Abstract: The subject of the research is the methods of its analysis and optimization based on indicators developed in the field of regulatory administrative and legal regulation. A qualitative assessment of the optimization of legislation is shown by the example of the decree of the Governor of St. Petersburg dated 07.09.2015 No. 61-pg, which defines the main directions of public administration of socio-economic phenomena and processes in St. Petersburg. A comparison of the indicators approved by this resolution, which serve the purposes of socio-economic development and administrative and legal regulation, with statistical socio-economic indicators will demonstrate how optimal regulatory regulation is. This optimality is assessed by the compliance of normative indicators (goals) with the most significant ones (for migration flows in inner-city municipalities) statistical indicators identified on large data sets by machine learning methods. Machine learning on large data sets made it possible to identify two of the most significant indicators of them — the goals of socio-economic development and regulatory regulation (the costs of landscaping and the costs of holding local holidays and sporting events), as well as to identify a statistical indicator that is not recognized as a goal of territorial development (environmental protection costs). The results obtained made it possible to identify the most important areas of activity of higher levels of public authority corresponding to the significance of indicators for the migration flow: preschool and school education, healthcare for children and elderly citizens, creation of an accessible (comfortable) environment for them. The results obtained are of methodological importance, since they have the potential to use numerical statistical indicators, and can be useful for evaluating the optimization of regulation and legal (regulatory) policy. Machine learning based on big data in the social, demographic, economic and environmental fields can become an important tool for optimizing administrative legislation and public administration.

Abstract: The subject of this research is the social relations that arise in terms of committing petty theft, as well as research means and methods for assessing optimization of the protective legal regulation. The author substantiates and tests the indicator of humanization of legal regulation, which is identified and used on the big data of judicial acts on administrative and criminal cases of petty theft (the Article 7.27 of the Code of the Russian Federation on Administrative Offenses and the Article 158.1 of the Criminal Code of the Russian Federation). The research is based on the original interdisciplinary methodology, which includes indicator approach and a set of legal and computer aided techniques (dogmatic, systemic analysis, expert assessment, data mining, correlation analysis, cluster analysis, classification, regression, etc.). The author substantiates the need to view humanization of protective legal regulation in the context of balanced interests of all parties involved in the legal conflict, namely: the state (society) interested in the effective preventive function of protective legal regulation; the victim interested in compensation for the caused harm; the wrongdoer interested in imposition of fair punishment adequate in its severity to facts in the case. These interests were compared to the empirical data and knowledge extracted from the vast arrays of judicial acts, as well as the corresponding methods of research. The use of humanization indicator for big data in cases of petty theft demonstrates that administrative responsibility in general is more humane than criminal responsibility (by three out of four indicators); there is disproportionality of repression in criminal cases; the level of humanism to the victim in cases of administrative offences is extremely low; individualization of criminal penalty is lower than of administrative penalty, despite the more complicated, time and cost consuming form of criminal proceedings.

Abstract: The subject of this research is the interdisciplinary legal and computer research tools and methods. The authors substantiate the interdisciplinary (legal-computational) methodology for automated analysis and assessment of qualitative changes in legislation and law enforcement practice. Interim results of the research project that are of methodological nature and cover methodological paradigm, principles, means and methods of scientific research are provided. The formulated conclusions represent a summary of heuristic search and computational experiments carried out in the domain field of administrative tort law, as well as comprehension of the process and results of research from both, legal and computer perspectives. Explanation is given to the interdisciplinary paradigm in the indicated methodological area. Leaning on the empirical evidence and observations, the author formulates the three research principles: principle of heterogeneity of domain, principle of discreteness of legal practice, and principle of identity of the model. As the key research tools, the author substantiates and tests in computational experiments the scientific information-analytical system, mathematical and social indicators have been developed, justified and tested in computational experiments. Computer methods (knowledge modeling, natural language processing, machine learning) that ensure automation of identification and usage of indicators mate with the dogmatic method, systemic analysis and expert assessment responsible for legal interpretation of computations.  The legal and computer tools are determined for identification and usage of the principal indicators. In conclusion, the author outlines a number of problems and restrictions determined in the course of the conducted research.

Abstract: The subject of this research is the interdisciplinary legal and computer research tools and methods. The authors substantiate the interdisciplinary (legal-computational) methodology for automated analysis and assessment of qualitative changes in legislation and law enforcement practice. Interim results of the research project that are of methodological nature and cover methodological paradigm, principles, means and methods of scientific research are provided. The formulated conclusions represent a summary of heuristic search and computational experiments carried out in the domain field of administrative tort law, as well as comprehension of the process and results of research from both, legal and computer perspectives. Explanation is given to the interdisciplinary paradigm in the indicated methodological area. Leaning on the empirical evidence and observations, the author formulates the three research principles: principle of heterogeneity of domain, principle of discreteness of legal practice, and principle of identity of the model. As the key research tools, the author substantiates and tests in computational experiments the scientific information-analytical system, mathematical and social indicators have been developed, justified and tested in computational experiments. Computer methods (knowledge modeling, natural language processing, machine learning) that ensure automation of identification and usage of indicators mate with the dogmatic method, systemic analysis and expert assessment responsible for legal interpretation of computations.  The legal and computer tools are determined for identification and usage of the principal indicators. In conclusion, the author outlines a number of problems and restrictions determined in the course of the conducted research.

