Decision-Making under Deep Uncertainty
A growing body of industry projects and academic research advocates for embracing the challenge of modelling changing environmental and socio-economic systems using Decision-Making under Deep Uncertainty (DMDU) techniques.
This approach alleges that there is limited confidence that the world of 2050 will reflect our best predictions, and that there is limited agreement and knowledge surrounding the consequences of actions taken in the present day. Whilst we know that climate change is happening, we do not know with certainty at what pace it will manifest or the extremity of potential changes in precipitation patterns under different emissions scenarios.
The DMDU toolkit aims to minimize uncertainty through the simulation of multiple plausible futures, different changes in the intensity of rainfall or droughts, different levels of urban creep, varied changes in consumption patterns. It then seeks to find solutions which optimize for multiple objectives (e.g. cost, pollution, emissions) across multiple possible futures, minimizing the chance that a model or proposed solution fails due to mistaken assumptions about future changes.
Whilst many practical applications of DMDU have concerned water resources planning, Casal-Camous, et al. (2018) [see article] applied these techniques to a fictional wastewater network to assess the resilience and sustainability of grey and green approaches to increasing sewer capacity. Further, Wongburi & Park (2018) [see article] developed an Excel-based system to explore the robustness of different approaches to improving Thailand’s treatment of wastewater.