The BBN was constructed using Hugin Developer 6.3, a commercial software package developed and distributed by Hugin Expert A/S, Aalborg, Denmark (http://www.hugin.com/). The BBN was constructed by representing variables as nodes connected by directed links (see Appendix 1), which are indications of conditional dependence. A link from node A (parent node) to node B (child node) indicates that A and B are functionally related, or that A and B are statistically correlated. Each child node (i.e. a node linked to one or more parents) contains a conditional probability table (CPT). The CPT gives the conditional probability for the node being in a specific state given the configuration of the states of its parent nodes. When networks are compiled, Bayes’ theorem is applied according to the values in the CPT, so that changes in the probability distribution for the states at node A are reflected in changes in the probability distribution for the states at node B.
Nodes were created for each of the five types of capital asset and given two possible states, ‘high’ and ‘low’, representing the amount of capital available prior to commercialization. A second set of five nodes was created representing the change in capital assets available resulting from commercialization; these were given five possible states, namely ‘Large decrease’, ‘Small decrease’, ‘No change’, ‘Small increase’, and ‘Large increase’. According to the framework described above, each of the nodes representing the availability of capital assets prior to commercialization was linked to each of the nodes representing change in capital assets resulting from commercialization.
Each of the factors that influences the NTFP commercialisation process (see Appendix 3) was represented as an individual node, linked to one of the five nodes representing capital asset types available prior to commercialization. Factors were grouped according to capital type, such that each factor was linked to a node representing only a single type of capital asset. To avoid unmanageably large CPTs, where necessary nodes representing factors were arranged in sub-groups such that the number of parent nodes per child node was limited to five (following Neil et al. 2000). The states defined for the factor nodes differed between factors, according to the scoring process outlined previously. In most cases, two Boolean states were defined, to indicate whether or not there was any evidence that a given factor had influenced a particular case.
The behaviour of any BBN depends on the conditional probabilities incorporated in the CPTs. These may be derived from a variety of sources, such as expert opinion or appropriate datasets. In the current example, probabilities were entered such that each factor had an equal influence on the state of the appropriate capital asset node to which it was linked. In this way, if all factors were in an appropriate (positive) state, then the probability associated with availability of the capital asset being ‘high’ would equal 1. Conversely, if all factors were in an alternative (negative) state, then the probability of availability of the capital asset being ‘low’ would equal 1. It is important to note that the factors were not otherwise weighted, and therefore the BBN is a form of ‘null’ model, assuming that all factors have an equal effect. However, the number of factors differed between different capital asset types (i.e. 14 in the case of natural capital, 10 for physical capital, five for social capital, 15 for human capital, and 22 for financial capital). As a result, any factor linked to financial capital will have had less individual influence on capital availability than (for example) factors linked to social capital assets, simply because of the difference in the number of factors grouped with these asset types.
In the case of the five nodes representing the change in availability of capital assets resulting from commercialization, CPTs were again constructed based on different combinations of the states of the parent nodes. For example, for the state of an asset node to be ‘Large increase’ with a probability of 1, then the state of all parent nodes (representing asset availability prior to commercialization) would need to be ‘High’. Conversely, if the state of all parent nodes were ‘Low’, then the state of the post-commercialization asset node would be ‘Large decrease’ with a probability of 1. Other combinations of ‘High’ and ‘Low’ states in the parent nodes were accorded intermediate probabilities in a way that was consistent across capital types. However, the CPTs were weighted in favour of the same capital type. A ‘High’ state in the parent node of a particular asset type was given a slight positive weighting in the child node of the same asset type, such that ‘High’ availabilities of specific assets were reflected in a higher probability of an increase in these same assets post-commercialization. This weighting was applied in a consistent way across all five post-commercialization asset nodes, and was included to reflect the beliefs of the research team.
According to this model structure, the nodes representing availability of the different types of capital asset available prior to commercialization behave independently of each other. The probabilities associated with their different states (‘high’ or ‘low’) are determined by the states of the nodes representing factors to which they are individually linked. The behaviour of the nodes representing availability of capital assets post-commercialisation is more complex, but is dependent on the probabilities associated with the states of each of the pre-commercialisation asset nodes, inferred according to Bayes’ theorem. In this case there is potential for interaction between the capital types, according to the values incorporated in the CPTs.