Networks of Echoes

Abstract

A series of authored and edited monographs that utilize quantitative and computational methods to model, analyze, and interpret large-scale social phenomena. Titles within the series contain methods and practices that test and develop theories of complex social processes through bottom-up modeling of social interactions. Of particular interest is the study of the co-evolution of modern communication technology and social behavior and norms, in connection with emerging issues such as trust, risk, security, and privacy in novel socio-technical environments.Computational Social Sciences is explicitly transdisciplinary: quantitative methods from fields such as dynamical systems, artificial intelligence, network theory, agent-based modeling, and statistical mechanics are invoked and combined with state-of-the-art mining and analysis of large data sets to help us understand social agents, their interactions on and offline, and the effect of these interactions at the macro level. Topics include, but are not limited to social networks and media, dynamics of opinions, cultures and conflicts, socio-technical co-evolution, and social psychology. Computational Social Sciences will also publish monographs and selected edited contributions from specialized conferences and workshops specifically aimed at communicating new findings to a large transdisciplinary audience. A fundamental goal of the series is to provide a single forum within which commonalities and differences in the workings of this field may be discerned, hence leading to deeper insight and understanding.

Self-organizing Complex Networks: individual versus global rules

“…The average imitation strength moves very quickly from the vanishing initial value, corresponding to no social interaction, toward a maximal value which is K ≈ 1.8. Notice that in the absence of interaction with the Prisoner's Dilemma process, the Ising-like DMM for the case of a regular two-dimensional lattice (West et al, 2014) would require the critical value K C ≈ 1.65 for M = ∞ and, as earlier mentioned, K C ≈ 1.5 for M = 100.…”

Self‐Organized Temporal Criticality: Bottom‐Up Resilience versus Top‐Down Vulnerability

“…The Decision-Making Model (DMM) [14], which is used in Sections 2 and 3, was proved [15] to generate phase transition as a function of its control parameter identical to that of the Ising model, where the control parameter is the temperature. In other words, the DMM belongs to Ising universality class [14].…”

“…The choice of a new flying direction is thus determined by an external stimulus of even weak intensity occurring at the same time as the collapse, thereby implying the property of complexity matching between the perturbed and the perturbing complex system [14].…”

“…The DMM has been shown [14] to undergo critical phase transitions and to be a member of the Ising universality class in which all the members of the network can act cooperatively, depending on the magnitude of the interaction strength [14]. However, this important result is obtained by assigning to all the individuals the same degree of attention to the opinions of their nearest neighbors, called .…”

A mathematics for medicine: The Network Effect

“…Instead we examine the properties of physiologic time series and identify those properties that emerge from the underlying network dynamics. In particular we focus on the inverse power-law (IPL) statistics that result from the temporal complexity generated by network dynamics, separate and distinct from the topological complexity that is the result of the connectivity of such networks, see for example Turalska et al ( 2009 ), West et al ( 2014 ) and references therein. Note that there is probably some nomenclature adopted in this paper that is not familiar and therefore a Glossary is provided in Appendix 6.1 for handy reference.…”