Cliodynamics

This paper discusses how the production rate of historical and archaeological data might contain unique information about past societies. The case study is the frequency of entries in the Annals of Ulster, a primary early medieval source from Ireland, which was compared to the frequency of archaeological material from early medieval Ireland. The two datasets were found to contain similar trends, namely a rapid increase in activity in the 7^th Century, followed by a decline in the Early 9^th Century, low levels of activity in the 10^th Century, until recovery in the Late 10^th / Early 11^th Centuries. This overall pattern of activity had not been noticed before. Turning to the archaeological record of Britain, although there are certain similarities between Ireland and Scotland especially in the early part of the period, we find that the 9th and 10th centuries there were a stable period, and thus contrast with Ireland. We argue that environmental pressures are unlikely to be driving the signal, and instead various socio-cultural factors in the past coalesced in Ireland, leading to circumstances powerful enough to attenuate the enduring evidence for human activity, but expressing themselves silently, perhaps even in a way that was not immediately obvious to those witnessing them in the past. The simplest explanation, we contend, is that population levels fell throughout the period. This finding offers insight into the relationship between long-term change and the primary production of history, and supports the idea that the quantity of certain historical data can contain information about past realities.

This article presents a general statistical approach suitable for the analysis of time-resolved (time-series) cross-cultural data. The goal is to test theories about the evolutionary processes that generate cultural change. This approach allows us to investigate the effects of predictor variables (proxying for theory-suggested mechanisms), while controlling for spatial diffusion and autocorrelations due to shared cultural history (known as Galton’s Problem). It also fits autoregressive terms to account for serial correlations in the data and tests for nonlinear effects. I illustrate these ideas and methods with an analysis of processes that may influence the evolution of one component of social complexity, information systems, using the Seshat: Global History Databank.

Editor's note: the link to the R scripts in the footnote on p.1 of the Supplemental Materials is now broken. Please use the following link instead: https://datadryad.org/stash/dataset/doi:10.17916/P6159W.

Our empirical tests generally support the hypothesis that up to certain values of the average per capita income its growth tends to lead to increased risks of sociopolitical destabilization, and only in the upper range of this indicator its growth tends to be associated with the decrease of sociopolitical destabilization risks. However, our analysis has shown that for various indices of sociopolitical destabilization this curvilinear relationship can be quite different in some important details. On the other hand, we detect the presence of a very important exception. We show that the relationship between per capita GDP and the intensity of coups and coup attempts is not curvilinear; in this case we are rather dealing with a pronounced negative correlation; a particularly strong negative correlation is observed between this index and the logarithm of GDP per capita. We demonstrate that this fact makes the abovementioned bell-shaped relationship with respect to the integral index of sociopolitical destabilization considerably less distinct and makes a very significant contribution to the formation of its asymmetry (when the negative correlation between per capita GDP and sociopolitical destabilization among the richer countries looks much stronger than the positive correlation among poorer countries). However, our analysis shows that for all the other indices of sociopolitical destabilization we do witness the bell-shaped relationship. On the other hand, for example, in relation to such indices, as political strikes, riots and anti-government demonstrations we deal with such an asymmetry that is directly opposite to that mentioned above - with such an asymmetry, when a positive correlation between GDP and instability for poorer countries is much stronger than the negative correlation for richer countries.

Humans are social beings; people are predisposed to join groups, categorize the social world into groups, and prefer fellow in-group members over out-group members. Social groups in turn compete for individuals and especially for the resources of individuals to maintain the cultural practices and symbolic markers of the group. We modeled the effect of this competition on population level cooperation. Using game theoretic and network science methods, we found that groups would develop and maintain norms that restrict their members to join other groups. If every group can maintain such norms against every other group (the topology of the group-network is complete), the society is composed of closed communities which do not cooperate with each other. Changing the topology of the group-network can yield larger cooperating components within the population, because, in this case, members of antagonistic groups can join a third group, thereby allowing cooperation between them. The results suggest that the individuals’ ability to join more than one social group is crucial for maintaining cooperation in large populations.

A Review of The Great Leveler: Violence and the History of Inequality from the Stone Age to the Twenty-First Century by Walter Scheidel (Princeton University Press, 2017)

The Seshat: Global History Databank was founded in 2011 with the goal of systematically collecting data about social, political, and economic organization of human societies and how they have evolved over time. From the beginning the first guiding principle of the Seshat project was to reflect the current state of knowledge about past societies as accurately as possible within practical constraints (I’ll discuss practical limitations later on). Second, and equally important, our aim for the database was to reflect not only what is known, but what is unknown, or poorly known.