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The Role of Knowledge Heterogeneity in Industrial Clusters Knowledge Dynamics: An Application of the Boolean Network Simulation Modeling
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Author(s): Lucio Biggiero (University of L'Aquila, Italy & CIRPS, Italy)and Marco Valente (University of L'Aquila, Italy)
Copyright: 2016
Pages: 26
Source title:
Relational Methodologies and Epistemology in Economics and Management Sciences
Source Author(s)/Editor(s): Lucio Biggiero (Università dell’ Aquila, Italy), Pier Paolo Angelini (Interuniversity Research Centre for Sustainable Development (CIRPS), Italy), Mario Basevi (Italian National Institute of Statistics, Italy), Nunzia Carbonara (Politecnico di Bari, Italy), Antonio Mastrogiorgio (Interuniversity Research Centre for Sustainable Development (CIRPS), Italy), Eliano Pessa (University of Pavia, Italy), Enrico Sevi (Università dell’ Aquila, Italy)and Marco Valente (Università dell’ Aquila, Italy)
DOI: 10.4018/978-1-4666-9770-6.ch009
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Abstract
In high-tech industrial clusters as the aerospace most collaborations for innovations are highly knowledge-specific and form a (relatively dense) knowledge network. With reference to the case of the aerospace industrial cluster of the Lazio Region, in this work we study the network dynamics of its core organizations (firms, universities, and research centers). By applying the methodology of NK simulation modeling, we explore what happens lacking the initial contribution of knowledge provided by universities and research centers. Further, we investigate the effects of the introduction of minimum requisites in terms of knowledge heterogeneity and knowledge amount. We show that, within a general favorable condition of activation rules, trajectories are quite short and knowledge dynamics is sensitive to the requisite of knowledge heterogeneity, when it is set-up around half of its potential range. We will conclude that, despite some interesting results like the ones we have found and many others that could be discovered, this methodology has substantial failures mostly due to its requisites of computational burden and topological and behavioral invariance, which makes it hardly applicable and scarcely informative into empirical analysis of phenomena within economics and management sciences.
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