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Constructing a foresight scenario’s narrative with Ego Networks

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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.

Ego network

An ego network is, basically, the network that surrounds or is centered upon a single variable or node, called, in this case, an “ego.” This network will be the backbone of our narrative.

The depth of an ego network is the length of the path between the selected ego and a linked node or variable. An ego network of depth 1 will thus display all variables or nodes that are linked to the selected ego by only one edge (link, arrow), either incoming or outgoing. If we take the example of the network centred on the ego “country’s geopolitical position s3” (reminder: s3 means step 3 in our dynamic mapping), which is the first variable we shall use to set the stage for Everstate, then we obtain the graph above.

An ego network of depth 2 would show all variables linked to the ego with a path between each variable and our ego equal to a maximum of 2 edges (for an ego network centered around variable A, examples of paths of length 2, or 2 edges are A->B->C and D->F->A). Always using the same example, we would have a much larger graph for an ego network of depth two, as shown on the left hand side.

Save for rare exceptions, we shall use ego networks of depth 1. The analyst can try different depths for her/his ego network and choose the depth that allows her/him to tell the clearer story.

Working with Ego Network in Gephi

Once the overall graph is constructed, it is extremely easy to obtain any ego network with Gephi.

In “Overview,” as shown on the screenshot below, select “filters” on the right hand side, and choose “Ego Network” in the section “Topology.” Then drag with the mouse “Ego Network” in the bottom right hand window “Queries.” In the “Node ID,” enter either the name of the variable you want to use or its ID, here 34. Then press OK and filter. The ego network redraws itself automatically in the “Graph” window. You can then apply any layout (bottom left hand window).

The complete network is accessed by clicking again on filter. One can then change ego or variable, or proceed with other tasks.

It is best to re-run the usual layout between filtering for two different ego networks to be able to benefit from the best possible visualisation for each ego network.

Using Ego Network to write the narrative

With one ego network

To start telling the narrative for a scenario, then one needs obviously to begin with a variable to which a value will have been attributed.

Continuing with the same example, we have set as variable “country’s geopolitical position s3” the value “medium range power,” as explained with the previous posts (Revisiting influence analysis and Variables, values and consistency in dynamic networks). This variable will be chosen as ego for the Ego Network (depth 1).

Then one uses one after the other linked variables to tell the story, each time attributing a value to that variable. Cross-consistency will need to be checked mentally and the analyst will have, of course, to remember the values s/he attributed to make sure the overall story is consistent and logical.

Here the first variables affecting our ego are “country geopolitical position s2,” “geographical location” “ecological setting” “new external military threats s3.” Thus, the corresponding narrative, as shall be fully seen in the post “Setting the Stage,” runs as follows (variables are inserted between brackets for the sake of explanation):

“As a medium state power [country’s geopolitical position s3] located on the Eurasian land mass [geographical location], Everstate had not seen its geopolitical position fundamentally altered since the end of the Cold War, and even since the end of World War II [country geopolitical position s2]. However, recently, some tensions had begun building up and Everstate had to start contending with them as they could easily transform in very concrete new external military threats [new external military threats s3].

What had contributed to maintain its geopolitical position were different factors. If the impact that its ecological setting could have had on its geopolitical position was remote and long forgotten, it nevertheless played a part [ecological setting]. Similarly, its continental climate, soften for the southeastern part by the influence coming from the sea was not seen as a factor influencing geopolitics anymore [geographical location, ecological setting]. The harshness of the snowy and mountainous North had long been seen as a bounty for tourism [geographical location, ecological setting]. The large river crossing the country from Northwest to Southeast was seen from the perspective of industry, trade and tourism and no longer as a possible way in for invaders [geographical location, ecological setting]. Finally, it had been centuries since the rich agricultural eastern plain had not attracted invaders or greedy neighbours looking for rich lands [geographical location, ecological setting].”

We can now move to the next group of influencing variables :

“Everstate’s army was performing, considering military techniques, expertise and previous experience, even if its size had been reduced [army’s size and performance s3].” Etc.

One then describes all the values for each variable, paying attention to the types of links, either cause or impact.

Moving from one ego network to another

When one sets the stage for the ideal-type (here Everstate), then one moves from one criteria initially selected to another, using each of them as ego, while the related network is used to develop the narrative. There is thus no difficulty regarding the choice of the next ego network.

