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Sketching the Unthinkable

Posted By Administration, Thursday, July 26, 2018
Updated: Monday, February 25, 2019

Monica Porteanu has written her sixth installment in our Emerging Fellows program. Here, she explores the evolving nature of scenarios. The views expressed are those of the author and not necessarily those of the APF or its other members.

In private and public administration, preparing for the future by “thinking the unthinkable” was first introduced by the RAND Corporation in the early 1960s. With time, sketching the unthinkable has become a common futuristic practice. Its results are summarized in stories about tomorrow, or scenarios.

And yet, scenarios are as old as humanity. Ancient civilizations imagined them in oracles or magic while building scenarios for the military (e.g., Sun Tzu’s Art of War), describing both present traditions and future visions, especially during uncertain times. Scenarios are fundamental in military, policy, and business, being developed using a mix of disciplines such as mathematics, economics, anthropology, and story-telling.

Futurists Bishop and Kahane remind us about the three critical types of scenarios: (1) predictive, i.e., forecasts and what-ifs, asking “what will happen?”; (2) explorative, i.e., external and strategic, asking “what can happen?”; and (3) normative, e.g., preserving and transforming, asking “what should happen?”.

The most known and used scenarios seem to fall into the first category. They are mostly based on statistics and assume they are bulletproof, based on scientists’ never-ending proof of the “unreasonable effectiveness of mathematics.” Nevertheless, when applied without checking the underlying nature of the relationships amongst the model’s variables, predictions provide a false sense of security about futures. Time series are particularly prone to mistakes, as they might carry over underlying presumptions from past and present into futures, challenging the statistical condition for independence when extrapolating from one value to the next.

All three types of scenarios tell stories about possible futures, paving the path for envisioning adaptive strategies, but only normative scenarios expand the futures paradigm from predicting or thinking to practical actions that have the potential to shape the future.

The normative scenarios seem to be the least used. They initiated out of challenges brought on by significant shifts, such as a political regime change. In recent years, disciplines that promote open creativity, collaboration, and innovation have increasingly embraced normative scenarios.

Design-led disciplines such as design thinking, strategic design, or research through design bring to scenario development effective new methods such as visualization, aesthetics, ethnography, or experience design. They have taken the telling of a story to showing, feeling, and experiencing it. Such immersion creates that magical circle of trust around scenarios that gives leaders the confidence to embark on a hero’s journey to act now and create “what should happen.”

Nonetheless, design, futurism, and scientific methods for scenario development can further benefit from learning about each other.

For example, design’s approach falls somewhere in between a binary selection (e.g., optimistic/pessimistic) and a high-medium-low style (e.g., most to least likely) which, most of the times, leads either to an optimistic-only path or, as the game theory demonstrates, to a sensible middle of the road but mediocre outcome. Futurism, on the other side, advocates for multiple, alternative futures that might have unpleasant or unexpected outcomes. At the same time, scientists look for theories that can provide evidence for the stories foresight scenarios aim to portray.

Could experiencing scenarios and the quest for hard facts be ever reconciled? Where might scenarios go from here? Would the futurists of 20018 still develop foresight scenarios? What would their toolset be?

In more immediate futures, data and ways to consume them are increasingly making their way into scenario development. Data have become essential in providing evidence of emerging blips that could turn into disruptors. Although digital and visual storytelling based on these data has progressed, the human brain, functioning in a 3-dimensional environment, still has difficulty making sense of large amounts of data presented on 2-dimensional screens.

These days it seems possible to narrow the gap between the 2-sided digital and the 3-dimensional physical worlds through augmented reality. This new technology enhances humans’ ability to make sense of data by juxtaposing digital information onto the real world. Furthermore, as humans process information through their five senses, the visualization side of augmented reality could be paired up with sound, touch, scent, and even taste to portray the envisioned images of the future.

It is up to us now to test whether scenarios born out of signals, sifted through the growing universe of data, and felt through augmented reality experiences, can be more potent than existing scenario consumption methods. Can these envisioned stories generate action, agency, and resilience for building preferred futures?


© Monica Porteanu 2018

Tags:  economics  politics  scenarios 

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Benefits of Big Data – predictions vs. foresight

Posted By Administration, Monday, February 16, 2015
Updated: Saturday, February 23, 2019

Julian Valkieser  shares his thoughts with us about “Benefits of Big Data” in this blog post for our Emerging Fellows program. The views expressed are those of the author and not necessarily those of the APF or its other members.

In my last articles, I have already mentioned the power of Big Data. My blog colleague Jason adopted it and expressed his own thoughts. In his last article, he has shown wonderfully how technology has already overturned business models and efficiency in other sectors and renewed them. In comparison to this, it could happen in the area of futurist and industry’s foresight as well.

Now, there are foresight methods that work well or best with uncertainty. Indeed, Delphi-Interviews are planned preciously, e.g. interviewees are pre-selected. But this does not mean that the statements can be processed for hard facts of future reality. And, they should not. That’s the exciting thing about scenarios. They give a way to stimulate the imagination and to derive recommendations for action.

But again, you try to keep the “cone of plausibility” as narrow as possible. (See Jason’s blog). You are looking for certain experts. You force certain issues. This is done in order to build the scenario reasonably.

Now you can imagine how neutral subjective responses and subjective questions are. Anyone who read “Thinking Fast and Slow“ from Daniel Kahneman knows what I mean. And right here data comes into play. Information could passively express motives and interests of groups. I have already indicated this in my last article.

In this article, I already referred to the fact that you can only get the most out of Big Data, if one applies the prediction to a trigger event. One extracts motives and interests out of big data for one or more so-called, trigger events. These are events that can be relatively easily predicted in the near future based on data, because the circumstances are (should be) less complex. Based on these trigger events you can create a scenario. In principle, this is nothing new. Just the basic information is extracted out of big data instead of interviews and subjective insights.

Let’s take an example. A major mobile phone company has 50 million customers. Each customer has a phone and moves every day with this turned-on phone – in this case between different radio towers (See Triangulation). Let’s suppose further that the company receives 20-100 motion information’s by any customer. Provided the company may cache this information for a longer period of time, the result is a huge amount of data information, how people move, how long they stay in which locations, etc. Of course, each individual could now be afraid of privacy. But the individual is not of interest. It’s about the mass.

Imagine what you can do with this information now available. Road offices could optimize the logistics. Infrastructure projects could be optimized. Where should the new stadium be built? How is the highway to be calculated? How many trains must be set on this track?

In a rising urban environment, where sheer masses of people are moving, all these data are exciting as the basis for trigger events and scenarios.

And finally, I have another wonderful example for these ideas. Eric Fischer has evaluated geo-tagging data from photo cameras. He compared where locals and tourists take pictures in certain cities in the world and displayed this information on maps.

Tags:  big data  foresight  scenarios 

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