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, implicit-organizational, 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
knowledge-representation, 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.
