Learning Bayesian decision analysis by doing: lessons from environmental and natural resources management

The planet we are living on is getting small; each decade the number of people here grows by almost 1 billion. Due to the escalating pressure that mankind puts on natural resources and the environment, there is a pressing need to develop management schemes and approaches that acknowledge the pragmatic character of the problems: We scientists should not just passively observe and measure but also need to assist policy makers for better action. This requires the ability to combine, interconnect, link, and analyze jointly information, knowledge, and judgment across scientific disciplines. The methodological development is blooming and rich. However, the way to applications tends to be long. It is not enough that one has learned and applied a methodology; it has also to be comprehended and accepted by many others who often are not all that devoted to methodological challenges; and launched to responsible institutions. In this paper, we make an overview of lessons learned from studying, applying, and launching of Bayesian decision analysis—influence diagrams and belief networks in particular—in the field of resource and environmental management. A number of case studies from water resources and fisheries are used as an illustration.