Logic and Order

Abstract

Traditional human representation is unable to conserve complete information. Therefore ignorance, uncertainty, ambiguity to mankind's best conceivable worldview are even more amplified. To minimize this problem, we need to develop a reliable and effective ontological uncertainty management (OUM) approach. To reach this goal requires starting from traditional mankind worldview to arrive at a convenient OUM framework. Learning from neuroscience helps to develop neuromorphic systems able to overcome previous representation limitations by appropriate OUM solution. Furthermore, according to CICT (computational information conservation theory), the information content of any symbolic representation emerges from the capturing of two fundamental coupled components, i.e. the linear one (unfolded) and the nonlinear one (folded), interacting with their environment. Thanks to its intrinsic self-scaling properties, this system approach can be applied at any system scale, from single quantum system application to full system governance strategic assessment policies and beyond. A detailed OUM application example, taking advantage of the well-known EPM (elementary pragmatic model) by De Giacomo & Silvestri, to achieve full information extraction and conservation, is presented. This chapter is a relevant contribution to effective OUM solution development framework for learning and creativity, emerging from a Post-Bertalanffy General Theory of Systems.