Mathematical model for forecasting of violations in the field of veterinary medicine using the Markov chain

Abstract

Activities within the field of veterinary medicine require an efficient governmental administration, including planning, programming, accounting and result analysis, in addition to control and supervision. As a result, it is crucial to have a theoretical understanding of the new forms of interaction between executive authorities at different levels and the economic entities carrying out these activities. Therefore, the improvement of the regulations governing this field is highly required. The solution is also linked to the improvement of the current legislation governing socio-economic processes, using a new reliable model. In this context, the main goal of this study is to develop proposals to improve the current legal regulation of administrative infractions in the field of veterinary medicine. The authors of this paper applied a multi-agent approach to identify groups of law subjects. They also developed a mathematical model using Markov chains to predict infractions in the field of veterinary medicine. The model is formed of “transition matrices” and takes into account the various reaction strategies of regulatory authorities. Such a strategy shows the likelihood of committing infractions and can form a basis for reforming the current legislation ruling the conduct of control and surveillance procedures using a risk-based approach.