It’s rare to find an interesting paper on history in the Proceedings of the National Academy of Sciences, so it was interesting to stumble upon Peter Turchin et al.’s “War, Space, and the Evolution of Old World Complex Societies” who developed a model that uses cultural evolution mechanisms to predict where and when the largest-scale complex societies should have arisen in human history.

From their abstract:

How did human societies evolve from small groups, integrated by face-to-face cooperation, to huge anonymous societies of today, typically organized as states? Why is there so much variation in the ability of different human populations to construct viable states? Existing theories are usually formulated as verbal models and, as a result, do not yield sharply defined, quantitative predictions that could be unambiguously tested with data. Here we develop a cultural evolutionary model that predicts where and when the largest-scale complex societies arose in human history. The central premise of the model, which we test, is that costly institutions that enabled large human groups to function without splitting up evolved as a result of intense competition between societies—primarily warfare. Warfare intensity, in turn, depended on the spread of historically attested military technologies (e.g., chariots and cavalry) and on geographic factors (e.g., rugged landscape). The model was simulated within a realistic landscape of the Afroeurasian landmass and its predictions were tested against a large dataset documenting the spatiotemporal distribution of historical large-scale societies in Afroeurasia between 1,500 BCE and 1,500 CE. The model-predicted pattern of spread of large-scale societies was very similar to the observed one. Overall, the model explained 65% of variance in the data. An alternative model, omitting the effect of diffusing military technologies, explained only 16% of variance. Our results support theories that emphasize the role of institutions in state-building and suggest a possible explanation why a long history of statehood is positively correlated with political stability, institutional quality, and income per capita.

The model simulation runs from 1500 B.C.E. to 1500 C.E.—so it encompasses the growth of societies like Mesopotamia, ancient Egypt and the like—and replicates historical trends with 65 percent accuracy.

Smithsonian Magazine summarizes:

Turchin began thinking about applying math to history in general about 15 years ago. “I always enjoyed history, but I realized then that it was the last major discipline which was not mathematized,” he explains. “But mathematical approaches—modeling, statistics, etc.—are an inherent part of any real science.”

In bringing these sorts of tools into the arena of world history and developing a mathematical model, his team was inspired by a theory called cultural multilevel selection, which predicts that competition between different groups is the main driver of the evolution of large-scale, complex societies. To build that into the model, they divided all of Africa and Eurasia into gridded squares which were each categorized by a few environmental variables (the type of habitat, elevation, and whether it had agriculture in 1500 B.C.E.). They then “seeded” military technology in squares adjacent to the grasslands of central Asia, because the domestication of horses—the dominant military technology of the age—likely arose there initially.

Over time, the model allowed for domesticated horses to spread between adjacent squares. It also simulated conflict between various entities, allowing squares to take over nearby squares, determining victory based on the area each entity controlled, and thus growing the sizes of empires. After plugging in these variables, they let the model simulate 3,000 years of human history, then compared its results to actual data, gleaned from a variety of historical atlases.

Click here to see a movie of the model in action.

Of particular interest to me was the discussion of the limitations of the model (100-year sampling and exclusion of city-states of Greece):

Due to the nature of the question addressed in our study, there are inevitably several sources of error in historical and geographical data we have used. Our decision to collect historical data only at 100-year time-slices means that the model ‘misses’ peaks of some substantial polities such as the Empire of Alexander the Great, or Attila’s Hunnic Empire. This could be seen as a limitation for traditional historical analyses because we have not included a few polities known to be historically influential. However, for the purposes of our analyses this is actually strength. Using a regular sampling strategy allows us to collect data in a systematic way independent of the hypothesis being tested rather than cherry-picking examples that support our ideas.

We have also only focused on the largest polities, i.e those that were approximately greater than 100,000 km2. This means that some complex societies, such as the Ancient Greek city states, are not included in our database. The focus on territorial extent is also a result of our attempt to be systematic and minimize bias, and this large threshold was chosen for practical considerations. Historical information about the world varies partly in the degree to which modern societies can invest in uncovering it. Our information about the history of western civilization, thus, is disproportionately good compared to some other parts of the world. Employing a relatively large cut-off minimizes the risk of “missing” polities with large  populations in less well-documented regions and time-frames, because the larger the polity the more likely it is to have left some trace in the historical record. At a smaller threshold there are simply too many polities about which we have very little information, including their territories, and the effects of a bias in our access to the historical record is increased.

Overall, I think the supporting information for the model is actually a lot more interesting read than the paper itself.