A situation-modulated minimal change account for causal inferences about causal networks

Although causal Bayes networks are applicable to examining causal inferences about different static objects and about a changing object with different states, previous studies investigated the former, but not the latter. We propose a situation-modulated minimal change account for causal inferences. It predicts that dynamic situations are more likely to elicit minimal revisions on causal networks and adherence to the Markov assumption than static situations. Two experiments were conducted to investigate qualitative causal inferences about causal networks with binary and numerical variables, respectively. It was found that qualitative causal inferences were more likely to adhere to the Markov assumption in dynamic situations than in static situations. This finding supports the situation-modulated minimal change account rather than the other alternative accounts. We conclude that dynamic situations are more likely to elicit minimal revisions on causal networks and adherence to the Markov assumption than static situations. This conclusion is beyond the previous predominant view that causal inferences are apt to violate the Markov assumption.

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

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.

Data Mining Approach in Determining the Relationships Between the Economy and the General Government Sector Size

This article is dedicated to a study of the relations between the economy and the size of the general government sector. The main aim of the article is an identification of the most important variables that are used to determine relations between the economy and size of the sector, as well as to identify frequency of their occurrences in relations to pairs of variables which describe an economy and the size of the sector. In order to explore these relations, the authors used Bayes networks. The economies of EU member states and their public finance systems were the object of analyses in this article. The period that was selected for the research covered the years 2000-2013 (inclusive). In order to describe economies, the authors selected 18 variables, whereas to describe the general government sector - 15 variables. These variables were sourced from databases of Eurostat, OECD and the World Bank. Among an economy’s measures and general government sector measures, there were also some benchmarks found (standard and classic) as well as measures proposed by the authors, which had not been used in the scientific descriptions that were dedicated to research on size of the general government sector. Ipso facto, this article fits in the discussion on not only the size of the general government sector, but also attempts to answer the question of whether the economy determines the size of the sector. To date, the research questions on the impact of the size of the general government sector on the economy of a particular country have been common. This article inverts the investigated dependence and its content concentrates on the attempt to determine if the size of the sector in a particular country is a function of its economy expressed by ratios adopted in conducted the research.