Can digital twins incentivise good modelling practice?

It has been said that culture eats strategy for breakfast. The effect of legacy over adequacy in modelling practice exemplifies the difficulty in changing behaviours to improve modelling outcomes. Ideally, good modelling practice would be incentivised by the systems in which modellers operate, and moreover, that modelling practice would have a learning orientation that gradually improves over time, seeking an ever closer alignment with organisational and societal needs.

Digital twins institutionalised within organisational operations provide a possible opportunity to incentivise these behaviours. A digital twin is a time-varying representation of a system that brings together observed information and predictive model capabilities. Juxtaposing model predictions with other sources of information forces models to demonstrate their value, in continually changing conditions. Operational use of a digital twin means that models need to be fit for purpose. The need to prioritise investment across a digital twin means that the model suite needs to address a broad range of purposes and model augmentation is more likely to be driven by consideration of value of information and prioritisation of efforts to reduce uncertainty over time.

These theoretical benefits are explored with example use cases in the context of cross-scale catchment water resource, landscape, and irrigation management, drawing on preliminary experiments in Australia.