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Multilevel Analysis of Industrial Clusters: Actors, Intentions and Randomness Model

Dilaver, O, Uyarra, E and Bleda, M (2014) Multilevel Analysis of Industrial Clusters: Actors, Intentions and Randomness Model In: Simulating Knowledge Dynamics in Innovation Networks. Complexity . Springer-Verlag.

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Abstract

The literature on industrial clusters indicates a symbiotic relationship between innovation and geographical concentration of firms working in similar industries. Innovative processes require different forms of knowledge and exper- tise, which are distributed across individuals and organisations at different levels of industrial clusters. In this chapter, we present fundamental extensions to the SKIN model for representing such multi-level interactions. We introduce individual actors in addition to firms as agents. These agents are placed in a two-regions environment that simulates evolution of two competing regions. We also integrate elements of intentionality in addition to randomness in our model. Through sub- jective assessments of their managers, firms investigate and design research projects. These extensions help to open up the black box of the firm and relate firms to the creative agency of individuals in starting up new firms, establishing their research objectives and creating new knowledge. Within this broad range of issues we focus in this chapter on the role of entrepreneurship to illustrate how the extended model can be used. In experiments focusing on entrepreneurship, we generate the relative success of Silicon Valley in comparison to Boston in silico.

Item Type: Book Section
Authors :
NameEmailORCID
Dilaver, Oo.dilaver@surrey.ac.ukUNSPECIFIED
Uyarra, EUNSPECIFIEDUNSPECIFIED
Bleda, MUNSPECIFIEDUNSPECIFIED
Date : 2014
Uncontrolled Keywords : Entrepreneurship, Industrial clusters, Multilevel modelling, Modelling intentionality, Uncertainty, Knowledge dynamics
Depositing User : Symplectic Elements
Date Deposited : 16 May 2017 15:31
Last Modified : 17 May 2017 14:36
URI: http://epubs.surrey.ac.uk/id/eprint/820216

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