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Useful Rules for Foresight from Taleb’s The Black Swan

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This second post on The Black Swan: the impact of the highly improbable by Nassim Nicholas Taleb emphasises some of the author’s points that could be useful to foresight and warning and all “predictive work. Many of those themes are not really new, but already integrated in F&W and, more broadly, analysis. Nonetheless, it is always useful to underline them, as it is so easy to forget best practice. (The first post can be accessed here).

Humility

humility, doubt(Notably pp.190-200) Considering uncertainty, but also our imperfect condition of human beings, the complexity of the social world, feedbacks, our more than insufficient knowledge and understanding, we must be very humble, accept our partial ignorance, our imperfection and mistakes (and make sure those essentially human flaws are accepted by others, which may be more difficult). Yet, we must also struggle to improve ourselves, increase our understanding and our capability to foresee the future. Doubts, humility, real dialogue between those different communities which try to understand the world, reflection upon mistakes – to correct what can be identified as wrong or inefficient – and successes – to reproduce what worked (according to conditions) – are keys for this improvement.

Taleb’s use of and reference to Montaigne’s wisdom also points to the importance of struggling against the loss of memory - institutional, scientific and general – that plagues us. Some things that were understood in the past are now misconstrued or ignored. It would appear, sometimes, that we are part of a race where youth, novelty, fads and shortening of time rule as masters. Yet, shouldn’t we pause for a while and wonder about this behaviour, and its origin. Should we not question the results stemming from this new race forward? For example, in science (soft and hard), it is not because something has been understood, discovered or written decades or centuries ago that it has become wrong. On the contrary, good science starts with knowledge and understanding of past scientific discoveries. Some understandings are outdated, but some are not. Novelty and justness of analysis are not synonymous, while discarding all the past only makes us lose time. Consumerism cannot and should not be applied everywhere.

“Black Swans events” (unpredictability, outliers)

As underlined last week, Taleb makes a distinction between “Mandelbrotian Gray Swans” (rare but expected event that are scientifically tractable, pp. 37, 272-273) and real “Black Swans events,” which are never identified in advance. From that we could make the following “rules”:

  • Making swans gray

Try to imagine as many improbable events as possible, initially suspending disbelief. This is already done; methods, however tentative, exist: wild cards scenarios (e.g. James A. Dewar, “The Importance of “Wild Card” Scenarios”); brainstorming; what if stories and narratives; use of alternative thinkers and thinking.

innovative idea, dangerous idea, wild card, gray swan

The key, here, is imagination and allowing oneself to go beyond groupthink, norms (institutional, social, cultural), belief-systems, even if ideas may feel dangerous (read for example “In defense of dangerous ideas” by Steven Pinker, Harvard college professor, cognitive scientist, July 2007). Then, and as suggested and explained by Dewar, because resources are limited and also because even Swans have to follow a few rules, those potential “gray swans” should be examined in the light of all the other rules. The least likely (or the most absurd) should be discarded. For example, we may always assume that gravity on earth could disappear, or that lambs will become carnivorous, yet, the chances are so minimal that we may choose to dispense with these situations. For those events that remain on the gray swans list, potential impacts can be estimated and highly improbable-high impact scenarios developed.

  • The absence of certainty

Because we may assume that the likelihood of the existence of Black Swans is very high, then we must consider them. This will influence our estimation of probability. We may just forget certainty. This may look like nothing, but I suspect that in the world of security and politics where the issues of power and control – including in personal terms – are so crucial, truly accepting uncertainty and insecurity is a major effort.

Continuing the struggle against biases

(pp.1-164) Cognitive biases being a fatality of human beings, the least we can do is being aware of them, and persist in our struggle against them. Using our increasing knowledge and awareness of cognition, we may continue applying and creating specific training and systematically incorporate related safeguards in methodologies and processes, from organization (for example people joining the exercise at different stage) to teams-composition (people with different background, psychological makeup, etc.).

