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

Methodological approach	Technique	Keywords or a (very) brief description	Source of information
Judgement	Genius forecasting	“Think the unthinkable” [55]	Opinion
Visualisation	Intuitive images are combined to scenarios that are in juxtaposition to analytical strategies, e.g. [56]	Opinion
Role playing	A group judgement technique where individuals create a response to a hypothetical situation which is considered as a scenario. Playing the devil’s advocate is an example of scenario forming with focus on unprecedented or highly unlikely scenarios [57].	(Group) opinion
Coates and Jarratt	For a given time frame and domain, four to six scenario themes regarding the most significant kinds of potential future developments based on judgement are formulated, cf. [58]	Opinion
Baseline scenario: the expected future	Trend extrapolation	Measures existing trends and extrapolates effects into the future; both judgement and empirical analyses are possible. The Manoa technique defines three strong trends which are analysed w.r.t. their implications separately, and in conjunction using a cross-matrix, cf. [26]	Opinion or statistical data
Elaboration of fixed scenarios	Incasting	Based on an (extreme) state of the world participants judge potential impacts in various respects as politics, economics, etc. Qualitative or quantitative for example life cycle assessment technique [59]	Opinion/data
SRI matrix (Stanford Research Institute matrix)	From a column-wise classification of fixed scenarios as for example worst case or expected future the dimensions (e.g. population, environment) are evaluated row-wise, cf. [60]	Opinion
Event sequences	Probability trees and scenario trees	Different future conditions constitute individual paths which are assigned probabilities. Probability trees are used in risk management. A related technique is scenario trees where a reduction of probable paths to relevant paths is carried out, cf. [61]	Opinion
Intuitive scenario building	Probability trees are evaluated to identify characteristics that are common to several branches. Summarising these branches is considered as a way to create coherent scenarios, e.g. sociovision [62]	Opinion
Divergence mapping	A set of events, derived from brainstorming, is aligned in different time horizons forming the storyline of a scenario. The relation of earlier events to the later events is seen to be a plausible sequence, cf. [63]	Opinion
Backcasting	Horizon mission methodology	Supposing a hypothetical situation (the scenario) was an actual situation, the ways and necessary components at present are analysed to achieve the scenario [31, 64]	Opinion, state-of-the-art technology data
Impact of future technologies	Multiple future scenarios are the basis from which experts work backward and identify necessary (technological) breakthroughs, e.g. [65]	Opinion
Future mapping	An expert elicitation technique where pre-defined events and pre-defined end-states are arranged to investigate interrelations and consequences, cf. [66]	Opinion
Dimensions of uncertainty	GBN (Global Business Network)	Based on two dimensions of uncertainty and polarities, four combinations are seen to constitute plausible futures, cf. [62]	Opinion
Morphological analysis and field anomaly relaxation	Multiple dimensions of uncertainty captured in columns are related to alternative events in rows. A scenario is created by the alignment of alternatives of each column, cf. [67]	Opinion
Cross-impact analysis	Interactive future simulation IFS	Based on a set of variables (descriptors) an assessment of their mutual relevance based on expert judgement is carried out. Consistent scenarios are constructed in the sense that variable combinations are computed that have been judged to be compatible. There are probabilistic versions of cross-impact analyses [37]	Opinion arranged by mathematical method
Modelling	Trend impact analysis	Based on a business-as-usual trend assumption, the impact of a potential event on that baseline scenario is evaluated in distinguished impact sequences (first depart from trend continuation, maximum impact, and effect integration), cf. [68]	Opinion (and statistical data)
Sensitivity analyses	Given a model, exogenous variables or model parameters are varied. The changes of model results given varying input/parameter assumptions are evaluated as “sensitivity”. Often the one-parameter-at-a-time technique is employed, a kind of ceteris paribus approach [69]	(Opinion and) statistical data
Dynamic scenarios	From brainstormed scenario themes a system is mapped using causal models. The variables figuring in different causal models are combined in a meta-model mapping the whole domain. The meta-model is analysed for different uncertainties involved in the variables, cf. [70]	Opinion and statistical data
Bayesian model averaging (BMA)	Scenarios are constructed from statistical data records of phenomena most relevant for an input variable of a consequent quantitative model. Uncertainty is evaluated based on the explanatory power of influencing phenomena most relevant in the historical record, cf. [24]	Opinion and statistical data