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Proximity Relations, Partnership Structure and Supporting Institutions in an Agent-Based Model of an Industrial District Prototype

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  • Riccardo Boero, Flaminio Squazzoni

Abstract

Industrial districts are complex systems based on an evolutionary network of interactions among heterogeneous, functionally integrated, complementary and localised firms. The paper describes an agent-based model that allows to investigate some theoretical hypotheses on the relation among behavioural styles of industrial district firms, different forms of proximity and of inter-firm relations, and their effect on technology and market adaptation of the industrial district as a whole. Moreover, we focus on some hypotheses on the role of partnership externalisation and the function that supporting institutions can exert as scaffolding structures able to improve effective connections between the industrial district and external environment.By using Swarm and Java programming language, we have created an industrial district prototype that allows to test some issues in the literature. Hypotheses which we concentrate upon are as follows: a) proximity relations can be a tool of technological learning for firms, but if firms are able to set up stable cooperation and coordination inter-firm structures; b)to do that, firms have to behave far from optimising-like behavioural styles; c)supporting istitutions matter as scaffolding structures between industrial districts and technology and market environment. To test the hypotheses, we have created and compared different simulation settings, where little modifications are gradually introduced in the model.Concerning the first hypotheses, the simulation outcomes show that proximity-based information is fundamental for learning and coordination of industrial firms. Different proximity metrics have different learning functions. Spatial proximity matters for circulation of relevant technological information among firms in phase of technological innovation, while organisational proximity matters for defining partnership structures able to make technological learning a collective and coordinated process. Concerning the second one, the simulation outcomes show that to transform proximity relations into learning tools what matters is the style of behaviour of ID firms. In a context of interdependence among firms, it is important that strategic industrial district firms are able to define long term partnerships with other complementary firms that should be based on commitment, cooperation, circulation of information, and mutual coordination. This is possible only if firms behave far from pure market style (short-run optimising economic agents), exchanging optimisation in a short period for adaptation in the long term, and taking care about the stability of their interaction context. Concerning the third one, the simulation outcomes show that supporting institutions matter to sustain firms in externalisation processes towards external environment.

Suggested Citation

  • Riccardo Boero, Flaminio Squazzoni, . "Proximity Relations, Partnership Structure and Supporting Institutions in an Agent-Based Model of an Industrial District Prototype," The Electronic Journal of Evolutionary Modeling and Economic Dynamics, IFReDE - Universit√© Montesquieu Bordeaux IV.
  • Handle: RePEc:jem:ejemed:1028
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    Keywords

    Agent-Based Models; Industrial Districts; Technological Change; Proximity Relations;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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