The Guardian: “History as a giant data set: how analysing the past could help save the future”

In recent decades, the letter went on, a number of worrying social indicators – such as wealth inequality and public debt – had started to climb in western nations, indicating that these societies were approaching a period of upheaval. The letter-writer would go on to predict that the turmoil in the US in 2020 would be less severe than the American civil war, but worse than the violence of the late 1960s and early 70s, when the murder rate spiked, civil rights and anti-Vietnam war protests intensified and domestic terrorists carried out thousands of bombings across the country.

The author of this stark warning was not a historian, but a biologist. For the first few decades of his career, Peter Turchin had used sophisticated maths to show how the interactions of predators and prey produce oscillations in animal populations in the wild. He had published in the journals Nature and Science and become respected in his field, but by the late 1990s he had answered all the ecological questions that interested him. He found himself drawn to history instead: could the rise and fall of human societies also be captured by a handful of variables and some differential equations?

Laura Spinney

Fascinating approach! Reminds me almost exactly of Asimov’s concept of psychohistory in his Foundation series. Considering how accurate Peter Turchin’s predictions for 2020 have proven, I intend to follow his work more closely.

General well-being (that is, of the overwhelming majority of population) tends to move in the opposite direction from inequality: when inequality grows, well-being declines, and vice versa

There is a visceral feeling, not only among historians but also among many ordinary people, that humans cannot be reduced to data points and equations. How can an equation predict a Joan of Arc, or an Oliver Cromwell? History is not a science, says Diarmaid MacCulloch, a historian at the University of Oxford, summing up that view. At the bottom of it is human behaviour, and that is terrifyingly unpredictable.

This argument gets it exactly wrong, argues Turchin, who since the early 1990s has been a professor in the department of ecology and evolutionary biology at the University of Connecticut, and is now also affiliated with the Complexity Science Hub in Vienna. It is because social systems are so complex that we need mathematical models. Importantly, the resulting laws are probabilistic, not deterministic, meaning that they accommodate the element of chance. But this does not mean they are hollow: if a weather forecast tells you there is an 80% chance of rain, you pack your umbrella.