Practically applying the idea of “strategic surprise” when anticipating new threats is difficult as soon as one moves away from the general idea, and tries to be more specific about the strategic impact a surprise could have. The surprise part of the concept is relatively easily understood and envisioned. When imagining a threat or danger occurring, we don’t have any problem identifying and explaining the many reasons why this event could happen unexpectedly and find us unprepared. Assessing, estimating and understanding these incriminated causes, then remedying them, is more complex, indeed the raison d’être of strategic foresight and warning and risk management, and the topic of many studies. The strategic dimension, for its part, is more elusive and far less […]
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.
In this article we explain and discuss the methodological background that allows us to set the criteria for Everstate – or for any country or issue chosen – as exemplified in the post “Everstate’s characteristics.” Meanwhile, we also address the problem of consistency.
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.
Go back to Part 1
Actually, any SF&W model as it primarily deals with time should be a dynamic network. How can we expect obtaining any potential outline for the future if our model for understanding is static?
Our map thus aims at representing the potential dynamics of polities. We shall notably use Ertman’s work on past state-building, but making it adaptable to present and future conditions.