Typically, self-organisation (SO) is defined as the evolution of a system into an organised form in the absence of external pressures. SO within a system brings about several attractive properties, in particular, robustness, adaptability and scalability. In the face of perturbations caused by adverse external factors or internal component failures, a robust self-organising system continues to function. Moreover, an adaptive system may re-configure when required, degrading in performance “gracefully” rather than catastrophically. In certain circumstances, a system may need to be extended with new components and/or new connections among existing modules — without SO such scaling must be preoptimised in advance, overloading the traditional design process. In general, SO is a not a force that can be applied very naturally during a design process. In fact, one may argue that the notions of design and SO are contradictory: the former approach often assumes a methodical step-by-step planning process with predictable outcomes, while the latter involves non-deterministic spontaneous dynamics with emergent features. Thus, the main challenge faced by designers of self-organising systems is how to achieve and control the desired dynamics. Erring on the one side may result in over-engineering the system, completely eliminating emergent patterns and suppressing an increase in internal organisation with outside influence. Strongly favouring the other side may leave too much non-determinism in the system’s behaviour, making its verification and validation almost impossible. The balance between design and SO is the main theme of guided self-organisation (GSO). In short, GSO combines both task-independent objectives (e.g., information-theoretic and graph-theoretic utility functions) with task-dependent constraints.
The new science of complex systems will be at the heart of the future of the Worldwide Knowledge Society. It is providing radical new ways of understanding the physical, biological, ecological, and techno-social universe. Complex Systems are open, value-laden, multi-level, multi-component, reconfigurable systems of systems, situated in turbulent, unstable, and changing environments. They evolve, adapt and transform through internal and external dynamic interactions. They are the source of very difficult scientific challenges for observing, understanding, reconstructing and predicting their multi-scale dynamics. The challenges posed by the multi-scale modelling of both natural and artificial adaptive complex systems can only be met with radically new collective strategies for research and teaching (...)
One of the defining tensions throughout the development of cities has been between our desire for quality of life and our need to move ourselves and the things we depend on around. The former requires space, peace, and safety in which to work, exercise, relax and socialise; the latter requires transport systems which, since the use of horsedrawn transport in medieval cities, have taken up space, created noise and pollution – and are often dangerous. This tension will intensify rapidly in coming years. Not only are our cities growing larger and denser, but (...) our interactions within them are speeding up and intensifying...
Several decades ago, Edward Lorenz noticed something seemingly minor: his computational weather models yielded very different results if he changed the input variables only a tiny bit. Lorenz published a paper on this topic in 1963 and that led to the modern field of chaos.
Urban Emergencies : Emergent Urbanism (UE:EU) is an independent research group exploring international and interdisciplinary perspectives on the implications of emergent risks on the built environment and its inhabitants.
If you look at our planet from space, what you see is something like a neural network with the cities as its nodes, and that is as good an image of the planet as a complex system of systems as one could hope for.
With the emergence of the internet in the mid-90's, the world became one global commons. In the past, we could understand that there was some mysterious unity to the various dimensions of life but we couldn't understand its dynamics, we couldn't observe and measure their interactions. We basically operated like the drunk who looks under the streetlight for his keys because that's where he can see.
Video featuring, from IBM: Mike Wing, Irving Wladawsky-Berger and Julia Grace.
Consider an image: hubs and spokes sprawling across a map. At the Bank, we work in many fields that could be portrayed this way – finance, trade, transportation, infrastructure or urban and regional development.
Venezuelan economist Ricardo Hausmann and Chilean physicist César Hidalgo, in a joint effort of Harvard University and the Massachutes Institute of Technology MIT, draw a new world map of economic adventure, and suggest the Earth may not be flat.