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Inter-Organizational Learning and Collective Memory in Small Firms Clusters: an Agent-Based Approach

Author

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  • Francesca Borrelli
  • Cristina Ponsiglione
  • Luca Iandoli
  • Giuseppe Zollo

Abstract

Literature about Industrial Districts has largely emphasized the importance of both economic and social factors in determining the competitiveness of these particular firms' clusters. For thirty years, the Industrial District productive and organizational model represented an alternative to the integrated model of fordist enterprise. Nowadays, the district model suffers from competitive gaps, largely due to the increase of competitive pressure of globalization. This work aims to analyze, through an agent-based simulation model, the influence of informal socio-cognitive coordination mechanisms on district's performances, in relation to different competitive scenarios. The agent-based simulation approach is particularly fit for this purpose as it is able to represent the Industrial District's complexity. Furthermore, it permits to develop dynamic analysis of district's performances according to different types of environment evolution. The results of this work question the widespread opinion that cooperative districts can answer to environmental changes more effectively that non-cooperative ones. In fact, the results of simulations show that, in the presence of turbulent scenarios, the best performer districts are those in which cooperation and competition, trust and opportunism balance out.

Suggested Citation

  • Francesca Borrelli & Cristina Ponsiglione & Luca Iandoli & Giuseppe Zollo, 2005. "Inter-Organizational Learning and Collective Memory in Small Firms Clusters: an Agent-Based Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(3), pages 1-4.
    • Handle: RePEc:jas:jasssj:2005-2-2
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    References listed on IDEAS

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    1. Rosaria Conte, 1999. "Social Intelligence Among Autonomous Agents," Computational and Mathematical Organization Theory, Springer, vol. 5(3), pages 203-228, October.
    2. Brusco, Sebastiano, 1982. "The Emilian Model: Productive Decentralisation and Social Integration," Cambridge Journal of Economics, Oxford University Press, vol. 6(2), pages 167-184, June.
    3. Troy J Strader & Fu-ren Lin & Michael J Shaw, 1998. "Simulation of Order Fulfillment in Divergent Assembly Supply Chains," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(2), pages 1-5.
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    Cited by:

    1. Santiago Quintero Ramírez & Walter Lugo Ruiz Castañeda & Jorge Robledo Velásquez, 2017. "Representation of unlearning in the innovation systems: A proposal from agent-based modeling," Estudios Gerenciales, Universidad Icesi, vol. 33(145), pages 366-376, November.
    2. Riccardo Boero & Flaminio Squazzoni, 2005. "Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-6.
    3. Prenkert, Frans & Følgesvold, Atle, 2014. "Relationship strength and network form: An agent-based simulation of interaction in a business network," Australasian marketing journal, Elsevier, vol. 22(1), pages 15-27.

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