Modelling dynamical processes in complex socio-technical systems

Abstract

In recent years the increasing availability of computer power and informatics tools has enabled the gathering of reliable data quantifying the complexity of socio-technical systems. Data-driven computational models have emerged as appropriate tools to tackle the study of dynamical phenomena as diverse as epidemic outbreaks, information spreading and Internet packet routing. These models aim at providing a rationale for understanding the emerging tipping points and nonlinear properties that often underpin the most interesting characteristics of socio-technical systems. Here, using diffusion and contagion phenomena as prototypical examples, we review some of the recent progress in modelling dynamical processes that integrates the complex features and heterogeneities of real-world systems.Q uestions concerning how pathogens spread in population networks, how blackouts can spread on a nationwide scale, or how efficiently we can search and retrieve data on large information structures are generally related to the dynamics of spreading and diffusion processes. Social behaviour, the spread of cultural norms, or the emergence of consensus may often be modelled as the dynamical interaction of a set of connected agents. Phenomena as diverse as ecosystems or animal and insect behaviour can all be described as the dynamic behaviour of collections of coupled oscillators. Although all these phenomena refer to very different systems, their mathematical description relies on very similar models that depend on the definition and characterization of a large number of individuals and their interactions in spatially extended systems.The modelling of dynamical processes is a research field that crosses different disciplines and has developed an impressive array of methods and approaches, ranging from simple explanatory models to realistic approaches capable of providing quantitative insight into real-world systems. Initially these models used simplistic assumptions for the micro-processes of interaction and were mostly concerned with the study of the emerging macro-level behaviour. This interest has favoured the use of techniques akin to statistical physics and the analysis of nonlinear, equilibrium and non-equilibrium physical systems in the study of collective behaviour in social and population systems. In recent years, however, the increase in interdisciplinary work and the availability of system-level high-quality data has opened the way to data-driven models aimed at a realistic description of complex socio-technical systems. Modelling approaches to dynamical processes in complex systems have been expanded into schemes that explicitly include spatial structures and have thus grown into a multiscale framework in which the various possible granularities of the system are considered through different approximations. These models offer a number of interesting and sometimes unexpected behaviours whose theoretical understanding represents a new challenge that has considerably transformed the mathematical and conceptual framework for t...

Memory in network flows and its effects on spreading dynamics and community detection

“…Many ranking 6,35 and community detection 30,31 methods, as well as epidemic models 9 build directly on this first-order Markov process. In fact, also maximal-entropy random walks are Markovian, although they build on modified transition probabilities 36,37 .…”

“…We considered two classical models for spreading processes 9 : a meta-population model that we implemented for the cities and is related to disease spreading, and a simpler model for spreading of ideas or rumours that we studied on the email data set. Both models are stochastic compartmental models.…”

“…In this data-driven approach, the complex structure is represented with a network of nodes and links, and the dynamics are modelled with random flow on the network [5][6][7][8][9] . The flow can represent ideas circulating among colleagues, passengers travelling through airports or patients moving between hospital wards.…”

Identification of Patient Zero in Static and Temporal Networks: Robustness and Limitations

“…One of the most prevalent types of dynamic processes of public interest characteristic for the real-life complex networks are contagion processes [1][2][3][4][5][6][7]. Epidemiologists detect the epidemic source or the patient-zero either by analysing the temporal genetic evolution of virus strains [8][9][10], which can be time-demanding or try to do a contact backtracking [11] from the available observed data.…”

On the validity of use of physical equations and principles in the socio-economic field and on the predictability of socio-economic system dynamics

“…The mathematical formalism for analysing different aspects of the evolution of the socio-economic system [27,33,36], including network methods [23], has been intensively developed. Moreover, in the studies of the dynamics of socio-economic systems, the various phenomenological approaches based on physical principles [12,16,22] as well as the mathematical models related to the class of so-called predator-prey models [13,20,34,38,39], have been actively developed and applied.…”