Bayesian Belief Networks (BBN’s) provide a useful integrative tool for linking science knowledge on components of complex land-water-social systems to explore scenarios to optimise benefits of different practices on environmental, economic and social values.
A BBN is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph. As a graphical structure BBN allows for the representation, of and reasoning about, an uncertain situation. The nodes in a network represent a set of variables in the domain being modelled. The nodes are connected by links representing the relationship between variables.
These relationships can be learned from the data if these are available or can be elicited from experts in the field. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. BBNs can answer ‘how’ questions by selecting a desired outcome and looking at how the independent variables are changed. ‘What-if’ scenarios can also be tested by changing predictor variables to states expected in the future, then observing changes in dependent variables. Any information supplied to the network will update the probabilities throughout the network immediately, and the strength of the prediction can be judged by the probability value for a given outcome.
|State of Development||Please Select|
|Outcome Areas||Economic, Environmental, Social, Cultural|
|Steady State or Dynamic||Unknown|
|Level of Integration||Economic, Environmental, Social, Cultural|
|Open/Closed Source||Open Source|
|User Interface||Please Select|
|Ease of Use||Moderate|
|Use in Policy Process||Plan (Policy Formulation), Review (Issue Identification)|
|Analytical Techniques||Bayesian Belief Network|
|Keywords||land, water, social, economic, environmental|
For a range of software packages see - http://www.cs.ubc.ca/~murphyk/Software/bnsoft.html
This case study describes a causal linkage model between practices on dairy farms and on-farm and in-stream values in Bog Burn, Southland.
Bayesian Networks: A tool for making good decisions in river catchment management. By Dr Richard Storey, NIWA
A free version of the software can be downloaded from https://www.norsys.com/download.html
You can find more information about Bayesian Networks, and some examples, at