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of multiple, heterogeneous parts, then demonstrate the consequences of these interac-
tions and the circumstances in which they occur, including the emergence of macro-
level patterns.
There are a number of stylised facts inviting explanation of which some of the
most relevant to simulation models of innovation follow. Firstly, innovation can be
progressive. New ideas and technologies solve problems, create new capabilities and
render obsolete and replace old ideas and technologies. A second stylised fact may be
found in the rate of quantitative innovation, that is, the rate at which a type of item
becomes better, faster, cheaper, lighter etc. Perhaps the best known example of this is
Moore's Law, which holds that the number of circuits that can be fitted on a chip
increases exponentially over time, but examples of exponential growth rates exist for
many other technologies, such as land and air transport, and the particular growth
rates also appear to have grown exponentially over time since 1840 [9, 10]. Thirdly,
there is the rate of qualitative innovation, that is, the rate at which qualitatively new
types of good or service appear. One rough illustration of this is that humans 10000
years ago had a few hundred types of good available to them, while today in a US city
there are barcodes for 10^10 types of goods [11]. Various stylised facts exist for the
frequency distribution of innovation size, where measures of size include the eco-
nomic returns from innovation [12, 13] and the number of citations received by a
particular patent [14]. Given Schumpeter's famous description of the “perennial gale
of creative destruction” [15], there is also interest in the size of the web of interde-
pendent technologies and services blown away (rendered obsolete and uncompetitive,
thereafter becoming extinct) by the emergence of a particular innovation. The innova-
tion literature typically distinguishes between incremental and radical innovations
[16], the former meaning a minor improvement in an existing technological approach,
and the latter a switch to a new approach. In addition, it may be recognised that tech-
nologies' components are grouped into modules. This leads to the concepts of
architectural and modular innovations [17], the former meaning a rearrangement of
existing modules, while the latter means a change of a single module. Finally, emer-
gent structures should be mentioned. Networks of firms, suppliers and customers
emerge to create and make use of particular interlinked technologies. If empirical
studies of such networks can identify regular structural features, then simulation mod-
els can investigate the circumstances under which these structures emerge.
This paper summarises some of the main points from a critical survey of several
models of organisational learning, knowledge dynamics, technological evolution and
innovation networks, undertaken as part of the ESRC-funded SIMIAN project
(www.simian.ac.uk) and described in detail in a forthcoming topic by the authors.
Particular areas for model comparison include the ways these models represent
knowledge and/or technologies, how novelty enters the system, the degree to which
the models represent open-ended systems, the models' use of networks, landscapes
and other pre-defined structures, and the patterns that emerge from the models' opera-
tions, primarily network structures and frequency distributions. In addition, based on
our experiences with these models and some recent literature, suggestions are made
about the form and features that future innovation models might contain.
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