Implicate Order as Ecosystem Consciousness, where Knowledge exits Non-localized and Represented as Probabilistic Ontology – Complexity Theory

Exploring Implicate Order as a way for modeling the ecosystem knowledge that exists non-localized, and also that the knowledge  are formations as evidence those are probabilistically deterministic. This means the ecosystem knowledge representation requires probabilistic ontology. Also, what is evident in a system is – in the method there exists Cartesian dilemma, This means the method used to study the macro behavior and the micro behavior both follow different mathematical scheme. Almost all micro behaviors are characterized by Cartesian methods, these breakdown when they are scaled to study macro behavior. This particular vexation is the reason why arriving at unified equation in physics is fleeting that reconciles macro physics with quantum behavior. Probing for a way to describe a system with such dilemma, Implicate Order was hypothesized by David Bohm as an ontology to fit the probabilistic paradigm.

Every system strategically has a challenge to resolve at macro level pertinent to a “context” – for instance healthcare management efficiency and then at micro level it is challenge to be met at “functional” level – example personalized healthcare delivery – which is efficacy. These two combined seems to present a compelling complex problem.

Integrative approach involving Bioinformatics, Quantum Maths, A.I and linguistics semantic allows us to develop algorithmic methods suitable to represent the ecosystem. These approaches help in creating a probabilistic ontology as envisioned in the implicate order. Furthermore, the way probabilistic inferencing keeps growing as the system progress in time continuum they provide a means to study Complex Adaptive System and also the Generative development.

Most common method in creating the probabilistic inference has been Bayesian network. But these are being contested as suitable for a lower order complex system, where the data is held constant and hypothesis are random. When the system complexity becomes messy, meaning both the hypothesis and the data become random, then we need advanced knowledge graphing methods.

Ingine Inc, for its cognitive computing platform BioIngine proposes “Hyperbolic Dirac Net” as a way to render Bayes Net into true Bayesian by overcoming the acyclic constraints. The approach incorporates a certain advancement to Bayesian statistics which makes it reversible. In general, reversibility, cyclic paths and feedback abound in the real world, especially the medical domain which is as such probabilistic and we need probabilistic knowledge networks that are general graphs, or even more diffuse fields of influence, not DAGs. In our response as the Hyperbolic Dirac Net (HDN), “Dirac” relates to use of Paul A. M. Dirac’s view of quantum mechanics (QM). QM is not only a standard system for representing probabilistic observation and inference from it in physics, but also it manages and even promotes concepts like reversibility and cycles. The significance of “hyperbolic” is that it relates to a particular type of imaginary number rediscovered by Dirac. Dirac notation entities, Q-UEL tags, and the analogous building blocks of an HDN all have complex probabilities better described as probability amplitudes.

There are several developments going on in the area of probabilistic modeling. Stanford has begun to offer free course on probabilistic graphical modeling based on Bayesian. In the link below go to preview link to access entire video coursework. Awesome coursework for beginners in system modeling.


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