A machine-learning software-systems approach to capture social, regulatory, governance, and climate problems

This paper will discuss the role of an artificially-intelligent computer system as critique-based, implicitorganizational, and an inherently necessary device, deployed in synchrony with parallel governmental policy, as a genuine means of capturing nation-population complexity in quantitative form, public contentment in societal-cooperative economic groups, regulatory proposition, and governance-effectiveness domains. It will discuss a solution involving a well-known algorithm and proffer an improved mechanism for knowledgerepresentation, thereby increasing range of utility, scope of influence (in terms of differentiating class sectors) and operational efficiency. It will finish with a discussion of these and other historical implications. Introduction The world created by humans to manage their daily affairs is growing in complexity beyond the comprehension capability of the vast majority of them. The political classes are vulnerable to implementation of policy that proves incorrect and damages the credibility of the state over the long term.