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Open Access Publications from the University of California


The Structure and Dynamics eJournal welcomes articles, book reviews, data, simulations, research material, and special issues that examine aspects of human evolution, social structure and behavior, culture, cognition, or related topics. Our goal is to advance the historic mission of anthropology in the broadest sense to describe and explain the range of variation in human biology, society, culture and civilization across time and space. Submissions of databases, software tutorials, programs, and teaching materials are welcomed, as are communications on research materials of interest to a wide variety of science and social science researchers, including networks, dynamical models, and complexity research and related genre.


Introduction to Structure and Dynamics: Inaugural Issue

In inaugurating the Structure and Dynamics journal, we offer a conduit for refereed electronic publication, debate, and editorial communication in the domain of anthropology and human sciences. We as editors dedicate our effort to facilitating and disseminating interdisciplinary discussion and research in anthropology and related sciences. We invite you—as a subscribed reader at no cost—to contribute and to participate in raising the aspirations of the human sciences today.

The Sumerian Takeoff

Economic geographers correctly note that regional variations in economic activity and population agglomeration are always the result of self-reinforcing processes of resource production, accumulation, exchange, and innovation. This article proposes that essentially similar forces account for the emergence of the world’s earliest cities in the alluvial lowlands of the Tigris and Euphrates rivers (Southern Mesopotamia), sometime during the second half of the fourth millennium BC.

That emergence of early cities in the southern Mesopotamian alluvium must be understood in terms of the unique ecological conditions that existed across the region during the fourth millennium, and the enduring geographical framework of the area, which allowed for the efficient movement of commodities via water transport and facilitated interaction between diverse social units alongside natural and artificial river channels. These conditions promoted evolving long-term trade patterns that, inadvertently, differentially favored the development of polities in the southern Mesopotamian alluvium over contemporary societies in neighboring regions.

More specifically, my contention is that by the final quarter of the fourth millennium the social and economic multiplier effects of trade patterns that had been in place for centuries – if not millennia – had brought about substantial increases in population agglomeration throughout the southern alluvial lowlands. Concurrent with these increases, and partly as a result of them, important socio-economic innovations started to appear in the increasingly urbanized polities of southern Mesopotamia that were unachievable in other areas of the Ancient Near East where urban grids of comparable scale and complexity did not exist at the time. Most salient among these innovations were (1) new forms of labor organization delivering economies of scale in the production of subsistence and industrial commodities to southern societies, and (2) the creation of new forms of record keeping in southern cities that were much more capable of conveying information across time and space than the simpler reckoning systems used by contemporary polities elsewhere. These innovations furnished southern Mesopotamian polities of the fourth millennium with what turned out to be their most important competitive advantage over neighboring societies. More than any other factor, they help explain why complex regionally organized city-states emerged earlier in southern Iraq than elsewhere in the Near East, or the world.

Dynamical Feedbacks between Population Growth and Sociopolitical Instability in Agrarian States

Most preindustrial states experienced recurrent waves of political collapse and internal warfare. One possible explanation of this pattern, the demographic-structural theory, suggests that population growth leads to state instability and breakdown, which in turn causes population decline. Mathematical models incorporating this mechanism predict sustained oscillations in demographic and political dynamics. Here I test these theoretical predictions with time-series data on population dynamics and sociopolitical instability in early modern England, the Han and Tang China, and the Roman Empire. Results suggest that population and instability are dynamically interrelated as predicted by the theory.

A Primer on Statistical Analysis of Dynamical Systems in Historical Social Sciences (with a Particular Emphasis on Secular Cycles)

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.

Networks and Small Groups

Homans' insights that interaction and sentiment are in a feedback loop that includes clique formation, social ranking and leadership are formalized and derived from a set of limited assumptions and propositions. Freeman's model of groups is used to detect pure informal groups, those that are not consequential upon anything else than sheer hanging around. It produces a system of cliques and rankings based purely on the rates of transitive triads that may include a third who is only weakly connected to the other two. Two assumptions about motivation in networks – the need for safety and efficacy are then combined with Gould's modeling of asymmetric relations in which members are valued for either intrinsic or extrinsic merits. The ratings tend to cascade according to the "Matthew Effect." Finally, people like to choose others who are more attractive than they are, subject to the condition that too much unrequited love is painful. Gould's Formalization of these ideas for the case of small groups produces, among other results, the consequence that those who are chosen more often than others also tend to direct interaction more towards others with lesser rank, a non-intuitive result observed by Homans, but explained by him in substantive rather than formal terms. The logic of rank further dictates that groups become segmented by structural equivalence. Other results are also summarized.

