Abstract for: Better Robustly Right than Accurately Wrong

This paper presents a System Dynamics approach for dealing with complex issues that are characterized by deep uncertainty. Deep uncertainty refers to situations in which experts disagree on the formulation of ‘the’ underlying model, probabilities of inputs, and the valuation of outcomes. Instead of waiting for full information and accurate data to become available, consensus to be reached, or irrefutable scientific proof to be established, one could address such issues with approaches that enable one to simultaneously take alternative theories/models, sets of possible functions, different distributions, and distinct valuation frameworks into account. Exploratory Modelling and Analysis is such an approach: it allows for simulating sets of alternative models across vast uncertainty spaces, generating multi-model ensemble forecasts, exploring the resulting ensembles of outcomes using all sorts of machine learning techniques, identifying and selecting exemplar scenarios, performing directed searches to answer specific questions, and optimizing the robustness of potential adaptive policies that are designed to always work, especially when really needed. After introducing the approach, it will be illustrated in this overview paper with different applications for each of these typical use cases.