The mechanical paradigm

The basis of scientific understanding has traditionally been the mechanical model (Prigogine and Stengers, 1987; Allen, 1985, 1988). In this view, the behaviour of a system could be understood, and anticipated, by classifying and identifying its components and the causal links, or mechanisms, that act between them. In physical systems, the fundamental laws of nature such as the conservation of mass, momentum, and energy govern these mechanisms, and determine entirely what must happen. This was such a triumph for classical science, that it was believed (erroneously) that analogous ideas must apply in the domains of biology, ecology, the human sciences, and particularly, of course, economics (Arrow and Debreu, 1954). It is exactly this vision that underlies Adam Smith's idea of an "invisible hand" working collective improvement through the self-seeking behaviour of individuals.

But in such a vision the problem of change remains unsolved. If we study a system over time, we find that its structure changes. Any system developed at a given time somehow transforms itself over time. In order to anticipate the changes that will occur in the system we must try to understand how this creative self-transformation can possibly occur. Obviously, it is not contained in the set of mechanical equations that characterize the system at any one time. It is clearly beyond the behaviour of a closed system.

In human systems what happens depends very strongly on what decisions are taken. Although, of course, the natural laws always work, they no longer suffice to determine what must occur. Systems open to flows of energy and matter can attain varying degrees of autonomy, where it is the interplay of nonlinear interactions that decide how the system structures and evolves.

In the traditional scientific view, the future of a system is predicted from the mathematical equations which govern the "motion" of its components. But in order to write down the equations of motion for a real system it is always necessary to make approximations. The assumption that must be made is that the elements making up the variables (molecules in a structure, individuals within a population, firms in a sector, etc.) are all identically that of the average type. In this case, the model reduces to a "machine" which represents the system in terms of a set of differential (perhaps non-linear) equations which govern its variables.

Fig. 1 At any different time we may analyse the components and functioning of the system, but this remains fixed while reality changes

This shows us the paradox underlying the scientific approach. At any given time, we can always analyse a system, and imagine that we have "understood" its structure and constituent mechanisms. From this, we may feel that we can even make predictions, and use it as a base for our policies and actions. However, the very act of formulating the structure as a set of mechanisms actually excludes the non-average individual microdiversity, which will be responsible for structural change and the qualitative evolution of the system.

In other words, the elements that will lead to invention and innovation are precisely what is excluded from the traditional scientific description of an economic or even an ecological system. The interactions of economic sectors, described through input-output relations, are comparable to the interacting population dynamics of an ecosystem, but neither contain the mechanisms of their own selftransformation. This is the paradox: in order to know the future, we use an analytic tool that throws away the factors that are important in creating that future.

The attraction of rational analysis is strong, and the scientific reader will certainly be using it in order to assimilate this paragraph and chapter, but clarity is bought at the expense of vital details, and it is the dialogue between the apparent structure and the deviations from it that provides the power of self-transformation and emergence in systems. The Newtonian vision of the world as a collection of "clockwork mechanisms" that can be laid out and examined is fine for the actual machines that humans produce, but is an inadequate representation of the world in which these are embedded. Real systems are in fact coupled in a multiplicity of ways with factors in their environment, through flows of matter, energy, and information, and although in vitro experiments can be useful in understanding some simple physical systems, the essential behaviour of ecosystems arises in vivo, through the visible and invisible dialogue with their environment. It is this science-inspired tendency to separate the "inside" of a system from its "outside" that is at the root of environmental problems.

Equally, it is this separation of inside from out, together with the mechanical description, that has produced a methodology in technology and engineering which is often characterized by "optimized" solutions under fixed conditions. But this fails to allow for the fact that information flows, learning, and change are all taking place and that in the real world the inside is co-evolving with the outside.

From the discussion we see that evolutionary change must result from what has been removed in the reduction to the deterministic description, that is, the non-average. Systems evolve through the interplay of two kinds of terms. First there are deterministic average mechanisms operating between typical components, whose identity and nature are revealed by rational, scientific analysis. Second, however, there is what has been suppressed in the rational picture, the non-average behaviour and detail which probes the stability of the existing structure and on occasions can be amplified and lead to qualitative structural changes and a reorganization of the average mechanisms.

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