Meanwhile, introspection and reflection should be promoted by and for those who deal with foresight, forecast and prediction, as, exemplified (here on the question of ethics and potential biases induced by “conflict of interests”) by this very recent post by Jay Ulfelder. “Introspective phases” could and should be included at different stages of the foresight process.

Opium, Dutch East Indies, anachronistic projectionThose phases should notably fight against the known phenomena of anachronistic projections and cultural projections. Anachronistic projections are usually done with regard to past history (judging past actions from the point of view of today’s moral norms; understanding the past through today’s lenses), which obscures understanding. For example, we currently struggle against drug trafficking, notably because this endangers our societies and is seen as morally bad. Yet, opium has been an accepted state activity at least from the 19th century to well into the 20th century. This does not mean going backwards in terms of norms or accepting things that are seen as morally reprehensible or are damaging or hurtful, but would help locating phenomena in their proper context, and thus focusing on dynamics, processes and understanding. This would also (ideally) help us move from an attitude that favours judging and casting blame, with all the power struggles and violence that this implies, towards a much more constructive behaviour, promoting understanding, preventing and healing.

A political scientist* gives somewhere a great trick that we could usefully apply: if you read (we can change it to think/say) somewhere the word “always,” then stop and think.

Not being prey to anachronistic projections would imply considering too the evolution of ideas and norms and setting time-dependent struggles within historical processes. Coming back to foresight, anachronistic projections may as well be done regarding the future. What does this imply? Can we devise methods to try minimizing them? How can we best proceed to include ideas, norms, and beliefs in our models?

Cultural projections are even better known and may be easier to consider. Not falling prey to them will demand knowing our own cultural sets of norms on top of, or even prior to, those of others (e.g., in the anticipation field, Werther, 2008). Just asking ourselves this question during foresight exercises could improve results. Similarly we must struggle against being victims of ideology. This does not mean rejecting this or that belief, just being aware of what influences us.

Finally, the impact of emotions on our cognition, emotional biases, should be considered, as human beings are definitely not rational, emotionless beings.

Falsification rather than confirmation

(Notably pp.55-61) The risks of induction, which are so important to us because so many of our analyses come from collected evidence, are linked to our tendency to seek confirmation rather than falsification (looking for an element, a fact, an event that would prove our hypothesis or explanation wrong). All our analyses – this is valid for all our explanations and understanding, not only for foresight and warning – should include an effort at falsification, however without denying confirming facts. We should always wonder:  which evidence, fact, should I look for to disprove my theory, analysis, estimation, conjecture?

Furthermore, this effort should be mentioned in the final anticipatory product (potentially in an appendix, according the specificities demanded by the delivery needs of the customer) to allow for follow-up and update. Meanwhile, indicators, specifically designed for falsification, should be created. For example, in foresight, if we have concurrent scenarios, the happenstance of an event or any indication showing that one scenario is becoming less likely should be considered and the set of scenarios should be revised accordingly.

Careful causality: “silent evidence”

silence, National Security, silent evidence, causality

(pp.100-121) Taleb starts first by cautioning against the dangers of applying causality when there is none or when “silent evidence” could potentially distort causal reasoning. “Silent evidence” is what we don’t know, cannot know, cannot hear, do not hear. To explain “silent evidence”, Taleb uses Cicero’s story: “Diagoras, a nonbeliever in the Gods, was shown painted tablets bearing the portraits of some worshippers who prayed, then survived a subsequent shipwreck. The implication was that praying protects you from drowning. Diagoras asked, ‘Where were the pictures of those who prayed, then drowned?’” Taleb, however, does not reject causality but encourages to use it with care and caution, trying to think about the possibility of existence of “silent evidence”.

