Seen a lot of Turchin citations lately but I think the bar for buying into cyclical views of history should be *really* high.
There were other, similar comments on Twitter. I respond on my blog, because it can be hard to keep track of multiple threads on Twitter, and a blog post has more staying power, compared to a tweet.
What my colleagues and I do is Cliodynamics, which is very different from typical cyclical views of history. “Cyclical history” suffers from two problems. First, mechanisms producing cycles are either entirely missing, or inadequately specified. There is almost never an explicit mathematical model that would clarify these mechanisms. Second, cyclical theories in history are not subjected to empirical tests with independently gathered data. It’s all retrospective eyeballing together with “Procrustean” forcing of the historical record to fit the postulated cycle by stretching in some places and cutting off a bit here and there. For a specific critique, looking at the Strauss-Howe cyclical theory, see my post The Prophecy of the Fourth Turning.
Cliodynamics is entirely different. Its roots are in nonlinear dynamical systems. We don’t go out looking for cycles; but we don’t shy away from them when there is robust evidence for them. In Structural-Demographic Theory, in particular, oscillations arise because of nonlinear feedbacks between different interacting components of the social system (state-level society). We model the postulated feedbacks mathematically and determine whether our intuition that they should lead to cycles is correct. See Why Do We Need Mathematical History?
Note these oscillations are not strictly periodic cycles, because there are always exogenous influences that continuously perturb the trajectories. Additionally, nonlinear feedbacks often induce the modeled system to behave chaotically.
Furthermore, human societies evolve. This means that many oscillations we see in history occur around a moving target. They are no less real because of that, but our statistical approaches need to be sophisticated enough to detect and characterize them. For an example on how “detrending” works see the graphs on the average age of first marriage here.
When we test predictions of cliodynamic models that generate cycles, we not only use standard statistical methods for detecting periodicity (such as spectral and time-series analysis). Even more importantly, we want to see whether the observed dynamics of different variables in the model change in the way model predicts. For example, when we see that a variety of proxies for population well-being (a key variable in structural-demographic theory) all wax and wane together, our degree of belief in the theory is strengthened. When we see that well-being proxies and elite overproduction proxies oscillate in almost perfect anti-phase, our confidence in the theory is increased further:
For details, see here. Note that this cyclic pattern may look “too good to be true”, but it is true! You can trace all steps of the analysis down to raw data to ascertain this. Our societies, including the US, are social systems, and they behave in a systemic fashion, meaning that when one component changes, this influences other components in the system. When we see such strong patterns in the data, and especially when we have theory translated into explicit mathematical models that explain these patterns, we must conclude that the theory is capturing something important about how our societies function and change.
As a final note, this is just one example. In our book Secular Cycles we applied structural-demographic theory to a number of historical societies. Other colleagues have extended such empirical investigations to more regions and historical periods, so currently we have good data on about 30 secular cycles. And we are building the Crisis DB to add to this number. In fact, all large-scale societies organized as states, for which we have good data, go through these oscillations. It’s like a law of history, or something.