Robust Decision Making (RDM)

Abstract

The quest for predictions-and a reliance on the analytical methods that require them-can prove counter-productive and sometimes dangerous in a fast-changing world. • Robust Decision Making (RDM) is a set of concepts, processes, and enabling tools that use computation, not to make better predictions, but to yield better decisions under conditions of deep uncertainty. • RDM combines Decision Analysis, Assumption-Based Planning, scenarios, and Exploratory Modeling to stress test strategies over myriad plausible paths into the future, and then to identify policy-relevant scenarios and robust adaptive strategies. • RDM embeds analytic tools in a decision support process called "deliberation with analysis" that promotes learning and consensus-building among stakeholders. • The chapter demonstrates an RDM approach to identifying a robust mix of policy instruments-carbon taxes and technology subsidies-for reducing greenhouse gas emissions. The example also highlights RDM's approach to adaptive strategies, agent-based modeling, and complex systems. • Frontiers for RDM development include expanding the capabilities of multiobjective RDM (MORDM), more extensive evaluation of the impact and effectiveness of RDM-based decision support systems, and using RDM's ability to reflect multiple world views and ethical frameworks to help improve the way organizations use and communicate analytics for wicked problems.

On Computational Thinking and STEM Education

“…To many scientists, the use of computing is not only a tool, but also a way of making discoveries, involving problem solving, design, and model building. For example, computational modeling is used in biology research (Brodland 2015), climate risk management (Garner et al 2016), and robust decision making under deep uncertainty (Lempert 2019). CT is naturally part of STEM, including of course, CS.…”

Anthropocene flooding: Challenges for science and society

“…Sensitivity analysis tools can be used to guide scenario generation by identifying dominant controls of human–natural systems (Razavi & Gupta, ). Recent advances in the field of Decision Making Under Uncertainty (Lempert et al, ; Maier et al, ) can help us to move away from crisp solutions that induce a false sense of security, to focus on critical scenarios that need attention, characterize associated trade‐offs and enable decision‐makers to think beyond “return periods” and the likelihood of floods (Gober, ).…”

The desirability of transitions in demand: Incorporating behavioural and societal transformations into energy modelling

“…Meanwhile, the modelling community should rapidly accelerate its initial efforts to investigate alternative deliberation options: those that go beyond the “cost-effectiveness” mode [157] , which prioritises understanding how to achieve the warming cap at the lowest possible cost. Such options can include: exploring a diversity of scenarios that reflect a range of desirable transitions to achieve different decarbonisation goals [158] ; understanding “robust” strategies to achieving such goals [159] in the face of a range of future scenarios; seeking mechanisms to handle counter-GDP-growth scenarios of negative growth; and downscaling and degrowth/post-growth implications. At the same time, the modelling community has realised that there is no “one model fits all” approach and it should build on its efforts to make IAMs increasingly sophisticated by supplementing them with a range of other tools and analytical techniques, to cover temporal and spatial scales that currently cannot realistically be represented [148] .…”