By Dirk Helbing
Each bankruptcy in Managing Complexity makes a speciality of studying real-world advanced structures and moving wisdom from the complex-systems sciences to functions in company, and society. The interdisciplinary contributions variety from markets and construction via logistics, site visitors regulate, and important infrastructures, as much as community layout, info platforms, social conflicts and development consensus. They serve to elevate readers' know-how in regards to the frequently counter-intuitive habit of complicated platforms and to assist them combine insights received in complexity learn into daily making plans, selection making, strategic optimization, and policy.
Intended for a wide readership, the contributions were saved mostly non-technical and handle a basic, scientifically literate viewers serious about company, educational, and public institutions.
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Extra info for Managing Complexity: Insights, Concepts, Applications
E 64 (2001) 026118 19. J. : Random graph models of social networks. Proc. Natl. Acad. Sci. USA 99 (2002) 2566–2572 20. J. : Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99 (2003) 7821–7826 21. : Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103 (2006) 8577–8582 22. , Arenas, A. : Comparing community structure indentiﬁcation. J. Stat. Mech. (2005) P09008 23. J. : Finding and evaluating community structure in networks. Phys. Rev. E 69 (2004) 026113 36 J.
Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64 (2001) 026118 19. J. : Random graph models of social networks. Proc. Natl. Acad. Sci. USA 99 (2002) 2566–2572 20. J. : Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99 (2003) 7821–7826 21. : Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103 (2006) 8577–8582 22. , Arenas, A. : Comparing community structure indentiﬁcation. J. Stat. Mech. (2005) P09008 23.
Though this analysis does not yet give a quantitative measure of the statistical signiﬁcance of the cluster analysis it certainly indicates a strong deviation from the RNM. However, more reﬁned calculations in principle also allow the calculation of the cluster detection accuracy. Instead, here the analysis of the temporal development of the user interests below will provide additional validation. Until now we have only found groups of bidders that have an increased probability to meet other members of their groups in the auctions they take part in.
Managing Complexity: Insights, Concepts, Applications by Dirk Helbing