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.
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I thank B. Goncalves and N. Perra for their help with the figures and a critical reading of the manuscript. This work has been partially funded by the NIH R21-DA024259, DTRA-1-0910039 and NSF CCF-1101743 and NSF CMMI-1125095 awards. The work has been also partly sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the US Government.
The author declares no competing financial interests.
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Vespignani, A. Modelling dynamical processes in complex socio-technical systems. Nature Phys 8, 32–39 (2012). https://doi.org/10.1038/nphys2160
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