A Temporal Variant-Invariant Validation Approach for Agent-based Models of Landscape Dynamics

Agent-based modeling provides a means for addressing the way human and natural systems interact to change landscapes over time. Until recently, evaluation of simulation models has focused on map comparison techniques that evaluate the degree to which predictions match real-world observations. However, methods that change the focus of evaluation from patterns to processes have begun to surface; that is, rather than asking if a model simulates a correct pattern, models are evaluated on their ability to simulate a process of interest. We build on an existing agent-based modeling validation method in order to present a temporal variant-invariant analysis (TVIA). The enhanced method, which focuses on analyzing the uncertainty in simulation results, examines the degree to which outcomes from multiple model runs match some reference to how land use parcels make the transition from one land use class to another over time. We apply TVIA to results from an agent-based model that simulates the relationships between landowner decisions and wildfire risk in the wildland-urban interface of the southern Willamette Valley, Oregon, USA. The TVIA approach demonstrates a novel ability to examine uncertainty across time to provide an understanding of how the model emulates the system of interest.