Reading analysis by John Tamny in Forbes, I lighted on the following paragraph — as perfect an encapsulation of why looking to the future in timely and in a high-quality way is essential, and how quality horizon scanning is integral to it:

“As often happens as companies grow, Blockbuster concentrated on perfecting its existing service while beating competitors offering the same instead of looking into ways that outsiders might destroy its business model altogether… For Blockbuster, the “disrupter” in question was Netflix. Indeed, popular as the Blockbuster brand was, getting to the video store in order to take advantage of its services was a hassle for customers–as was returning videos on time to avoid paying late fees. The rise of Netflix from well outside the traditional retail space meant these problems were solved in one fell swoop.” (my italics)

Change that matters, that is, relatively sudden and acutely disruptive to incumbent business-model success, always comes from outside an industry. Britannica wasn’t beaten by another encyclopedia. Eastman Kodak was beaten by digital photo startups, not by Fuji. And so on, and so on, through industry failure, whether it leads merely to value hemorrhage or all the way to Chapter 11.

Looking vs seeing

Sure there are companies that lose because they are simply outcompeted, that is, are less capable than the competition in doing the same thing. Hertz is currently in this category. But when a clear market leader, with brand and capital and customers galore comes totally unstuck, it is always new technology and/or new business model coming from the outside that has done it. In these cases, as with Blockbuster, companies fall to industry entrants that change ways of doing things, solving pain or trade-offs that buyers suffer, or otherwise provide consumers with more value.

These are always, theoretically, innovations incumbents could have done themselves if they were ready to think ahead (and brave enough, when required, to cannabalize existing products that stood in the way of important future steps) and therein lies a conundrum about looking at new, external competitors. It’s seldom that the incumbent can’t see the intruder, that is, is not looking. Often they are looking intensively. It is that they don’t see the absolute disruption in the new until it is too late. It is a problem of perception. This is why industry horizon scanning is a little about the easy task of looking, and a lot about the much harder job of seeing. And why putting one’s corporate head down and making an existing product or service ‘more perfect’ is part of not seeing.

* Interestingly, the Blockbuster demise was called exactly right in November 2007 by Don Reisinger on CNET.

It was in this spirit that I picked up the UK launch issue (aka May 2009) of Wired. Actually it’s not the first launch. Wired was in the UK ten years ago, but Condé Nast withdrew it in the dot.com crash. In the US at the time, I remember when Wired, the poster child of the Silicon Valley / Nasdaq bonanza, was almost as thick as a phone book each month. But those days were soon over.

Anyway, who could resist an offering that was about to tell me about my “Life in the future. “Fake Meat, Robots and Electro-Sex: the World is About to Change.” On the cover are, I kid you not, flying cars!

Now, I wouldn’t take this stuff seriously for a moment, if everyone else promised not to. But they don’t. So here we go. In the “What’s Next?” cover story 46 experts make 99 predictions about the next 40 years, and none of them will happen, or not in the time frame expressed.

Oh, moon settlement?

I shrink from sharing the list. Meal replacement patches, check. Moon settlement, check. The male pill, check. Every techno-fantasy of the jockish sci-fi world, check. Well, let’s stop on the male pill for a moment. Can we not do it? Sure we can do it – today. What’s stopping it is not technology. It is attitudes (machismo, essentially). So Wired experts are telling us that this will go away in a decade. Puh-leez.

I hardly need mention there’s no method given behind any of these expert forecasts.

Don’t you think Wired should be asking themselves why, in 2009, they are producing 186 pages of dead tree and carting it around the country in carbon-emitting trucks? Technology-vision may lead you to a view of the future. But it’s unreliable. The future is determined by what consumers are ready for. Well, that’s one of the 20-or-so key forecast filtering principles of Future Savvy.

Perhaps we should look at the cover story for what it is really about – which is selling magazines. Because, there’s no doubt that tech is changing, and many new capabilities are coming on stream, and this is very, very fascinating to imagine uses for. And this fascination is what Wired packages and sells. Don’t bet any money on the predictions though, certainly not their timeline.

But sturdy in some areas

Aside from the predicting lark, it’s a good magazine of its kind. The features are well-conceived, well-written, for example, one about how the BBC iPlayer business was built; a feature on sea salvage; a profile of PayPal founder Elon Musk; the David X Li formula and how it mis-calculated risk, and so on. Great stuff. Actually quite a sturdy business-oriented-view of techno-change, if you can get past the boys-with-toys riff of the magazine as a whole.

