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How Network Visibility and Strategic Networking Leads to the Emergence of Certain Network Characteristics: A Complex Adaptive System Approach


  • Somayeh Koohborfardhaghighi

    () (College of Engineering, Seoul National University)

  • Jorn Altmann

    () (College of Engineering, Seoul National University)


Person-to-person interactions within an organization form a network of people. Changes of the structural properties of these networks are caused through a variety of dynamic processes among the people. We argue in this paper that there is a feedback loop between individual actions and the network structure. Therefore, a proper interaction model is needed to explain the emerging structural changes among networked individuals. According to our proposed interaction model, which is based on a complex adaptive system approach, changes in the network properties are consequences of four factors: (1) the initial underlying network structures; (2) the process of network growth; (3) the adoption of strategic responses to what other individuals do in the network; and (4) the network visibility. The experimental results show that all of these factors have influence. If the process of network growth triggers strategic responses of all direct neighbors, we observe a heavy drop in the average shortest path length between the individuals. The value of the average shortest path length shrinks to three, even independently of the visibility of the global network topology. We observe the same trend for the clustering coefficient. Fluctuations in the clustering coefficients are not significant, if visibility of the network topology is set to a high value. However, in the presence of only small number of strategic responses and a high network visibility, a short average shortest path length and a high clustering coefficient can be observed.

Suggested Citation

  • Somayeh Koohborfardhaghighi & Jorn Altmann, 2016. "How Network Visibility and Strategic Networking Leads to the Emergence of Certain Network Characteristics: A Complex Adaptive System Approach," TEMEP Discussion Papers 2016130, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Aug 2016.
  • Handle: RePEc:snv:dp2009:2016130

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    References listed on IDEAS

    1. Jackson, Matthew O. & Wolinsky, Asher, 1996. "A Strategic Model of Social and Economic Networks," Journal of Economic Theory, Elsevier, vol. 71(1), pages 44-74, October.
    2. Buechel, Berno, 2011. "Network formation with closeness incentives," Center for Mathematical Economics Working Papers 395, Center for Mathematical Economics, Bielefeld University.
    3. Gallo Edoardo, 2012. "Small World Networks with Segregation Patterns and Brokers," Review of Network Economics, De Gruyter, vol. 11(3), pages 1-46, September.
    4. M. Koenig & Claudio J. Tessone & Yves Zenou, "undated". "A Dynamic Model of Network Formation with Strategic Interactions," Working Papers CCSS-09-006, ETH Zurich, Chair of Systems Design.
    5. Lynne Hamill & Nigel Gilbert, 2009. "Social Circles: A Simple Structure for Agent-Based Social Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(2), pages 1-3.
    6. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    7. Thurow, Lester, 1983. "Dangerous Currents: The State of Economics," OUP Catalogue, Oxford University Press, number 9780198771838.
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    More about this item


    Co-Author Model; Strategic Behavior; Utility Maximization; Network Growth Models; Complex Adaptive System Approach; Agent-based Modeling and Simulation.;

    JEL classification:

    • A13 - General Economics and Teaching - - General Economics - - - Relation of Economics to Social Values
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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