From our machines and software to our legal frameworks and urban infrastructure, Arbesman explores the forces that lead us to continue to make systems more complicated and more incomprehensible, despite our best efforts to make them simpler. He goes on to identify a new framework for thinking about (and planning within) complex systems. After a summer of meeting handsome French boys and getting a tiny bit sunburnt. OK, fine - a lot sunburnt, Lottie's heading off on a week-long residential school trip. A whole week away from embarrassing parents and Toby's tasty air biscuits! The original text, published 1990, was the first quantitative introduction to chaos for science undergraduates. This has now been revised and skilfully extended … Suitable as a source text for lecture courses.’

In addition, this book highlights certain group phenomena that have been given only cursory attention in many group textbooks, including women in authority, group metaphors, regressive groups, and the transpersonal potential of small groups. Imagine trying to understand a stained glass window by breaking it into pieces and examining it one shard at a time. While you could probably learn a lot about each piece, you would have no idea about what the entire picture looks like. This is reductionism--the idea that to understand the world we only need to study its pieces--and it is how most social scientists approach their work.Small differences in initial conditions, such as those due to errors in measurements or due to rounding errors in numerical computation, can yield widely diverging outcomes for such dynamical systems, rendering long-term prediction of their behavior impossible in general. [7] This can happen even though these systems are deterministic, meaning that their future behavior follows a unique evolution [8] and is fully determined by their initial conditions, with no random elements involved. [9] In other words, the deterministic nature of these systems does not make them predictable. [10] [11] This behavior is known as deterministic chaos, or simply chaos. The theory was summarized by Edward Lorenz as: [12] Over the past two decades, no field of scientific inquiry has had a more striking impact across a wide array of disciplines–from biology to physics, computing to meteorology–than that known as chaos and complexity, the study of complex systems. Now astrophysicist John Gribbin draws on his expertise to explore, in prose that communicates not only the wonder but the substance of cutting-edge science, the principles behind chaos and complexity. He reveals the remarkable ways these two revolutionary theories have been applied over the last twenty years to explain all sorts of phenomena–from weather patterns to mass extinctions. The acclaimed author of The Half-Life of Facts explains the challenges of overly complex technology. It bridges the gap between the popular books and the technical tomes, by employing computer experiments in place of calculations, and by concentrating on examples … Written by people for whom chaos is not an end but a means to the understanding of physical phenomena.’

But the trip soon turns into a total disaster. The other girls staying at the camp are MEGA-MEAN, best friend Jess is spending all her time with new girl Isha, and Lottie's diary gets stolen!Chaos theory concerns deterministic systems whose behavior can, in principle, be predicted. Chaotic systems are predictable for a while and then 'appear' to become random. The amount of time for which the behavior of a chaotic system can be effectively predicted depends on three things: how much uncertainty can be tolerated in the forecast, how accurately its current state can be measured, and a time scale depending on the dynamics of the system, called the Lyapunov time. Some examples of Lyapunov times are: chaotic electrical circuits, about 1 millisecond; weather systems, a few days (unproven); the inner solar system, 4 to 5 million years. [18] In chaotic systems, the uncertainty in a forecast increases exponentially with elapsed time. Hence, mathematically, doubling the forecast time more than squares the proportional uncertainty in the forecast. This means, in practice, a meaningful prediction cannot be made over an interval of more than two or three times the Lyapunov time. When meaningful predictions cannot be made, the system appears random. [19] We must abandon the idea that we will understand the rules, and instead become field biologists for technology--relying on description and observation to uncover facts about how a system might work.