So, actually, much to like. Just, please, don’t think a lad’s mag is going to tell you anything coherent about the future.

First, there is the prediction game on offer from ABC, taglined: “The Oscars Live Challenge: Think you can Predict a Winner? Make Your Picks Now!”

It’s all part of the marketing drive of course, but, nevertheless how would one play it best and what might that tell us? Let’s assume there is something at stake, like you’re really going to sit in front of the TV and mark off your right vs. wrong predictions, and compare your score with that of your spouse for year-long bragging rights – now there’s pressure – how would you predict? Would you think (a) “this is the best movie so I predict it will win”? Hardly. You would think: (b) “this is the one that I think most people will pick, so that’s the one I think will win.”

You would be making a meta-prediction – going with what you think most are going to choose. In this particular case you would also know that that Oscar winners are chosen by balloting the 6,000 members of the Academy of Motion Picture Arts and Sciences. So your more exact question would be: who is this special group likely to choose in each category?

What’s going on? In future situations that are heavily dependent on aggregate human choices – which is very many situations – the savviest predicting strategy is to figure out the choices most people are going to make. Oscars aside, figuring out the choices most people will make on any issue – hybrid cars, tighter securities legislation, public health care, etc. – is an excellent guide to what will really happen. It’s a mass market-led view of the future to be sure, but that’s exactly what makes it dependable in mass-opinion situations. (Not all situations are determined by mass-market choices – predicting a presidential election winner is; predicting a superbowl winner is not.)

Playing the game

I had a shot at the Oscar prediction game, joining the alleged 1,680 other “players” who were then online. From what I could tell via the rather gristly Flash interface is that the game is not (yet) “social” in that you can’t see what other people are predicting – there is no access to aggregate opinion. No matter. One can instantly get this in hundreds of prediction market forums right now, for example Intrade, where the price of each outcome in each Academy Awards category directly reflects how strongly players as a whole have bid up that outcome.

At Intrade, at time of writing, Slumdog Millionaire is at $87.30 (max is $100; the other 4 movies share the remaining $12.70). When used as a prediction this means that the aggregate opinion of people staking real money has been effectively captured: it is that Slumdog Millionaire is 87% likely to be the choice of the Academy members in its category.

This is a guide to Oscar night that I would not bet against if I wanted to hold onto my bragging rights. Even in situations less overwhelmingly agreed on by players, it has been shown that prediction markets, tapping the aggregate “wisdom of crowds” (working like “Ask the audience” on Who Wants to be a Millionaire) are a fabulous tool for capturing what most people think will happen, resulting in excellent predictions. Caveat Emptor: prediction markets are poor at predicting long-term, open-ended situations, particularly where the outcome alternatives are unknown or can’t be clearly bounded, as blogged a few months back.

The story covers the pilot project of two San Francisco newspapers seeking to create an online edition. The presenter starts: “Imagine if you will sitting down with your morning coffee and turning to your computer to read the day’s newspaper. Well it’s not as far fetched as it seems…”

28 years later it’s exactly what we do. But it seemed far-fetched then, and this was not a misjudgment: it has taken us until now, the full 28 years in most developed countries, to get to the point where mass online newspapers rival mass print editions in the market. What might that tell us about what seems far-fetched now, whether it will happen or not, and how long it will take? How does it improve our foresight?

$10 plays 20c, but not for long
The news clip features early 1980s computers – the text-only green screens – and achingly slow phone-set modems. A newspaper takes two hours to download (with no picture, ads, or comics). So there are technology limitations.

Then there are economic barriers: the local-call hourly charge is $5 (=$10 for the paper) while the print copy costs 20c.

And there are system-wide market-adoption issues: there are only “two to three thousand” home computers in the Bay Area at the time. Home computer penetration is obviously related to utility (usefulness/cost) of the machine.

But in 1981 home computers were about to get a whole lot better for a whole lot less – and with this programmers would be drawn into turning the technology into something we actually need, and ultimately can’t do without – all driving towards the utility jump that signals mainstream adoption. But at the time home computers were an unimaginably small niche of the total media market.

Fast forward to 2037 and what might we be able to say about it? First, that the pilot projects of important new mainstream markets already exist today (along with great business opportunities). The technologies involved are, now, incredibly clunky and expensive, meaning consumer utility is laughably low. But this will steadily unravel to the point where the technology is fantastic and affordable, and voila! We will have fundamental transition and entirely new mainstream markets.

