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components that are referred to as 'complex systems'. These same processes, includ-
ing co-evolutionary mechanisms among dif erent system components and levels, give
complex systems an
adaptive quality
, whereby their structures change in response either
to changes in a system's external environment, or from within through what some have
termed 'self-organised criticality' (Bak, 1996), in which the system evolves to a particu-
lar 'critical' state that then generates chain reactions between components to produce a
major change in the system's structure and/or dynamics (a sort of 'punctuated equilib-
rium' form of system evolution).
Finally, because of these various attributes, complex systems are fundamentally
non- deterministic
. Even if we have complete information on the function and inter-
relationships of components, it is not possible to anticipate their behaviour precisely.
Complex systems are inherently stochastic in nature. But that does not mean their
behaviour is random in the sense of being haphazard. There are causal processes at work,
but they operate through complex, distributed feedback and self-reinforcing mecha-
nisms that are unlikely to be detected by standard measures of association (correlation)
between assumed determinants and presumed ef ects (McGlade and Garnsey, 2006).
According to some exponents, together these various characteristics form a sort of
'vocabulary of complexity' (Lissack, 1999; Nicolis and Prigogine, 1989). In ef ect, they
constitute a 'complexity ontology', a particular view of how reality (from physical
systems to biological systems to social systems) is structured and behaves (evolves). Of
itself this ontology does not imply a specii c set of methods or 'tools' of analysis, though
as mentioned above, many advocates of complexity do seek to express complex systems
and their dynamics in terms of formal mathematical models. This distinction between the
ontological and methodological aspects of complexity thinking is an important one, as
developments and debates within economics demonstrate.
3. Economics and complexity thinking
In recent years complexity theory has attracted increasing interest from economists,
and is now believed by many to be a novel and powerful framework of thought capable
of challenging the fundamental principles of the mainstream economic canon (see, for
example, Arthur et al., 1997; Auyang, 1998; Colander, 2000a, 2000b; Krugman, 1996;
Metcalfe and Foster, 2004; Potts, 2000; Rosser, 2004; Schenck, 2003).
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According to
one of its main advocates, 'complexity changes everything; well maybe not everything,
but it does change quite a bit in economics' (Colander, 2000a, p. 31). It is suggested that
complexity ideas 'are beginning to map out a radical and long-overdue revision of eco-
nomic theory' (Buchanan, 2004, p. 35). In
one such manifesto,
for example, Beinhocker
(2006) suggests the term 'complexity economics' as an umbrella for a number of streams
of theoretical and empirical work that can be linked directly or indirectly with 'complex-
ity thinking'. While he emphasises that 'complexity economics' is still more a research
programme than a single synthesised theory, he identii es i ve key dimensions - or what
he calls 'big ideas' - that mark out 'complexity economics' from 'traditional economics'
(Table 4.2): the economy as an open, non-linear system; made up of agents with bounded
rationality, who learn and adapt; who interact through constantly changing networks;
whose micro-behaviours and interactions are the source of emergent pattern and order at
the macro-level; and who are the source of the constant novelty that imbues the economy
with its evolutionary momentum.
