Discovering the Dynamics of Smart Business Networks
In an earlier paper ,was discussed the necessary evolution from smart business networks, as based on process need satisfaction and governance, into business genetics  based on strategic bonds or decay and opportunistic complementarities. This paper will describe an approach and diffusion algorithms whereby to discover the dynamics of emergent smart business network structures and their performance in view of collaboration patterns over time. Some real life early analyses of dynamics are discussed based on cases and date from the high tech sector. Lessons learnt from such cases are also given on overall smart network dynamics with respect to local interaction strategies, as modelled like in business genetics by individual partner profiles, goals and constraints. It shows the weakness of static "business operating systems", as well as the possibly destabilizing clustering effects amongst nodes linked to filtering, evaluation and own preferences.
|Date of creation:||03 Dec 2007|
|Date of revision:|
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- Nier, Erlend & Yang, Jing & Yorulmazer, Tanju & Alentorn, Amadeo, 2007.
"Network models and financial stability,"
Journal of Economic Dynamics and Control,
Elsevier, vol. 31(6), pages 2033-2060, June.
- Giorgio Gnecco & Marcello Sanguineti, 2009. "The weight-decay technique in learning from data: an optimization point of view," Computational Management Science, Springer, vol. 6(1), pages 53-79, February.
- Philippe Robert-Demontrond & R. Ringoot, 2004. "Introduction," Post-Print halshs-00081823, HAL.
- Dietmar Maringer & Tikesh Ramtohul, 2012. "Regime-switching recurrent reinforcement learning for investment decision making," Computational Management Science, Springer, vol. 9(1), pages 89-107, February.
- L. Randall Wray & Stephanie Bell, 2004. "Introduction," Chapters, in: Credit and State Theories of Money, chapter 1 Edward Elgar Publishing.
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