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ideas from evolutionary biology, so there are questions concerning the interpretation of
metaphors and analogies transferred from 'complexity theory' to economic geography.
The chapter is intended to throw some light on this issue. We begin, then, in the next
section, with identifying some of these generic principles or properties that are held to
characterise complex systems.
2. 'Complexity thinking': some generic concepts and principles
At the very outset, we need to distinguish between 'complexity' and 'complication'. The
description 'complex' is often inappropriately attributed to systems by virtue of their
having a large number of component parts, when these systems are merely complicated.
A system of this kind can be understood by taking it apart and rebuilding it, like a clock
or a car - the system is explicable through a description of is component parts. On the
other hand a system is complex when it comprises non-linear interactions between its
parts, such that an understanding of the system is not possible through a simple reduc-
tion to its component elements. A complicated system, then, need not be complex, in
the sense of exhibiting complex behaviour: of course, a complex system may also be
complicated.
As mentioned above, while there is as yet no generally agreed set of well-dei ned 'law-
like' statements that together constitute a universal theory of complexity, nevertheless
what distinguishes complex systems is the way they exhibit emergent self-organising
behaviour, driven by co-evolutionary interactions, and an adaptive capacity that enables
them to rearrange their internal structure spontaneously (Pavard and Dugdale, 2000).
More specii cally, seven generic properties can be identii ed as characteristic of complex
systems (see Table 4.1).
First, a complex system has a distributed nature and representation, in the connection-
ist sense, whereby the system's resources are physically or virtually distributed across
various sites, and its functions and the relationships and feedbacks that exist among its
elements occur over various spatial ranges and scales: complex systems are characteristi-
cally multi-scalar. Second, it is often supposed that a system changes only inside its own
frontier: this is the dei nitional notion of 'operational closure'. In contrast, it is typically
dii cult to determine the boundaries of a complex system: the boundary between a
complex system and its environment is usually dependent on the purpose of the analysis,
or on the context, and not on any intrinsic property of the system itself. In short, open-
ness (or non- isolation ) is an inherent feature of complex systems. This in turn is closely
related to the idea that such systems tend also to be dissipative , in the sense that they
are in constant interaction and exchange with their environments, and thus experience
a continual inl ow and outl ow of energy, matter and information. When these charac-
teristics are combined with non - linear dynamics , arising from the mutually reinforcing
feedbacks among a complex system's parts, the result is an irreversibility of change and
a tendency towards path dependence in the system's trajectory and behaviour. At the
same time, however, openness implies a susceptibility to externally induced l uctuation
and perturbation, and such forces can cause a shift to a new regime. In other words, 'the
passage to complexity is intimately related to the bifurcation of new branches of solutions
[trajectories] following from the instability of a reference state, caused by the nonlineari-
ties and [environmental] constraints acting on an open system' (Nicolis and Prigogine,
1989, p. 73; original emphasis). Because of its inherent connectivity, non-linearity and
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