(This article is a fully updated version of the original article published in November 2011 under the title “Creating a Foresight and Warning Model: Mapping a Dynamic Network (I)”). Mapping risk and uncertainty is the second step of a proper process to correctly anticipate and manage risks and uncertainties. This stage starts with building a model, which, once completed, will describe and explain the issue or question at hand, while allowing for anticipation or foresight. In other words, with the end of the first step, you have selected a risk, an uncertainty, or a series of risks and uncertainties, or an issue of concern, with its proper time frame and scope, for example, what are the risks and uncertainties to […]
2018 could be the year when the U.S. takes back the lead over China with the most powerful supercomputer in the world. It could be the year when the AI-power war over computing power started. 2017 is the year when Artificial Intelligence started creating Artificial Intelligence (AI). It is the year when China overtook the US […]
Here we shall present the drivers and forces behind the current exponential development of Artificial Intelligence (AI). Deep Learning, a sub-field of AI, leads this expansion, as we explained in “When Artificial Intelligence will Power Geopolitics – Presenting AI” (open access) and in “Artificial Intelligence and Deep Learning – The New AI-World in the Making” (semi-open […]
This series of two articles focuses on the current development of the Russian Arctic region, while explaining and demonstrating the importance of using strategic thinking for governments as well as for business actors. Indeed, the international dynamics of geopolitical and environmental changes, including their interactions, are becoming so rapid and powerful that political and business actors have […]
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.