Publications

We thank Tosh et al. (1) for their interest in our research (2) but note that their analyses do not undermine the main findings of our article. Their suggestion that polity population divided by polity area should be one of the social complexity dimensions raises a number of issues. What does this ratio mean at large spatial scales, where populations are concentrated in large urban centers and much of the territory is not heavily populated? How are societies distributed across this variable and why? For example, a small-scale “simple” society could have a very high population density if it has access to a rich resource base. Tosh et al. (1) do not provide sufficient information or context to meaningfully interpret their results. The study by Chick (3), cited by Tosh et al. (1), was based on the Standard Cross-Cultural Sample, which is heavily weighted toward low-complexity, small-scale societies. The second component that Chick (3) found captures the differences in mobility and mode of production between agricultural and foraging societies, rather than complexity of organization per se. Tosh et al. (1) also argue without evidence that the proportion of variance explained by princip

Do human societies from around the world exhibit similarities in the way that they are structured, and show commonalities in the ways that they have evolved? These are long-standing questions that have proven difficult to answer. To test between competing hypotheses, we constructed a massive repository of historical and archaeological information known as “Seshat: Global History Databank.” We systematically coded data on 414 societies from 30 regions around the world spanning the last 10,000 years. We were able to capture information on 51 variables reflecting nine characteristics of human societies, such as social scale, economy, features of governance, and information systems. Our analyses revealed that these different characteristics show strong relationships with each other and that a single principal component captures around three-quarters of the observed variation. Furthermore, we found that different characteristics of social complexity are highly predictable across different world regions. These results suggest that key aspects of social organization are functionally related and do indeed coevolve in predictable ways. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history.

We thank Thomas for his comments (1) on our recent paper, in which we show that a very simple model can explain a remarkably high proportion of variance in historical data on the origins and spread of macrostates in Afroeurasian history (2). The core of Thomas’s critique is that our model is “too simple and too abstract” (1). We, however, see model simplicity as a desirable feature in developing theory that is both grounded in sociological mechanisms and tested with historical data. Too many previous attempts at theory building in the social sciences have foundered as a result of being highly complex and including in them too many mechanisms. The ability of our simple model to accurately predict data speaks for itself.

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.

In times of violence, people tend to hide their valuables, which are later recovered unless the owners had been killed or driven away. Thus, the temporal distribution of unrecovered coin hoards is an excellent proxy for the intensity of internal warfare. We use this relationship to resolve a long-standing controversy in Roman history. Depending on who was counted in the early Imperial censuses (adult males or the entire citizenry including women and minors), the Roman citizen population of Italy either declined, or more than doubled, during the first century BCE. This period was characterized by a series of civil wars, and historical evidence indicates that high levels of sociopolitical instability are associated with demographic contractions. We fitted a simple model quantifying the effect of instability (proxied by hoard frequency) on population dynamics to the data before 100 BCE. The model predicts declining population after 100 BCE. This suggests that the vigorous growth scenario is highly implausible.