Abstract
Human beings’ approach to the real world is about incompleteness: incompleteness of understanding, representation, information, etc. It focuses on the unknown, rather than on the production of mathematical certainties based on weak assumptions. The human brain is at least a factor of 1 billion more efficient than our present digital technology, and a factor of 10 million more efficient than the best digital technology that we can imagine. The unavoidable conclusion is that we have something fundamental to learn from the brain and biology about new ways and much more effective forms of computation and information managing. The presented approach, based on CICT (Computational Information Conservation Theory), has shown to be quite helpful with high application flexibility. It can be applied at any system scale and open the door towards a more effective post-Bertalanffy Systemics Complexity modeling, taking into consideration system incompleteness, quasiness, and beyond.
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Acknowledgements
The author acknowledges the continuous support from the CICT CORE Group of Politecnico di Milano University, Milano, Italy, for extensive computational modeling and simulation resources.
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Fiorini, R. (2019). Embracing the Unknown in Post-Bertalanffy Systemics Complexity Modeling. In: Minati, G., Abram, M., Pessa, E. (eds) Systemics of Incompleteness and Quasi-Systems. Contemporary Systems Thinking. Springer, Cham. https://doi.org/10.1007/978-3-030-15277-2_22
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