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evolutionary algorithm so that he does not have to rely on natural analogies. However,
natural metaphors continue to be permeate complexity economics and are used when
convenient. Beinhocker (2006) later identii es general laws of evolutionary systems that
subsume the biological:
The claim of the modern algorithmic view of evolution is that evolutionary systems are a uni-
versal class with universal laws. We can then ask whether the economy is part of that class and
subject to those laws. If the answer is yes, then the economic and biological worlds are both
members of that universal class. They may be very dif erent in their implementations of the
algorithm, and thus asking what a parent and an of spring are in economics makes no sense.
Nonetheless, the two worlds are still subject to the same general laws of evolutionary systems,
thus explaining the strong (pardon the metaphor) family resemblance. (p. 217)
Indeed, he relies heavily on i nding a business equivalent to DNA which can be dif-
ferentiated, selected and replicated. But do we need to search for this if socio-economic
evolution is fundamentally dif erent from natural evolution? Moreover, he also uses
evolutionary psychology to explain what he means by wealth as 'i t order'. Goods
and products that push people's 'pleasure buttons' are those that satisfy the needs and
instincts derived from our long evolutionary history. Thus, his explanation proves reliant
on drawing out the implications of natural evolution.
Furthermore, other complexity economists are more willing to use direct natural evo-
lutionary analogies. For instance, one of the most interesting aspects of self-organisation
identii ed in the complexity literature is the 'Red Queen' (or competitive co-evolution)
ef ect in which two competing species become locked into an intensive and adaptive race,
equivalent to running in order to stand still. There have been several applications of this
idea in complexity economics (for example, Markose, 2005; Robson, 2005). Thus it has
been argued that this ef ect may explain the dynamics of innovation in high-technology
and i nancial sectors as it forces competitors to continuously introduce new variety. It
is not hard to envisage a spatially dei ned version of Red Queen ef ects in which i rms in
clusters are driven to innovate more by the pressure of close competitors (Porter, 1998).
The synthesis of evolutionary ideas with complexity means that recent versions of com-
plexity economics still appear to be vulnerable to criticisms of evolutionary approaches.
One of these is that they still portray human agents as mainly adapting to their envi-
ronments rather than actively making these environments. There seems to be little in
complexity economics on the ways in which powerful i rms transform their economic
environments through buying out competitors, switching investments into new sectors
and locations, introducing major innovations in processes or products, or remaking
markets through the ef ects of mass advertising.
Notwithstanding this continued use of the natural metaphor, the main dif erence
between complexity economics and neo-Darwinian views of economic evolution appears
to rest on the relative importance of system self-transformation relative to selection.
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The key point here is that some complexity accounts argue that selection cannot operate
on complex economies. This is either because there is too much innovation and mutation
happening simultaneously or because interdependencies between system components
mean that the i tness landscape is l at and that these co-evolutionary pockets become
trapped in suboptimal i tness (Kauf man, 1993; McKelvey, 1999). In these conditions
environmental pressures will not work even if they are strong. This surely implies that
