Dynamics of Human Behavior

I review network aspects of human dynamics that link macro-historical to micro-sociological and evolutionary processes. The ability to bond in communities of varying spatial scales is a special property of humans that happens through social networks. These networks have greater cohesion through invulnerability to disconnection without removal of k nodes. Menger’s (1927) connectivity theorem shows that this property of k-cohesion mutually entails k node-independent paths between every pair of group members. Because of this property, i.e., by redundancy of communication, humans in such communities can utilize language and long-range communication to compensate for diminishing face-to-face interaction as groups grow large. For a given level k of cohesion, the maximally extensive e(k) group size is unbounded and scalable because, for each cohesive intensity level k, the maximal group size e(k) can expand indefinitely without the need to increase the average number of ties per member. Hence, the growth of human community size is scalable at a fixed cost in number of ties per person, unlike those species unable to take advantage of k-connectivity. Strong causal effects, using the k cohesion-level measure of empirical groups whose boundaries and extent are defined by e(k), have been replicated and validated in various sociological and anthropological network studies. This allows me to explore here the micro-macro linkages, the social and historical dynamics of socially cohesive networks, between scalable properties of k-cohesive groups and concomitant sociopolitical processes. Qualitative dynamics of major historical processes in human behavior, which are related for example to warfare and empire formation, are consistent with scale-up of sociopolitically k-cohesive groups. Such groups expand across metaethnic frontiers to evoke resistance that operates through scale-up of k-cohesive growth-by-opposition. Some current studies of such issues (Turchin 2003, 2005) use sufficient levels of aggregation to successfully assess dynamic interactions between macro-variables in sociopolitical processes (some of which involve political unit cohesion and scale). Others, such as the conflict studies of Lim, et al. (2007), use field-theory models of spatial interaction. New hypotheses, questions, and results may help link scalable k-cohesive groups to human evolutionary modeling and to variables used in evolutionary models of cooperativity and of transitions in sociopolitical organization. These various kinds of empirical studies illustrate concepts and methods in dynamics and complex systems applicable to human behavior in the domains I review. The mainline arguments illustrated here are expanded by reviews of work on other causal process models that combine micro-analysis of sociopolitical and economic behavior in the context of institutions, networks, historical ethnography, and network economic experiments. I note new directions flourishing in causal modeling, including multifractality and agent behavior, that evince further need for development of historically longitudinal databases, advancement of methods for dynamical analyses, and use of multilevel modeling that incorporates network representation and conceptualization.