Creation of new adapted quantitative tools

(The whole book) Except in the cases when they can be applied, the author warns us to distrust correlations, which furthermore do not allow for understanding (according to the famous “correlation does not imply causation“), linear trends and Gaussian distribution. Instead, research involving fractals and scalability, complexity theory (as done for example by the New England Complex Systems Institute) should be favoured. Building upon this, we can imagine creating new tools allowing for multi-disciplinary research, articulating, when necessary (there is no need to use something very complicated if a simple analysis or logic are sufficient), complex modeling, agent-based models, and mixing quantitative and qualitative assessments (notably including processes and feedbacks), and integrating within foresight methodologies. What should guide us is always the issue or the problem at hand.

* Unfortunately I cannot remember who wrote this, nor in which book or article. Initially I thought it was Benedict Anderson in Imagined Communities but after having skimmed again through the book, I cannot locate the citation, thus it could be Anthony Smith, or Eric Hobsbawm, or… if anyone knows, I would welcome the reference!

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References

Dewar, James A. “The Importance of “Wild Card” Scenarios,” Discussion Paper, RAND.

Pinker, Steven, “In defense of dangerous ideas”, July 2007.

Ulfelder, Jay, “Advocascience“, 27 January 2013, Dart-Throwing Chimp.

Werther, Guntram F. A., Holistic Integrative Analysis of International Change: A Commentary on Teaching Emergent Futures, The Proteus Monograph Series, Volume 1, Issue 3, January 2008.

Taleb’s Black Swans: The End of Foresight?

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Meteorids and Earth, Taleb, Black Swan Events, Black SwansSince Nassim Nicholas Taleb published his bestseller The Black Swan: the impact of the highly improbable back in 2007, “Black Swans” and “Black Swans events” have become part of everyday language. They are used as a catchphrase to mean two different things. First, as was the case recently in the Brookings interesting interactive “briefing book” Big Bets and Black Swans: Foreign Policy Challenges for President Obama’s Second Term, “black swans” represent high impact, low probability events, what is also known as wild cards.[i] Second, “black swans” refer to events that could absolutely not be predicted, as, for example for the Economist in ”The prediction games: Our winners and losers from last year’s edition”. Unfortunately, in this case, the label “black swans” excuses foresight errors. It tends to stop explanations and evaluation. Similarly, some will make statements along the line of “oh, but there is no point to do any foresight (or futures work or forecast), did you not read Taleb’s Black Swan? One cannot predict or foresee anything.”

This is a rather crucial assertion for us and it needs to be investigated.

What are exactly those Black Swans about which Taleb writes: they cannot be absolutely unpredictable and low probability events at the same time? Thus, what did Taleb really describe? After having read this book, should we just resign, abandon any work related to anticipation, and, instead, do something else? Or is there more to Taleb’s argument than that? Can we use this book to improve our foresight methodologies, consider deep uncertainty, yet without giving up?

This first post will review the whole book and see if it really points to the absurdity of foresight. The next one will outline some of Taleb’s points that could be more than useful to foresight, forecast, warning, and more generally all anticipation methodologies.

Black and Grey Swans

Taleb is interested in understanding better uncertainty and randomness, notably those “Black Swan events” defined as unpredictable (outliers), with an extreme impact and which are, after the fact, revised as explainable and predictable.

The existence of black swans is logical and even may be seen as obvious, but it does not imply that foresight is doomed, only that it cannot be 100% certain (for the anecdote, the ancient Chinese divination method, the Yi Ching, which used the tossing of yarrow stalks, always removed one stalk before the cast, to account for this unpredictability).

Black Swans and Pacific Black Ducks East Basin Lake Burley Griffin Canberra by Celcom, GFDL or CC-BY-SA-3.0, from Wikimedia CommonsIn a nutshell, The Black Swan denounces the problem and risks of induction, building upon David Hume and Karl Popper. Extremely briefly, an inductive reasoning runs as follows: all the swans observed are white, thus all swans are white… which is proven wrong when one black swan is spotted. Hence the danger of this reasoning, if it is not done cautiously. Incidentally, this is quite crucial for us as so many of our analyses come from collected evidences, and we should always keep the danger of induction in mind, but more on that with the next post.

