A significant class of decision making problems consists of choosing actions, to be carried out simultaneously, in order to achieve a trade-off between different objectives. When such decisions concern complex systems, decision support tools including formal methods of reasoning and probabilistic models are of noteworthy helpfulness. These models are often built through learning procedures, based on an available knowledge base. Nevertheless, in many fields of application (e.g. when dealing with complex political, economic and social systems), it is frequently not possible to determine the model automatically, and this must then largely be derived from the opinions and value judgements expressed by domain experts. The BayMODE decision support tool (Bayesian Multi Objective Decision Environment), which we describe in this paper, operates precisely in such contexts. The principal component of the program is a multi-objective Decision Network, where actions are executed simultaneously. If the noisy-OR assumptions are applicable, such a the model has a reasonably small number of parameters, even when actions are represented as non-binary variables. This makes the model building procedure accessible and easy. Moreover, BayMODE operates with a multi-objective approach, which provides the decision maker with a set of non-dominated solutions, computed using a multi-objective genetic algorithm. © Springer Science+Business Media, LLC 2007.
|Titolo:||A decision support tool coupling a causal model and a multi-objective genetic algorithm|
|Data di pubblicazione:||2007|
|Tipologia:||1.1 Articolo in rivista|