Abstract
BackgroundScenario 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.MethodsThe 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.ResultsThe results are scenarios assessable with respect to (1) the consistency of scenario assumptions in terms of statistical confirmation, (2) the suitability of a quantitative model to represent the scenario, and (3) the statistical uncertainty of the scenario for a given quantitative model. A transparent scenario construction process results in traceable assumption documentation (an exemplary communication is provided in the Appendix). Perhaps, the most important novelty of the approach is the possibility of communicating to decision-makers the associated uncertainty in easily understandable terms. The distinction between provable possible assumptions (based on statistical evidence) and hypothetical assumptions is a novelty and significantly improves the aptitude of scenario study recipients to evaluate scenarios on their part.ConclusionsBMA provides the possibility for decision-makers (and all recipients of outlooks based on scenario technique) to trace back results to assumptions and provide an evaluation of these assumptions in terms of statistical confirmation. As such, the approach adds to the currently limited methodological diversity in scenario construction techniques.
Highlights
Scenario design is currently not a standardised process
The Bayesian model averaging (BMA) technique is a well-established methodology today and, as I will argue, is an appropriate conceptual setting for consistent scenario construction for application cases where cause-effect relations are uncertain and the mathematical representation in models should account for that uncertainty
In many circumstances, investigating unprecedented situations is the very reason for creating scenarios! BMA offers a way that uses “knowledge of the past” about parts of the world—say, the number of unemployed people documented in statistical data—to formulate expectations about these parts of the world in different states of accompanying phenomena
Summary
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. Exemplified for the case of energy modelling in this paper, the idea of the BMA scenario technique is that what is observed in the past (documented by statistical data) is a proven possible state of the world. It is important to understand that the statistical method tries to find relations of phenomena expressed as statistical data, based on similarities or differences in the changes these phenomena undergo. It is the expected impact of an assumption on other assumptions of the scenario, given the data record we consider. The technique is suitable for scenarios that figure as assumptions for consequent processing in quantitative models, e.g. optimization models and simulation models
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