The theme of Peter Coveney and Roger Highfield's book, too, is mess - the wonderful messiness of the natural world and how nature works with that mess to generate the rich complexity of ourselves and our environment. Their writing makes it obvious why a wise corporate leader should want to focus on mess. They also describe attempts to found a new "science of complexity", and make clear the profound implications that holds for our understanding of science itself.
Traditional science of the Newtonian sort is reductionist. It assumes that every whole, or every system, can be broken down into its most simple working parts, and that the whole can then best be understood through understanding the parts. Thus my body consists of a heart, lungs, kidneys, a brain and so on. Reductionist medicine holds that I am an assemblage of these parts, and that an illness in me originates with a malfunction in some part. Western medical experts who understand the whole body as an interrelated system are very rare. Western universities that teach the interrelationship of academic disciplines are equally rare.
Newtonian science is also linear. Things move from A to B along smooth paths. Progress develops through smooth and steady increase, systems evolve in predictable, rule-bound ways. The world is an orderly place offering no real surprises to the scientist with tools to understand its few simple laws.
But the sciences of the 20th century - relativity theory, quantum mechanics, and more recently the "new sciences" of chaos and complexity - try to cope with the fact that some natural phenomena are not inherently simple. There are some wholes or systems that are greater than the sum of their parts. Attempts to reduce them to simple bits lose something vital. There are kinds of development and progress that are not linear - they happen in sharp, dramatic, cascades of change, in "quantum leaps". Nature and the man-made environment contain some systems that defy reductive analysis and predictability: things like beehives, ecological systems, economic trends, the human brain and nervous system, computer networks and so on. All have a tendency to self-organise, to follow inner patterns of development inherent within, or specific to their unique evolution. These complex systems demand a new kind of respect, almost humility, from the scientist who hopes to study them. They inspire awe rather than a confident urge to control.
But scientists will be scientists, and science writers will continue to laud their Faustian dreams of all-embracing, simple understanding. So Frontiers of Complexity is as much about "brave efforts" to found a science of complexity as it is about the wonder and intricacies of complex systems themselves. This throws a shadow over the book, and raises some doubts about the philosophical sophistication of its authors.
The "science of complexity" is a computer science. Non-linear equations that apply to complex systems are too difficult for human beings to solve. But if they are run through computers, amazing trends and patterns begin to appear out of the "mess". The illusion is given that there are, after all, quite simple laws at work within the complexity. The computer simulation of one complex system, a beehive, appears quite similar to that of another, the human brain, and voila! - the scientist has found a "unifying principle". The grave doubts now being raised in the scientific community focus on the question of whether all that's being discovered is the unifying principle behind how computer-generated simulations look.
Coveney and Highfield bring their own major assumption to the surface when they say: "The ultimate test of our understanding of the brain will come with the design and simulation of an artificial one which displays such attributes as intelligence and consciousness." But if I design my computer to produce a cackle every time I type a joke, it does not mean that the computer has a sense of humour. It simply behaves as though it does. There is a world of difference here. Just as there may be a world of difference between the genuine complexity of a beehive and the "artificial life" program of a complexity scientist which simulates that complexity.
Complexity is real, and perhaps genuinely very complex. "Complexity science" may be no more than a disguised effort to apply reductionist science to that which cannot be reduced. Frontiers of Complexity describes many good examples of the real thing, but it is too much in thrall to the claims of those who would simplify it.