A new agent-based model provides insight into deep uncertainty faced in simulated forest management

Exploratory modeling in forestry uses a variety of approaches to simulate forest management. One important assumption that every approach makes is about the deep uncertainty—the lack of the knowledge required for making an informed decision—that future forest management will face. This assumption can strongly influence simulation results and their interpretation but is rarely studied. Our objective was to explore how differences in modeling approaches influence the deep uncertainty faced in simulated forest management. We used SOSIEL Harvest, a new agent-based extension to a landscape-change model, LANDIS-II, to simulate three approaches to modeling forest management. For each, we used the same forest and management data from Michigan, US, which isolated the differences among approaches as the only variable factor. We then used a new method, also introduced here, to measure and compare the deep uncertainty faced during simulated management. Finally, we used a typology of sources of uncertainty to categorize the sources responsible for this deep uncertainty. The simulated forest management in the three modeling approaches faced substantially different degrees of deep uncertainty, which translated into considerable differences in simulation results. There was an overall negative relationship between deep uncertainty and the ability of the management to respond to forest change and adapt decisions accordingly. While inherent deep uncertainty faced in simulated forest management can be substantial, it is overestimated by exploratory models that underestimate management’s ability to respond to forest change. Reducing such model-related uncertainty will allow for more realistic results from exploratory studies of forest management.