Ontological Uncertainty & Decision Making

We inevitably see the universe from a human point of view and communicate in terms shaped by the exigencies of human life in a natural uncertain environment. Although there are many sources of uncertainty, two basic areas of uncertainty that are fundamentally different from each other were recognized as traditional reference knowledge: natural and epistemic uncertainty. Intrinsic randomness of a phenomenon or natural uncertainty cannot be reduced by the collection of additional data and it stems from variability of the underlying stochastic process (if any). Unlike natural uncertainty, epistemic uncertainty can be reduced by the collection of additional data. Statistical and applied probabilistic theory is the core of traditional scientific knowledge; it is the logic of "Science 1.0"; it is the traditional instrument of risk-taking. Unfortunately, epistemic uncertainty sources are still treated with the traditional approach of risk analysis, which provides an acceptable cost/benefit ratio to producer/manufacturer, but in some cases it may not represent an optimal solution to end user. In fact, deep epistemic limitations reside in some parts of the areas covered in decision making. More generally, decision theory, based on a "fixed universe" or a model of possible outcomes, ignores and minimizes the effect of events that are "outside model". In fact, contemporary human made systems can be quite fragile to unexpected perturbation because Statistics can fool you, unfortunately. Then, our ontological perspective can be thought only as an emergent, natural operating point out of, at least, a dichotomy of two coupled irreducible complementary ideal asymptotic concepts: a) reliable predictability and b) reliable unpredictability. To overcome and solve this conundrum, a few implementation system examples are presented and discussed.