How Strategic Networking Impacts the Networking Outcome: A Complex Adaptive System Approach
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.
|Date of creation:||Aug 2016|
|Date of revision:||Aug 2016|
|Publication status:||Published in Proceedings of the 18th International Conference on Electronic Commerce, ICEC2016.|
|Contact details of provider:|| Postal: 599 Gwanak-Ro, Gwanak-Gu, Seoul 151-744|
Web page: http://temep.snu.ac.kr/
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- 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.
- 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. Full references (including those not matched with items on IDEAS)
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