Bayesian Belief Nets (BBNs) have proven to be an extremely powerful technique for reasoning under uncertainty. We have used them in a range of real applications concerned with predicting properties of critical systems. In most of these applications we are interested in a single attribute of the system such as safety or reliability. Although such BBNs provide important support for decision making, in many circumstances we need to make decisions based on multiple criteria. For example, a BBN for predicting the safety of a critical system cannot be used to make a decision about whether or not the system should be deployed. This is because such a decision must be based on criteria other than just safety (cost, politics, and environmental factors being obvious examples). In such situations the BBN must be complemented by other decision making techniques such as those of multi-criteria decision aid (MCDA). In this article we explain the role of BBNs in such decision-making and describe a generic decision-making procedure that uses BBNs and MCDA in a complementary way. The procedure consists of identifying the objective and perspective for the decision problem, as well as the stakeholders. This in turn leads to a set of possible actions, a set of criteria and constraints. We distinguish between, uncertain and certain criteria. The BBN links all the criteria and enables us to calculate a value (within some probability distribution in the case of the uncertain criteria) for each criterion for a given action. This means that we can apply traditional MCDA techniques to combine the values for a given action and then to rank the set of actions. The techniques described are demonstrated by real examples, including a safety assessment example that is being used by a major transportation organisation.
As part of their decision making tool, authors in REF used MCDA (multi-criteria decision aid) in order to support a decision in a nuclear-engineering application.
Making Decisions: Using Bayesian Nets and MCDA
Knowledge-Based Systems
Computer Science