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| future_fabulators:scenario_methods [2014-03-04 06:29] – [Change Drivers & Weak Signals] maja | future_fabulators:scenario_methods [2014-03-04 06:34] – [Scenarios] maja | ||
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| - | * __Two axes method__: Scenarios generated using the ‘two axes’ process are illustrative rather than predictive; they tend to be high-level (although additional layers of detail can subsequently be added). They are particularly suited to testing medium to long-term policy direction, by ensuring that it is robust in a range of environments. Scenarios developed using this method tend to look out 10-20 years.[[http:// | + | * __Two axes method__: Scenarios generated using the ‘two axes’ process are illustrative rather than predictive; they tend to be high-level (although additional layers of detail can subsequently be added). They are particularly suited to testing medium to long-term policy direction, by ensuring that it is robust in a range of environments. Scenarios developed using this method tend to look out 10-20 years. |
| - | * __Branch analysis method__: The ‘branch analysis’ method is suited to developing scenarios around specific turning-points that are known in advance (e.g. elections, a referendum or peace process). This approach works best for a shorter time horizon: generally up to five years.[[http:// | + | * __Branch analysis method__: The ‘branch analysis’ method is suited to developing scenarios around specific turning-points that are known in advance (e.g. elections, a referendum or peace process). This approach works best for a shorter time horizon: generally up to five years. |
| - | * __Cone of plausibility__ method: offers a more deterministic model of the way in which drivers lead to outcomes, by explicitly listing assumptions and how these might change. Of the three techniques, this approach is most suitable for shorter-term time horizons (e.g. a few months to 2-3 years), but can be used to explore longer-term time horizons. It also suits contexts with a limited number of important drivers. </ | + | * __Cone of plausibility__ method: offers a more deterministic model of the way in which drivers lead to outcomes, by explicitly listing assumptions and how these might change. Of the three techniques, this approach is most suitable for shorter-term time horizons (e.g. a few months to 2-3 years), but can be used to explore longer-term time horizons. It also suits contexts with a limited number of important drivers. |
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| From: [[http:// | From: [[http:// | ||
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| - | Another suggestion (from [[integral scenario development]] by Christ C Stewart: | ||
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| - | * Apply 6 root questions (relating to factors and actors) and the AQAL framework (four quadrants by Wilber) to deepen the scenario stories | ||
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| - | Also, the layers from Causal Layered Analysis | + | Other possibilities: |
| + | An option from [[integral scenario development]] by Christ C Stewart is to Apply 6 root questions (relating to factors and actors) and the AQAL framework (four quadrants by Wilber) to deepen the scenario stories. | ||
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| - | More on [[possible_futures_parallel_presents]] [[design fiction]], [[guerilla | + | More on [[possible_futures_parallel_presents]] [[design fiction]], [[guerrilla |
| ==== Prehearsals ==== | ==== Prehearsals ==== | ||
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