5. Conclusion

As noted in the Introduction (Chapter 1), the computational and conceptual challenges of the multi-model, transdisciplinary workflows that characterize ambitious projects such as IM3 have limited UC and UQ analyses. Moreover, the very nature and purpose of modeling and diagnostic model evaluation can have very diverse philosophical framings depending on the disciplines involved (see Figure_1_1 and Chapter 2.2). The guidance provided in this text can be used to frame consistent and rigorous experimental designs for better understanding the consequences and insights from our modeling choices when seeking to capture complex human-natural systems. The progression of sections of this text provide a thorough introduction of the concepts and definitions of diagnostic model evaluation, sensitivity analysis and UC. In addition, we comprehensively discuss how specific modeling objectives and applications should guide the selection of appropriate techniques; broadly, these can include model diagnostics, in-depth analysis of the behavior of the abstracted system, and projections under conditions of deep uncertainty. This text also contains a detailed presentation of the main sensitivity analysis methods and a discussion of their features and main limitations. Readers are also provided with an overview of computer tools and platforms that have been developed and could be considered in addressing IM3 scientific questions. The appendices of this text include an overview of UQ methods, a terminology glossary of the key concepts as well as example test cases and scripts to showcase various UC related capabilities.

Although we distinguish the UC and UQ model diagnostics, the reader should note that we suggest an overall consistent approach to both in this text by emphasizing “exploratory modeling” (see review by Moallemi et al. [9]). Although data support, model complexity, and computational limits strongly distinguish the feasibility and appropriateness of various UC diagnostic tools (e.g., see Fig. 3.5), we overall recommend that modelers view their work through the lens of cycles of learning. Iterative and deliberative exploration of model-based hypotheses and inferences for transdisciplinary teams is non-trivial and ultimately critical for mapping where innovations or insights are most consequential. Overall, we recommend approaching modeling with an openness to the diverse disciplinary perspectives such as those mirrored by the IM3 family of models in a progression from evaluating models relative to observed history to advanced formalized analyses to make inferences on multi-sector, multi-scale vulnerabilities and resilience. Exploratory modeling approaches can help fashion experiments with large numbers of alternative hypotheses on the co-evolutionary dynamics of influences, stressors, as well as path-dependent changes in the form and function of coupled human-natural systems [37]. This text guides the reader through the use of sensitivity analysis and uncertainty methods across the diverse perspectives that have shaped modern diagnostic and exploratory modeling.