Existing methodologies of sensitivity analysis may be insufficient for a proper analysis of Agent-based Models (ABMs). Most ABMs consist of multiple levels, contain various nonlinear interactions, and display emergent behaviour. This limits the information content that follows from the classical sensitivity analysis methodologies that link model output to model input. In this paper we evaluate the performance of three well-known methodologies for sensitivity analysis. The three methodologies are extended OAT (one-at-a-time), and proportional assigning of output variance by means of model fitting and by means of Sobol’ decomposition. The methodologies are applied to a case study of limited complexity consisting of free-roaming and procreating agents that make harvest decisions with regard to a diffusing renewable resource. We find that each methodology has its own merits and exposes useful information, yet none of them provide a complete picture of model behaviour. We recommend extended OAT as the starting point for sensitivity analysis of an ABM, for its use in uncovering the mechanisms and patterns that the ABM produces.

The use of serious games in the governance of natural resources and the environment is progressively increasing and includes games used for research and data collection, teaching and training, and fostering a change of practices. However, this diversity remains underexplored and underreported. In view of a growing interest in the use of serious games in natural resource and environmental governance, the absence of discussions about how differences in intended use and delivery influence the performance, assessment, and outcomes of games is problematic. Here we present an inventory, and a description, of such different uses then, by focusing on serious games used as interventions, we discuss when, and how, games could be used to generate learning and social learning. To that end we use a narrative review of selected literature, and insight from research on social learning, to develop an inventory of game use, and within that inventory we conceptualize the use of serious games as a social learning intervention. Also, by means of an illustrative case of a serious game (developed as part of the Assessing the Learning Effects of Games on Attitude of Stakeholders toward Sustainable Shrimp Farming – ALEGAMS research project) we reflect on a few key aspects of game use. We suggest that developing a serious game needs several iterations and, although the learning outcomes can be assessed, the impact of games aiming at changes in current practice and policy will likely fall beyond the timespan of usual project periods. This is something future research should consider as it has implications for the research design and methodology.