Bayesian Belief Network (BBN) starts from a diagrammatic representation of the system that is being studied, developed by pulling together the knowledge of scientists and practitioners (both are stakeholders) about the processes leading to the supply and demand of ES. As a knowledge representation tool, this initial development of a BBN generates a framework of nodes and links, similar to many other representations of an ecological system or a human decision process. This structure is then parameterised using conditional probability tables (CPTs), equations, and/or learning from data cases and can then be run for a range of options and scenarios including tests on the structure, sensitivity analyses, etc.
