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The opportunity cost of social relations: on the effectiviness of small worlds

Author

Listed:
  • Lorenzo Cassi

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Lorenzo Zirulia

Abstract

The aim of this paper is to extend the theoretical literature on knowledge and network structure by studying the use of social networks as a learning mechanism. The novelty of this approach is suggested by the empirical evidence on informal trading of know-how. In the model, we consider a set of actors who create and diffuse knowledge with the aim of increasing their own personal knowledge. They are located on a lattice (identifying the social space) and are directly connected to a small number of other individuals. We assume that individuals can learn individually or socially, and that individuals choose how to learn on the basis of a cost-benefit comparison. Within this framework, we compare network structures in terms of efficiency and equity. We find that the opportunity cost of using the network affects its optimal structure in terms of aggregate performance and that the small world does not emerge unambiguously as being the most efficient.

Suggested Citation

  • Lorenzo Cassi & Lorenzo Zirulia, 2008. "The opportunity cost of social relations: on the effectiviness of small worlds," Post-Print hal-00354449, HAL.
    • Handle: RePEc:hal:journl:hal-00354449
    DOI: 10.1007/s00191-007-0073-5
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    References listed on IDEAS

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    Cited by:

    1. Zhang, JingJing & Yan, Yan & Guan, JianCheng, 2019. "Recombinant distance, network governance and recombinant innovation," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 260-272.
    2. Bogner, Kristina, 2019. "Knowledge networks in the German bioeconomy: Network structure of publicly funded R&D networks," Hohenheim Discussion Papers in Business, Economics and Social Sciences 03-2019, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    3. Mario Maggioni & Teodora Uberti, 2011. "Networks and geography in the economics of knowledge flows," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(5), pages 1031-1051, August.
    4. Mario Maggioni & Teodora Uberti & Mario Nosvelli, 2014. "Does intentional mean hierarchical? Knowledge flows and innovative performance of European regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(2), pages 453-485, September.
    5. Cecere, Grazia & Corrocher, Nicoletta & Gossart, Cédric & Ozman, Muge, 2014. "Technological pervasiveness and variety of innovators in Green ICT: A patent-based analysis," Research Policy, Elsevier, vol. 43(10), pages 1827-1839.
    6. repec:wsi:acsxxx:v:21:y:2018:i:08:n:s0219525918500182 is not listed on IDEAS
    7. Muge Ozman & Andrew Parker, 2023. "The effect of social networks, organizational coordination structures, and knowledge heterogeneity on knowledge transfer and aggregation," Journal of Evolutionary Economics, Springer, vol. 33(2), pages 249-278, April.

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    More about this item

    Keywords

    knowledge; networks; small worlds; diffusion; learning;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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