Creating a Foresight and Warning Model: Mapping a Dynamic Network (I)

Map, graph or network as model:

Once an initial question is defined – in our case, what will be the future of the modern nation-state for the next twenty years – most methods start with building a strategic foresight and warning model, which will describe and explain the issue or question at hand. In other words, we construct our underlying model for understanding. As Epstein underlines, making explicit models is nothing else than explicating the hidden model we, as human beings, are using when thinking. Furthermore, in terms of analysis and more specifically intelligence analysis, making the model explicit will help first identifying various unconscious biases, thus allowing minimising them. It will then help defining areas of uncertain understanding, which can then be marked for further research.

What is a map, graph or network?

Most futures or foresight methods start looking for variables (also called factors or drivers) that are part of their model. A variable is a symbol or symbolic name that stands for a value that may vary. Some methodologies then link those variables. The link between two variables represents an influence (A influence B), most often causality. For example, in a model on demographics, one might have as variables birth rate and total population, and a link from birth rate to total population.

Whatever the question at hand, the construction of the model must be grounded in science, i.e. accumulated knowledge and understanding. Brainstorming sessions are crucial but should not dispense with using what others have understood beforehand, even if debates exist. Ideally the model should also be regularly updated to consider new findings.

One may see such maps, for example, in the British foresight product, Dimensions of Uncertainty done by the Foresight department of the Government Office for Science (2010?), notably Annex A.

Actually, maps are nothing else than graphs or networks – in our case directed graphs – and thus will benefit from the long scientific history that is attached to them, from Graph Theory, as graphs started being studied in mathematics with Euler in 1735 to the more recent Network Science. The development of the field has seen the emergence of new tools, such as network visualization software that greatly facilitate working with and on networks. Gephi, open source software, has been used here for the development of the underlying model, considering both its ease, its flexibility and yet its power.

The map and its use

Once the model is built, it is used to develop the scenarios that will constitute the history of Everstate, notably thanks to ego networks as will be explained in a few weeks. It will also give the indicators that are necessary for warning. Were capabilities available, it could be a step towards developing proper simulations that could then be mixed with the narratives.

The map itself, if it is seen as a whole by neophytes, may appear as complicated and difficult to use. It is however not so. It is just a tool and as all tools it demands understanding and training. Computers or mobile phones are far from being simple and yet they are now almost universally used. Once mastered, working with networks greatly facilitates the task of the analyst. It can be used as reference and give support to analytical conclusion, as statistics, trends or indications do. It is indeed one of the purposes of the Chronicles of Everstate to show how simple using a map for strategic foresight and warning is.

In terms of analytical management, a map is an investment. Indeed, once a graph has been properly built for a specific issue, it will most likely remain valid for a large period of time, especially if it is regularly updated with scientific findings. It can thus be used again each time the issue it covers comes into play. For example, if one wanted to do some foresight and warning on pandemics, the future of nuclear energy, of weapons of massive destruction (WMD), or cybersecurity, then at one stage or another the dynamics linked to state and government would have to be introduced and thus the map constructed here for the future of the state could and should be used again.

Constructing the initial model

The core ideal-type model

Rather than attempting to build from scratch the overall graph in all its complexity, it is easier to start building a minimalist core ideal-type model. This core graph will allow understanding the fundamental dynamics at work and then will be used as basis for developing the full model.

In the case of the future of the nation-state, I have started from Weber’s ideal-type, which gives the following graph.

This approach to understanding politics, which, obviously must include the population, a variable so often forgotten, would have helped understanding the 2011 uprisings in North Africa and the Middle East as well as the more recent protests in Europe and the Americas. We may only assume, with hindsight, that, had it been applied to classical F&W countries’ analysis, the likelihood to have been able to foresee the events would have been greatly heightened.

Including dynamics

As the graph shows, s0 (“step 0”) and s1 (“step 1”) have been added to variables, so as to include a dynamic dimension. Indeed as the model was being constructed, tested and revised, it appeared that using uniquely broad static conceptual variables was inadequate. The system constituted by the polity evolves; each action has consequences; the aggregation of all actions, reactions and consequences, as well as creativity, lead to evolution and change…. Read more next post.

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Image: Human interactome -CC BY-SA 3.0 Keiono – self-made. Dataset created by Andrew Garrow at Unilever UK. via wikimedia commons.