scholarly journals Modeling of Intelligent System Thinking in Complex Adaptive Systems

2014 ◽  
Vol 36 ◽  
pp. 93-100 ◽  
Author(s):  
Ben Khayut ◽  
Lina Fabri ◽  
Maya Avikhana
Author(s):  
Mario Tani ◽  
Ornella Papaluca ◽  
Pasquale Sasso

The new logics of competitions are mostly based on exploiting relationships to implement new mechanisms in managing Knowledge. Today, a successful company should be, lean, modular, and with a smart approach to new products development. In this context, the source of competitive advantage cannot be found into a static heterogeneity of resources, but companies must be able to create and manage a dynamic competitive process to continuously reinvent their products/services and to re-combine their resources with their partners’ ones. A paradigm for this behavior is the Open Innovation one, as created by Chesbrough. According to the rules of this paradigm, companies have to acknowledge that they operate in a network of relationships, they must be open to cooperate with their external partners, and they must not try to limit their actions in reaching only for some pre-defined result. So, Open Innovation Networks appear to be similar to those described by the scholars in the Complex Adaptive Systems field where the actions of the system, and of its parts, are the result of the various actors’ interactions in an emergent way. In this paper, we use a Systematic Literature Review approach to explore how the main topics in the System Thinking Perspective, and in particular, those related to Complex Systems, are linked to the Open Innovation studies.


Author(s):  
Peter R. Monge ◽  
Noshir Contractor

To date, most network research contains one or more of five major problems. First, it tends to be atheoretical, ignoring the various social theories that contain network implications. Second, it explores single levels of analysis rather than the multiple levels out of which most networks are comprised. Third, network analysis has employed very little the insights from contemporary complex systems analysis and computer simulations. Foruth, it typically uses descriptive rather than inferential statistics, thus robbing it of the ability to make claims about the larger universe of networks. Finally, almost all the research is static and cross-sectional rather than dynamic. Theories of Communication Networks presents solutions to all five problems. The authors develop a multitheoretical model that relates different social science theories with different network properties. This model is multilevel, providing a network decomposition that applies the various social theories to all network levels: individuals, dyads, triples, groups, and the entire network. The book then establishes a model from the perspective of complex adaptive systems and demonstrates how to use Blanche, an agent-based network computer simulation environment, to generate and test network theories and hypotheses. It presents recent developments in network statistical analysis, the p* family, which provides a basis for valid multilevel statistical inferences regarding networks. Finally, it shows how to relate communication networks to other networks, thus providing the basis in conjunction with computer simulations to study the emergence of dynamic organizational networks.


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