baldassarri baronchelli barrat caglioti castellano cattuto complexity dattoli evolutionary_dynamics granular_media granular_media herrmann information_theory innovation_dynamics kreyon language_dynamics loreto mari mukherjee petri pietronero puglisi quantum_optics self_organization servedio statistical_physics techno_social_systems tria zapperi zippers
2012 |
Miguel, Maxi San; Johnson, Jeffrey H; Kertesz, Janosz; Kaski, Kimmo; Diaz-Guilera, Albert; Mackay, Robert S; Loreto, Vittorio; Erdi, Peter; Helbing, Dirk Challenges in complex systems science Journal Article THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS, 214 , pp. 245–271, 2012. Abstract | BibTeX | Tags: complex_systems, diaz-guilera, Erdi, helbing, johnson, kaski, kertesz, loreto, mackay, san miguel, social_dynamics @article{b, title = {Challenges in complex systems science}, author = {Maxi San Miguel and Jeffrey H. Johnson and Janosz Kertesz and Kimmo Kaski and Albert Diaz-Guilera and Robert S. Mackay and Vittorio Loreto and Peter Erdi and Dirk Helbing}, year = {2012}, date = {2012-01-01}, journal = {THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS}, volume = {214}, pages = {245--271}, publisher = {SPRINGER HEIDELBERG}, abstract = {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.}, keywords = {complex_systems, diaz-guilera, Erdi, helbing, johnson, kaski, kertesz, loreto, mackay, san miguel, social_dynamics}, pubstate = {published}, tppubtype = {article} } 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. |
Conte, Rosaria; Gilbert, Nigel; Bonelli, Giulia; Cioffi-Revilla, Claudio; Deffuant, Guillaume; Kertesz, Janosz; Loreto, Vittorio; Moat, Susy; Nadal, Jean-Pierre; Sanchez, Ancho; Nowak, Andrzej; Flache, Andreas; Miguel, Maxi San; Helbing, Dirk Manifesto of computational social science Journal Article THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS, 214 , pp. 325–346, 2012. Abstract | BibTeX | Tags: Bonelli, cioffi-revilla, conte, deffuant, flache, Gilbert, helbing, kertesz, loreto, moat, nadal, nowak, san miguel, sanchez, social_dynamics @article{b, title = {Manifesto of computational social science}, author = {Rosaria Conte and Nigel Gilbert and Giulia Bonelli and Claudio Cioffi-Revilla and Guillaume Deffuant and Janosz Kertesz and Vittorio Loreto and Susy Moat and Jean-Pierre Nadal and Ancho Sanchez and Andrzej Nowak and Andreas Flache and Maxi San Miguel and Dirk Helbing}, year = {2012}, date = {2012-01-01}, journal = {THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS}, volume = {214}, pages = {325--346}, abstract = {The increasing integration of technology into our lives has created unprecedented volumes of data on society’s everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about.}, keywords = {Bonelli, cioffi-revilla, conte, deffuant, flache, Gilbert, helbing, kertesz, loreto, moat, nadal, nowak, san miguel, sanchez, social_dynamics}, pubstate = {published}, tppubtype = {article} } The increasing integration of technology into our lives has created unprecedented volumes of data on society’s everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about. |
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