Simulating Innovation: Comparing Models of Collective Knowledge, Technological Evolution and Emergent Innovation Networks

  • Christopher WattsEmail author
  • Nigel Gilbert
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 229)


Computer simulation models have been proposed as a tool for understanding 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 book. A critical review of existing innovation-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 innovation networks [6], and the hypercycles model of economic production [7]. The models are compared for the ways they represent knowledge and/or technologies, 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 frequency distributions. Suggestions are then made as to what features future innovation models might contain.


Innovation Novelty Technological Evolution Networks 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Ludwig-Maximilians UniversityMunichGermany
  2. 2.University of SurreyGuildfordUK

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