Here are some sound pieces of advice: the more you know about a system, the better you are at predicting its behavior. If you want a large outcome, then put a large amount of effort into the process. For the best execution, plan ahead. These are all powerful strategies – but only if you are dealing with a linear system. For a complex system, this approach spells disaster
The living world is filled with striped and mottled patterns of contrasting colours; with sculptural equivalents of those patterns realised as surface crests and troughs, with patterns of organisation and behaviour even among individual organisms. People have long been temped to find some ‘intelligence’ behind all these biological patterns. In the early twentieth century the Belgian Symbolist playwright Maurice Maeterlinck, pondering the efficient organisation of bee and termite colonies asked; What is it that governs here? What is it that issues orders? Foresees the future? Elaborates, plans and preserves equilibrium? Administers and condemns to death?
We are increasingly aware that many our living systems – human and natural – are at risk today, as we face incredibly complex and interconnected challenges related to global security, environmental degradation, and inter-woven economies. Understanding the nature and dynamics of living systems, therefore, can shed light on how we think about our problems and our resources, and about the assumptions and the choices we make.
Chaos is a purely mathematical concept; it is an undeniable mathematical fact. We know that theoretical physics is built on mathematics, and that all theoretical physicists are applied mathematicians. The first question that I want to examine, then, is: why is it that, among all the practitioners of science, applied science, engineering disciplines, and human sciences, physicists were practically the last ones to be interested in chaos and to use it in their work?
Wicked problems such as climate change, poverty, and geopolitical instability, tend to be ill-defined, multifaceted, and complex such that solving one aspect of the problem may create new, worse problems. Thus, trying to engineer solutions to such problems is exacerbated by our inability to measure overall improvement.
This book is about collective intelligence: the creativity and resourcefulness that a group or team can bring to a collaborative problem.
How can we use knowledge of complexity in a practical way? I am often asked this question. I am confused by it. Practical at what level? By “practical” what is meant?