In this day and age of speed, not to say haste, unequally shared resources and wish to relatively easily obtain answers to complex questions, we are faced in strategic foresight and warning analysis (or political risk analysis) with a very serious challenge. We must choose a methodology that: allows for a “good enough” analysis (Fein, 1994), …
Tragic events strike Everstate. We witness tornadoes and drought, war in the Middle East and even a major industrial accident, while a new episode of financial crisis starts. These are instances of the various conditions presiding to Everstate’s destiny, considering what has been done, or not, globally, regionally and within Everstate.
The same set of events should be used to stress test each scenario. The logic of the scenario will however comes first, assuming it impacts the plausibility of the event. In that case, ….
In many foresight methods, once you have identified the main factors or variables and reach the moment to develop the narrative for the scenarios, you are left with no guidance regarding the way to accomplish this step, beyond something along the line of “flesh out the scenario and develop the story.”*
Here, we shall do otherwise and provide a straightforward and easy method to write the scenario. We shall use the dynamic network we constructed for Everstate – or for another issue – and the feature called “Ego Network” that is available in social network analysis and visualisation software to guide the development and writing of the narrative.
Once variables (also called factors and drivers according to authors) have been identified – and in our case mapped, most foresight methodologies aim at reducing their number, i.e. keeping only a few of those variables.
Indeed, considering cognitive limitations, as well as finite resources, one tries obtaining a number of variables that can be easily and relatively quickly combined by the human brain.
The problem we here face methodologically is how to reduce this number of variables at best, making sure we do not reintroduce biases or/and simplify our model so much it becomes useless or suboptimal.
Furthermore, considering also the potential adverse reactions of practitioners to complex models, being able to present a properly simplified or reduced model (however remaining faithful to the initial one) is most often necessary.