A Bayesian network for recurrent multi-criteria and multi-attribute decision problems: Choosing a manual wheelchair

This paper discusses recurrent multi-criteria, multi-attribute decision problems. Because of the possibility of decision-maker ignorance or low decision-maker involvement the decision problem structuring is done once for all by a group of experts and does not involve the implication of the decision makers. We propose an original model based on Bayesian networks, which provides a decision process that helps the decision-maker to select an appropriate alternative among a set of alternatives, taking into account multiple criteria that are often conflicting. Our model makes it possible to represent in the same model the decision case (i.e., the decision-maker characteristics, contextual characteristics, their needs and preferences), the set of alternatives with the different attributes, and the choice criteria. The model allows us to compute the value of three essential elements: the importance of each criterion, which is based on the decision-case characteristics; each criterion’s evaluation index in terms of the alternative; and each criterion’s satisfaction index. The recurrent problem of choosing a manual wheelchair (MWC) illustrates the construction and use of our model.

► We propose a Bayesian network model for recurrent multi criteria decision problems, such as choosing a manual wheelchair. ► Our model provides a list of probabilistic recommendations for the attributes of the alternatives. ► Our model does not require any preference information, because of the wide variety of decision-makers due to recurrent problems. ► Our model integrates the personal characteristics and the decisional context.