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

Background

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.


Methods

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.
... mehr


Results

The 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.


Conclusions

BMA 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.