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The Opportunity Cost of Social Relations: on the Effectiveness of Small Worlds

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  • Lorenzo Cassi

    (CESPRI, Università Commerciale Bocconi, Milano, Italy)

  • Lorenzo Zirulia

    (CESPRI, Università Commerciale Bocconi, Milano, Italy)

Abstract

The aim of this paper is to extend the theoretical literature on knowledge and network structure by considering explicitly the choice to use the social network as a learning mechanism. In the model, we consider a set of actors that creates and diffuses knowledge. They are located on a lattice (identifying the social space) and they are directly connected with a small number of other individuals. Their aim is to increase their personal knowledge. We assume that individuals can learn in two ways: individually, by elaborating their personal knowledge; or socially, by interacting with other individuals in their social neighbourhood. Given this framework, we compare network structures in terms of efficiency and equity. We find that networks characterized by low average distance perform well in the short run, when the opportunity cost of using the network is low, but cliquish networks are more efficient in the long run, when the opportunity cost is high. However, a "small world" structure, characterized both by low average distance and high cliquishness, is the most equal structure in terms of knowledge distribution.

Suggested Citation

  • Lorenzo Cassi & Lorenzo Zirulia, 2005. "The Opportunity Cost of Social Relations: on the Effectiveness of Small Worlds," KITeS Working Papers 175, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Sep 2005.
  • Handle: RePEc:cri:cespri:wp175
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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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