The Black Swan attacks quantitative predictions made with a Gaussian distribution (or normal distribution or bell curve) when applied to a world that is increasingly complex, notably because of the evolution of social interactions, and includes unexpected events. Such a world should rather be understood through fractals, that would then allow us to anticipate those events that Taleb names “Mandelbrotian Gray Swans.” Here I trust the author because of the consistency and logic of his argument, while I don’t have the mathematical knowledge necessary to go more in-depth into fractals.

The Economist usage is thus correct, whilst Brookings foreign policy experts’ actually refer to ”Gray Swans.”

All doomed: forecasting, prediction, foresight, and also meaning

Taleb’s attack is directed at statistics and quantitative methodologies (correlations, trends, statistical forecasts, etc.), NOT qualitative ones, thus concerns only one part of “foresight.” Nonetheless, we are not safe yet, as Taleb also denounces a “narrative fallacy” and underlines various cognitive biases, which make us believe we can understand history or the present or try to anticipate the future, when, according to him, such endeavours are near impossible as everything is “ruled” by randomness or luck. For Taleb, most historical events with large impact are Black Swans, thus could not be predicted, not even with a low probability of success.

This brief summary would let us believe that, indeed, prediction, forecast, foresight, and even worse understanding and finally meaning are impossible human endeavours. Those stem from our human needs and cognitive make up. To believe in their possibility emerges from our lack of introspection and reflection.

This book is thus not only about anticipating the future, and more broadly the philosophy of science and epistemology but also about meaning. The specific narrative Taleb uses – intertwining personal experience and stories as examples, with logic, (sceptical) empiricism and references to philosophers, novelists and scientists – is part and parcel of the demonstration. Don’t even hope to skim through The Black Swan to understand it. It is here in the literary part of the book, in its very construction, that we find the key to Taleb’s work and to answering our question “is foresight doomed?”

The choice of hope over despair

The demonstration made by the author is extremely well done, very consistent, save for one single paragraph. Our very smart author could not be unaware that someone, somewhere, would tell him: “Hey, wait a minute, if you say that nothing can be predicted and understood, that something random and unexpected (the famous black swan) may always happen, then it may also happen that your argument will be proven wrong by something unexpected or something you do not know.”

Taleb had thought about that, but used something quite akin to a sophist argument (p.192-193, hardback edition): He started by explaining the “Black Swan asymmetry,” which allows you only to assert, for example that “all swans are not white” (nothing more but nothing less), stops his reasoning and then quotes Popper, who, asked about “a possible falsification of falsification,” answered that his questioner was an idiot. Conclusion, the reader does not dare anymore to question Taleb (or Popper or anyone for that matter) for fear of being an idiot. Well done, but unfair, and somehow a shame to use such a device because some new insight could have emerged. Actually, I don’t really care about being named an idiot, but as a reader, I would very much prefer, if I don’t understand, to be explained why, and if I do, to see the author pushing his argument further.

This does not challenges most of the points made in the book, but allows questioning its overall conclusion. Thus, foresight might still be possible, should the impossibility to understand the world, the “narrative fallacy” be questionable too.

If human cognitive biases are numerous, witness the amount of research and findings of cognitive sciences, and as wonderfully synthesised by Heuer, if indeed our knowledge and understanding of the world through historical, political and more broadly social science is most of the time imperfect and still minimal, Taleb here, despite his huge erudition, seems himself to be prey – as all human beings – to the problem of induction, to generalization and to seeking confirmation as validation. Only one properly foreseen event should be sufficient to make foresight and understanding not impossible.

I’ll give here an example (because it is simple, but we could quote many others, as Jay Ulfelder correct 2012 forecast of coups in Mali and Guinea-Bissau or all the correct Economist 2012 predictions etc.). In 1919, Max Weber explained and underlined the main characteristics of the State, including the crucial importance of the legitimate monopoly of violence.[ii] Armed with this knowledge and common sense, and referring to the second Iraq War, it comes that if one destroys the State, it is very highly likely that civil war will occur. It could thus have been (very easily) anticipated that if one destroyed the Iraqi State (through the Ba’ath party), then civil war would more than probably occur, something that was carefully avoided in Germany after WWII despite the “denazification goal.”[iii] This is NOT a reinterpretation of facts after the events, but could have been made beforehand. Why this was not done or rather not heard is another story, let’s not confuse issues. We have here at least one instance of a potentially accurate anticipation (actually many with the other examples). Thus foresight is not impossible.

