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

• Published in: Management science Vol. 52; no. 4; pp. 514 - 528
• Main Authors: Lempert, Robert J, Groves, David G, Popper, Steven W, Bankes, Steve C
• Format: Journal Article
• Language: English
• Published: Linthicum, MD INFORMS 01.04.2006; Institute for Operations Research and the Management Sciences
• Series: Management Science
• Subjects: 21st century; adaptive planning; Afterlife; Analysis; Applied sciences; Climate change policy; Computer simulation; Control theory; Decision making; Decision making models; decision making under uncertainty; Decision theory; Decision theory. Utility theory; deep uncertainty; Design; Economic growth; Economic growth rate; Economic value; Economics; Emissions policy; Environmental policy; Environmental quality; Exact sciences and technology; Expected utility; General aspects; Hedging; Management information systems; Management science; Methods; Modeling; Operational research and scientific management; Operational research. Management science; Pollutant emissions; Probability; robust decision making; Robust statistics; scenario planning; Simulation; Strategic planning; Studies; Sustainable development; Term weighting; Uncertainty; United States; Script; Warranty; Decision support system; Decision making; Pollution control; Iterative process; robust decision making; Cluster; Vulnerability; deep uncertainty; Adaptive method; decision making under uncertainty; Uncertain system; Economic growth; scenario planning; Robustness; adaptive planning; Decision criterion; Planning
• ISSN: 0025-1909; 1526-5501
• DOI: 10.1287/mnsc.1050.0472

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.