Bayesian networks: A guide for their application in natural resource management and policy
Bayesian networks have been successfully used to assist problem solving in a wide range of disciplines including information technology, engineering, medicine, and more recently biology and ecology. There is growing interest in Australia in the application of Bayesian network modeling to natural resource management (NRM) and policy. Bayesian networks offer assistance to decision-makers working in complex and uncertain domains by assembling disparate information in a consistent and coherent framework and incorporating the uncertainties inherent in natural systems and decisionmaking.
Bayesian networks as modeling tools have been shown to fulfill the following needs:
Integration – of models, data types and qualitative information;
Prioritisation – through cost benefit analysis and ranking variables against a stated objective;
Flexibility – as they can be modified to suit the context in which they are applied and can be updated as new knowledge is obtained; and
Communication – as they are graphically based and allow explicit documentation of assumptions and uncertainties, making them easier to understand and use than most modeling frameworks.
A key feature of the successful adoption of Bayesian networks as a modelling tool in decision-making is their relative simplicity when compared with other modelling approaches. They are graphical models, capturing cause and effect relationships through influence diagrams. The use of probabilities to characterise the strengths of linkages between variables means that these can be defined using both quantitative and qualitative information while the use of Bayes’ theorem provides a formalised process to update models as new knowledge or data becomes available. Being
probabilistic, Bayesian networks can readily incorporate uncertain information, with these uncertainties being reflected in model outputs. Sensitivity analysis tools allow characterisation of uncertainties so that key causal factors and knowledge gaps can be identified. Model outcomes are testable, both quantitatively and through formal review processes.
|Product type:||Report or paper|
|Landscape elements:||Research methods|
|Catchments / sites:||none|
|Contact Person:||Carmel Pollino (firstname.lastname@example.org), Integrated Catchment Assessment and Management Centre, ANU|
|Last Updated:||June 11, 2010 17:05|
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- Incorporating dynamics and feedback into Bayesian modelling of natural resource systems (Report or paper)
- Development of Bayesian networks to explore the adoption of riparian management practices in Tasmania (Report or paper)