| This thesis reports findings from the Agent Based Landuse Experiment, where agent based modelling is combined with discrete choice theory to examine cumulative impacts of moose hunting in forested landscapes. This approach accounts for complexity in terms of agent heterogeneity, agent learning, biophysical feedbacks, and spatial relationships over time that defines the resource system under analysis. Furthermore, a defensible method of representing human decision making in multi-agent systems is presented.; Outcomes are examined for various assumptions regarding agent characterization and alternative resource management scenarios implemented on the landscape. Assumptions regarding agent preference heterogeneity, perception heterogeneity and agent learning are found to yield markedly different outcomes for agent behavior, utility accruing from the landscape, and the sustainability of moose populations. Resource management scenarios simulating disaggregated heterogeneous agents show that earlier decommissioning roads and regeneration of access/linear disturbances appear to negatively impact the sustainability of moose populations. |