Once this task accomplished, then one starts a new phase of the scenario building, really telling the story.

The variable that must be chosen to begin narrating the story depends upon the understanding the analyst has of the overall dynamics for the issue at hand. In our case, I chose, as shall be seen in a later post, the variable “pop level of satisfaction (sec) s3”  (the level of satisfaction regarding security as felt by the population – step 3) because it is crucial for understanding the overall dynamics of a polity. However, another analyst could have chosen to start with another variable. As no variable has been removed from the graph, all variables will be used anyway.

The variable is then used as ego, as explained previously. One starts with the influencing or causing variables. For each causing or influencing variable, to obtain details on this variable and thus better develop the narrative and explain the dynamics, the analyst will be able to use it as ego network. This will allow her to better flesh out the story.

Once causing variables have been detailed and a coherent story developed, then the analyst must move to the impacts or consequences. Each impact will be used as new ego, and a new paragraph or part written using the network of this ego.

The process is repeated until completion of the scenario.

Exception: groups or clusters of variables

For some variables that appear as tight groups or clusters, it makes more sense to develop a narrative including both influencing and influenced variables. In those case, then one shall tell the story for the whole group in one or two paragraphs. The task of detecting those groups of variables is eased by the use of network visualisation tools as those groups are literally shown by the lay-out (here Force Atlas).

Ultimately, it will be up to the analyst to decide how to tell the story according to which outline and how to handle the variables for the best possible result.

As we progress with the Chronicles of Everstate, the reader will become familiar with the method. To help the reader, the first post dealing with Everstate’s future, “The Chronicles of Everstate (2011 EVT – 2012 EVT): Discontent” (to be published on January 15), will also serve as example. It will recall briefly the methodology and the words corresponding to an influencing or influenced variable or node for the main narrative will be in bold.

Ego networks, an analyst’s weapon

Using ego network to develop the narrative is not only a support but it also helps ensuring that neither influencing factors nor impacts are forgotten. It allows fighting against many biases and gives a structured framework and outline to the analysis, thus assisting the analyst.

It will facilitate verification, revision and discussions among various analysts.

Furthermore, assuming the model has been developed scientifically, it can be used as proof or evidence of the validity of the scenario, which is crucial to obtain the trust of policy-makers and decision-makers, or more largely of all potential users of the strategic foresight scenarios.

* For example: Andrew Curry & Wendy Schultz, “Roads Less Travelled: Different Methods, Different Futures,” Journal of Futures Studies, May 2009, 13(4): 35 – 60; Jerome C. Glenn and The Futures Group International, “Scenarios,” The Millennium Project: Futures Research Methodology, Version 3.0, Ed. Jerome C. Glenn and Theodore J. 2009, Ch 19; Tom Ritchey, “Morphological analysis,” The Millennium Project: Futures Research Methodology, Version 3.0, Ed. Jerome C. Glenn and Theodore J. 2009, Ch 17.

Variables, values and consistency in dynamic networks

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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.

References

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).

Revisiting influence analysis

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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.

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.

Ranking analysis

When the foresight methodology does not include links between variables, thus if we don’t have a graph or a map, then the way to select variables is by ranking them according to specific criteria. Among the criteria most used, one finds likelihood and impact, or impact and uncertainty (i.e. one does not know how the variables will evolve). However, in a complex world, which includes feedback loops and where ripple effects exit, not linking variables is a serious handicap.

Influence analysis

When variables are linked (as here), the method most commonly used is to identify the most influential variables – influence analysis – and then to reduce the foresight analysis to those variables. Hence, scenarios will be constructed around those influential variables. There are different ways to proceed.

Some systems, such as Parmenides Eidos™ and all similar approaches, e.g. Singapore RAHS 1.6 (watch the technological demonstration video, especially from 3:32mn onwards), use what graph and network theory call indegree and outdegree centrality measures. The indegree of a node (our variable) is the number of head endpoints adjacent to the node, i.e. how many edges (arrows) arrive to this node. It represents the number of variables that influence this node. The larger the indegree, the more influenced the variable is.

The outdegree of a node is the number of tail endpoints adjacent to the node, i.e. how many edges (arrows) leave from this node. It represents the number of variables that are influenced by this node. The larger the outdegree, the more influential the variable is.

