question, ‘How much of the time do you trust the government in Washington?’ obtained over the course of routine opinion polling of the US population.
The dynamics of the indicator of political stress (Ψ) was originally calculated for the period from 1958 to 2011, and then its movement was predicted from 2011 to 2020:
GRAPH: Political stress indicator dynamics. Solid line: calculated values; dashed line: predicted.
Source: P. Turchin, A. Korotayev / PLoS ONE
In a new paper, scientists drew information from the Cross-National Time-Series Data Archive (CNTS) database. It contains information on the 200 most important indicators for more than 200 countries around the world from 1815 to the present. The researchers were most interested in data on anti-government demonstrations, riots, government crises, revolutions and purges (although for the United States and Great Britain there is little data for reliable statistical analysis with regard to the last two phenomena). An independent dataset from the US Political Violence Database (USPVD) and an archive of publications from The New York Times were also used to check and correct the information.
It turned out that in full accordance with the forecasts for 2010-2012 in the United States over the past 10 years, the number of anti-government demonstrations has sharply increased, and the number of street riots has increased significantly (see graph below). It is important to note that the prediction made at that point in time was completely at variance with the current trends and could not be a simple extrapolation, since from the early 1980s to 2010 the level of social unrest remained consistently low.
Blue line: anti-government demonstrations. Brown line: riots.
Source: P. Turchin, A. Korotayev / PLoS ONE
In 1845, Karl Marx published Theses on Feuerbach. The 11th and final thesis read: ‘Philosophers have only explained the world in different ways, but the point is to change it.’ Almost two centuries later, it can be reformulated as follows: ‘Previously, historians only described the events of the past and could never reliably explain them, but now their job is to predict the future course of history.’
Yes, it still seems like science fiction. Indeed, the majority of traditional historians are sceptical of Pyotr Turchin’s work and other supporters of cliodynamics and structural-demographic theory, especially since almost all of them are Russians and do not belong to the Western academic establishment.
From the point of view of most historians, any coup, terrorist attack, protest or crisis is due to a complex constellation of preconditions and reasons, completely unique, which can be scientifically described only through an idiographic approach—a careful description of individual phenomena. They believe that it is impossible to reduce them to a system of several relatively simple equations and describe the laws of history nomothetically, or by means of mathematically verified laws, as in physics or other natural sciences.
Nevertheless, these studies from 2010–2012 and their current validation convincingly show that certain socio-historical aspects can be predicted with high accuracy. However, for this, history—a descriptive discipline in the humanities—must become data-driven, i.e., a discipline resting on conclusions that are based on large arrays of empirical data and statistical processing. This requires a paradigm shift in the scientific community.
Moreover, the validity of the conclusions and predictions of models based on the structural-demographic theory shows that it correctly captures the main—structural—driving forces of society. This means that if we can predict popular disturbances, we will be able to prevent them by proposing and implementing the necessary reforms in time or by pursuing a social policy that people are demanding. All this makes SDT a powerful tool not only for scientists, but also for politicians.
It is also important to note that the events of 2020 do not affect or change the simulation results in any way. All the trends that have clearly manifested themselves in the USA, Great Britain, and a number of European countries have been slowly but steadily growing throughout the decade. The COVID-19 pandemic, of course, has also had an impact, and it was impossible to predict it based on historical data (although virologists and epidemiologists have regularly written about the potential danger of coronaviruses in scientific periodicals since the 2000s). But epidemics of dangerous diseases often arise during periods of social crisis and hit the most vulnerable sectors of society (as happened in the United States), which only mobilizes the masses even more and takes them to the streets.