Of course, something completely unexpected may always happen, forbidding certainty. Of course, our imperfect knowledge and understanding will only at best help us outlining possible futures. Yet, in the meantime, do we have the right to disregard an understanding that could save lives and protect our security (individual, national, and global)? Using our previous example, the knowledge on State and its processes could definitely be helpful in assessing needs and potential impacts before to decide about austerity policies and IMF backed structural adjustment policies as was done worldwide with disastrous consequences, before to enter post-conflict countries and determine strategies. It would be crucial in evaluating what is happening in countries such as those that made the Arab (winter)-spring, and the evolution of situations where the international community intervenes,  such as Afghanistan.[iv] Do we have the right not to work towards improving our understanding? We have to live with uncertainty but isn’t it our job? We have to live with incomplete mastery and imperfection, but isn’t it what it is to be just human?

despair, black swan, TalebWhat seems to me to lie at the core of the book and to lead to Taleb’s overall conclusion, is a very deep despair and a revolt when confronted to uncertainty, to the unexpected, to unfairness, to death and war and suffering (hence the importance of the autobiographical and literary part of the book). For the author, the world has become meaningless, and he finds hope only in the miracle of being alive and of building upon the opportunities of “positive Black Swans.”

Someone else could very well use similar valid points (the problem of induction, the risk of anachronistic projections, etc.), but also see and emphasize connections (even hidden ones, or different ones, for example the probably not yet completely understood fractals, where one can also marvel at the self-similarity property) rather than disconnections, meaning rather than meaninglessness and reach a different conclusion. From a physicist such as Omnes, you would get a completely different outlook on life. This is often the same with astrophysicists, who truly wonder (in both meanings of the word) about the world.

Berlin Cathedral (Germany). Altar area – Stained glass windows ( 1905 ) shwowing an angel with banner of victory als allegory of hope by Anton von Werner.Thus, ultimately, there might be an individual choice to be made on the way one approaches the world.

If we choose not to despair but, on the contrary, to wonder and hope and work hard, we may and even must continue our foresight and warning endeavour, assuming that a few rules are respected, those very rules that Taleb underlines (common sense, humility and using the right causality or the right tool for the right problem, etc.), as we shall see next.

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[i]“A wild card is a future development or event with a relatively low probability of occurrence but a likely high impact on the conduct of business,” BIPE Conseil / Copenhagen Institute for Futures Studies / Institute for the Future: Wild Cards: A Multinational Perspective, (Institute for the Future, 1992); then popularised with John L. Petersen, Out of the Blue, Wild Cards and Other Big Surprises, (The Arlington Institute, 1997, 2nd ed. Lanham: Madison Books, 1999).

[ii] For those interested in comprehending the modern State and its processes, here are a few enlightening works by social scientists, among a more detailed biography:

Ertman, T., (1997), Birth of the Leviathan: Building States and Regimes in Medieval and Early Modern Europe, (Cambridge, Cambridge University Press).

Mann, M., (1986), The sources of social power. 1, A history of power from the beginning to A.D. 1760, (Cambridge: Cambridge University Press).

Tilly, C., (1990), Coercion, capital, and European states, AD 990-1990, (Oxford: Blackwell).

Weber, M. (1919) Le savant et le politique, (Paris : 10/18, 1963) Paru originalement en allemand «Wissenschaft als Beruf » & « Politik als Beruf » 1919.

