Multilevel selection is a powerful theoretical framework for understanding how complex hierarchical systems evolve by iteratively adding control levels. Here I apply this framework to a major transition in human social evolution, from small-scale egalitarian groups to large-scale hierarchical societies such as states and empires. A major mathematical result in multilevel selection, the Price equation, specifies the conditions concerning the structure of cultural variation and selective pressures that promote evolution of larger-scale societies. Specifically, large states should arise in regions where culturally very different people are in contact, and where interpolity competition – warfare – is particularly intense. For the period of human history from the Axial Age to the Age of Discovery (c.500 BCE–1500 CE), conditions particularly favorable for the rise of large empires obtained on steppe frontiers, contact regions between nomadic pastoralists and settled agriculturalists. An empirical investigation of warfare lethality, focusing on the fates of populations of conquered cities, indicates that genocide was an order of magnitude more frequent in steppe-frontier wars than in wars between culturally similar groups. An overall empirical test of the theory’s predictions shows that over ninety percent of largest historical empires arose in world regions classified as steppe frontiers.
Welcome to the inaugural issue of Cliodynamics: The Journal of Theoretical and Mathematical History.
This primer explicates the conceptual foundations of the statistical approach to detecting dynamical feedbacks. It is assumed that we have time-series data on several aspects of the studied system. The basic idea of the approach is to regress discrete rates of change of measured variables on variables themselves. I discuss several issues involved in the analysis, such as how to select the appropriate time step, or the delay parameter. The goal of the analysis is to determine whether a particular predictor variable, or set of variables, has a statistically detectable effect on the response. This is accomplished by cross-validation.
This short report describes the publication of the first batch of historical data produced by Seshat: Global History Databank. The data is available as free, open access material here; see also our website for more information on the Seshat project as a whole.