Spontaneous group formation in the seceder model

The emergence of interstellar molecular complexity explained by interacting networks

Recent years have witnessed the detection of an increasing number of complex organic molecules in interstellar space, some of them being of prebiotic interest. Disentangling the origin of interstellar prebiotic chemistry and its connection to biochemistry and ultimately, to biology is an enormously challenging scientific goal where the application of complexity theory and network science has not been fully exploited. Encouraged by this idea, we present a theoretical and computational framework to model the evolution of simple networked structures toward complexity. In our environment, complex networks represent simplified chemical compounds and interact optimizing the dynamical importance of their nodes. We describe the emergence of a transition from simple networks toward complexity when the parameter representing the environment reaches a critical value. Notably, although our system does not attempt to model the rules of real chemistry nor is dependent on external input data, the results describe the emergence of complexity in the evolution of chemical diversity in the interstellar medium. Furthermore, they reveal an as yet unknown relationship between the abundances of molecules in dark clouds and the potential number of chemical reactions that yield them as products, supporting the ability of the conceptual framework presented here to shed light on real scenarios. Our work reinforces the notion that some of the properties that condition the extremely complex journey from the chemistry in space to prebiotic chemistry and finally, to life could show relatively simple and universal patterns.

Hidden order across online extremist movements can be disrupted by nudging collective chemistry

Disrupting the emergence and evolution of potentially violent online extremist movements is a crucial challenge. Extremism research has analyzed such movements in detail, focusing on individual- and movement-level characteristics. But are there system-level commonalities in the ways these movements emerge and grow? Here we compare the growth of the Boogaloos, a new and increasingly prominent U.S. extremist movement, to the growth of online support for ISIS, a militant, terrorist organization based in the Middle East that follows a radical version of Islam. We show that the early dynamics of these two online movements follow the same mathematical order despite their stark ideological, geographical, and cultural differences. The evolution of both movements, across scales, follows a single shockwave equation that accounts for heterogeneity in online interactions. These scientific properties suggest specific policies to address online extremism and radicalization. We show how actions by social media platforms could disrupt the onset and 'flatten the curve' of such online extremism by nudging its collective chemistry. Our results provide a system-level understanding of the emergence of extremist movements that yields fresh insight into their evolution and possible interventions to limit their growth.

Situating Social Influence Processes: Dynamic, Multidirectional Flows of Influence Within Social Networks

Social psychologists have studied the psychological processes involved in persuasion, conformity, and other forms of social influence, but they have rarely modeled the ways influence processes play out when multiple sources and multiple targets of influence interact over time. However, workers in other fields from sociology and economics to cognitive science and physics have recognized the importance of social influence and have developed models of influence flow in populations and groups—generally without relying on detailed social psychological findings. This article reviews models of social influence from a number of fields, categorizing them using four conceptual dimensions to delineate the universe of possible models. The goal is to encourage interdisciplinary collaborations to build models that incorporate the detailed, microlevel understanding of influence processes derived from focused laboratory studies but contextualized in ways that recognize how multidirectional, dynamic influences are situated in people's social networks and relationships.

Local-global interaction and the emergence of scale-free networks with community structures

Understanding complex networks in the real world is a nontrivial task. In the study of community structures we normally encounter several examples of these networks, which makes any statistical inferencing a challenging endeavor. Researchers resort to computer-generated networks that resemble networks encountered in the real world as a means to generate many networks with different sizes, while maintaining the real-world characteristics of interest. The generation of networks that resemble the real world turns out in itself to be a complex search problem. We present a new rewiring algorithm for the generation of networks with unique characteristics that combine the scale-free effects and community structures encountered in the real world. The algorithm is inspired by social interactions in the real world, whereby people tend to connect locally while occasionally they connect globally. This local-global coupling turns out to be a powerful characteristics that is required for our proposed rewiring algorithm to generate networks with community structures, power law distributions both in degree and in community size, positive assortative mixing by degree, and the rich-club phenomenon.

Networking the seceder model: Group formation in social and economic systems

The seceder model illustrates how the desire to be different from the average can lead to formation of groups in a population. We turn the original, agent based, seceder model into a model of network evolution. We find that the structural characteristics of our model closely match empirical social networks. Statistics for the dynamics of group formation are also given. Extensions of the model to networks of companies are also discussed.

The dynamics of multidimensional secession: fixed points and ideological condensation

We explore a generalized, stochastic seceder model of societal dynamics with variable size polling groups and higher-dimensional opinion vectors, revealing its essential modes of self-organized segregation. Renormalizing to a discrete, deterministic version, we pin down the upper critical size of the sampling group and analytically uncover a self-similar hierarchy of dynamically stable, multiple-branch fixed points. In d>/=3, the evolving, coarsening population suffers collapse to a 2D ideological plane.

Artificial chemistries--a review

This article reviews the growing body of scientific work in artificial chemistry. First, common motivations and fundamental concepts are introduced. Second, current research activities are discussed along three application dimensions: modeling, information processing, and optimization. Finally, common phenomena among the different systems are summarized. It is argued here that artificial chemistries are "the right stuff" for the study of prebiotic and biochemical evolution, and they provide a productive framework for questions regarding the origin and evolution of organizations in general. Furthermore, artificial chemistries have a broad application range of practical problems, as shown in this review.