Information cohomology is computed in low degrees to establish multivariate mutual informations as k-coboundaries, with simplicial structures yielding free-energy interpretations and a topological minimum free energy complex.
Complex Systems: A Survey
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
A complex system is a system composed of many interacting parts, often called agents, which displays collective behavior that does not follow trivially from the behaviors of the individual parts. Examples include condensed matter systems, ecosystems, stock markets and economies, biological evolution, and indeed the whole of human society. Substantial progress has been made in the quantitative understanding of complex systems, particularly since the 1980s, using a combination of basic theory, much of it derived from physics, and computer simulation. The subject is a broad one, drawing on techniques and ideas from a wide range of areas. Here I give a survey of the main themes and methods of complex systems science and an annotated bibliography of resources, ranging from classic papers to recent books and reviews.
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Proposes a cross-layer intellicise network architecture grounded in multiple theories to support intelligent complex systems, with reviews of enabling technologies and a case study.
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The Poincar\'e-Boltzmann Machine: from Statistical Physics to Machine Learning and back
Information cohomology is computed in low degrees to establish multivariate mutual informations as k-coboundaries, with simplicial structures yielding free-energy interpretations and a topological minimum free energy complex.
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Evolving Intelligent Complex Systems via Intellicise Networks: Architecture, Technologies, and Pathways
Proposes a cross-layer intellicise network architecture grounded in multiple theories to support intelligent complex systems, with reviews of enabling technologies and a case study.