Coupled catastrophes: sudden shifts cascade and hop among interdependent systems

An important challenge in several disciplines is to understand how sudden changes can propagate among coupled systems. Examples include the synchronization of business cycles, population collapse in patchy ecosystems, markets shifting to a new technology platform, collapses in prices and in confidence in financial markets, and protests erupting in multiple countries. A number of mathematical models of these phenomena have multiple equilibria separated by saddle-node bifurcations. We study this behavior in its normal form as fast--slow ordinary differential equations. In our model, a system consists of multiple subsystems, such as countries in the global economy or patches of an ecosystem. Each subsystem is described by a scalar quantity, such as economic output or population, that undergoes sudden changes via saddle-node bifurcations. The subsystems are coupled via their scalar quantity (e.g., trade couples economic output; diffusion couples populations); that coupling moves the locations of their bifurcations. The model demonstrates two ways in which sudden changes can propagate: they can cascade (one causing the next), or they can hop over subsystems. The latter is absent from classic models of cascades. For an application, we study the Arab Spring protests. After connecting the model to sociological theories that have bistability, we use socioeconomic data to estimate relative proximities to tipping points and Facebook data to estimate couplings among countries. We find that although protests tend to spread locally, they also seem to "hop" over countries, like in the stylized model; this result highlights a new class of temporal motifs in longitudinal network datasets.Comment: 20 pages, 4 figures, plus a 6-page supplementary material that contains 5 figures. Accepted at Journal of the Royal Society Interfac

Mathematical Modelling of Social Factors in Decision Making Processes at the Individual and Population Levels

In this thesis we apply mathematical modelling techniques to investigate the implications of social influence on decision making processes in two related contexts. The first problem concerns the mathematical modelling of civil unrest. We consider the collective action problem facing individuals who are deciding whether or not to join a political revolution or protest in a dictatorial regime that employs censorship and repression. In studying this problem we develop both a population-level model and a network-based individual-level (or agent-based) model. The population-level model establishes a conceptual framework that can be used to understand the role that new communication technologies (e.g. the Internet, satellite television, Short Message Service (SMS) text messaging, social media, etc.) may have played in facilitating the political revolutions of the Arab Spring. We establish the consistency between the individual-level model and the population-level model, and show methodologically how these two modelling strategies can be applied to complement one another, establishing a hierarchy of differential equation models that explicitly take the structure of the social network into account. Finally, using proxy network data for network structure pre- and post-adoption of new communication technologies, we perform small-scale computational simulations of our individual-level model in order to establish quantitative evidence that the political revolutions of the Arab Spring may have been facilitated by new communication technologies. The second problem concerns the spread of smoking and obesity in populations. We consider two conformity problems that individuals face when deciding whether to join one population sub-group over another (or possibly over many others) in the context of two non-communicable diseases. We begin by studying the smoking epidemic over the past century, where individuals are given the choice to smoke or not to smoke. We establish a new data set for smoking prevalence over the past century in seven developed countries and use it to calibrate a population-level mathematical model for the dynamics of smoking prevalence. We compare our model's predictions to an independently established measure of individualism/collectivism, i.e. Hofstede's Individualism versus Collectivism (IDV) measure, and find evidence that a society's culture can have a quantitative effect on the spread of a contagion. Finally, we study the dynamics of individuals' body mass index (BMI - defined as weight divided by height squared). We establish an individual-level model that also has implications at the population level. At the population level our model fits empirical BMI distributions better than the log-normal and skew-normal distribution functions, i.e. two distributions commonly used to fit right-skewed data, and provides a mechanistic explanation for the right-skewness observed in empirical BMI distributions. At the individual level our model is able to reproduce the average and standard deviation in individuals' year-over-year change in BMI. At both the individual and population levels our model finds evidence in support of the hypothesis that social factors play a role in the dynamics of individuals' BMI

An Agent-Based Model Of Centralized Institutions, Social Network Technology, and Revolution

