A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios

Abstract

Robustness is a key criterion for evaluating alternative decisions under conditions of deep uncertainty. However, no systematic, general approach exists for finding robust strategies using the broad range of models and data often available to decision makers. This study demonstrates robust decision making (RDM), an analytic method that helps design robust strategies through an iterative process that first suggests candidate robust strategies, identifies clusters of future states of the world to which they are vulnerable, and then evaluates the trade-offs in hedging against these vulnerabilities. This approach can help decision makers design robust strategies while also systematically generating clusters of key futures interpretable as narrative scenarios. Our study demonstrates the approach by identifying robust, adaptive, near-term pollution-control strategies to help ensure economic growth and environmental quality throughout the 21st century.decision making under uncertainty, robust decision making, deep uncertainty, adaptive planning, scenario planning

A comprehensive review of climate adaptation in the United States: more than before, but less than needed

“…In general, these approaches seek options that allow reversibility, preserve future options, can resist a variety of impacts, and are flexible, such that mid-course adjustments are possible (OTA 1993;Wilby and Vaughan 2011). Among these approaches are Robust Decision Making (RDM), Iterative Risk Management (IRM), Adaptive Management or CoManagement, Portfolio Management, and Scenario Planning (Gregg et al 2011;Groves and Lempert 2006;Kareiva 2008;Lee 1993;Lempert et al 2006;Moore et al 2012;Moser 2012;NPS 2010;NRC 2004;Williams and Brown 2012).…”

Operationalizing climate targets under learning: An application of cost-risk analysis

“…One way of dealing with the shortcomings described above is to switch focus to scenario frameworks and notions of robust decision making (Lempert et al 2006), thereby abandoning the appealing notion of optimality. Part of the community sees climate targets as a viable alternative (IPCC AR5 WG-III Chapter 6: Clarke et al (2014)) in the light of deep ("Knightian") uncertainty and preferences in-line with the precautionary principle (Iverson and Perrings 2012).…”

Landscape Survey to Support Flood Apex National Flood Decision Support Toolbox: Definitions and Existing Tools

“…In developing a conceptual framework for considering DSTs for flood risk management, the decision-framing approaches developed in Lempert, Groves, et al, 2006, andBennett, Cumming, andPeterson, 2005, provide useful starting points to scope the problem. Both approaches require a conceptualization and explicit modeling of the system, paying attention to critical uncertainties and the relationships between model elements.…”

“…One approach to decision structuring that has been used in a variety of instances to develop a well-posed question is the XLRM framework (Lempert, Groves, et al, 2006). The XLRM framework provides a simple and intuitive approach to distinguishing among goals motivating the decisions, uncertain and largely uncontrollable factors, factors that are within decisionmaker control, and the relationships among these factors.…”