But the most important lesson of all is this: it will take a generation. The future never cuts corners. All fundamental changes in social and market patterns take at least a generation, if not more. There’s a well-known truism in foresight work, which is this: we tend to overestimate the pace of change, but underestimate how all-encompassing it will be, once it comes.

Future Savvy is intended to be a book for business and policy professionals, not academics. But it does speak to students and scholars who need to assess and evaluate foresight work. In any event, professors Jay Gary and Dennis Walters have included it alongside works by Wendel Bell, Jerome Glenn, and Ted Gordon (no relation) — household names in the foresight field — so the comparison is of course very happily accepted.

The following is from the course outline. I’ve included the full bibliography, which is in itself a valuable collection of sources in the futures field, and merits attention all the way down the list.

Course Description:
Surveys traditional forecasting theory and methods. After a consideration of forecasting in general, students learn how to conduct research using both qualitative (secondary sources, interviews and questionnaires) and quantitative (data analysis, numerical forecasting and trend decomposition). They also apply critical thinking skills to existing forecasts. [Learning objectives:] 1. Managing: understand the principles and applications of operational forecasting within organizations. 2. Assessing: decide when to use statistical or judgmental methods in strategic forecasting, and how to combine foresight methods to generate 10 to 20 year outlooks. 3. Evaluating: gather information in a specific domain that can be used to forecast baseline as well as alternative futures. 4. Researching: construct a long-term strategic forecast for a client organization that draws upon both quantitative and qualitative sources.

Required Resources
* Bell, Wendell. 1996. Foundations of futures studies: History, purposes, and knowledge. (Human Science for a New Era), vol. 1. New Brunswick, NJ: Transaction. ISBN: 0765805391
* Carlberg, C. G. (2005). Excel sales forecasting for dummies. Hoboken, NJ: Wiley. ISBN: 0764575937
* Glenn, J. C., and Gordon, T. J. Futures Research Methodology V2.0 CD-ROM American Council for the UNU. ISBN: 097220511X
[This item is available through http://www.acunu.org/millennium/FRM-v2.html]
* Gordon, A. (2009). Future Savvy: Identifying trends to make better decisions, manage uncertainty, and profit from change. New York: American Management Association. ISBN: 0-8144-0912-1
* Jain, C. L. ed. (2001). Practical guide to business forecasting. Flushing, NY: Graceway. ISBN: 092126758

Recommended and supplemental resources:
*  Coates , Joseph F 2025, John B. Mahaffie, and Andy Hines. 2025: Scenarios of US and Global Society Reshaped by Science and Technology. Oak Hill Press. ISBN: 1886939098, also available in .pdf files via http://www.josephcoates.com/2025_PDF.html
* Armstrong, J. S. (1985). Long-range forecasting: From crystal ball to computer (2nd ed.). New York: Wiley. ISBN: 0471823600, also available in .pdf files via http://www.forecastingprinciples.com/Long-Range%20Forecasting/contents.html
* Armstrong, J. Scott. 2001. Principles of Forecasting . Kluwer. ISBN: 0792374010.
* Caplow, T., Hicks, L., & Wattenberg, B. J. (2001). The first measured century: An illustrated guide to trends in America , 1900-2000 . Washington , DC : AEI Press. Download chapters at: http://www.pbs.org/fmc/book.htm
* Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34, 571-582.
* Duberley, J., & Johnson, P. (2000). Understanding management research: An introduction to epistemology. Thousand Oaks, CA: Sage.
* Einhorn, H.J. (1986). Accepting error to make less error. Journal of Personality Assessment, 50, 387-395.
* Fischoff, B. (1994). What forecasts (seem to) mean. International Journal of Forecasting, 10, 387-403.
* Gawiser, Sheldon R., and G. Evans Witt. 1994. A Journalist’s Guide to Public Opinion Polls . Praeger. ISBN: 0275949893.
* Gillham, Bill. 2000. The Research Interview. Continuum International. ISBN: 082644797X.
* Hetman, F. (1969). Le Langage de la prévision, the language of forecasting: With a French-English-German vocabulary. Paris: S.ÉD.ÉI.S. http://www.cnam.fr/lipsor/eng/data/langageprevision.pdf
* Jantsch, E. (1967). Technological forecasting in perspective. Paris: OECD. http://www.cnam.fr/lipsor/recherche/laboratoire/data/prevtech_en_final.pdf
* Makridakis, S. G., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and applications (3rd ed.). New York: John Wiley & Sons.
* Molitor, G. T. (2003). The power to change the world: The art of forecasting. Potomac, MD: Public Policy Forecasting.
* Moore, D. A., Kurtzberg, T., Fox, C. R., & Bazerman, M. H. (1999). Positive illusions and forecasting errors in mutual fund investment decisions. Organizational Behavior & Human Decision Processes, 79, 95-114.
* Orrell, D. (2007). The future of everything: The science of prediction. New York, NY: Thunder’s Mouth.
* Rescher, N. 1998. Predicting the future: An introduction to the theory of forecasting. Albany: SUNY Press. ISBN: 0-7914-3553-9
* Salant, Priscilla, and Don A. Dillman. 1994. How to Conduct Your Own Survey . Wiley. ISBN: 0471012734.
* Seidensticker, R. B. (2005). Future hype: The myths of technology change. San Francisco, CA: Berrett-Koehler.
* Schnaars, S. P. (1989). Megamistakes: Forecasting and the myth of rapid technological change. New York: Free Press
* Sherden, William A. (1998). The fortune sellers: The big business of buying and selling predictions. New York: John Wiley.
* Wood, G. (1992). Predicting outcomes: Sports and Stocks. Journal of Gambling Studies, 8, 201-222.

