I like the idea of applying decision theory to real-world problems. The ideal concept is that decision makers and stakeholders have a body of highly relevant objective information available to them at the time they are also applying judgment and values (and politics) to make those decisions. There is a huge academic endeavor, but the hard part in my work experience is pulling together relevant information in a timely manner. Today’s schedule and budget expectations just don’t allow complex analyses in many cases, so we default to relatively simplistic approaches to complex problems. Maybe AI can help apply these methods more quickly, alongside more traditional methods like benefit-cost analysis. I like the focus on robust decisions in this paper, and I like the collection of practical tools including some R packages.

A review of tools and resources to support Decision-Making Under Deep Uncertainty

Decision-making under Deep Uncertainty (DMDU) offers approaches to support robust, adaptive strategies for complex decision-making. However, practical uptake of DMDU remains limited, partly due to fragmented access to resources and a lack of an inventory of available tools. This study introduces a comprehensive catalogue of tools and resources. Through a structured survey and expert elicitation, we identify 28 resources and 16 tools that support DMDU research and practice and classify them using an established DMDU taxonomy. Our analysis reveals a focus on introductory guidance regarding theory and methods of DMDU application, with some bias toward water-related applications. Technical, method-specific resources on how to implement existing frameworks remain limited. Our results identify tools supporting all core DMDU components, though they highlight persistent scalability challenges. The resulting online catalogue provides a foundation for expanding the use of DMDU in practice and is intended as a living, community-driven platform.