Self-Evolvable Systems Machine Learning in Social Media
This monograph presents key method to successfully manage the growing complexity of systems where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limits and new ways are n
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Editorial and Programme Advisory Board Dan Braha New England Complex Systems, Institute and University of Massachusetts, Dartmouth Péter Érdi Center for Complex Systems Studies, Kalamazoo College, USA and Hungarian Academy of Sciences, Budapest, Hungary Karl Friston Institute of Cognitive Neuroscience, University College London, London, UK Hermann Haken Center of Synergetics, University of Stuttgart, Stuttgart, Germany Viktor Jirsa Centre National de la Recherche Scientifique (CNRS), Université de la Méditerranée, Marseille, France Janusz Kacprzyk System Research, Polish Academy of Sciences, Warsaw, Poland Kunihiko Kaneko Research Center for Complex Systems Biology, The University of Tokyo, Tokyo, Japan Scott Kelso Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, USA Markus Kirkilionis Mathematics Institute and Centre for Complex Systems, University of Warwick, Coventry, UK Jürgen Kurths Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany Linda Reichl Center for Complex Quantum Systems, University of Texas, Austin, USA Peter Schuster Theoretical Chemistry and Structural Biology, University of Vienna, Vienna, Austria Frank Schweitzer System Design, ETH Zürich, Zürich, Switzerland Didier Sornette Entrepreneurial Risk, ETH Zürich, Zürich, Switzerland
For further volumes: http://www.springer.com/series/5394
Understanding Complex Systems Future scientific and technological developments in many fields will necessarily depend upon coming to grips with complex systems. Such systems are complex in both their composition - typically many different kinds of components interacting simultaneously and nonlinearly with each other and their environments on multiple levels - and in the rich diversity of behavior of which they are capable. The Springer Series in Understanding Complex Systems series (UCS) promotes new strategies and paradigms for understanding and realizing applications of complex systems research in a wide variety of fields and endeavors. UCS is explicitly transdisciplinary. It has three main goals: First, to elaborate the concepts, methods and tools of complex systems at all levels of description and in all scientific fields, especially newly emerging areas within the life, social, behavioral, economic, neuroand cognitive sciences (and derivatives thereof); second, to encourage novel applications of these ideas in various fields of engineering and computation such as robotics, nano-technology and informatics; third, to provide a single forum within which commonalities and differences in the workings of complex systems may be discerned, hence leading to deeper insight and understanding. UCS will publish monographs, lecture notes and selected edited contributions aimed at communicating new findings to a large multidisciplinary audience.
Springer Complexity Springer Complexity is an interdisciplinary program publishing the best research and academic-level teaching on both fundamental and applied aspects of complex systems - cutting across all traditional disciplines
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