However, as underlined by Arcade, Godet et al. (Structural Analysis with the MICMAC Method, 2009), this method only considers direct influence. What happens if one variable exerts only a single influence on one group of variables, but if this group of variable exerts a strong influence on the whole system? The importance of the initial variable would be downplayed if we were considering only direct influence.

In graph and network theory, various measurements – centrality measures – exist that allow identifying various types of importance of a node in relation to the whole network or graph. However, those measurements were created initially with social analysis in mind, not for foresight analysis.

8 most important nodes according to Eigenvector centrality measures

After having tested them against the direct influence idea and with foresight in mind, the measure that is chosen here to determine the initial set of criteria is the Eigenvector centrality. Again, using Gephi allows for easy and instantaneous calculation of all centrality measures.

We should however underline that we lose here the information differentiating between influenced and influential nodes. This is why graph and network analysis use an array of measurement and not a single one, and why we used here Eigenvector centrality measures against indegree and outdegree when selecting Eigenvector centrality measures.

Further tests should be designed to refine the choice of measurement for this revisited influence analysis.

The influence graph

Influence graph

Once influence (or degree centrality in graph and network theory) is measured, the variables are positioned on a graph, the “influence graph,” with as abscissa (x axis) dependency or influence received, and as ordinate (y axis) influence.

This can be easily done with Gephi by choosing the layout called “Geo Layout” and entering, in the case of the degree measurements, “indegree” for longitude and “outdegree” for latitude.

The location of variables on the graph expresses their type of influence and they are labelled accordingly:

  • Top left quadrant – most influential variables: drivers (usual) or influent variables (MicMac method, Godet)
  • Top right quadrant – most influenced and influential nodes: pivots (usual) or relay variables (MicMac method, Godet)
  • Bottom right quadrant – most influenced variables: outcomes (usual) or depending variables (MicMac method, Godet)
  • Bottom left quadrant – neglected variables, considered as less important in strict influence analysis terms.
  • The MicMac method of Godet adds further distinctions, as shows in red on the graph.

Influence graph (degree) with Eigenvector centrality (size of the node) for the entire graph

Once variables have been sorted out according to influence, then the variables that are seen as most important are usually selected and used to proceed to the next step. For example, on may redraw a map using only the drivers, pivots and outcomes and then move to creating foresight scenarios or to Morphological Analysis with those selected variables.

However, as seen, foresight methods usually start from an explicitly limited number of variables, which allow them to select at the end of the influence analysis between 2 and 10 variables. Here, on the contrary we use as many variables as needed for the map to be a good enough model, and thus the influence analysis, whatever the measurement chosen, does not lead to the easy selection of this very limited number of variables.

Furthermore, the selection of only those variables, if it can seem helpful initially, becomes a disadvantage when we move to the creation of scenarios. Experience shows that it is practically impossible to construct a serious narrative with only those selected variables. When constructing narratives, one automatically and unconsciously reintroduces other variables that had been previously eliminated. As those variables are now reintroduced unsystematically and without guidelines, the door is opened for any kind of mistake and biases can easily creep in.

Finally, it is scientifically absurd and wrong to dispense with one variable, when we know it is there. It could lead to erroneous foresight and then warning. Furthermore, in the case of warning, ignoring variables could deprive us of crucial indicators and thus impede the overall warning on the issue at hand.

Revisiting influence analysis: from reduction to setting initial criteria

Hence, in the methodology used here, we shall do otherwise. We shall NOT reduce the number of variables but use a method that could be called propagation and is made possible by the existence of ego networks in social network analysis.

Does it mean that we can fully dispense with influence analysis? The answer is evidently no – or this post would not have been written – but we shall use it for a different purpose.

We shall use influence analysis to set the initial criteria that will give us the point of departure for constructing the narrative of our scenarios.

According to cases, between 5 and 10 variables can be chosen as initial criteria. The rule of thumb is to try to determine variables truly standing out. Visualization is here very helpful. If the software used is Gephi, then one will also be able to choose various features such as filters allowing selecting various ranges of variables according to measurement. As no variable has been suppressed, fixing the number of initial variables is not a crucial problem and cannot lead to major mistakes. It is more a matter of convenience to be able to start telling the stories of the future (narrating the scenarios).

