Social Dynamics and Complexity

We consider what effects could be produced by the long-term interaction of millennial macrotrends of the World System development and shorter-term cyclical dynamics. Among other things this will make it possible for us to demonstrate how even rather simple mathematical models of pre-industrial political-demographic cycles could help us to account for a paradox that has been encountered recently by political anthropologists.

Our research confirms that, notwithstanding recent arguments to the contrary, population density was a major determinant of warfare frequency in pre-industrial societies. However, the relationship between the two variables is dynamic, and could only be adequately described by nonlinear dynamic mod-els. Hence, we confront a rather paradoxical situation. On the one hand, we observe a millennial trend leading to the growth of both population density and warfare frequency. As a result, from a long-term perspective, we observe a very strong positive correlation between these two variables. But on the other hand, we also observe secular demographic-warfare cycles, which produce negative correlations both for individual cultures and for subsamples of cultures with similar levels of technological and/or political development. So finally, if we make a straightforward cross-cultural test of the linear rela-tionship between the two variables using a world-wide sample including cul-tures with all levels of technological and political development, we do not find any significant correlation at all. However it appears that hiding behind this "non-correlation" is the presence of an extremely strong and significant dy-namic non-linear relationship.

Longitudinal network analysis is coupled in this study to a systematic analysis of the results of long-term ethnography of a nomadic group. Data collection using genealogical, interview and observational methods is complemented by analytic methods using graph theoretic concepts and dynamical as well as structural methods to assess various cross-cutting and hierarchical levels of social cohesion (nuclear and extended families, lineages, clans, tribal groups, and village or nationality affiliations as found within the nomad group) to formulate and test hypotheses about social mobility and political leadership. Predictive hypotheses about the inverse relation between out-mobility and social cohesion versus the direct relation between cultural transmission and marital relinking as a form of cohesion are thought to validate the basic approach. The model of distributed cohesion developed from these data provides a new understanding of processes supporting the emergence of leaders in egalitarian nomadic groups.

Necessity, purpose, distancing the over-familiar, familiarizing the distant, making absences visible, and testing answers provide a framework for considering comparison in sociocultural anthropology. The methods reviewed include intrasocietal, dyadic, triadic, controlled, and broad comparison; units of comparison, necessary precautions about what is comparable; the operations of comparison and contrast and their respective productivity; differences, similarities and concomitance; polarity and dimensionality of comparison; comparison of norms, of behaviors, of processes; comparisons in time and issues of comparison in historical time; dynamics and models.

The paper concludes with a short history of methods of contrast and comparison.

_____________________________________________________________________________________________ Reprinted with permission of the author from Methodology and Fieldwork, pp. 94-111, 2004, edited by Vinay Kumar Srivastava. Delhi: Oxford University Press.

The Standard Cross-Cultural Sample contains the best-described society in each of 186 cultural provinces of the world, chosen so that cultural independence of each unit in terms of historical origin and cultural diffusion could be considered maximal with respect to the others societies in the sample. Often the time period chosen is that of the earliest high quality ethnographic description. Hence the SCCS is primarily a sample of preindustrial societies.

The original paper, published in the journal Ethnology in 1969, presented the first research results of the Cross-Cultural Cumulative Coding Center (CCCCC), a unit established in 1968 by Murdock and White at the University of Pittsburgh, with support from the National Science Foundation. The center was organized to offer to scholars a representative sample of the world's known and well described cultures, each "pinpointed" to the smallest identifiable subgroup of the society in question at a specific point in time, and to provide the first set of coded data for the sample. The sample selected by Murdock and White has the advantages of providing a sample size sufficiently large to test multivariate hypotheses but sufficiently small to allow complete coding by different authors; one with pinpointing of dates and focal groups and with a rich ethnographic bibliography and multiple data quality ratings that facilitate cumulative coding of comparative data on a wide variety of topics.

Although the SCCS maximizes the relative independence of sample cases, the 1969 article also provides standard Galton's problem controls for historical nonindependence of cases, and demonstrates with illustrations of their use that Galton’s problem controls are needed to make valid statistical inferences even with a relatively large cross-cultural sample. This cautionary scientific result has not been properly emphasized by the Human Relations Area Files, with which the SCCS has no connection other than the fact that for some SCCS societies, but not all, there exist partial ethnographic materials housed by HRAF.

