Are there laws of history? If more like science, could it predict the future?


EXCERPT: In February 2010, Peter Turchin, an ecologist from the University of Connecticut, predicted that 2020 would see a sharp increase in political volatility for Western democracies. [...] all indications were that serious problems were looming. In the decade since that prediction ... Turchin’s ‘quantitative historical analysis’ seem remarkably prophetic.

A couple of years earlier, in July 2008, Turchin had made a series of trenchant claims about the nature and future of history. Totting up in excess of ‘200 explanations’ proposed to account for the fall of the Roman empire, he was appalled that historians were unable to agree ‘which explanations are plausible and which should be rejected’. The situation, he maintained, was ‘as risible as if, in physics, phlogiston theory and thermodynamics coexisted on equal terms’. Why, Turchin wanted to know, were the efforts in medicine and environmental science to produce healthy bodies and ecologies not mirrored by interventions to create stable societies? Surely it was time ‘for history to become an analytical, and even a predictive, science’. Knowing that historians were themselves unlikely to adopt such analytical approaches to the past, he proposed a new discipline: ‘theoretical historical social science’ or ‘cliodynamics’ – the science of history.

[...] There is a long tradition of modelling scientific history, of studying the past in order to shape the future. ... By the late 1960s, optimism about the human capacity to manage the future was becoming rather clouded. ... Simulations of isolated systems had been done before but what had been produced here was a model of how these systems might interact globally. The more powerful the computers, the more complex the systems they could model. But of course, the accuracy of any model depended on the starting assumptions of its programmers and the nature of the data they fed into it – a point not lost on the many critics of The Limits to Growth.

When datasets are created and archives accessed digitally, users aren’t viewing a simple facsimile of the original materials. They are looking at computer files that will have undergone a series of transformations that mask the assumptions built into the digital architecture, as well as the conditions under which the data was produced. Besides, for the majority of historians, ‘historical facts’ are not discrete items that exist independently, awaiting scholars who will hunt them down, gather them up and catalogue them safely. They need to be created and interpreted. Textual archives might seem relatively easy to reproduce, for example, but, just as with archaeological digs, the physical context in which documents are found is essential to their interpretation: what groups, or items, or experiences did past generations value and record, and which of these must be salvaged from the margins of the archives? What do the marginalia tell us about how the meanings of words have changed?

[...] Inspired by the work of the American sociologist Jack Goldstone, who in the 1990s had tried to translate Alexis de Tocqueville’s philosophy into mathematical equations, Turchin began to relate population size to economic output (and, critically, levels of economic inequality) as well as social and political instability. In order to measure changes in these three variables across time, he had to identify a range of different data sources. Social structure, for example, could be treated as a product of health and wealth inequality – but to measure either, you need to choose approximate and appropriate proxies. The process was further complicated by the fact that, when you’re working with a chronology that spans millennia, these proxies must change over time. The texture of that change might be qualitative as well as quantitative and, if qualitative, then – well, are you still actually measuring the same thing?

[...] Turchin claimed to have identified manageable datasets that enabled him to track population, economy and political change over thousands of years. In particular, he identified two repeating patterns as crucial to making sense of political history: secular sociodemographic cycles and father-son cycles. The first referred to centuries-long periods of time in which waves of sociopolitical instability rose and fell on the back of population growth. As the size of the population reached the carrying capacity of the land, living standards would decline. Previously elite groups, experiencing a loss of resources or status, would begin to revolt against the established political system. In the ensuing chaos, population levels would decline, new technologies or novel strategies for exploiting old ones might be found, and a new wave would begin. Inside these centuries-long cycles were to be found the shorter, 50-year ‘father-son’ oscillations, where, for example, the experience of war by one generation leads the next to reject violence, while the third (grandson) generation, having no direct experience of the horror of conflict, is willing to begin the cycle all over again. This cycle, incidentally, was the primary basis for Turchin’s prediction of chaos in 2020.

[...] Turchin passionately rejected the idea that complexity made human societies unsuitable for quantitative analysis, arguing that it was precisely that complexity which made mathematics essential. ... Turchin insisted that the cliodynamic approach was not deterministic. It would not predict the future, but instead lay out for governments and political leaders the likely consequences of competing policy choices.

[...] cliodynamicists and their colleagues actually have more in common with 19th-century armchair anthropologists than they might, perhaps, wish to acknowledge. Their meta-analyses and abstract mathematical models depend, by necessity, on manipulating data gathered by other scholars, and a key criticism of their programme is that cliodynamicists are not sensitive to the nuances and limitations of that data. What’s fascinating about this, of course, is that there is a group of evolutionary scientists who have an extremely well-developed critical sense of the need to interpret and contextualise data – especially when it pertains to social behaviour: these are field scientists. When they study behavioural biology or ecology, field scientists frequently adopt a deeply reflexive approach to the recording and interpretation of data, always questioning how their observational records refract the events that took place. Field scientists need constantly to engage with interpretive subjectivity, given that the key variables of their study are not under their control. In situations where data is radically incomplete, as when Western scientists are operating on postcolonial territory, they use local field assistants and volunteers to gather data for later interpretation at home.

In sum, it is not at all clear that creating a science of history is actually a good thing. But what’s certainly dangerous is letting one particular perspective on what it means to study something scientifically take centre-stage in debating the issue. The methodological reflections of field scientists on how to do science outside the laboratory, and how to relate mathematical models to lived behaviour, should be invaluable to any serious effort to develop an evolutionary understanding of history. And since the only person to have created a sustained exploration of what happens when you apply cliodynamics to social policy is Isaac Asimov – I’m thinking of his deployment of ‘psychohistory’ in his Foundation series of novels (1942-93) – perhaps we should ask novelists to participate in this experiment too... (MORE - details)

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