Variables, values and consistency in dynamic networks

In this post we shall explain and discuss the methodological background that allows us to set the criteria for Everstate – or for any country or issue chosen for foresight analysis – as exemplified in the post “Everstate’s characteristics.”

Variables, Values and initial criteria

Values must now be attributed to each selected initial criterion, as would be done with Morphological Analysis. However, here, those values will be those corresponding to the present and not to the future (as in Morphological Analysis).

If the foresight were done about a specific known country, then it would be (relatively) easy to attribute real values for the selected criteria. In our case, those values will help us creating Everstate, putting flesh on our ideal-type and starting making it look real. A similar approach could be used for any issue. It is not limited to the future of the nation-state.

Even if we are working with an ideal-type, we nevertheless must remain in the realm of the plausible and thus values must not be far-fetched. However, we must also make sure that we are not prey to our various biases when choosing values.

High, medium and low values can be selected to cover a broad spectrum. Our variables are most of the time continuous variables, i.e. they can take an infinite number of values. However, borrowing from Bayesian Networks, we discretise variables, i.e. we transform continuous variables into discrete ones by using ranges, or intervals (e.g. rather than use for oil prices o to +∞, we use intervals such as x≤30, 30<x<70, etc.) (Fenton and Neil, 2007). The use of notions such as high, medium, low, for example, corresponds to the discretisation of variables that are not typically quantitative.

Consistency and dynamic networks

Now, and again as with Morphological Analysis, consistency must be checked, i.e. we verify that a variable can take value x while another can take value y. For example, in our case, one could not have at the same time a very high level of domestic escalation (Everstate is on the brink of civil war) and a geopolitical position of strong power rising (at least in terms of country; if we talk about factions or political movements, then the conclusion would not be the same).

Compared with Morphological Analysis that systematically checks all pairs (or rather half of all pairs), because its variables are not linked, our task is greatly simplified. We only have to check all pairs of values for variables that are linked. This considerably reduces the task, at least for the variables chosen as initial criteria.

Typically, inconsistent scenarios are discarded and consistent sets chosen. Then narratives are developed for those specific scenarios, attributing one fixed value to each variable.

This approach cannot be used here, except when setting initial criteria, as we have introduced a dynamic element. Indeed, because we deal with living systems that are by nature always evolving, the value for one variable will most probably change with time, considering the interactions of all the other variables’ values. Furthermore, new unexpected values may appear. Ideally, if values have been properly entered this novel occurrence should not be possible, but, remember, we deal here most of the time with qualitative variables and it will be extremely difficult to cover the whole range of potential values, considering often limited imagination as well as biases.

Part of the interest of the method developed here is to ber able to follow the very evolution of the variables’ values, which will help us determine impact and, ideally, timelines, evaluate alternative policy options and thus will lead to a fully actionable foresight.

We should nevertheless pay attention to the imperative of cross-consistency. We encounter here a formidable obstacle. As seen in “Revisiting influence analysis,” other methodologies reduce the number of variables. Thus, they only look at specific values and their cross-consistency for a very limited number of variables. On the contrary, we keep all variables. Thus, logically, we should systematically enter values for each and every variable and then check the consistency across values for each variable, and if a value appears over time, recheck the consistency.

The task is enormous and cannot be done systematically – except with very large resources. The imperfect solution chosen here, but that should be “good enough,” (Fein, 1994) will be to attribute values and pay attention to consistency while constructing the narrative using ego networks, as we shall see in a next post. Consistency will thus not be ignored but checked progressively. This heightens the role and responsibility of the analysts who will develop the scenarios, as they will need to have the related training and inner qualities.

Fixing values for the initial criteria

As we start here with present criteria and not future ones, we are not confronted, at this stage, with the difficult task of selecting sets of values and determining how many scenarios should be prepared. This will come later on as we shall see in the course of the scenario telling.

As Everstate is an ideal-type that aims at being as representative as possible of existing countries, a plausible medium range set of values will be selected.

Readers interested in other specific conditions will be able to run the whole process again, changing values where necessary.

This selected set of values will allow constructing the first narrative that will set the stage for the scenarios on the future.


Fein, Helen, ‘Tools and Alarms: Uses of Models for Explanation and Anticipation”, Journal of Ethno-Development, Vol. 4, No. 1, 1994.

Fenton, Norman and Neil, Martin, Managing Risk in the Modern World Applications of Bayesian Networks, A Knowledge Transfer Report from the London Mathematical Society and the Knowledge Transfer Network for Industrial Mathematics, (London: London Mathematical Society, Nov 2007).

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