Identifying the degree of influential effects for the causal relationships in a Bayesian network model using group decision making technique

In this paper, we propose a methodology based on group decision making for weighting expert opinions or the degree of an expert's belief in identifying influential effects from parent variables to child variables in a BN model. The methodology consists of three sequential steps. The first step is a pre-processing process where the experts need to identify and select every pair of variables that have a causal relationship between them for the BN model. All the experts in the group must agree with each other for the selections. Second, we map every pair of causal variables into alternatives. Next, experts sort the alternatives by means of a fixed set of linguistic categories; each one has associated a numerical score. We apply a method of weighting individual expert opinions in order to arrange the sequence of alternatives in each step of the decision making procedure. The sequential decision procedure is repeated until it determines a final subset of experts where all of them positively contribute to the agreement for group decision making. Lastly, we transform the alternatives and the collective scores that we obtain from previous step into the BN models. We select a simple diagnostic model of a vehicle fuel system as a case study.