Cliodynamics, the science that predicted Trump and a surge of violence to come
How science tackled history and came out predicting Donald Trump susceptibility.
History, many think, does not repeat itself. Considering we as humans have never been at this level of technology, knowledge and intelligence, it might be hard to image that anything repeats itself, at least for our generation. But now scientists are putting out some data and start to tackle this question.
Historians believe that almost every development in modern times has an equivalent in the past, that the rise and fall of empires and dictators, wars and political conflict, socio-economic factors leading to revolution, genocides and racial/religious prosecution are just repeating occurrences throughout time. Which begs the question:
If history repeats itself, should we not be able to make predictions about the future?
And in fact, some scientists started investigating this notion of historians and tried to back it up with numbers. With the help of mathematical models and a holistic approach, scientists use the immense amount of historic data available and look for correlations in population rates, economic fluctuations, state collapses and many more parameters, trying to integrate them into a “big history” theory. Funnily enough, this model does not only work to recapitulate past events, but might as well be able to predict future events using prediction data of these diverse parameters.
What sounds like science-fiction is actually done, field pioneers like Peter Turchin have analysed historical records on economic activity, demographic trends and outbursts of violence in the United States, and have come to the conclusion that a new wave of internal strife is already on its way. With colleagues, Turchin’s data allowed to define two trends or “endless repeating cycles”, the longer “secular cycle” runs around 200–300 years.
“It starts with a relatively egalitarian society, in which supply and demand for labour roughly balance out. In time, the population grows, labour supply outstrips demand, elites form and the living standards of the poorest fall. At a certain point, the society becomes top-heavy with elites, who start fighting for power. Political instability ensues and leads to collapse, and the cycle begins again.”
More interestingly, they also observered a shorter cycle, spanning approximately two generations (50 years) and named thereafter “father-and-son cycle”. The father perceives a social injustice and responds violently, and the son lives with the miserable legacy of the resulting conflict. The third generation starts afresh with a violent response to an injustice which has it’s roots in the father’s generation.
These two cycles fit patterns of instability across Europe and Asia from the fifth century BC onwards.
Furthermore, Turchin’s model was able to predict the uprising of revolution in Egypt in 2011, something that was rather unforeseen as economy was growing and poverty at relative bay. But Turchin explained:
However, in the last decade egypt saw a quadruple of graduates with no prospect, a marker of elite overproduction and hence, trouble.
So math is able to predict our future based on data from our past?
Not quite that easy. While it is true that some global trends seem to run in cycles, mathematical models can not predict how certain things are going to play out exactly, or which factors and parameters are going to shift. Herbert Gintis, a retired economist who is still actively researching the evolution of social complexity, thinks that the patterns and causal connections that cliodynamics reveal can teach policy-makers valuable lessons about pitfalls to avoid, and actions that might forestall trouble. He offers the analogy to aviation:
“You certainly can’t predict when a specific plane is going to crash, but engineers recover the black box. They study it carefully, they find out why the plane crashed, and that’s why so many fewer planes crash today than used to.”
So taking a look into past mistakes can prevent future disaster, I think many of us can agree intuitively, that this makes sense.
Shockingly, policy makers did not get the memo. Peter Turchin’s work was even featured in scientific press “nature news” 2012, with a clear warning for US politicians:
“Therefore, if Turchin’s prediction of unrest in the United States around 2020 is correct, the Whitehouse would expect the next few years to see an increase in tightly knit US groups whose rituals have a threatening quality but promise great rewards.”
What seems very speculative back in 2012, when the economy was recovering well from the 2008 stock market crisis and President Obama was starting his second term trying to unify the American people, became now reality with the 2016 US presidential race.
Consider the tightly knit group of Donald Trump supporters. When he stands on stage and shouts: “Who is gonna pay for that wall?” , the crowd chants: “Mexico, Mexico!!!”… those are rituals directed at our primal heritage, when the world was deadly and everything that was foreign automatically considered dangerous. If you incite fear in people with terrorism and propaganda, they will strive for strong leaders who promise quick solutions. Unfortunately, a black and white dualistic view on the world, an “us versus them”-mentality, is not reflecting the reality and complexity of our everyday lives. So why do populistic promises always work in (perceived or real) crisis?
Our brains are wired to love easy solutions, especially to complex and difficult topics.
The less we know about the nuances, the more confident we are in our convictions. To quote psychologist and best selling author Daniel Kahneman, our brain works on a “what you see is all there is”-basis, meaning the less facts we know about something, the more we believe in the few things we know about it. That’s why so many people believe in the great promises Donald Trump delivers with so little concrete plans, because it’s easy to understand, convenient, it’s the ever-old “us-versus-them” illusion. He is not the first nor will he be the last political figure to abuse our primal mindsets, the only thing that changed is that:
We had the data to predict population susceptibility and should have tried to stir against it.
This susceptibility is what got the US in the dilemma that is populist Trump, but they are not the only one. Many states in the European Union are seeing a wide shift to far right-wing politics and propaganda because of the refugee crisis and terror attacks in Paris. Populism is a global problem that could be solved if more effort would be put into educating people and policy makers alike about the dangers of “easy solutions”, or at least if people would remember the troubled past of populist dictators.
Now even if Trump does not win the US presidency (which is pretty much a coin toss by now), the masses of people aroused by his appearance are not suddenly going to be less radical and susceptible to propagandist bullshiting, they are here to stay. And it will be everyones job to re-integrate them.
The only thing we can hope for, is that the surge of violence that is predicted to happen can be counteracted with milder and more integrative policies sooner rather than later.
As for cliodynamics, and the science of predictive modeling, I hope that it will serve more policy makers as a black box, a tool to avoid accidents and helping them engineer a better future for all of us.
This article is biased with respect to characterizing D. Trump as a populist, which you might disagree on. Although I strongly believe the facts might be on my side given the amount of data presented by his speeches and actions. I do not care for or against any particular person, but against the methods of populism used by any person; no matter if it is Trump, Hillary, left- or right-wing populism has to be kept at bay to find real solutions to real problems; and to pave the way for a better future.
Cliodynamics, or the science behind it, is non-political and in this case merely predicting population susceptibilities and predisposition to violence and social unrest, taking into account data from the past. These data include parameters like income, economic strength, economic inequality, social injustice, employment rates, education levels and many more. It is basically the science of understanding the past to not make the same mistakes all over again in the future.