Choosing diverse sets of plausible scenarios in multidimensional exploratory futures techniques. (March 2016)

• Record Type: Journal Article
• Authors: Lord, Steven; Helfgott, Ariella; Vervoort, Joost M.
• Is Part Of: Futures. Volume 77(2016:Jan.)
• Page Start: 11
• Page End: 27
• Publication Date: 2016-03
• Subjects: Morphological analysis -- Scenario diversity -- Exploratory scenarios -- Regional environmental change scenarios -- Robust optimization -- OLDFAR; Economic forecasting -- Periodicals; Technological forecasting -- Periodicals; Economic policy -- Periodicals; Prévision économique -- Périodiques; Prévision technologique -- Périodiques; Politique économique -- Périodiques; Economic forecasting; Economic policy; Technological forecasting; Periodicals; Electronic journals; 330.0112
• DOI: 10.1016/j.futures.2015.12.003
• Languages: English
• ISSNs: 0016-3287

Abstract

Highlights: ⬢ New method to extract a maximally diverse set of scenarios from morphological analysis. ⬢ Uses robust optimization to account for uncertainty in diversity between scenarios. ⬢ Provides users with multiple sets of diverse scenarios for qualitative assessment. ⬢ Implemented in graphic user interface software tool for use in participatory workshops. ⬢ Key lessons learned through the application in four regional scenario case studies with CGIAR and 240 partner organizations. Abstract: Morphological analysis allows any number of dimensions to be retained when framing future conditions, and techniques within morphological analysis determine which combinations of those dimensions represent plausible futures. However, even a relatively low number of dimensions in future conditions can lead to hundreds or even thousands of plausible future scenarios. Creating highly diverse but conceivable visions of the future in which to explore decision-making, exploratory futures techniques rely on the selection of a small number of plausible scenarios from the larger set. In this paper we describe a new method for finding maximally diverse sets containing a small number of plausible scenarios from a multi-dimensional morphological analysis. It is based on a mathematical optimization of diversity that is robust to the uncertainty in the framing of future factors and states and in what stakeholders might consider diverse combinations of those factors and states. We also describe implementation of the method as a software tool and its performance in recent exploratory scenario development by CGIAR and partners for regional environmental change, food security and livelihoods.