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Knowledge diffusion in social networks under targeted attack and random failure: the resilience of communities

Author

Listed:
  • Piergiuseppe Morone

    (UnitelmaSapienza University of Rome)

  • Rocco Caferra

    (UnitelmaSapienza University of Rome)

  • Antonio Lopolito

    (University of Foggia)

Abstract

Knowledge diffusion is a complex and demanding process that requires coordination and collaboration between agents with different levels of knowledge, to establish fruitful learning interactions. In this paper, we develop an agent-based model to investigate how different behavioral/sociological rules can alter, strengthen, or weaken this process. We observe that, during normal times, different aggregation strategies are apparently irrelevant for determining differences in learning opportunities. However, under crisis, there is an observable outperformance of social structures with established communities, characterized by both strong ties (i.e., intense contacts within communities) and weak ties (i.e., knowledge spillover across communities). We further test system resilience, considering interruptions to the knowledge diffusion of expert agents and the random temporary removal of agents (simulating a viral outbreak). We discuss how these scenarios may explain economic phenomena and explore the implications for policies aimed at mitigating knowledge and economic inequalities.

Suggested Citation

  • Piergiuseppe Morone & Rocco Caferra & Antonio Lopolito, 2024. "Knowledge diffusion in social networks under targeted attack and random failure: the resilience of communities," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(2), pages 283-303, April.
    • Handle: RePEc:spr:jeicoo:v:19:y:2024:i:2:d:10.1007_s11403-023-00392-x
    DOI: 10.1007/s11403-023-00392-x
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    1. Midgley, David F. & Morrison, Pamela D. & Roberts, John H., 1992. "The effect of network structure in industrial diffusion processes," Research Policy, Elsevier, vol. 21(6), pages 533-552, December.
    2. Jutta Günther & Dirk Meissner, 2017. "Clusters as Innovative Melting Pots?—the Meaning of Cluster Management for Knowledge Diffusion in Clusters," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 8(2), pages 499-512, June.
    3. Cowan, Robin & Jonard, Nicolas, 2004. "Network structure and the diffusion of knowledge," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1557-1575, June.
    4. Cowan, R. & Jonard, N., 2003. "The dynamics of collective invention," Journal of Economic Behavior & Organization, Elsevier, vol. 52(4), pages 513-532, December.
    5. Piergiuseppe Morone & Richard Taylor, 2004. "Knowledge diffusion dynamics and network properties of face-to-face interactions," Journal of Evolutionary Economics, Springer, vol. 14(3), pages 327-351, July.
    6. Vivi Alatas & Abhijit Banerjee & Arun G. Chandrasekhar & Rema Hanna & Benjamin A. Olken, 2016. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," American Economic Review, American Economic Association, vol. 106(7), pages 1663-1704, July.
    7. Lambiotte, R. & Panzarasa, P., 2009. "Communities, knowledge creation, and information diffusion," Journal of Informetrics, Elsevier, vol. 3(3), pages 180-190.
    8. Per Block & Marion Hoffman & Isabel J. Raabe & Jennifer Beam Dowd & Charles Rahal & Ridhi Kashyap & Melinda C. Mills, 2020. "Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world," Nature Human Behaviour, Nature, vol. 4(6), pages 588-596, June.
    9. Charlotte C. Greenan, 2015. "Diffusion of innovations in dynamic networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 147-166, January.
    10. Lin, Min & Li, Nan, 2010. "Scale-free network provides an optimal pattern for knowledge transfer," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 473-480.
    11. Todo, Yasuyuki & Matous, Petr & Inoue, Hiroyasu, 2016. "The strength of long ties and the weakness of strong ties: Knowledge diffusion through supply chain networks," Research Policy, Elsevier, vol. 45(9), pages 1890-1906.
    12. Wang, Jiang-Pan & Guo, Qiang & Yang, Guang-Yong & Liu, Jian-Guo, 2015. "Improved knowledge diffusion model based on the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 250-256.
    13. 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.
    14. Zhang, Haihong & Wu, Wenqing & Zhao, Liming, 2016. "A study of knowledge supernetworks and network robustness in different business incubators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 545-560.
    15. Anders Malmberg & Dominic Power, 2005. "(How) Do (Firms in) Clusters Create Knowledge?," Industry and Innovation, Taylor & Francis Journals, vol. 12(4), pages 409-431.
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