Computer Techniques and Indicators in the Policy of Optimization of Legislation and Law Enforcement

DOI

10.2991/assehr.k.201212.012How to use a DOI?

Keywords

legal policy, computer methods, indicators, machine learning, big data

Abstract

Assessment of optimization of legislation and law enforcement includes the use of numerous computer techniques and models designed for big data processing. In this study, indicators for assessing changes in legislation and law enforcement were divided into mathematical and social groups. Some of important for legal policy indicators and connected computer techniques are shown and discussed in the paper. The subject homogeneity indicator is based on an application of dimensionality reduction and used to assess the state of subject homogeneity of legal regulation. The time indicator is used in various time series analysis methods and shows specific legislative and law enforcement changes. The individualization of punishment indicator is based on correlation analysis and time series analysis and demonstrates implementation levels of the important principle of bringing to justice and legal politics. The indicator of rationality reflects the logic of decision making and based on machine learning. In modern legal policy, it is customary to operate with legal goals as the grounds for making certain decisions in the field of lawmaking, interpretation of law and law enforcement. The use of indicators makes it possible to check whether the goals of these political decisions have been achieved or not.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Egor TROFIMOV
AU  - Oleg METSKER
PY  - 2020
DA  - 2020/12/15
TI  - Computer Techniques and Indicators in the Policy of Optimization of Legislation and Law Enforcement
BT  - Proceedings of the International Conference Digital Age: Traditions, Modernity and Innovations (ICDATMI 2020)
PB  - Atlantis Press
SP  - 50
EP  - 53
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.201212.012
DO  - 10.2991/assehr.k.201212.012
ID  - TROFIMOV2020
ER  -