[iii] Nina Serafino, Curt Tarnoff, and Dick K. Nanto, (Foreign Affairs, Defense, and Trade Division), U.S. Occupation Assistance: Iraq, Germany and Japan Compared, CRS Report for Congress, March 23, 2006,http://www.fas.org/sgp/crs/natsec/RL33331.pdf

[iv] For example, one may wonder, considering that the modern state is essentially a territorial state, if the necessary resources to build or rebuild a state should not be proportionate to territory. If this is correct and if the amount spent on Germany is a good indication, although approximate, then the amount needed for Afghanistan might have been closer to 76 billion USD rather than to the 14 billion pledged. The way to implement a reconstruction, as well as the time necessary to succeed, would most probably have to be revisited. Hélène Lavoix « La construction de la paix et l’estimation des besoins et de l’impact, » Policy paper, Centre d’Etudes et de Recherches Internationales (CERI – Sciences Po) programme on « Peace and Human Sécurity » (CERI-CPHS), April 2008.

Scenarios: Improving the Impact of Foresight Thanks to Biases

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Alternative worlds NICForeseeing the future, whatever the name given to the endeavour*, faces two major tasks. First, we have the analysis, the process according to which the foresight, forecast, or, more broadly, anticipation will be obtained. Second, the result must be delivered to and understood by those who need it because they will act on it, to the least integrate the new knowledge received in the decisions they will take**. A huge challenge runs across both those tasks: overcoming the various natural and constructed biases that limit human understanding.

Much thought is usually given to analytical methodologies, which may be seen as nothing else than ways to overcome biases. Analysts commit themselves to many years of study, and force themselves to struggle against those biases, including through their own research and reflections. Managers look for ways to support them through training and constitution of best teams. Teachers and research institutions contribute to this generalised effort, as with, for example, the recent ongoing experiment funded by the Intelligence Advanced Research Projects Activity (IARPA), the “Good Judgement project“. Those enterprises are necessary, even crucial, if we want to improve our foresight, as underlined, for example, by political scientist and forecaster Jay Ulfelder.

We tend, unfortunately, to devote fewer efforts to deal with biases related to the second part of our work, the delivery of the anticipation to and its understanding by the recipients or customers.

This is certainly no easy task, as, there, we must deal with an “other” or worse with others. We have no power on their willingness to make an effort to overcome biases, assuming they accept being also prey to biases.

Results may also be obtained through the use of participatory methodologies, such as, for example, scenarios-building, where classical or analytical ways to mitigate biases are sought. This approach, despite its virtue, is limited because of the often busy agenda of decision-makers, or plainly impossible because of the sheer numbers of recipients. In those cases, only remains the final product that must, alone, reach the customer, be read, viewed or listened to, and understood. The strategy regarding biases, thus, must change. Rather than only focusing on struggling against biases, we may as well accept them and, even better, use them to our advantage.

The biases detailed in Heuer’s masterwork Psychology of Intelligence Analysis show us that fictionalized scenario narratives*** are perfect products to take advantage of some of those usual human cognitive traits to achieve our objectives, even more so if they are adequately combined with visual tools.

Playing with the “vividness criterion”

Crisis in ZefraFictionalized narratives obviously directly use this bias that Heuer describes as follows: “Information that is vivid, concrete, and personal has a greater impact on our thinking than pallid, abstract information that may actually have substantially greater value as evidence.” Heuer, p.116, knowing that, according to Nisbett & Ross, vivid information is information that is concrete, imagery-provoking, and emotionally rich (1980).

Among many, one interesting example is the narrative written by Karl Schroeder for the Directorate of Land Strategic Concepts of National Defense Canada in 2005, Crisis in Zefra. The four fictionalized scenarios of Global Trends 2030, use fiction characters and real or fictional organizations that will be familiar to their main readers, U.S. policy-makers, and a type of narrative as well as a design format that will similarly correspond to something very concrete and real for their clients.

Below are two examples of short fictionalized pieces, created out of material generated during a workshop, and aiming at making real threats related to algorithms.

