On qualitative uncertainty in modelling assumptions

Abstract

Researchers today have a range of advanced and efficient methods for quantifying uncertainty at their disposal. These methods effectively help them to understand how simulation results may change when a model is re-run or when input parameters are varied. However, models often contain assumptions that are not numerical or have uncertainties that cannot be quantified. Examples include assumed omissions, existing assumptions reused in new contexts, or assumptions based on partial evidence. This paper proposes a novel conceptual framework to investigate the uncertainty of modelling assumptions on a qualitative level. We aim to educate model developers on how to assess model quality beyond quantifiable uncertainties, understand how it can deteriorate, and identify measures that can improve quality or mitigate deterioration. The framework is designed to be broadly applicable to implemented models (simulations), conceptual models, and even mental models.