Engaging Multiple Worldviews With Quantitative Decision Support: A Robust Decision-Making Demonstration Using the Lake Model

Many of today's most pressing policy challenges are usefully characterized as wicked problems. With contested framings parties to a decision disagree not only on potential solutions, but on the nature of the problem they are trying to solve. The quantitative tools of risk and policy analysis, commonly designed to develop and compare choices within a single decision framing, are poorly designed to bring quantitative information into debates with contested framings. This study aims to build on recent advances in decision making under deep uncertainty (DMDU) to demonstrate methods and tools that may help resolve the tension between quantitative decision support and multiworldview approaches for addressing wicked problems. The study employs robust decision making (RDM), one common DMDU method, and a new version of the lake model, a simple and widely used model of a coupled human and natural system, to conduct a stylized analysis that reflects three different worldviews. The RDM analysis solves the decision challenge independently for each worldview and then compares each set of solutions from the vantage of the other worldviews. The resulting utopia-dystopia matrix informs problem reframing that seeks robust, adaptive strategies independently consistent with each worldview and thus provides a locus for agreement. The study describes how stakeholder engagements might use such analytic tools and their information products to provide overlapping but alternative entry points for groups with fundamentally different worldviews to engage with each other in deliberative processes appropriate for wicked problems.

Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision-Making Framework for Climate Change Adaptation

There is increasing concern over deep uncertainty in the risk analysis field as probabilistic models of uncertainty cannot always be confidently determined or agreed upon for many of our most pressing contemporary risk challenges. This is particularly true in the climate change adaptation field, and has prompted the development of a number of frameworks aiming to characterize system vulnerabilities and identify robust alternatives. One such methodology is robust decision making (RDM), which uses simulation models to assess how strategies perform over many plausible conditions and then identifies and characterizes those where the strategy fails in a process termed scenario discovery. While many of the problems to which RDM has been applied are characterized by multiple objectives, research to date has provided little insight into how treatment of multiple criteria impacts the failure scenarios identified. In this research, we compare different methods for incorporating multiple objectives into the scenario discovery process to evaluate how they impact the resulting failure scenarios. We use the Lake Tana basin in Ethiopia as a case study, where climatic and environmental uncertainties could impact multiple planned water infrastructure projects, and find that failure scenarios may vary depending on the method used to aggregate multiple criteria. Common methods used to convert multiple attributes into a single utility score can obscure connections between failure scenarios and system performance, limiting the information provided to support decision making. Applying scenario discovery over each performance metric separately provides more nuanced information regarding the relative sensitivity of the objectives to different uncertain parameters, leading to clearer insights on measures that could be taken to improve system robustness and areas where additional research might prove useful.

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

Deep uncertainty in future climatic and economic conditions complicates developing infrastructure designed to last several generations, such as water reservoirs. In response, analysts have developed multiple robust decision frameworks to help identify investments and policies that can withstand a wide range of future states. Although these frameworks are adept at supporting decisions where uncertainty cannot be represented probabilistically, analysts necessarily choose probabilistic bounds and distributions for uncertain variables to support exploratory modeling. The implications of these assumptions on the analytical outcomes of robust decision frameworks are rarely evaluated, and little guidance exists in terms of how to select uncertain variable distributions. Here, we evaluate the impact of these choices by following the robust decision-making procedure, using four different assumptions about the probabilistic distribution of exogenous uncertainties in future climatic and economic states. We take a water reservoir system in Ethiopia as our case study, and sample climatic parameters from uniform, normal, extended uniform, and extended normal distributions; we similarly sample two economic parameters. We compute regret and satisficing robustness decision criteria for two performance measures, agricultural water demand coverage and net present value, and perform scenario discovery on the most robust reservoir alternative. We find lower robustness scores resulting from extended parameter distributions and demonstrate that parameter distributions can impact vulnerabilities identified through scenario discovery. Our results suggest that exploratory modeling within robust decision frameworks should sample from extended, uniform parameters distributions.

Stress-Testing US Colorectal Cancer Screening Guidelines: Decennial Colonoscopy from Age 45 is Robust to Natural History Uncertainty and Colonoscopy Sensitivity Assumptions

PurposeThe 2023 American College of Physicians (ACP) guidelines for colorectal cancer (CRC) screening are at odds with the United States Preventive Task Force (USPSTF) guidelines, with the former recommending screening starting at age 50 y and the latter at age 45 y. This article "stress tests" CRC colonoscopy screening strategies to investigate their robustness to uncertainties stemming from the natural history of disease and sensitivity of colonoscopy.MethodsThis study uses the CRC-SPIN microsimulation model to project the life-years gained (LYG) under several colonoscopy CRC screening strategies. The model was extended to include birth cohort effects on adenoma risk. We estimated natural history parameters under 2 different assumptions about the youngest age of adenoma initiation. For each, we generated 500 parameter sets to reflect uncertainty in the natural history parameters. We simulated 26 colonoscopy screening strategies and examined 4 different colonoscopy sensitivity assumptions, encompassing the range of sensitivities consistent with prior tandem colonoscopy studies. Across this set of scenarios, we identify efficient screening strategies and report posterior credible intervals for benefits of screening (LYG), burden (number of colonoscopies), and incremental burden-effectiveness ratios.ResultsProjected absolute screening benefits varied widely based on assumptions, but strategies starting at age 45 y were consistently in the efficiency frontier. Strategies in which screening starts at age 50 y with 10-y intervals were never efficient, saving fewer life-years than starting screening at age 45 y and performing colonoscopies every 15 y while requiring more colonoscopies per person.ConclusionsDecennial colonoscopy screening initiation at age 45 y remained a robust recommendation. Colonoscopy screening with a 10-y interval starting at age 50 y did not result in an efficient use of colonoscopies in any of the scenarios evaluated.HighlightsColorectal cancer colonoscopy screening strategies initiated at age 45 y were projected to yield more life-years gained while requiring the least number of colonoscopies across different model assumptions about disease natural history and colonoscopy sensitivity.Colonoscopy screening starting at age 50 y with a 10-y interval consistently underperformed strategies that started at age 45 y.