Information Technology Reference
In-Depth Information
The models described are all capable of demonstrating that the emergence of some
system-level pattern (e.g. convergence on a peak solution, scale-free distributions in
change sizes, collaboration networks, self-maintaining systems) is sensitive to various
input parameters and structures controlling micro-level behaviour (e.g. initial network
structure, search and learning behaviour, knowledge structure).
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Points of Comparison
The models are now compared for the ways they represent knowledge and/or tech-
nologies, how novelty enters the system, the degree to which they represent open-
ended systems, their use of networks, landscapes and other pre-defined structures, and
the patterns that emerge from their operations, including networks and scale-free fre-
quency distributions. A summary is given in Table 1.
As may be clear from the above descriptions, the models differ widely in their rep-
resentation of knowledge and technologies: there were bit strings (L&F), nodes in a
grid (S&V), lists of components (A&P), kenes (SKIN model), vectors of continuous
variables (CJZ) and algorithmic chemistry rules (hypercycles model). These were
evaluated using NK fitness (L&F), connection to the base row and height of row
(S&V), a list of desired logic functions and cost in terms of number of base compo-
nents (A&P), and in terms of their ability to take input from and supply output to
other model components (SKIN, hypercycles). It seems that later models tended to be
better than earlier ones, and represent gains in knowledge.
How novelty enters the system varies in line with how knowledge or technologies
are represented. L&F's use of heuristic search methods reflects the idea that there are
two sources of novelty: new combination of existing parts, and mutation, during
copying or experimentation. This view of novelty in innovation is found not only
among Herbert Simon's disciples [31], but is also common among Schumpeterian
evolutionary economists [32, 33]. The A&P model recombines existing parts when
constructing new logic gate designs. The SKIN model's incremental and radical re-
search processes have effects analogous to mutation of knowledge, while recombina-
tion of parts is made through alliance partnerships. The hypercycles model sees a
self-maintaining system emerge via a process analogous to ant colony optimisation, as
new production runs are constructed out of firms and rules. But novel choices of rule
for transforming or route for transferring products can only be made between the rules
and firms still present in the model. Once rules have been forgotten and firms have
left the network, they do not return. So in the present version of the model there is no
equivalent of a mutation process to reintroduce novel firms and rules. The CJZ model
is based on the idea that innovation stems from collaborative use of existing knowl-
edge resources, but it does not specify a mechanism for this. The constant-elasticity-
of-substitution production function is a description of the pattern of innovation rather
than its explanation. The S&V model also omits an explanation of how a new innova-
tion has been made, beyond the simple concept of “search” from a position at the
current best-practice frontier. The regular two-dimensional grid in which technologies
