ParadigmOfComplexity The last few decades have seen the emergence of a growing body of literature devoted to a critique of the so-called “old” or “Cartesian-Newtonian” paradigm which, in the wake of the prodigious successes of modern natural science, came to dominate the full range of authoritative intellectual discourse and its associated worldviews. Often coupled with a materialistic, and indeed atomistic, metaphysics, this paradigm has been guided by the methodological principle of reductionism. The critics of reductionism have tended to promote various forms of holism, a term which, perhaps more than any other, has served as the rallying cry for those who see themselves as creators of a “new paradigm.” At the forefront of such a challenge, and in many ways the herald of the new paradigm, is the relatively new movement of transpersonal psychology. In taking seriously such experiences, transpersonal theory has been compelled to transcend the disciplinary boundaries of mainstream psychology. C.
Singularity (system theory) From Wikipedia, the free encyclopedia The attributes of singularities include the following in various degrees, according to context: Henri Poincaré developed Maxwell's ideas on singularities in dynamic systems. Poincaré distinguished four different simple singularities in the singular points of differential equations. he mentioned: In recent times, chaos theory has attracted a great deal of work, but deterministic chaos is just a special case of a singularity in which a small cause produces a large observable effect as a result of nonlinear dynamic behavior. In contrast the singularities raised by Maxwell, such as a loose rock at a singular point on a slope, show a linear dynamic behavior as Poincaré demonstrated. In the colloquial sense of disorder or confusion, however, chaos certainly occurs in social systems. The currently dominant theory of the origin of our universe postulates a physical singularity (specifically the Big Bang).
Complexity: It’s Not That Simple Complexity theory has been around for a generation now, but most people don’t understand it. I often read or listen to consultants, ‘experts’ and media people who proffer ludicrously simplistic ‘solutions’ to complex predicaments. Since it seems most people would prefer things to be simple, these ‘experts’ always seem to have an uncritical audience. Complexity theory argues that simple, complicated, complex and chaotic systems have fundamentally different properties, and therefore different approaches and processes are needed when dealing with issues and challenges in each of these types of systems. As the diagram above illustrates, natural systems (both social and ecological) are inherently complex. Human invention, for the most part, uses biomimicry, i.e. we attempt to manufacture, to replicate mechanically, things that appear to work in nature. Natural systems are highly effective but inefficient due to their massive redundancy (picture a tree dropping thousands of seeds).
Self-propelled particles SPP models predict robust emergent behaviours occur in swarms independent of the type of animal that is in the swarm. SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm.[6] Swarming systems give rise to emergent behaviours which occur at many different scales, some of which are turning out to be both universal and robust. It has become a challenge in theoretical physics to find minimal statistical models that capture these behaviours.[7][8][9] Overview[edit] The SPP model is based on a collection of points or particles, each functioning individually as an autonomous agent, and each following the same simple rules which govern their behaviour. Simulations demonstrate that a suitable "nearest neighbour rule" eventually results in all the particles swarming together, or moving in the same direction. Examples[edit] Locust nymph Marching locusts[edit] Bird landings[edit] Other examples[edit] References[edit]
Human Systems Dynamics Self-dissimilarity From Wikipedia, the free encyclopedia Self-dissimilarity is a measure of complexity defined in a series of papers by David Wolpert and William G. Macready.[1][2] The degrees of self-dissimilarity between the patterns of a system observed at various scales (e.g. the average matter density of a physical body for volumes at different orders of magnitude) constitute a complexity "signature" of that system. See also[edit] References[edit]
- Glossary of Terms Category: Resources Posted by: Aaron and Ken The following definitions are brief, loose, and approximate. But despite these features, they should give readers a decent understanding of what we mean when we use these terms. Is there a term you would like to see here? Adaptive system engineering- the general enterprise of engineering a system to be adaptable. Applies, Fails to Apply, or Does not Apply- This is an important distinction in philosophy. Autocatalytic Set- An collection of entities (molecules, people, nations, institutions, whatever) that produces as outputs the same elements which are necessary inputs for expanding the collection. Autopoietic Set- Autopoiesis is the process of dynamics self-maintenance. Building Block- When parts of a collection organize themselves into patterns that we recognize as coherent phenomena then we say that it is an emergent phenomena. Complicated- Sometimes complication is considered a degenerate concept to complexity. Phylogenetic Tree-.
Systems science Impression of systems thinking about society. Systems science is an interdisciplinary field that studies the nature of complex systems in nature, society, and science itself. It aims to develop interdisciplinary foundations that are applicable in a variety of areas, such as engineering, biology, medicine, and social sciences.[1] Systems science covers formal sciences such as complex systems, cybernetics, dynamical systems theory, and systems theory, and applications in the field of the natural and social sciences and engineering, such as control theory, operations research, social systems theory, systems biology, systems dynamics, systems ecology, systems engineering and systems psychology.[2] Theories[edit] Since the emergence of the General Systems Research in the 1950s,[3] systems thinking and systems science have developed into many theoretical frameworks. Systems notes of Henk Bikker, TU Delft, 1991 Systems analysis Systems design System dynamics Systems engineering Systems Methodologies
Activités - Chaire Edgar Morin de la Complexité Le programme des activités de la chaire Edgar Morin de la Complexité doit permettre : d’interroger le sens de la complexité du point de vue des disciplines de gestion et de quelques problématiques directrices des préoccupations de l’ESSEC (entrepreneuriat, business in society, etc).d’expliciter les enjeux de l’appréhension d’un environnement interne et externe complexe pour les praticiensexaminer les méthodes d’approche et de traitement de la complexité sur le terrainde considérer les qualités, compétences, savoir-faire requis pour gérer la complexité au sein des organisations sur les plans du management et du leadership individuel et collectif Quelques activités de la chaire dans ce contexte : les « Mises en boîtes » : présentation par des professeurs de l'ESSEC de leur vision et compréhension de la complexité au travers de certains de leurs résultats de recherche.
Social network Social networks and the analysis of them is an inherently interdisciplinary academic field which emerged from social psychology, sociology, statistics, and graph theory. Georg Simmel authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations. Overview[edit] History[edit] In the late 1890s, both Émile Durkheim and Ferdinand Tönnies foreshadowed the idea of social networks in their theories and research of social groups. Levels of analysis[edit] Self-organization of a network, based on Nagler, Levina, & Timme, (2011)[32] In general, social networks are self-organizing, emergent, and complex, such that a globally coherent pattern appears from the local interaction of the elements that make up the system.[33][34] These patterns become more apparent as network size increases. Micro level[edit] Social network diagram, micro-level. Meso level[edit] Social network diagram, meso-level Examples of a random network and a scale-free network. In J.A.
Energy transition and co-operative education and training - On Society (Image by rawpixel from Pixabay, CC0) While traditionally the integration of theory and practice is routine in vocational education, in tertiary education theoretical education is often separated from practice. After all graduates shall satisfy requirements in jobs and society. This is often not the case due to a lack of exchange between theory and practice during education. In contrast to classical education formats co-operative learning approaches offer alternative opportunities to solve this problem. Our research questions how co-operative formats can be defined and discusses which barriers and innovative values each format entails. What can be learned from the status quo is that co-operation in education and training plays only a minor role in the overall education system. To support energy transition HEIs need to provide a co-operation and co-ordination platform. View the latest posts on the On Society homepage (Image by rawpixel from Pixabay, CC0)