Abstract: The subject of this article is the research tools and assessment methods with regards to optimization of legislation and law enforcement. The paper reveals the experience of computational experiments on the judicial acts on administrative offenses established by the Chapter 18 of the Code of Administrative Offenses of the Russian Federation. The research employs various computer methods, including knowledge modeling, methods of natural language processing and machine learning, as well as the related within the framework of interdisciplinary paradigm methods of systemic analysis and expert assessment. Computational experiments were conducted on the empirical basis formed out of texts of 50,438 judicial acts. On the example of big data on administrative offenses, the article demonstrates the interdisciplinary (from computer and legal perspectives)  interpreted results in the context of usage and identification of a number of indicators for optimization of legislation and law enforcement, primarily – time indicator, indicator of individualization of punishment, and indicator of subject uniformity. The conclusions and generalizations are made pertaining to legislation and law enforcement in this area under consideration. Computational methods and the set of indicators can be the groundwork for making decisions in law policy. The advantages of the proposed methodology consist in objectivity of the conclusions that based on methodology open to public verification, as well as big legal data that ensures accuracy of research.

Abstract: The subject of this research is the changes that took place in law enforcement practice due to introduction in 2011 of the new revision of the Article 20.4 “On Violation of Fire Prevention Rules” of the Code of the Russian Federation on Administrative Offenses. The article presents the results of computational experiment conducted for the purpose of development and testing of high-performance software based on the intellectual analysis and computer-assisted learning that improves understanding of the new legal phenomena and processes associated with the impact of legislation upon law enforcement practice. For solving the research objective. For solving the research problem, the author uses the data of the State Information System “Justice” related to 56,500 orders on imposition of administrative punishment in accordance with the Article 20.4 of the Code of the Russian Federation on Administrative Offenses for the period of 2010-2017. The author extracts and factorizes the necessary data; JSON data was converted using the algorithm in MapReduce paradigm for the models of factorization and learning. As a result of computer-assisted learning, was obtained the “tree of decisions”. On the “tree of decisions” it is demonstrated that middle of 2011 marks qualitative improvement in judicial practice, which became more uniform and logical; as well as in the context of imposing administrative punishment, the court started using standard circumstances of the case. The more efficient revision of the Article 20.4 of the Code of the Russian Federation on Administrative Offenses allowed in a midterm period to enhance the rule of law in the area of satisfying formalized requirements to ensuring fire safety, by reducing the number of cases from 2012 to 2017 by more than 10 times. The author empirically substantiates the working version of the method of analysis and assessment of qualitative changes in legislation and law enforcement practice based on the computer-assisted learning technique in form of “tree of decisions”.

Abstract: The subject of this research is the changes that took place in law enforcement practice due to introduction in 2011 of the new revision of the Article 20.4 “On Violation of Fire Prevention Rules” of the Code of the Russian Federation on Administrative Offenses. The article presents the results of computational experiment conducted for the purpose of development and testing of high-performance software based on the intellectual analysis and computer-assisted learning that improves understanding of the new legal phenomena and processes associated with the impact of legislation upon law enforcement practice. For solving the research objective. For solving the research problem, the author uses the data of the State Information System “Justice” related to 56,500 orders on imposition of administrative punishment in accordance with the Article 20.4 of the Code of the Russian Federation on Administrative Offenses for the period of 2010-2017. The author extracts and factorizes the necessary data; JSON data was converted using the algorithm in MapReduce paradigm for the models of factorization and learning. As a result of computer-assisted learning, was obtained the “tree of decisions”. On the “tree of decisions” it is demonstrated that middle of 2011 marks qualitative improvement in judicial practice, which became more uniform and logical; as well as in the context of imposing administrative punishment, the court started using standard circumstances of the case. The more efficient revision of the Article 20.4 of the Code of the Russian Federation on Administrative Offenses allowed in a midterm period to enhance the rule of law in the area of satisfying formalized requirements to ensuring fire safety, by reducing the number of cases from 2012 to 2017 by more than 10 times. The author empirically substantiates the working version of the method of analysis and assessment of qualitative changes in legislation and law enforcement practice based on the computer-assisted learning technique in form of “tree of decisions”.

Abstract: The matter under research is judicial practice in review of administrative judgements (decisions) as stated by Article 20.4 'Fire Safety Violations' of the Administrative Offences Code of the Russian Federation. The absence of judicial statistics about individual categories of administrative offences at the federal and regional levels causes the need to use computational methods to collect, process and analyse data. To achieve research targets, the authors of the article have used data of state autmoated system 'Justice'. Empirical base of the research was developed with the help of crawler based on POST-inquiries with some JSON parameter. As a result of inquiries, the researchers have received complete records of judicial acts and have used these to make a classification. For detailed intellectual analysis, the researchers have referred to 4.9 thousand judicial solutions about review of administrative judgements (decisions) based on Article 20.4 of the Administrative Offences Code of the Russian Federation for the period since 2010 till 2017. As a result of the research, the authors have created and tested the methodology of extraction, analysis and interpretation of practical judicial data that are not provided by judicial statistics. In the course of interpretation of empirical data, the authors have discovered general Russia's trends in law enforcement as a result of increased efficiency of administrative law as well as have created three regional models of correlation of results for review of administrative judgements (decisions) that have been associated with the indicators of regional socio-economic statistics.