Cite as Helene Lavoix, (2011), “Revisiting influence analysis,” The Chronicles of Everstate, Red (Team) Analysis, http://wp.me/p1S3g8-50.

References

Arcade, Jacques, Godet, Michel, Meunier, Francis, and Roubelat, Fabrice, “Structural Analysis with the MICMAC Method & Actors’ Strategy with MACTOR Method,” The Millennium Project: Futures Research Methodology, Version 3.0, Ed. Jerome C. Glenn and Theodore J. 2009, Ch 11.

Glenn, Jerome C. and The Futures Group International, “Scenarios,” The Millennium Project: Futures Research Methodology, Version 3.0, Ed. Jerome C. Glenn and Theodore J. 2009, Ch 19.

Hanneman, Robert A. and Mark Riddle. 2005. Introduction to social network methods. Riverside, CA: University of California, Riverside ( published in digital form at http://faculty.ucr.edu/~hanneman/ )

Ritchey, Tom; “Morphological analysis,” The Millennium Project: Futures Research Methodology, Version 3.0, Ed. Jerome C. Glenn and Theodore J. 2009, Ch 1.

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

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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 strategic foresight and warning methods start with building a model that 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: The Seven Bridges of Königsberg, by Bogdan Giuşcă (Public domain (PD), based on the image, GFDL or CC-BY-SA-3.0, via Wikimedia Commons

Creating a Foresight or Warning Model: Mapping a Dynamic Network (II)

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[From Part I: 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.]

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.

Steps s0 and s1 will be used for the initial, simpler model. Then, what happens during s0 and s1 leads to the “evolution of society,” which thus starts the second step, s2. The hypothesis here is that we have a successful political organisation that provides the necessary security to the people. As a result, various developments take place, notably involving creativity, innovations, etc. The variable “evolution of society” (in red in the graph) is thus a cluster variable for all those developments that are not included in the graph. With s2, we shall build a more advanced model, representing the modern state. However, s2 will not display potential domestic escalation and stabilization. The underlying hypothesis for s2 is that at the end of s2 the overall socio-political model has not changed but starts showing signs of increasing inadequacies.

For s3 (step 3), we shall have the same model as for s2, but here we shall include variables related to potential domestic escalation or stabilization. Indeed, if the existing socio-political organization finally proves itself to be adequate or if it is changed in a timely fashion, then it will be possible to stop escalation, solutions can be found and finally there are possibilities to stabilize the situation.

Finally, s4 will focus on a potential failure of the s2s3 type of socio-political organisation. Actually, with s4 we shall also change scale as not all variables existing in s2 and s3 are replicated, for the sake of simplicity and clarity.

Ideally, if we had a simulation in mind, or if we wished to insert agent based modelling inside our larger conceptual framework, then n steps should be included and all variables used for each step.

Furthermore, network software give us the possibility to add a time component to a graph, as time can be attributed to each link between two variables.

The possibility to work in this direction is a very promising way forward to improve SF&W analysis and sufficient interest and funding should be made available to allow including this component. However, social science in general, international relations and political science in particular have not focused upon time. Effort should thus be made here, explicating the time factor when it is there, complementing existing findings when it has not been considered to allow for the proper, scientific inclusion of the time factor.

Adding nodes and sub-graphs

Having now our core fundamental model on the one hand and our broad dynamic structure on the other, we must progressively add the variables or groups of variables that are missing. For example, the core interactions take place within a milieu and against a normative backdrop that must both be considered. We now obtain the following graph, which is still considerably simple, with the nodes representing the milieu in green and the normative variables in violet.

One may also realise that some variables are actually generic and represent cluster of variables. For example, the variable “ruler,” which was indeed very convenient when starting our model, needed to be developed to be representative of our current polities. Thus for s2 and s3, to be as accurate as possible, the ruler was replaced with its corresponding nodes, using notably Susan E. Scarrow’s work, which gave the following subgraph.

There is no best or easiest way to add nodes, sub-graphs or develop a cluster: variables existing in both core graph and subgraph will serve as pivot and care will be taken not to have twice the same variable, then all links and dynamics must be rechecked.

Decision to detail or not a node will remain with the foresight analyst and depend upon the question as well as upon the resources available. A map that is too simplistic will lead to erroneous foresight and thus should not be favoured. A map that would take too long to construct would also deny foresight. Thus a middle ground must be found.

