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Title: Editorial: At the crossroads: Lessons and challenges in computational social science
Author: Borge Holthoefer, Javier  
Moreno Vega, Yamir
Yasseri, Taha
Others: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Universidad de Zaragoza
University of Oxford
Keywords: computational social science
simulation
models
big data
complex systems
Issue Date: 29-Aug-2016
Publisher: Frontiers in Physics
Citation: Borge-Holthoefer, J., Moreno Vega, Y. & Yasseri, T. (2016). Editorial: At the Crossroads: Lessons and Challenges in Computational Social Science. Frontiers in Physics, 4(). doi: 10.3389/fphy.2016.00037
Also see: https://doi.org/10.3389/fphy.2016.00037
Abstract: The interest of physicists in economic and social questions is not new: during the last decades, we have witnessed the emergence of what is formally called nowadays sociophysics and econophysics that can be grouped into the common term 'Interdisciplinary Physics' along with biophysics, medical physics, agrophysics, etc. With tools borrowed from statistical physics and complexity science, among others, these areas of study have already made important contributions to our understanding of how humans organize and interact in our modern society. Large scale data analyses, agent-based modeling and numerical simulations, and finally mathematical modeling, have led to the discovery of new (universal) patterns and their quantitative description in socio-economic systems. At the turn of the century, however, it was clear that huge challenges -and new opportunities- lied ahead: the digital communication technologies, and their associated data deluge, began to nurture those models with empirical significance. Only a decade later, the advent of the Web 2.0, the Internet of Things and a general adoption of mobile technologies have convinced researchers that theories can be mapped to real scenarios and put into empirical test, closing in this way the experiment-theory cycle in the best tradition of physics. We are nowadays at a crossroads, at which different approaches converge. We name such crossroads computational social science (CSS) : a new discipline that can offer abstracted (simplified, idealized) models and methods (mainly from statistical physics), large storage, algorithms and computational power (computer and data science), and a set of social hypotheses together with a conceptual framework for the results to be interpreted (Social Science). Despite its youth, the field is developing rapidly in terms of contents (articles, books, etc.), but also institutionally -either under the form of labs, institutes, and academic programs; or as consolidated events and scientific gatherings. This 'work-in-progress' spirit is reflected as well in this volume: the call was launched in late 2014 and 10 articles were eventually accepted and published, including reviews -a look behind-, one methods paper, and six original contributions -a look ahead- introducing a broad range of research, from models with a strong analytical flavor to data-driven problems.
Language: English
URI: http://hdl.handle.net/10609/78510
ISSN: 2296-424XMIAR
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