Formal Aspects of the Emergence of Institutions

We argue that social institutions emerge on the basis of the human cognitive ability to integrate an evaluation of the behavior and performances of other group members over long time periods. The results of those evaluations are condensed into the social status of an individual, and that status is the link between short time achievements and long term success within the group. Altruistic behavior on a short time scale can be advantageous for an individual on a longer time scale as it contributes to her or his status. Conversely, for example, building mating decisions not on events that may be quite random on a short time scale, but on long term accumulations is an evolutionarily rational behavior because it reduces stochastic fluctuations by averaging. Our proposal does not need any group selection scheme. It calls some approaches to computer simulations of social dynamics into question. It is based on considerations from system theory, in particular, concerning the integration of different temporal scales. It utilizes a new concept of emergence as opposed to self-organization through non-linear interactions of simple elements. It requires further studies from the social sciences to understand that scale shift as encoded in social status.

About the Image: Diffusion Dynamics in an Historical Network

The journal logo illustrates the use of dynamic visualization to complement network and statistical analysis in the study of social, political, economic and historical processes generally. In giving credit to the authors of the logo, this is an invited paper that summarizes earlier work on how existing social networks are transformed into political action in times of rapid social change. Citing Krempel and Schnegg (1988): "This general theoretical problem is exemplified for the 1848/49 Revolution in Esslingen, a middle-sized German town. We use data from more than 200 historical sources to identify patterns of activity and social linkages for more than 2000 inhabitants of Esslingen at the time of the revolution and during the 15 years preceding it."

Results indicate that while existing social structure plays a key role for mobilization processes, the picture needs to be differentiated in theory, explanation, and statistical analysis. To this end, dynamic visualization, as developed by Krempel (2005), can be extraordinarily powerful in understanding how network processes are interlinked. Structure does not have the same effect at each stage of an historical process and for every person involved. Mobilization does not only take place through the existing structure but also occurs in more distinct regions of the network where a common situation and an equivalent position in society at large are the driving forces behind the organization of protest. These differentiated processes are evident in the dynamic gif logo and the detailed explication of its visualized network components through time.


Comment on: Dynamical Feedbacks between Population Growth and Sociopolitical Instability in Agrarian States by Peter Turchin

Turchin’s article achieves significant progress in the modeling of demographic cycles as a basic feature of complex agrarian systems' dynamics. He suggests an extremely simple model accounting for an unusually high percentage of the political-demographic variation, including some features for which earlier models failed to account. Rather than modeling the recovery phase of the demographic cycle as starting immediately after the demographic collapse, which is not observed in reality, the recovery phases of Turchin’s model are, more accurately, separated from those of collapse by significant periods of internal warfare that blocks recovery growth. Such intercycles, systematically observed in the agrarian political-demographic dynamics, represent a problem that Turchin has managed to solve in a very elegant and compelling way.

Oscillations in Population Sizes - From ecology to history

A new mathematical theory is proposed to explain the population size oscillations described in the paper by P. Turchin. The model is based on Turchin's demographic-structural theory and includes the "inertia of war" phenomenon together with the tendency for retribution in military conflicts.

Critique of Guillermo Algaze’s “The Sumerian Takeoff”

Drawing upon modern economic theorists, Guillermo Algaze emphasizes continuous, interlocking, self-reinforcing processes of growth and external trade as keys to "takeoff" toward southern Mesopotamia's regional leadership in the fourth millennium B.C. But the search for historical causality, always complex, would better avoid supposed universals of individual motivation as determinate roots of behavior everywhere and concentrate in the first instance on fuller consideration of the specific context and time. Without denying a role for Algaze's factors, I suggest that ever-present risks of subsistence variability were probably more decisive in encouraging social stratification and a higher degree of regimentation within locally contending city-states there. Enhanced military effectiveness then surely played a part, alongside trade and possibly overshadowing it, in ensuing regional dominance.

Response to Oscillations in Population Sizes – From Ecology to History

Natalia Komarova brings up a very important issue in modeling dynamical phenomena—what mathematical framework to use. I respond to her critique.