Instrumenting good modelling practice as common practice

Good modelling practice has many requirements. Above all, the process should be complete and transparent enough so that the credibility of its conclusions can be comprehended, or even assessed, by its intended audience. And the more complex, uncertain and cross-sectoral the problem being modelled, or potentially devastating its consequences may be, the more the need for good practice. Consequently, good modelling practice is essential in addressing not just climate change issues, but also cross-sectoral issues such as occurs with water, energy, agriculture and the socio-economy. Yet despite widespread acknowledgment of the grand socio-environmental challenges facing the planet, practices as seen in the major literature largely remain meagre, and most often are pathetically inadequate.

The presentation begins with a list of specific technical complaints around poor practice, ones that could be easily remedied by modellers, to concede this unnecessary state of affairs. We argue for a suitable ontology around concepts for anchoring good modelling practice, including trustworthiness, assurance, robustness, reproducibility and credibility, along with fitness-for-purpose notions of usability, reliability and feasibility. We also emphasize the often-overlooked role of human factors in the modelling process, including assumptions and choices made by the modeller, and consider how consequent biases or uncertainties can be reduced. We then synthesize the steps in the modelling process as recognized in the scientific and grey literature, and provide examples of checklists of questions that merit addressing for each step. Many of these questions prompt consideration of methodological choices, especially around uncertainty and scale. Good modelling practice warrants greater transparency in documenting, justifying and, wherever possible, comparing methodological choices and related assumptions. We argue that the level of robustness to choices be made clearer.

The modelling community must however address how to advance modelling so that good practice becomes not just well-known but common practice. Instruments for achieving this are posited around: regulation by journals in terms of standards that they require for relevant papers published; developing incentives for following good practice; promoting an institutional/community culture around it, and expanding education and capacity building in modelling that focusses from the start on good practice as being fundamental.