Regulatory Cybernetics: Adaptability and Probability in the Public Administration's Regulations

Abstract

Dynamic perspectives from systems theory and cybernetics are used in this paper to introduce the self-adaptable legal regulation or individual decision-making based on Bayes networks. The latter, by using similar elements as systems theory or cybernetics can help decision-makers not only to quantify the evidential strengths of hypotheses but also to take the most probable decision. Nowadays legal science and the public administration with it that prepares the majority of draft legal rules, do not sufficiently address legal forms from which rules' content derives. The increasing speed of change and the consequent shortness of operative rules should force decision-makers to consider the new forms of legal norms and decisions that would still respect the objectivity and impartiality of decision-making.