Intensive agriculture is necessary for a complex society — which relies on costly bureaucracies, organized religion, and constraints on the ruling elites to promote the common good — but not sufficient. Complex societies only evolve when they compete against each other, and the losers disappear.
Simulated within a realistic landscape of the Afro-Eurasian landmass during 1,500 BC to 1,500 AD, the mathematical model was tested against the historical record. During the time period, horse-related military innovations, such as chariots and cavalry, dominated warfare within Afro-Eurasia. Geography also mattered, as nomads living in the Eurasian Steppe influenced nearby agrarian societies, thereby spreading intense forms of offensive warfare out from the steppe belt. On the other hand, rugged terrain inhibited offensive warfare.
The study focuses on the interaction of ecology and geography as well as the spread of military innovations and predicts that selection for ultra-social institutions that allow for cooperation in huge groups of genetically unrelated individuals and prevent large-scale complex states from splitting apart, is greater where warfare is more intense.
While existing theories on why there is so much variation in the ability of different human populations to construct viable states are usually formulated verbally, by contrast, the authors’ work leads to sharply defined quantitative predictions, which can be tested empirically.
The model-predicted spread of large-scale societies was very similar to the observed one; the model was able to explain two-thirds of the variation in determining the rise of large-scale societies.
There were several recurrent questions that came up in conversations with reporters:
Why does a question such as this need a computer model to answer?
Although our theory is relatively simple, it’s too complex to reason through using verbal arguments. There are important nonlinear feedback loops that can be captured only mathematically. Furthermore, the heart of our approach is getting detailed, quantitative predictions that can be compared to a large dataset on historical evolution of states in Afroeurasia. This can be done only with a quantitative, dynamical model.
Does this model tell us anything new, or is it a way to confirm what we already know?
The model results in new knowledge. Before we went through this exercise we did not know whether competition between societies, taking the form of warfare, was really an important driver in the evolution of large complex societies. Now we know that it is the main factor, with the presence of agriculture as a necessary condition, and various environmental effects (e.g., rugged terrain) also playing a role. Undoubtedly, cultural peculiarities are also important, although they were not included in the model. But since the model predicts 65 percent of variance in the data, such other factors must be of lesser importance than those included. At best, they provide the remaining 35 percent of the explanation. Our main result, that patterns of warfare are the most important factor explaining the rise and spread of large states is quite novel, and it will not be immediately accepted by most anthropologists and historians (although I expect that political scientists will be more sympathetic). Our results are likely to generate much controversy, which is why we plan to continue with this research program to address various criticisms that people will be bringing forth.
What are “large complex societies”?
In the simplest terms, societies counting a million of individuals or more. Such societies are invariably organized as states, have many complex institutions that are designed to prevent them from breaking up, extensive division of labor, complex internal organization, and so on. So they are ‘complex’ in many different ways
Why do you call these societies “anonymous”?
For most of our evolutionary history humans lived in small-scale societies — numbering just hundreds of people. These human groups were integrated by face-to-face interactions. In other words, everybody knew everybody else. Then there was a transition to large-scale societies, starting 10,000 years ago. In large societies of today each of us knows only a tiny proportion of people — the huge majority is strangers. In this sense our societies are anonymous. We interact all the time with people who are not personally known to us (think of taking a subway in New York City, or shopping in a supermarket).
How did you go about developing this model? How did you decide which data inputs to include?
Our model was guided by a general theoretical framework — cultural multilevel selection (CMLS). This theory predicts that competition between societies is the main driver of evolution of complex societies. Thus, emphasis on warfare. But then we needed to ‘operationalize’ such quantities as ‘warfare intensity’. What does it mean? It turned out that for the period of history we focused on, 1500 BC–1500 AD, we could capitalize on the spread of warhorse-related technologies as a proxy for intense warfare. The importance of rugged terrain was also suggested by the CMLS theory. It is easier to defend mountainous areas.
And it is clear that agriculture is a necessary condition for the rise of complex societies. That was already well known. However, our model shows that just the spread of agriculture does a not-so-great job explaining where and when large-scale societies arise. It’s a necessary, but far from sufficient condition. Warfare patterns do the bulk of explanation — that’s what allowed such a remarkably good match between model results and data.
Why do you think intense warfare and the spread of war technology turned out to be so important in deciding which large states would form?
That comes out from the CMLS theory, as I explained earlier. To evolve to a large size, societies need special institutions that are needed for holding them together; preventing them for splitting along the seams. But such institutions have large internal costs and, without constant competition from other societies, they collapse. Only constant competition between societies ensures that ultrasocial norms and institutions will persist and spread. So it really was war that made the state.
Were you at all surprised by these results?
I was certainly surprised by how well the model predicted the data. Even with first-guess parameters, the ones I tried during the early phase of the work, the model output looked very much like data. Quantitatively the model explained >50% of variance. Moderate adjustments of just 4 parameters increased prediction to 65%. This is much better than anyone, including myself, had thought can be done with historical data. Even though history is very complex, it turns out that simple models can capture very well many of its patterns.
What are the limitations of this sort of approach? Are there any nuances or particular cultures’ idiosyncrasies that affect their expansion and can’t be incorporated into mathematical models?
Of course, differences in culture, environmental factors, and thousands of other variables not included in the model all have effect. If you look carefully at Figure 1, you will see that there are lots of differences in detail between data and the model. That’s as it should be. A simple general model should not be able to capture actual history in all its glorious complexity. Both general processes and cultural idiosyncrasies play a role. But most historians and the lay public don’t realize that general processes can be very powerful in shaping history. Our model proves that they are wrong.