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

• URL: http://arxiv.org/abs/2002.11485v1
• Date: Sun, 23 Feb 2020 13:00:52 GMT
• Title: A machine-learning software-systems approach to capture social, regulatory, governance, and climate problems
• Authors: Christopher A. Tucker
• Abstract summary: It 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 in quantitative form, public contentment in societal-cooperative economic groups, regulatory proposition, and governance-effectiveness domains.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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