To Peter Turchin, who studies population dynamics at the University of Connecticut in Storrs, the appearance of three peaks of political instability at roughly 50-year intervals is not a coincidence. For the past 15 years, Turchin has been taking the mathematical techniques that once allowed him to track predator–prey cycles in forest ecosystems, and applying them to human history. He has analysed historical records on economic activity, demographic trends and outbursts of violence in the United States, and has come to the conclusion that a new wave of internal strife is already on its way. The peak should occur in about 2020, he says, and will probably be at least as high as the one in around 1970. “I hope it won’t be as bad as 1870,” he adds.
Turchin’s approach which he calls cliodynamics after Clio, the ancient Greek muse of history is part of a groundswell of efforts to apply scientific methods to history by identifying and modelling the broad social forces that Turchin and his colleagues say shape all human societies. It is an attempt to show that “history is not 'just one damn thing after another' ”, says Turchin, paraphrasing a saying often attributed to the late British historian Arnold Toynbee.
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What is new about cliodynamics isn’t the search for patterns, Turchin explains. Historians have done valuable work correlating phenomena such as political instability with political, economic and demographic variables. What is different is the scale Turchin and his colleagues are systematically collecting historical data that span centuries or even millennia — and the mathematical analysis of how the variables interact.
In their analysis of long-term social trends, advocates of cliodynamics focus on four main variables: population numbers, social structure, state strength and political instability. Each variable is measured in several ways. Social structure, for example, relies on factors such as health inequality measured using proxies including quantitative data on life expectancies — and wealth inequality, measured by the ratio of the largest fortune to the median wage. Choosing appropriate proxies can be a challenge, because relevant data are often hard to find. No proxy is perfect, the researchers concede. But they try to minimize the problem by choosing at least two proxies for each variable.
Then, drawing on all the sources they can find historical databases, newspaper archives, ethnographic studies Turchin and his colleagues plot these proxies over time and look for trends, hoping to identify historical patterns and markers of future events. For example, it seems that indicators of corruption increase and political cooperation unravels when a period of instability or violence is imminent. Such analysis also allows the researchers to track the order in which the changes occur, so that they can tease out useful correlations that might lead to cause–effect explanations.
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Claudio Cioffi-Revilla, a computer social scientist at George Mason University in Fairfax, Virginia, welcomes cliodynamics as a natural complement to his own field: doing simulations using ‘agent-based’ computer models. Cioffi-Revilla and his team are developing one such model to capture the effects of modern-day climate change on the Rift Valley region in East Africa, a populous area that is in the grip of a drought. The model starts with a series of digital agents representing households and allows them to interact, following rules such as seasonal migration patterns and ethnic alliances. The researchers have already seen labour specialization and vulnerability to drought emerge spontaneously, and they hope eventually to be able to predict flows of refugees and identify potential conflict hotspots. Cioffi-Revilla says that cliodynamics could strengthen the model by providing the agents with rules extracted from historical data.
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But Goldstone cautions that cliodynamics is useful only for looking at broad trends. “For some aspects of history, a scientific or cliodynamic approach is suitable, natural and fruitful,” he says. For example, “when we map the frequency versus magnitude of an event — deaths in various battles in a war, casualties in natural disasters, years to rebuild a state we find that there is a consistent pattern of higher frequencies at low magnitudes, and lower frequencies at high magnitudes, that follows a precise mathematical formula.” But when it comes to predicting unique events such as the Industrial Revolution, or the biography of a specific individual such as Benjamin Franklin, he says, the conventional historian’s approach of assembling a narrative based on evidence is still best.
Herbert Gintis, a retired economist who is still actively researching the evolution of social complexity at the University of Massachusetts Amherst, also doubts that cliodynamics can predict specific historical events. But he thinks that the patterns and causal connections that it reveals can teach policy-makers valuable lessons about pitfalls to avoid, and actions that might forestall trouble. He offers the analogy of aviation: “You certainly can’t predict when a 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.”
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