Quantifying human group dynamics represents a unique challenge. Unlike animals and other biological systems, humans form groups in both real (offline) and virtual (online) spaces-from potentially dangerous street gangs populated mostly by disaffected male youths to the massive global guilds in online role-playing games for which membership currently exceeds tens of millions of people from all possible backgrounds, age groups, and genders. We have compiled and analyzed data for these two seemingly unrelated offline and online human activities and have uncovered an unexpected quantitative link between them. Although their overall dynamics differ visibly, we find that a common team-based model can accurately reproduce the quantitative features of each simply by adjusting the average tolerance level and attribute range for each population. By contrast, we find no evidence to support a version of the model based on like-seeking-like (i.e., kinship or "homophily").

Despite the many works on contagion phenomena in both well-mixed systems and heterogeneous networks, there is still a lack of understanding of the intermediate regime where social group structures evolve on a similar time scale to individual-level transmission. We address this question by considering the process of transmission through a model population comprising social groups which follow simple dynamical rules for growth and breakup. Despite the simplicity of our model, the profiles produced bear a striking resemblance to a wide variety of real-world examples--in particular, empirical data that we have obtained for social (i.e., YouTube), financial (i.e., currency markets), and biological (i.e., colds in schools) systems. The observation of multiple resurgent peaks and abnormal decay times is qualitatively reproduced within the model simply by varying the time scales for group coalescence and fragmentation. We provide an approximate analytic treatment of the system and highlight a novel transition which arises as a result of the social group dynamics.

Coalescence-fragmentation problems are now of great interest across the physical, biological, and social sciences. They are typically studied from the perspective of rate equations, at the heart of which are the rules used for coalescence and fragmentation. Here we discuss how changes in these microscopic rules affect the macroscopic cluster-size distribution which emerges from the solution to the rate equation. Our analysis elucidates the crucial role that the fragmentation rule can play in such dynamical grouping models. We focus our discussion on two well-known models whose fragmentation rules lie at opposite

To what extent can one infer that superior capabilities are driving sustained superior performance? Modeling performance as some combination of differences in capabilities and processes of cumulative advantage, we argue that a Bayesian framework in which decision makers take into account the differences in cumulative advantage provides for a correct inference. We show, using both simulated and real performance data, that the Bayesian method gives rise to estimates relevant for the inference problem. The estimates also illustrate why a firm with superior performance during a longer period can be less likely to possess superior capabilities than a firm with superior performance during a shorter period. Our work has implications for the origins of competitive advantages and for organization learning in strategy research. Copyright © 2012 John Wiley & Sons, Ltd.

The many-body dynamics exhibited by living objects include group formation within a population and the nonequilibrium process of attrition between two opposing populations due to competition or conflict. We show analytically and numerically that the combination of these two dynamical processes generates an attrition duration T whose nonlinear dependence on population asymmetry x is in stark contrast to standard mass-action theories. A minority population experiences a longer survival time than two equally balanced populations, irrespective of whether or not the majority population adopts such an internal grouping. Adding a third population with predefined group sizes allows T(x) to be tailored. Our findings compare favorably to real-world observations.