Hard and Soft Sciences

Scientific disciplines that are concerned with linear systems are usually called 'hard' sciences. These disciplines include physics, chemistry, astronomy, and mathematics. They are characterised by the two facts that very accurate measurements of their components are possible, and that very accurate prediction of their behaviour is also possible. They are the oldest sciences in terms of scientific discovery, and they are associated with famous, but ancient, names such as Pythagoras, Archimedes, Euclid, Copernicus, Galileo, and Newton.

Conversely, scientific disciplines that are concerned with nonlinear systems are usually called 'soft' sciences. They embrace all the life sciences, including biology, medicine, economics, psychology, and sociology. Characteristically, their components are difficult to measure accurately, and predictions of their behaviour are either difficult or impossible. Everyone is aware of how difficult it is to measure such things as human creativity, originality, artistic ability, and personality. It is equally difficult to predict the future behaviour of the stock market, a horse race, the weather, or a human individual.

There is a traditional belief that the hard sciences are more fundamental, more accurate, more important, more factual, more 'scientific', and generally superior to the soft sciences. This tradition, which is a form of intellectual snobbery, has done enormous harm to the soft sciences. It has persuaded many scientists, particularly during the twentieth century, to treat soft sciences as if they were hard sciences. That is, to treat non-linear systems as if they were linear systems.

Twentieth century science has attempted to force non-linear systems into the mould of the hard sciences. The more difficult this has been, the less the discipline in question has been respected. The very 'soft' science of sociology, for example, is often dismissed entirely, on the grounds that it is not really a science at all. But, if we are to understand ourselves, sociology is perhaps the most important science of all. And this will probably be recognised only when sociology is studied as a non-linear system. Similar comments are probably valid for other non-linear disciplines, such as economics, and crop improvement.

Many of our current problems in crop science have resulted from the desire among crop scientists to be truly 'scientific'. That is, a desire to treat this soft science as if it were a hard science. This has led to linear analyses, and linear controls, being imposed on non-linear systems. Some major distortions have resulted, such as biological anarchy (see 6.7 & 7.16.1), the use of vertical resistance on a basis of uniformity (see 5.3), the errors stemming from parasite interference (see 7.16.2), and our heavy reliance on crop protection chemicals (see 11.3.2). Clearly, we must recognise that ecosystems, agro-ecosystems, and crop pathosystems are nonlinear systems. And we must analyse them and control them accordingly. Or, more precisely, we must allow them to self-organise as much as possible (see 11).

Some aspects of the hard sciences are non-linear. Physicists, for example, have been forced to use a non-linear approach in quantum mechanics and fluid dynamics. And complexity theory is now bringing about a scientific revolution that is probably at least as important as the work of scientists such as Newton. One of the unexpected outcomes of complexity theory is that the physicists themselves are now beginning to say that the soft sciences, the non-linear systems, are the more important, and the more fundamental. Indeed, the hard sciences, the linear systems, are somewhat elementary. They are simple and, when compared with the non-linear systems, they are relatively infrequent and and they are less important in the world as a whole. The implication is that science in the twenty first century will be very different from science in the twentieth century. It will be concerned primarily with non-linear systems, and it will require entirely new techniques of study.

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