Considering potential structural changes in the future

It is now time to envision what might happen to the ideal-type model of polity with time, and why, as this is the purpose of foresight.

Scientific historical knowledge tells us that war and the timing of its onset were some of the major causes for changes that led to state-building and, if we take the case of the fall of the Roman Empire, to collapse. However, political history, international relations and security studies have generally tended to focus on external military threats, while as a pendent, in the state security apparatus, security has by and large be seen as equal to external military threats.

Now, if we want to be able to envision the future as well as possible, we need to consider not only conventional variables but also unconventional ones. To be able to determine those supplementary variables, we need first to understand what they cover. Here, starting from the importance of war and its onset for prompting change, we may deduce that any type of pressure threatening the security of the polity will be cause for change, as, indeed, the society and its political authorities must adapt to face those pressures. Capability to adapt or not, which will vary according conditions, will lead to one or another type of outcome or plausible future. Using imagination, research, horizon scanning and, in a collaborative setting, brainstorming, will allow identifying various types of pressures that will then be included in the graph as new nodes. For example, the variable “evolution of society,” which starts s2, as seen previously, is a first intrinsic cause of pressure on the polity, as new phenomena must be integrated. The pressure is increased because evolution goes in the direction of an increasing complexity that political authorities must learn to harness. Each pressure identified is a cluster variable or group node that could – and ideally should – become a graph. Here, as our focus is the nation-state, we shall leave them as such.

Now, we also need to introduce the possibility for the appearance of new variables. For example, if we consider complexity theory, we know that complex systems generate emerging properties. Something that did not exist in the past emerges. For example, if we follow the modernist school of thoughts on nation and nationalism, as is done here, nationalism and nations in their current acceptance are a modern phenomenon that did not exist previously.

Such novelties correspond to a change of structure for a map. If the possibility for such new variables were not included, then the map created would most probably fail to envision some plausible futures. Only changes happening while the structure is fixed could be foreseen. For example, any foresight done during the Cold War – a stable period structurally – which would have focused only on Cold War related variables would have been unable to foresee the end of the Cold war and potential post Cold War futures. Indeed, if it had not included those “new variables” and processes, then it would have been unable to foresee changes once the structure changed. This is why it is much easier to practice foresight – and warning – when the structure is stable than when it is in transition as now.

How can we introduce the possibility for structural changes? One way is to add a node labelled along the line of “other types of,” then to explain the type of variable one refers to, and to fully include it within the map, with all necessary linkages. This generic variable may then be refined and divided into various more specific variables, still always allowing for something we did not think of at the time of the design of the graph and that may appear later, in one day, one month or one year, or that may be found somewhere else in the world.

In our case, we thus have a model that evolves under different kinds of pressures: previous pressures, cumulated and acting from a global level, new external military threats, new unconventional threats (those direct threats that have already been identified, such as cyber threats or bio weapons of mass destruction), cumulated/global unconventional threats (those unconventional threats that act at a global level), other kinds of pressure for survival (direct potential pressures that are generally not yet accepted or even identified). Known pressures such as peak oil (the end of cheap oil), global warming, biodiversity loss, etc. are covered by the cluster nodes. If need be, they can be detailed as subgraphs and the linkages previously identified for the initial cluster node will help integrating the subgraph into the overall map.

The potential for various changes of structure must be permanently kept in mind when constructing the map.

Conclusion

The overall dynamic map that is progressively constructed is the foundation for the entire strategic foresight and warning analysis and conditions the success of the next steps, and the quality of the various products that will be delivered.

In our case, the dynamic map looks as follows, and we shall see with the next posts how to work with it.

References

Epstein, Joshua M. “Why Model?” Santa Fe Institute Working Papers, 2008

Ertman, Thomas. Birth of the Leviathan : Building States and Regimes in Medieval and Early Modern Europe. Cambridge, UK ; New York: Cambridge University Press, 1997.

Zellman, Ariel Review: Birth of the Leviathan by Thomas Ertman, 2008.

Scarrow, Susan E. “The nineteenth-century origins of Modern Political Parties: The Unwanted Emergence of Party Based Politics,” in Richard S. Katz, William Crotty (eds), Handbook of Party Politics, London, Sage, 2006.