This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change

Modelling the Information-Psychological Impact in Social Networks

The paper considers the objects, subjects, purposes, tools, methods and implementation of information-psychological impact (IPI). It suggests a cellular automata model of the diffusion process of information-psychological impact in social networks, the hierarchy of the changes in the states of the subjects of information-psychological impact and the chart of transitions from state to state used in the cellular automaton algorithm. The suggested cellular automaton takes into account the effect of forgetting the information-psychological impact, as well as social and psychological parameters and probabilistic characteristics of the subjects of the social network. It therefore allows for the modelling of the diffusion of the information-psychological impact in the social network. The model can be used to determine the number of subjects affected by the information-psychological impact and the possibility of successful diffusion of the impact. The modelling of the suggested algorithm was performed. The results of the modelling are analysed in the paper

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Globalistics and Globalization Studies: Big History & Global History. Yearbook

This yearbook is the fourth in the series with the title Globalistics and Globalization Studies. The subtitle of the present volume is Global History & Big History. The point is that today our global world really demands global knowledge. Thus, there are a few actively developingmultidisciplinary approaches and integral disciplines among which one can name Global Studies,Global History and Big History. They all provide a connection between the past, present, andfuture. Big History with its vast and extremely heterogeneous field of research encompasses allthe forms of existence and all timescales and brings together constantly updated information fromthe scientific disciplines and the humanities. Global History is transnational or world historywhich examines history from a global perspective, making a wide use of comparative history andof the history of multiple cultures and nations. Global Studies express the view of systemicand epistemological unity of global processes. Thus, one may argue that Global Studies and Globalistics can well be combined with Global History and Big History and such a multidisciplinary approach can open wide horizons for the modern university education as it helps to form a global view of various processes

Globalistics and globalization studies big history and global history

This yearbook is the fourth in the series with the title Globalistics and Globalization Studies. The subtitle of the present volume is Global History & Big History. The point is that today our global world really demands global knowledge. Thus, there are a few actively developing multidisciplinary approaches and integral disciplines among which one can name Global Studies, Global History and Big History. They all provide a connection between the past, present, and future. Big History with its vast and extremely heterogeneous field of research encompasses all the forms of existence and all timescales and brings together constantly updated information from the scientific disciplines and the humanities. Global History is transnational or world history which examines history from a global perspective, making a wide use of comparative history and of the history of multiple cultures and nations. Global Studies express the view of systemic and epistemological unity of global processes. Thus, one may argue that Global Studies and Globalistics can well be combined with Global History and Big History and such a multidisciplinary approach can open wide horizons for the modern university education as it helps to form a global view of various processes

Scientific discovery and topological transitions in collaboration networks

We analyze the advent and development of eight scientific fields from their inception to maturity and map the evolution of their networks of collaboration over time, measured in terms of co-authorship of scientific papers. We show that as a field develops it undergoes a topological transition in its collaboration structure between a small disconnected graph to a much larger network where a giant connected component of collaboration appears. As a result, the number of edges and nodes in the largest component undergoes a transition between a small fraction of the total to a majority of all occurrences. These results relate to many qualitative observations of the evolution of technology and discussions of the “structure of scientific revolutions”. We analyze this qualitative change in network topology in terms of several quantitative graph theoretical measures, such as density, diameter, and relative size of the network's largest component. To analyze examples of scientific discovery we built databases of scientific publications based on keyword and citation searches, for eight fields, spanning experimental and theoretical science, across areas as diverse as physics, biomedical sciences, and materials science. Each of the databases was vetted by field experts and is the result of a bibliometric search constructed to maximize coverage, while minimizing the occurrence of spurious records. In this way we built databases of publications and authors for superstring theory, cosmic strings and other topological defects, cosmological inflation, carbon nanotubes, quantum computing and computation, prions and scrapie, and H5N1 influenza. We also built a database for a classical example of “pathological” science, namely cold fusion. All these fields also vary in size and in their temporal patterns of development, with some showing explosive growth from an original identifiable discovery (e.g. carbon nanotubes) while others are characterized by a slow process of development (e.g. quantum computers and computation). We show that regardless of the detailed nature of their developmental paths, the process of scientific discovery and the rearrangement of the collaboration structure of emergent fields is characterized by a number of universal features, suggesting that the process of discovery and initial formation of a scientific field, characterized by the moments of discovery, invention and subsequent transition into “normal science” may be understood in general terms, as a process of cognitive and social unification out of many initially separate efforts. Pathological fields, seemingly, never undergo this transition, despite hundreds of publications and the involvement of many authors