Information Technology Reference
In-Depth Information
horizontal links as well as vertical ones, it is possible for quite large jumps in the
BPF, whenever progress in one column has obstructed by an impossible technology
while progress continues in other columns from search radiuses can cover the column
with the obstruction. The frequency distribution of these innovation sizes is recorded
and plotted. For some values of the parameter search radius, this frequency distribu-
tion tends towards a scale-free distribution. In their basic [3] model, Silverberg and
Verspagen represent the same amount of search as occurring from every column in
the grid. Silverberg and Verspagen [2] extend this model with search agent firms who
can change column in response to recent progress. The firms' adaptive behaviour has
the effect of generating the scale-free distribution of innovation sizes without the need
for the modeller to choose a particular value of the search radius parameter, and thus
the system represents self-organised criticality [26].
Arthur and Polak [4] (A&P) also simulate technological evolution. Their technolo-
gies have a real-world meaning: they are designs for Boolean logic gates, made up of
combinations of component technologies, beginning from a base technology, the
NAND gate. Each time step a new combination of existing technologies is created
and evaluated for how well it generates one of a fixed list of desired logic functions. If
it replicates desired functions satisfied by a previously created technology, and is less
expensive, where cost is defined as the number of component instances of the base
technology, NAND, then the new technology replaces in memory the technology with
the equivalent function and higher cost. The replaced technology may have been used
as a component technology in the construction of other technologies, in which case it
is replaced in them as well. The total number of replacements resulting from the
newly created technology is its innovation size. As with the previous model, A&P
find example parameter settings in which the frequency distribution of innovation
sizes tends towards being scale-free.
The model for Simulating Knowledge dynamics in Innovation Networks (SKIN)
[5, 27, 28] simulates a dynamic population of firms. Each firm possesses a set of
units of knowledge, called kenes, and a strategy, called an innovation hypothesis (IH),
for combining several kenes to make a product. Input kenes not possessed by the firm
must be sourced from a market supplied by the other firms. Each kene is a triple of
numbers, representing a capability, an ability and expertise. Products are created as a
normalised sum-product of capabilities and abilities of the kenes in the IH, and given
a level of quality based on a sum-product of the same kenes' abilities and expertise.
Firms lacking a market for their products can perform incremental research to adjust
their abilities, radical research to swap a kene in their IH for another kene, or enter an
alliance or partnership with another firm to access that firm's kenes. Expertise scores
increase in kenes when they are used, but decrease when not in use, and kenes with 0
expertise are forgotten by the firm. Partners are chosen based on past experience of
partnership, customer and supplier relations, and the degree of similarity in kenes to
the choosing firm. Regular partners may unite to form an innovation network, which
then can create extra products in addition to those produced by its members. Products
on the markets have dynamic prices reflecting recent supply and demand, research has
costs, and firms' behaviour reflects their wealth.
