Second, “black swans” refer to events that could absolutely not be predicted, as, for example for the Economist in ”The prediction games: Our winners and losers from last year’s edition”. Unfortunately, in this case, the label “black swans” excuses foresight errors. It tends to stop explanations and evaluation. Similarly, some will make statements along the line of “oh, but there is no point to do any foresight (or futures work or forecast), did you not read Taleb’s Black Swan? One cannot predict or foresee anything.”
This is a rather bold statement, especially when one seeks to anticipate uncertainty and to foresee and warn. We thus need to explore the unpredictability claim further.
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.