Information Technology Reference
In-Depth Information
2
The Innovation Models
Simulation models of innovation focus on the production, diffusion and impact of
novel ideas, beliefs, technologies, practices, theories, and solutions to problems.
Simulation models, especially agent-based models, are able to represent multiple
producers, multiple users, multiple innovations and multiple types of interdependency
between all of these, leading to some hard-to-predict dynamics. In the case of innova-
tions, some innovations may form the components of further innovations, or they may
by their emergence and diffusion alter the functionality and desirability of other inno-
vations. All of the models surveyed below have these aspects.
Space permits only a few models to be surveyed here. Further models of techno-
logical evolution, with several points of similarity to the ones included here, may be
found in Lane [18]. Also related are science models [19], models of organisational
learning, for example March [20], models of strategic decision making, for example
Rivkin and Siggelkow [21], and models of language evolution [22, 23]. Treatments of
some of these areas can also be found in Watts and Gilbert [24, chapters 7, 5 and 4].
Space also does not permit more than brief indications of the functionality of the
models in this survey. For more details the reader is directed to the original source
papers and to Watts and Gilbert [24, chapter 7]. The brief descriptions of the models
now follow.
Lazer and Friedman [1] (hereafter L&F) simulate an organisation as a network of
agents attempting to solve a common complex problem. Each agent has a set of be-
liefs, represented by a bit string, that encode that agent's solution. Solutions are
evaluated using Kauffman's NK fitness landscape definition [25], a moderately “rug-
ged” landscape problem with N=20 and K=5. Agents attempt seek better solutions
through the use of two heuristic search methods: learning from others (copying some
of the best solution among the agent's neighbours), and trial-and-error experimenta-
tion (trying a solution different from your current solution by mutating one bit). The
eventual outcome of searching is that the population converges on a common solu-
tion, usually a better solution than any present among the agents initially, and ideally
one close to the global optimum for that fitness landscape.
Silverberg and Verspagen [3] (S&V) simulate technological evolution using nodes
in a grid lattice to represent interlinked technologies, and percolation up the grid to
represent technological progress. Technologies can be in one of four states: impossi-
ble, possible but yet-to-be-discovered, discovered but yet-to-be-made-viable, and
viable. At initialisation, technologies are set with a fixed chance, to be possible or
impossible. Technologies in the first row of the grid are then set to be viable. The
best-practice frontier (BPF) is defined as the highest viable technologies in each col-
umn. Each time step, from each technology in the BPF, R&D search effort is made
over technologies within a fixed radius. As a result of search some possible technolo-
gies within the radius may, with a chance dependent on the amount of effort divided
by the number of technologies in the radius, become discovered. Any discovered
technologies adjacent to viable technologies become themselves viable. Innovations
are defined as any increases in the height of the BPF in one column. Innovation size is
defined as the size of the increase. Since technologies may become viable because of
