Science, Models and Theories

author: Seth Bullock, University of Southampton
published: Feb. 25, 2007,   recorded: March 2006,   views: 6618


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Continuing advances in information and communications technology (ICT) are increasing the scale and connectivity of today's engineered systems. Managing the resultant complexity is becoming the central challenge for UK industry and government: from software, to cities and even stock exchanges. Across the UK, a wide range of internationally leading research groups are addressing this challenge. In many cases they draw inspiration from biology, which provides innumerable examples of systems that cope with complexity. From cells to ecosystems, biology achieves scalability, adaptability, self-repair, and robustness, often by exploiting "emergent" system-level behaviours. Achieving equivalent success in engineered systems is the root problem that we face.
In the first of our short courses, we introduce the core concepts of complexity in the context of both natural and engineered systems, and explore the ways in which new computational systems, models, and simulations are taking part in complexity science through a series of lectures and workshop activities.

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Reviews and comments:

Comment1 Victor MacGill, November 25, 2007 at 7:46 a.m.:

This is not an area that I am that conversant with, so it was useful to get some background. In the video voice took preference over pictures, so it dropped lots of frames and the movement was very jerky, but it meant the content kept going, which is preferable

Comment2 Blue Pilgrim, July 6, 2009 at 7:49 a.m.:

It depends on what you want: if you want to understand (and understanding can be overrated) the complex processes then a stochastic, empirical model may not be that helpful, except to experiment with, but if you want a practical solution, especially to a complex problem, then it may well be best -- a sort of high-powered trial and error system, like evolution. We use computer software whose internals we don't understand all the time.

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