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Quantitative scenario design with Bayesian model averaging: constructing consistent scenarios for quantitative models exemplified for energy economics

Culka, Monika



Scenario design is currently not a standardised process. The formulation of storylines representing different dimensions (for example economic or societal developments) demands an investigation of assumption compatibility, coherence, and consistency. Scenario techniques that use expert opinion as the sole information source are particularly appropriate for personal decisions. Contexts where scenarios serve as decision support on a societal level—for example in political decision-making—benefit from unbiased, fact-depicting, multi-dimensional information that is available in statistical data.


The presented approach uses the well-established method of Bayesian model averaging for the formulation of consistent, transparent, and intuitively understandable quantitative scenario assumptions. These assumptions are used in quantitative models to produce outlooks and forecasts. Illustrated by the example of quantitative energy models used to investigate developments of the energy system by scenario technique, the approach contrasts with other scenario methods. Bayesian model averaging (BMA) is a method that allows for an evaluation of both system relation stability in terms of observable co-evolvement of phenomena in the past and of future system states of interest based on expert opinion where past evolvements serve as a point of reference.
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Verlagsausgabe §
DOI: 10.5445/IR/1000089038
Veröffentlicht am 04.01.2019
DOI: 10.1186/s13705-018-0162-3
Zitationen: 13
Web of Science
Zitationen: 11
Zitationen: 14
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technikzukünfte (ITZ)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2018
Sprache Englisch
Identifikator ISSN: 2192-0567
KITopen-ID: 1000089038
Erschienen in Energy, Sustainability and Society
Verlag Springer Fachmedien Wiesbaden
Band 8
Heft 1
Seiten Article: 22
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 09.07.2018
Schlagwörter Scenario technique; Uncertainty modelling; Assumption consistency; Empirical adequacy
Nachgewiesen in Dimensions
Web of Science
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