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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 my investment portfolio within the next 18 months to 3 years, or what will be the future of the emerging artificial intelligence world over the next twenty years, or what are the risks and uncertainties to my activity within the next fiver years as a result of China’s rise.

Once this initial question is defined, the second stage is about constructing our underlying model for understanding, i.e. mapping our risk or issue.

As Professor Joshua Epstein underlines, constructing a model – i.e. modeling – is nothing more, actually, than making explicit the hidden, implicit, model we, as human beings, are using when thinking. Epstein lists 16 advantages that result from this explicit modeling, to which we can add a couple more. Among these, we can notably highlight that, in terms of intelligence and anticipation analysis, making the model explicit will help identifying various cognitive, normative and emotional biases, thus allowing for their mitigation. Thanks to this modeling we can think out of the box and overcome silos. Then, the model and its construction will help defining areas of uncertain understanding, which can then be marked for further study, inquiry and research. Meanwhile, an explicit model will also help us working collaboratively, while communication will be greatly eased and enhanced, notably by using tools developed for big data analytics.

How are we thus to transform our inner implicit and imperfect model about the risk and uncertainty of concern into a proper and efficient explicit model to assess correctly risks and uncertainties, design critical responses and communicate about both risks and the decisions taken to manage them?

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This post will focus on a third analytical challenge at the core of the foresight and warning process, the fact that actors and “factors”, or rather variables, are often mixed together. Using the example of the unfolding crisis in Ukraine, the first post of the series explained how to map a strategic foresight and warning question, notably how to move from factors to variables and the second underlined the importance to define and name the actors relevant to the question as objectively as possible and suggested ways to do it. The “black box” actor As we recall from the last post, during the first steps of a mapping for the future evolution of the crisis in Ukraine, both factors or rather variables and actors would … Continue reading Actors and Factors In Future Threats Analysis (3) – The Crisis in Ukraine

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This series of posts deals with the core and basis of the foresight and warning analytical process, explaining it while stressing three most common challenges analysts and participants to workshops face: identifying factors correctly (this post); specifying actors objectively (2-); overcoming an inadequate mix of “actors and factors” (3-). Practical ways forward will be suggested. The example that will be used as case study throughout those three posts is the 2013-2014 crisis in Ukraine, with, as corresponding strategic foresight and warning (SF&W) question, “What are the possible futures for the Ukrainian crisis over the next two years?” Compared with our previous methodological series, these posts may seem to address more basic problems. However, as workshop after workshop, participants, be they … Continue reading Actors and Factors In Future Security Threats Analysis (1) – the Crisis in Ukraine

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This article is the fourth of a series looking for a methodology that would fulfill the challenging criteria demanded by our time. Having clarified with the last post the approach and mindset for the building of our scenarios, we shall now move to the practical part, how to do it, focusing here, in this post on scenarios for war, before moving to scenarios for situations qualified as non-violent crises with the next article. Mutually exclusive scenarios As a preamble, it is necessary to emphasize a crucial rule. To quote Glenn and The Futures Group International: “When a set of scenarios is prepared, each scenario usually treats the same or similar parameters, but the evolution and actual value of the parameters described in each scenario are … Continue reading How to Analyze Future Security Threats (4): Scenarios and War

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This article is the third of a series looking for a methodology that would fulfill the challenging criteria demanded by our time. We shall now focus on scenarios, which are a way to simulate how the actors we defined and described during the previous step interact, not only among themselves but also with their environment, up until the end of the chosen timeframe. Using the precedent post’s game of chess analogy, with scenarios we imagine the various ways the game may “end”. Related Towards an Operational Methodology to Analyze Future Security Threats and Political Risk (1) Methodology to Analyze Future Security Threats (2): a Game of Chess How to Analyze Future Security Threats (3): Scenarios as an Organic Living System How to Analyze Future … Continue reading How to Analyze Future Security Threats (3): Scenarios as an Organic Living System

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This article is the second of a series looking for a methodology that would fulfill the challenging criteria demanded by our time. Previously, we saw that a single “story” initially told at a general level, the political dynamics that are at the core of a polity, could be used to build the very specific model needed to answer a strategic foresight and warning (national security) question or a political risk interrogation. Very practically, how shall we do that? How are generic dynamics going to help us with our task? How can we proceed? This is what we shall see now. Related Towards an Operational Methodology to Analyze Future Security Threats and Political Risk (1) Methodology to Analyze Future Security Threats (2): a … Continue reading Methodology to Analyze Future Security Threats (2): a Game of Chess

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Global Trends 2030 compares our current transition period with 1815, 1919, 1945 and 1989. Yet we have not known recently any global systemic war. Thus why choose such a comparison? What could explain such a puzzling choice and what could we learn from it, for our understanding of the world and its potential future(s)?

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The initial variables chosen to start building our scenario are the five most important variables according to Eigenvector centrality, as explained in Revisiting influence analysis. We shall now choose values for each criterion. Consistency is then checked, but only for the variables that are linked (see the consistency matrix). As we aim at finding a plausible and average, mild set of initial criteria, we shall start from the following set, which is also intuitively representative of the situation, real or perceived, in which many real world countries have found themselves for a couple of years. We then verify that the chosen scenarios are consistent with the consistency matrix. Even if the aim is to obtain timelines that are as precise … Continue reading Everstate’s characteristics

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Variables, Values and Consistency in Dynamic Networks

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.

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Revisiting influence analysis

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.

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