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,...

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Bibliographic Details
Published inarXiv.org
Main Author Tucker, Christopher A
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.02.2020
Subjects
Algorithms
Knowledge representation
Machine learning
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Summary: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.
ISSN:2331-8422