Agent-based models (ABM) have become an important tool for natural resource management in recent decades, and for the study of agricultural change in particular (e.g., Becu, Perez, Walker, Barreteau, & Page, 2003; Bell, 2011; Bert et al., 2010). Where household-level decisions, made in interaction with other households and the natural environment, shape outcomes at the landscape scale, ABM can provide insights that coarser equation-based models (EBM) or statistical models may not (Bankes, 2002; Paranuk et al. 1998). ABM provide as well a unique point of entry for non-expert stakeholders into the analytic process because it offers a 1:1 mapping of real-world actors to computational agents, which provides a level of conceptual understanding and familiarity that is not available when EBM or statistical models are used. Furthermore, the actor behaviours that are formalized within the computational code that defines an agent’s behavior can be represented in a variety of ways (e.g., heuristic decision trees, utility functions) that are again easier for non-expert stakeholders to than the equations and constraints of other modeling approaches.