Synthetic risk intelligence is a research project exploring how emergent areas of artificial intelligence can be applied in a network context, as a flow network based near real time proactive system.

One of the main issues with the implementation of Bayesian dynamic network awareness, whereby near real time threats are proactively countered, is the equivalent of automated "high level abstract" thinking. This is seen as a fundamental future capability for intelligent optical neworks and next generation routers.

Preliminary Vision

Synthetic risk intelligence is a set of exploratory guidelines for the development of flow network based near real time projective intervention in a context by a platform: (please click on diagram to expand)

The term "metaobject links + relations" in the diagram is roughly covariant to Imanuel Kants' categories in the Critique of Pure Reason though not in exactly the same sense, as in this case they are merely inferential through accumulated plasticity factors:

1) mathematical (mindGraph object subsets intrinsic)

• Quantity(Unity(%) | Plurality(%) | Totality(%))
• Quality(Reality(%) | Negation(%) | Limitation(%))

2) dynamical (mindGraph objects subsets wrt each other or "understanding")

• Relation(Inherence&Subsistence(%) | Causality&Dependence(%) | Community(%))
• Modality(Possibility-Impossibility(%) | Existence-Nonexistence(%) | Necessity-Contingence(%))

The "metaobject links + relations" factor is repeated in the diagram as it is inferred that some plasticity factors eventually push some of the type 1) mathematical or type 2) dynamical factors into object cascades which are then transformed into derivatives, before being selectively admitted to a simulation domain.