Semantics, Classifications and Evidence in a Model for Global Catastrophic Risks

摘要

Life on the surface of the Earth is fragile and can be deteriorated by outside influence, from nature, or inside influence, from humans. We present a macro perspective for the nation state as a knowledge discourse system. To detect what might happen, a surveillance model needs to classify emerging risks prior to occurrence, ihe state intelligence model presented here helps survey potential macro tactors. During risk analysis, a set of risk classification criteria was devised for linking inside and outside influence trigger points that can indicate existential catastrophes. The analysis is based on a classification of current risks rather than distant future potential risks. Each is measured according to its respective impact, and whether or not it is highly probable to occur or recur in the surveillance system. The inside influence is found most probable with a probability of P > 0.4 compared to outside influence with a probability of P > 0.28. The State Intelligence Surveillance Analysis Model presented here consists of an 8-by-8 risk matrix or, a 16 risk table with a computable 20.92 trillion risk combinations per second. The relationships between inside and outside influences have been studied and grouped into classification schemes, where it is imagined that one may trigger the other, and by chance, acting autonomously for any type of catastrophe. The current study gives more focus and awareness to classifiers and the problem of which surveillance components to detect, thereby improving simulations, being well aware that the exact calculations for catastrophes are impossible.