Our approach

 

brittGaiser

 Image: Britt gaiser                                                                                                                                                                                                                                                             

identification of risks and cost-effective management options  in the use of natural resources

 

Most environmental problems are created by man. Therefore, the solutions must be applicable from the point of view of society, and the provided scientific information must be relevant for the stakeholders. As the analysis of current state of the ecosystem and the predictions of future outcomes of policy actions are almost always uncertain, we argue that the probabilistic decision analysis is the right methodological approach to support in societal problem solving of environmental problems. We highlight the capacity of Bayesian approach to learn from several potential information sources, including observed data, theoretical models, publications and expert judgements. The role of informative prior knowledge is important in Bayesian inference, as it can include the knowledge obtained in earlier scientific studies, and therefore be crucial for the end user of policy relevant information, who is not only interested about the information content of the latest data obtained in a recent scientific study. Estimation of expert knowledge from stakeholders may also be an important way to commit them to a scientific policy process.

Bayesian decision analysis offers several ways to improve the effectiveness of scientific information in actual policy. The decision options can be ranked by the probabilistic information and the values of society, the value-of-information of the variables of the decision model can be estimated to direct future studies, and the value-of-control estimates can be used to suggest new ways to manage the system. These effects can be estimated as monetary values, which suggest directly the absolute amount of resources to be used, and how this allocation should be focused between different decision options.

We apply, especially, the Bayesian network analysis techniques to the scientific challenges we study. Some examples of our applications include:

· Cod fisheries management under uncertainty created by Baltic Sea inflow dynamic

· Oil spill risk model that can be used as strategic and tactical decision model

· Bio-economic risk governance model for oil spills in the Baltic Sea

· Bio-economic probabilistic decision model for salmon management