Black out scenario, fictional narrative, algorithm based threat, threat scenario, futures, foresight scenario, fictional narrative, algorithm based threat, threat scenario, futures, foresight, intelligence, spy,

Emphasizing “consistency”

Any good narrative will pay attention to consistency and thus will use the human “oversensitivity to the consistency (absence of contradiction) of evidence and insufficient sensitivity to the reliability of evidence.” (Heuer, pp.120-122)

Using our flawed perception of cause and effect

As Heuer describes throughout chapter 11, generally, story-telling and thus story coherence is usually wrongly favoured over scientific method and scientific findings/research. Meanwhile we display a need for causal explanations, that is indeed best served by this story-telling. Thus a fictionalized scenario narrative built upon a proper scientific model will allow us transforming scientific research into a product that can be attractive to and believed by customers.

This is what led me, among other motivations, and once the model built for the scenarios on the future of the nation-state, to develop the Chronicles of Everstate in a serialized way, rather than to adopt a more classical and shorter form.

Tweaking the “availability rule”

This rule refers to one of the components that leads us to reach flawed estimates for probabilities. Heuer, using work by Tversky and Kahneman (1973), underlines that “’Availability’ refers to imaginability or retrievability from memory. Psychologists have shown that two cues people use unconsciously in judging the probability of an event are the ease with which they can imagine relevant instances of the event and the number or frequency of such events that they can easily remember.” (Heuer p. 147).

Thus, an interesting narrative – when it is read – will most probably influence the ease with which people can imagine, by themselves, future instances of similar events. We could also wonder if becoming aware of the scenario narrative would affect, through memories, perception of occurrence of such events.

Heuer (p.149), indeed, underlines that the participation to scenario-building exercises impacts estimations of probabilities for participants. Here we suggest that people reading scenarios – or more broadly being exposed to products derived from those scenarios such as films, theatre pieces, games, etc. would similarly be affected.

Using our weakness in assessing probabilities

As judgments concerning the probability of a scenario are influenced by amount and nature of detail in the scenario in a way that is unrelated to the actual likelihood of the scenario (Heuer pp. 156-157), then narrating a scenario or part of it with details will impact the ability of the customer to believe in its plausibility. This should prove extremely useful in convincing recipients to pay attention to potentially least possible futures, in struggling against prejudice and more generally against all organizational and belief-based biases.

Fictionalized narratives are thus a very useful type of products for the delivery of foresight, that we should permanently keep in mind, be it to deliver the result of more or less long scenarios-building processes, as with The Millenium Project 2020 Global Energy Scenarios, or the Global Trends 2030 of the NIC, or in the case of short exercises as shown above for potential algorithms-related threats. Could it also be used for other methodologies, such as  Forecasting?

Comments, ideas and suggestions are welcome!

*The label used signals various assumptions, methodologies, processes, aims and groups of practitioners.

** Even doing nothing is an action.

*** Scenarios are one of the end products of the process known as scenario-building as, for example, presented by Glenn and The Futures Groups and as used here to assess the future of the nation-state. A practical way to write them has been presented with the post “Constructing a foresight scenario’s narrative with Ego Networks.”

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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.

Heuer, Richards J. Jr., Psychology of Intelligence Analysis, Center for the Study of Intelligence, Central Intelligence Agency, 1999.

Ulfelder, Jay, Forecasting Round-Up No. 3, Dart-Throwing Chimp, 6 Dec 2012.

Kahn, Herman, and Weiner, Anthony J. The Year 2000: A Framework for Speculation on the Next Thirty-Three Years. New York, NY: The Macmillan Co., 1967.

Tversky, Amos and Daniel Kahneman, “Availability: A Heuristic for Judging Frequency and Probability,” Cognitive Psychology, 5 (1973), pp. 207-232.

Durance, Philippe and Michel Godet, “Scenario building: Uses and abuses“, Technological Forecasting and Social Change », Volume 77, Issue 9, November 2010, Pages 1488–1492, doi:10.1016/j.techfore.2010.06.007

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.