The BNAS model allows the definition of dynamic variables (time-dependent drivers) at the start of each simulation.
The system of agents' iterations: The BNAS model is composed of static and variable parts.
Each agent has its own characteristics and field of vision and, in combination, they form a heterogeneous agent population in the BNAS model.
Given that the BNAS model is a dynamic model, the system runs each iteration 100 times.
The BNAS model does not define permanent data variables (such as land-use drivers).
In this study, the utility of the BNAS model in a PSS framework is tested by using it as a policy generator and a future land-use simulator.
The BNAS model can be employed in a PSS framework for processing information.
The BNAS model can incorporate land-use decisions through the links between the various drivers.
Figure 2 provides examples of BN structures for the household agents to illustrate how a BN in an agent type can be represented (these are examples and do not represent the BNAS model's variables).
This study expands on the initial BNAS model by using it as a policy generator through the backward inference capabilities of BNs.
IMPLEMENTATION OF THE BNAS MODEL IN A PSS FRAMEWORK WITH GROWTH SCENARIOS
The variables used in the BNAS model application for the city of Surrey are as follows: