Everstate: Setting the stage (2)

Summary of our scenario so far: Everstate is an ideal-type for our very real countries created to foresee the future of the modern nation-state. In the case of this specific scenario-building, we are setting the stage for Everstate, by attributing values to key influencing variable to be able to develop the scenarios.

As previously set, Everstate is thus a middle-range power located on the Eurasian land mass and part of the international liberal order. It is ruled under a democratic… Read More

Constructing a Foresight Scenario’s Narrative with Ego Networks

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.

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Variables, Values and Consistency in Dynamic Networks

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.

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Revisiting influence analysis

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.

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