Over the past few years many people, including readers of this blog, asked me to comment on the Strauss-Howe Generational Theory (SHGT). The “notoriety” of this theory has been recently given a boost by reports that it inspired the worldview of Steve Bannon, who was until recently Donald Trump’s chief strategist.

Superficially, SGHT shares several common elements with theories of Cliodynamics, and specifically the Structural-Demographic Theory (SDT):

• There are cyclic patterns in history
• Swings of social mood from one human generation to the next play a role in this cyclic change
• We in the US are entering a Time of Troubles, which will peak during the next decade.

Beyond this point, SHGT and SDT part ways. To put this difference in one sentence, SHGT is a prophecy (in fact, the main work of SHGT was titled The Fourth Turning: An American Prophecy), while SDT is a scientific theory. I explain the difference in my post Scientific Prediction ≠ Prophecy. Let’s see how this general distinction works out in the specific case of SHGT versus SDT.

The SDT is a scientific theory because (1) it presents a logically cohesive explanation of why change occurs and (2) it then tests critical assumptions and predictions of the theory with independently gathered data.

Let me quickly deal with the second point. SHGT is not a scientific theory because it uses what I call the “Procrustean” approach. Like the mythical Procrustes, one forces the historical record to fit a postulated cycle by stretching in some places and cutting off a bit here and there in others.

Here’s how the famous “Procrustean bed” works Source

In the scientific approach, one tests dynamical theories by collecting quantitative data and feeding the data to statistical analysis. For example, to collect data on political violence in the United States I did systematic searches of newspapers and other databases, and included in the resulting database all instances of political violence in which at least one individual lost life.

Let’s now deal with the first point at some length.

Unlike SDT, SHGT simply postulates that there is a recurring cycle of four generation-length stages in Anglo-American history from 1584 (later pushed back to 1433) to the present. These four stages, or “turnings” are: “The High”, “The Awakening”, “The Unraveling”, and “The Crisis”. Why do we see this specific sequence? It’s just because it is.

That’s not how it works in science. When we see a recurring pattern (why does winter come every twelve months?), we want to understand the mechanisms giving rise to it (in this case, rotation of the planet Earth around the Sun).

While doing research on the SDT, I observed that disintegrative periods of secular cycles tend to be “lumpy”: it’s not a continuous internal war dragging on for a century. Instead, there are typically peaks of political violence, recurring roughly every 50 years or so. I proposed that such cycles could arise as shifts of social mood between generations (“fathers-and-sons cycles”). Next, I built a mathematical model to see whether this mechanism could actually result in 50-year cycles, and whether such dynamics would be possible for realistic assumptions about model parameters.

Let me give you a bit more detail, so that you can appreciate the flavor of such modeling (full details are in Chapter 2 of Ages of Discord, in section Wheels within Wheels: Modeling Complex Dynamics of Sociopolitical Instability).

The model makes no assumptions about generations. Individuals enter the population when they become adult (at age 20) and leave it upon “retiring” (at age 55). Initially they all are “naives”, who are neither for, nor against violence. But naives can become radicalized after encountering a “radical” and converting to their ideology (by a process akin to “social contagion”, which is why I called it a social contagion model). When there are many radicals, social instability and political violence become so high that many radicals become disenchanted with their radicalism and turn into “moderates” who value peace and order above all, and who work actively to bring about an end to violence. In this way high levels of political radicalization, by breeding moderates, create a backlash against violence and yearning for peace.

This is it. It’s a very simple model that makes no assumption about discrete generations. Yet, for a broad spectrum of plausible parameters, it predicts that the proportion of population that is radicalized (and therefore political violence) would peak every 50 years. In other words, we will see an alternation of violent and peaceful “generations” (remember, generations are not built in, they arise as a result of interactions in the model). Here’s what model predictions look like:

Source: Figure 2.3 in Ages of Discord.

This is, of course, a very simple model. But in science this is a virtue. The model shows that it’s not so difficult to get a pattern of alternating peaceful/violent generations. Incidentally, it’s much more difficult to get a pattern of four different generations – in fact, my modeling experience says that it would take truly “heroic” assumptions to get the pattern assumed by SHGT, if it is even possible.

In SDT social contagion is just one of the social mechanisms explaining political violence outbreaks. This mechanism interacts with structural-demographic cycles, producing a complex dynamical pattern, like this one:

Source: Figure 2.4 in Ages of Discord.

This is why this section of Ages of Discord is called Wheels within Wheels—because the two cycles superimpose on each other, and the longer structural-demographic cycle can suppress an outbreak driven by social contagion (simply because most everybody is feeling good and doesn’t want to radicalize and fight a civil war).

I realize that my critique of SHGT will strike many readers as overly academic. But the scientific method is the best way we have to understand how the world operates, so that we can nudge it to better outcomes. Science requires a lot of “slogging”. It takes a lot of work to build and analyze models, and to collect and analyze data. Prophesies are much easier. They are also much more likely to persuade people, than careful science. The reason is that prophesies are vague. People who listen to them are free to supply their own content, in a kind of internalization of a prophesy, which makes it more convincing. This is why, I think, many prophesies enjoy a good run for a while. But in the long run science wins.

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