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they balance between no order and too much order
(Potts, 2000). In this critical state,
complex systems have a dynamic ei ciency that allows them to meet evolutionary and
competitive pressures, whereas excessively ordered as well as chaotic, unstable systems
are both eliminated. Self-organised systems have a high degree of resilience and robust-
ness as they are marked by distributed and dispersed, rather than centralised, control, as
well as by strong positive and negative feedback loops, and by a high level of redundant
variety (Heylighen, 1999). It is not surprising then that this vision of complex systems has
been used in a normative fashion and has been used to explain the resilience and adapt-
ability of complex industrial clusters (Lindsay, 2005).
At the same time, however, complexity economics also suggests that once co-
evolutionary complexity, in terms of a system's internal and external co-evolutionary
linkages, passes a certain threshold, the system may become unresponsive to environ-
mental pressures. Foster (1997), for example, points out that all dissipative systems have
a tendency to degrade through time and the renewal of their links depends on the con-
tinual import of information, energy and resources. He also argues that complex systems
tend to become more specialised as their order, integration and 'knowledge' increase. As
their coherence increases, they specialise in adapting to particular environmental niches
and if these niches suf er from resource depletion or the entry of new competitors, then
even complex systems may disintegrate and start the process of self-organisation anew.
Thus complex linkages and connections that constitute complex economic systems may
over time prove to be too specialised. In the case of 'complexity catastrophes', too many
interdependencies act to trap a system within a basin in a i tness landscape so that envi-
ronmental selection can not operate. McKelvey (1999) applies this thinking to i rms and
Beinhocker (2006) also argues that hierarchical systems in organisations are often more
ei cient and adaptable because they can make and implement decisions more rapidly.
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Once again however, whether this can be applied to the evolution of urban and regional
economies is unclear. How can we judge when their connectivity has exceeded a benei -
cial value and started to move towards a complexity catastrophe? What would an excess
of external connectivity mean in this case? Under this logic also, the increasing com-
plexity of many production networks and regional economies might imply that we will
witness more 'complexity catastrophes' in future, although this will depend, of course,
on whether any such ef ect is of set by other trends in innovation and the evolution of
knowledge.
6. Complexity and regional economic evolution
An issue of particular interest in the present context, given the above discussion and our
comments in the introduction, is whether and how far complexity economics represents
an advance on other types of evolutionary economics, and evolutionary economic geog-
raphy, that draw insights by using Darwinian and other natural evolutionary analogies
and metaphors. While some complexity economists argue that their approach is mac-
roscopic and supersedes a microscopic, generalised Darwinianism (Foster, 1997), as we
have already noted, most argue that complexity economics can synthesise complexity
with evolutionary approaches based on natural selection. At the same time complexity
economics tends to claim that it no longer has to rely on restrictive natural analogies
and metaphors. Instead it aims to tell stories about its own subject matter (Wakeley,
2002). Beinhocker (2006), for example, argues that his approach is based on a universal
