Foreseeing 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”

Fictionalized 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.

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.”

Featured image: Stanley Kubrick exhibit at EYE Filminstitut Netherlands, Amsterdam – The War Room (Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb)- By Marcel Oosterwijk from Amsterdam, The Netherlands [CC-BY-SA-2.0 (http://creativecommons.org/licenses/by-sa/2.0)], via Wikimedia Commons

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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