Analytical support of the procedural decision-making process
DOI:
https://doi.org/10.34121/1028-9763-2020-4-20-32Keywords:
evaluation concept, legal norm, fuzzy set, multilayer neural network, fuzzy conclusion, оціночне поняття, правова норма, нечіткий набір, багатошарова нейронна мережа, нечіткий висновокAbstract
Abstract. In the process of legal research, private scientific methods are developed and used to study legal reality, such as the method of comparative jurisprudence, the method of interpretation (explanation) and the formal legal method. However, at the present stage of legal research, it is impossible to be limited only by these methods. Even legal scholars who consistently defend the status of dogmatic jurisprudence recognize that the application of these methods, with all their merits, sets a limiting framework in understanding the practical action of positive law and the originality of its theoretical vision. Nevertheless, the application of these methods in the study of legal reality allows us to draw a conclusion about the general trends in the development of evaluative concepts that are fundamental in civil procedural law. It is offered an approach to the formation of a system of information support of procedural decision-making based on the application of fuzzy inference mechanism implemented in the logical basis of the feedforward multilayer neural network. Under this approach, a method to overcome the semantic uncertainty in the evaluation terms of procedural law is developed by using appropriate terms (fuzzy sets) of corresponding linguistic variables. As an example it is selected the Articles on “Violation of copyright or neighboring rights” of the Criminal Code of the Azerbaijan Republic based on which has been proposed formalism for the evaluation concept of “significant harm” in relation to the sanction applied. For making an adequate to evaluation concept notion it is proposed grading scale of possible sanctions, obtained on the basis of the description of the relevant legal norms in terms of fuzzy implicative rules.References
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