The SCCS has served as a basis for a cumulative series of data coded by diverse authors in hundreds of publications on many different types of societal characteristics. Cumulative ethnographic codes and codebooks are published in the World Cultures electronic journal, which adds the geographical coordinates and computerizing mapping through SPSS and the journal's MAPTAB program, written by Douglas R. White. The SCCS and its SPSS database and codebooks currently contain more than 2,000 variables contributed by nearly a hundred different studies and as many authors. These variables include those of Murdock’s Ethnographic Atlas in a form most useful for testing hypotheses because they are collated with the standard sample societies.

This paper is reprinted in modified form with permission of the journal editorial office as published in Ethnology 8:329-369.

A theory of demographic cycles is developed by the author out of the contributions of many sciences. F. Braudel named these cycles as "secular trends," and R. Cameron used the concept of "logistics cycles." The author constructs a mathematical model of a demographic cycle. With the help of this model the cycle is divided into phases for which the author is able to determine about 40 qualitative attributes of the cycle. These attributes allow a demographic cycle to be identified in the real course of a history even in the absence of quantitative data about a population. With the help of this method 57 demographic cycles are identified in the history of the various countries of the East. In particular, it is shown that the increase of demographic pressure at the end of a cycle results in establishment of étatist monarchy.

Agents producing and exchanging knowledge are forming as a whole a socio-semantic complex system. Studying such knowledge communities offers theoretical challenges, with the perspective of naturalizing further social science, as well as practical challenges, with potential applications enabling agents to know the dynamics of the system they are participating in. The present thesis lies within the framework of this research program. Alongside and more broadly, we address the question of reconstruction in social science. Reconstruction is a reverse problem consisting of two issues: (i) deduce a given high-level observation for a considered system from low-level phenomena; and (ii) reconstruct the evolution of high-level observations from the dynamics of lower-level objects. In this respect, we argue that several significant aspects of the structure of a knowledge community are primarily produced by the co-evolution between agents and concepts. In particular, we address the first reconstruction issue by using Galois lattices to rebuild taxonomies of knowledge communities from low-level observation of relationships between agents and concepts; achieving ultimately an historical description (including inter alia field progress, decline, specialization, interaction -- merging or splitting). Using the framework of a socio-semantic network, or "epistemic network," we then micro-found several stylized facts regarding the empirically observed structure: we exhibit processes at the level of agents accounting for the emergence of epistemic community structure. After assessing empirical interaction and growth processes, and assuming that agents and concepts are co-evolving, we successfully propose a morphogenesis model rebuilding relevant high-level stylized facts. We finally defend a general epistemological point related to the methodology of complex system reconstruction, eventually supporting our choice of a co-evolutionary framework.

We investigate a simple generative model for network formation. The model is designed to describe the growth of networks of kinship, trading, corporate alliances, or autocatalytic chemical reactions, where feedback is an essential element of network growth. The underlying graphs in these situations grow via a competition between cycle formation and node addition. After choosing a given node, a search is made for another node at a suitable distance. If such a node is found, a link is added connecting this to the original node, and increasing the number of cycles in the graph; if such a node cannot be found, a new node is added, which is linked to the original node. We simulate this algorithm and find that we cannot reject the hypothesis that the empirical degree distribution is a q-exponential function, which has been used to model long-range processes in nonequilibrium statistical mechanics.

We investigate a simple generative model for network formation. The model is designed to describe the growth of networks of kinship, trading, corporate alliances, or autocatalytic chemical reactions, where feedback is an essential element of network growth. The underlying graphs in these situations grow via a competition between cycle formation and node addition. After choosing a given node, a search is made for another node at a suitable distance. If such a node is found, a link is added connecting this to the original node, and increasing the number of cycles in the graph; if such a node cannot be found, a new node is added, which is linked to the original node. We simulate this algorithm and find that we cannot reject the hypothesis that the empirical degree distribution is a q-exponential function, which has been used to model long-range processes in nonequilibrium statistical mechanics.