Machine Learning and Big Data for Optimization of Administrative Law (Computing Experience)

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.