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Dynamics of collaboration among high-growth firms: results from an agent-based policy simulation

Author

Listed:
  • Anna Varga-Csajkás

    (ELRN
    University of Pécs)

  • Tamás Sebestyén

    (ELRN
    University of Pécs)

  • Attila Varga

    (ELRN
    University of Pécs)

Abstract

This study aims to show how supporting new business formation could promote the emergence of new ties in the knowledge network. Stimulating start-ups is conducive to the innovation system and economic development, however, we show how it has additional beneficial effects by the increasing the density of the innovation network. An innovation network is a complex socioeconomic phenomenon, which emerges from the decisions of many heterogeneous agents, that justifies the use of an agent-based model (ABM). We introduce an application of an ABM that is appropriate for simulating network formation among organizations. Agents are placed in the two-dimensional abstract social space where they are moving toward each other to find cooperation partners, following the gravity principle. The attraction is determined by the mass and the distance of agents. These parameters are specified by regression analysis, the result of which shows that the geographical, social, and technological distance has a negative impact on innovation-related cooperation. For the empirical underpinning of the model, we used survey data on the Hungarian high-growth firms’ (known as gazelles) egocentric network that contains information about innovation-purpose cooperation.

Suggested Citation

  • Anna Varga-Csajkás & Tamás Sebestyén & Attila Varga, 2023. "Dynamics of collaboration among high-growth firms: results from an agent-based policy simulation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 70(2), pages 353-377, April.
    • Handle: RePEc:spr:anresc:v:70:y:2023:i:2:d:10.1007_s00168-022-01150-w
    DOI: 10.1007/s00168-022-01150-w
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    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
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