Experiences in applying Bayesian network models in interdisciplinary water quality decision analysis

I review the experience obtained in using integrative Bayesian models in interdisciplinary analysis focusing on oil spill risk analysis and water quality in the Baltic Sea. We have applied BBN especially to interdisciplinary analysis, which is easily needed in decision analysis. In the environmental risk analysis, we have mainly focused on the oil spills, where the data sets are poor and also the published papers are scarce. More importantly, the aim of the decision analysis here is to avoid seeing the data, i.e. to avoid accidents. Therefore, some of our applications are based mainly on the use of expert knowledge, especially when we consider decision options that have not been applied before.

  In the oil spill risk analysis, we have chosen the state of the threatened species as the decision criteria, as they have a status in other parts of Finnish legislation. There is no single clear objective in oil spill legislation which could be used as a utility function, and our analysis have shown, that the legislation should be updated to include well defined objectives. One of the scientific quality criteria for using the Bayesian decision analysis for management is that the uncertainty estimates are scientifically justified. Especially in cases where society is assumed to be highly risk averse, the uncertainty estimates related to alternative management options may have a crucial role.

   Biology, sociology and environmental economics have their own scientific traditions. Bayesian models are becoming traditional tools in fisheries biology, where uncertainty estimates of management options are frequently required. In sociology, the traditions allow the subjective treatment of the information, which supports the use of prior probability distributions.  Many of the environmental risks have also economic components, or at least actions have a price, which favors the use of quantitative risk analysis. However, the traditions and quality criteria of these three scientific fields are in many respects different. This creates both technical and human challenges to the modeling tasks.

 

Keywords: Bayesian networks, fisheries management, environmental management, interdisciplinary risk and decision analysis