Understanding Complex Adaptive Social Systems By Means Of The Models Of Brain And Psyche

This paper provides a introduction to complex adaptive systems and of the ways of analyzing and modeling them or certain categories of complexity such as external and internal complexity. For external complexity, which refers to the inputs of the system, we chose the model proposed by JurgenJost based on entropy and adopted by researchers in the field. I did research on the application of the model to practical situations and we concluded that when is applied on samples of the population may lead to incorrect conclusions or "hasty". To counter were proposed improvements and additions through a version of the model adapted to operate with Onicescu's informational energy. For internal complexity this paper introduces a model used to determine the complexity of brain circuits which can be used. This model, together with the framework called PILOTS (based on entropy and fined with necessary changes to restore confidence in the conclusions suggested by information offered) will be adapted and tested for application to determine the complexity of groups / social networks modeled as relational networks. Here is defined a formal framework for representing social and economic networks as multi-graph to which is associated probabilities matrices. It is proposed a method to realize graph specific measurements (and including determining the probabilities) based on the description of nodes and the association between them. Based on probability matrices later on we can perform measurements of the internal and external complexity at the level of the node subgraph and at the level of the integral graph.