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How Strategic Networking Impacts the Networking Outcome: A Complex Adaptive System Approach


  • Somayeh Koohborfardhaghighi

    () (College of Engineering, Seoul National University)

  • Jorn Altmann

    () (College of Engineering, Seoul National University)


In this study, we provide an interaction model based on complex adaptive system theory, to explain how different methods of network growth and strategic responses of existing network members towards them impact the outcome of networked individuals (i.e., utility gain at the individual level or a society’s collective utility known as social welfare). The proposed interaction model allows us to perform our experiments with dynamic utility computation, while individuals act strategically in response to what other individuals do in the network. We utilized the formulation of the co-author model, as it augments the concept of network structure for modeling individuals’ utilities. The experimental results show that different methods of a network growth lead to different networking outcome for its members. We observed that total networking outcome is the highest (with respect to the co-author model), if newly entered individuals establish their links strategically to other existing members in a way to maximize their own payoffs. We believe that reduction in the total utility due to strategic responses within the network is acceptable in exchange of having a homogenous utility distribution within the population. Our observations give us the idea that, with the help of strategic responses, central network members can be prevented from gaining very high utilities compared to others. Furthermore, network structures can be prevented, in which the utilities of network members are widely dispersed. In such a setting, individuals experience no discrimination in utility gain against other people in their community.

Suggested Citation

  • Somayeh Koohborfardhaghighi & Jorn Altmann, 2016. "How Strategic Networking Impacts the Networking Outcome: A Complex Adaptive System Approach," TEMEP Discussion Papers 2016131, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Aug 2016.
  • Handle: RePEc:snv:dp2009:2016131

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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.
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    More about this item


    Co-Author Model; Social Welfare; 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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