Abstract :
During the last two decades, much of the theoretical and practical advances in Bayesian decision analysis have been closely linked to the adaptation of emerging new computational — usually Artificial Intelligence — techniques and to progress in computer software, respectively. This paper documents and discusses experience on the use of two recent network model approaches, influence diagrams and belief networks, and relates those approaches to decision trees. They both allow probabilistic, Bayesian studies with classical decision analytic concepts such as risk attitude analysis, value of information and control, multi-attribute analysis, and various structural analyses. The theory of influence diagrams dates back to the early 1980s, and a variety of commercial software are on market. Belief network is a more recent concept that is under process of finding its way to applications. Illustration on environmental and resource management is provided with examples on freshwater and fisheries studies
