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Simulating Innovation: Comparing Models of Collective
Knowledge, Technological Evolution and Emergent
Innovation Networks
Christopher Watts
1
and Nigel Gilbert
2
1
Ludwig-Maximilians University, Munich, Germany
c.watts@lmu.de
2
University of Surrey, Guildford, UK
n.gilbert@surrey.ac.uk
Abstract.
Computer simulation models have been proposed as a tool for under-
standing innovation, including models of organisational learning, technological
evolution, knowledge dynamics and the emergence of innovation networks. By
representing micro-level interactions they provide insight into the mechanisms
by which are generated various stylised facts about innovation phenomena. This
paper summarises work carried out as part of the
SIMIAN
project and to be
covered in more detail in a forthcoming topic. A critical review of existing in-
novation-related models is performed. Models compared include a model of
collective learning in networks [1], a model of technological evolution based
around percolation on a grid [2, 3], a model of technological evolution that uses
Boolean logic gate designs [4], the SKIN model [5], a model of emergent inno-
vation networks [6], and the hypercycles model of economic production [7].
The models are 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 net-
works and scale-free frequency distributions. Suggestions are then made as to
what features future innovation models might contain.
Keywords:
Innovation; Novelty; Technological Evolution; Networks.
1
Introduction
Simulation models of innovation, including organisational learning, knowledge dy-
namics, technological evolution and the emergence of innovation networks may pro-
vide explanations for stylised facts found in the literatures in innovation, science and
technology studies. As computer simulation models of social systems, they can pro-
vide one with something one cannot easily obtain from the system itself [8]. They
offer a third approach to research, combining the ethnographer's interest in complex
contexts and causal relations with the quantitative data analyst's interest in large-scale
patterns. They can represent rigorously in computer code the micro-level interactions
