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Small World Dynamics and The Process of Knowledge Diffusion: The Case of The Metropolitan Area of Greater Santiago De Chile

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Abstract

This paper aims to understand some of the mechanisms which dominate the phenomenon of knowledge diffusion in the process that is called ‘interactive learning’. We examine how knowledge spreads in a network in which agents have ‘face-to-face’ learning interactions. We define a social network structured as a graph consisting of agents (vertices) and connections (edges) and situated on a grid which resembles the geographical characteristics of the metropolitan area of Greater Santiago de Chile. The target of this simulation is to test whether knowledge diffuses homogeneously or whether it follows some biased path generating geographical divergence between a core area and a periphery. We also investigate the efficiency of our ‘preference’ model of agent decision-making and show that this system evolves towards a small-world type network.

Suggested Citation

  • Piergiuseppe Morone & Richard Taylor, 2004. "Small World Dynamics and The Process of Knowledge Diffusion: The Case of The Metropolitan Area of Greater Santiago De Chile," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(2), pages 1-5.
  • Handle: RePEc:jas:jasssj:2004-9-1
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    Cited by:

    1. Lorenzo Cassi & Lorenzo Zirulia, 2008. "The opportunity cost of social relations: On the effectiveness of small worlds," Journal of Evolutionary Economics, Springer, vol. 18(1), pages 77-101, February.
    2. Piergiuseppe Morone & Richard Taylor, 2012. "Proximity, knowledge integration and innovation: an agenda for agent-based studies," Journal of Evolutionary Economics, Springer, vol. 22(1), pages 19-47, January.
    3. Mueller, Matthias & Bogner, Kristina & Buchmann, Tobias & Kudic, Muhamed, 2015. "Simulating knowledge diffusion in four structurally distinct networks: An agent-based simulation model," Hohenheim Discussion Papers in Business, Economics and Social Sciences 05-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    4. Yang, Guang-Yong & Hu, Zhao-Long & Liu, Jian-Guo, 2015. "Knowledge diffusion in the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 429-436.
    5. Widad Guechtouli, 2014. "Agent-based modeling of knowledge transfer within social networks," Working Papers 2014-148, Department of Research, Ipag Business School.
    6. Zhao, Liming & Zhang, Haihong & Wu, Wenqing, 2017. "Knowledge service decision making in business incubators based on the supernetwork model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 249-264.
    7. repec:eee:asieco:v:54:y:2018:i:c:p:53-68 is not listed on IDEAS
    8. Piergiuseppe Morone & Richard Taylor, 2004. "A Laboratory Experiment of Knowledge Diffusion Dynamics," Experimental 0407004, EconWPA.

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