Abstract
A model based on a Bayesian Belief Network (BBN) has been constructed for the Baltic Sea with the aim of investigating future scenarios of human activities in the region and informing environmental management strategies, such as those developed under a Science and Policy Integration for Coastal Zone Assessment Systems Approach Framework application. This paper describes necessary refinements to take into account historical influences on this relatively enclosed system. BBNs are static models and therefore do not incorporate feedback loops, whereas natural systems clearly display feedback mechanisms. This paper describes the implementation of one step feedback loops into a BBN model in an attempt to partly remove this constraint. Feedback loops within this stochastic model were shown to improve its accuracy. The drivers, both natural and anthropogenic, having greatest impact on the environment are identified. These refinements were made to improve its accuracy in modelling the system and gives insights into the functioning of that system.
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