INTERAX—An interactive model for studying future business environments: Part I

Enzer, Selwyn; o̊Associate,

doi:10.1016/0040-1625(80)90049-9

Cited by 35 publications

(9 citation statements)

References 4 publications

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“…Since Jouvenel's works, introducing time in scenarios has been proposed on the one hand by early regional planning works emphasizing political and social changes [19], and on the other hand by operations research based works [20][21][22]. To go further and to study the issue of the transformation of scenarios in time, our two researches sought to introduce action processes into scenarios using a phenomenological approach [6].…”

Section: Methodological Frameworkmentioning

confidence: 99%

Designing action based scenarios

Marchais-Roubelat¹,

Roubelat²

2008

Futures

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Section: Methodological Frameworkmentioning

confidence: 99%

Designing action based scenarios

Marchais-Roubelat¹,

Roubelat²

2008

Futures

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“…[2]. The analysis of trends and event/trend combinations were included [3][4][5][6][7][8]. Ways in which to inform special types of coupled dynamic systems using cross-impact data were developed [9][10][11][12].…”

Section: Cross-impact Analysismentioning

confidence: 99%

Cross-impact balances

Weimer-Jehle¹

2008

Physica A: Statistical Mechanics and its Applications

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“…However, my criticisms apply to all techniques based on expert opinion. Consistent scenario construction is a relevant prerequisite for the legitimation of energy model results and one approach addressing this issue is cross-impact analysis [37,38], reviewed by [39]. Briefly described, the method presented in [37] defines the so-called descriptors figuring as a representation of context assumptions for a scenario.…”

Section: Horizon Mission Methodologymentioning

confidence: 99%

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

Culka¹

2018

Energ Sustain Soc

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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. 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.

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INTERAX—An interactive model for studying future business environments: Part I

Cited by 35 publications

References 4 publications

Designing action based scenarios

Designing action based scenarios

Cross-impact balances

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

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