No longer. The paradigm was broken by the Hans Rosling (Gapminder) video “Debunking Myths About the Third-World,” 2006. By Rosling’s own admission, his analysis is not based on new or better data. The (UN) data has always been there (yes now it’s becoming more available). But the seachange is new software which makes it easy to filter and present it in dynamic, graphic form. And, no surprise, this is popular. According to Gapminder, this video has seen by 500,000 people, not bad for a 20-minute treatise on perceptions of developing world countries.

Data turned into dynamic moving pictures is, one might say, required in our era (trends: visual literacy, short attention span, computing power) so thankfully we can expect more of this. What’s important, for forecast evaluation purposes, is the power of explanation and mental-model challenge that the improved communication provides. As Rosling says of his Swedish graduate students: “Their problem was not lack of data, it was preconceived ideas” (an outdated world view of “1st world” vs “3rd world.”) An endless amount of poring over dusty tables of figures would be unlikely to change that. But it’s hard to watch Rosling’s moving bubbles and not have one’s paradigm shaken.

Another site, in a similar vein, is worldmapper, a University of Sheffield initiative. Worldmapper communicates hundreds of world indicators, from infant mortality to military spending and so on, by manipulating the size of territory of each country to indicate presence or absence of the variable in question, as the following maps show:

Prisoners as percentage of population

Girls not at secondary school

Strikes and lockouts, 2002

Again it is basically UN data that is being sourced, but now presented in a way that cuts through the obscurity tells and the story much more vividly. As we know, humans “get it” better and faster via images than via words or figures. It challenges our perceptions in a way that figures in dusty tables cannot. They payoff is it’s harder to miss what’s really going on. So we have a better view of the world: our mental model aka ‘paradigm” more closely approximates reality. That means we will make better assumptions going forward which will, on balance (no guarantees of course), convert into better predictions.

This is the item:

There’s nothing new about this of course – the product has been around for a while, and ways to take vinyl and digitize it have been offered since the CD became the music industry standard in the mid-1980s. What’s interesting is that it is still being offered in 2008, more than 20 years after the technology transition. And still being bought, despite a sticker price of gpb 299 (nearly $600. In fact, this is the special newspaper-tie-in deal price.) The producers and marketers have, no doubt, done their homework: there are still enough people out there with vinyl records to justify a product and a campaign, including big newspaper spots that don’t come cheap.

What does this tell us about the future, and about predictions? It illustrates a key principle in thinking circumspectly and more accurately about the future. Legacy investments and legacy situations are a reality. They often represent a significant slice of daily practice or market share, well beyond the time when things have, officially, moved on. For all practical purposes, in any future the past continues to exist for a long time.

A slow and measured exit
This is common sense. But often missed by breathless techo-forecasters whose eyes are fixed on the next new thing. The implication of many forecasts is, when a new technology emerges into the market (which often takes longer than expected) that is also when previous solutions fall away. Not so. Yes, sometimes a new product is clearly advantageous, and adoption is rapid and pervasive. But when there are real investments in prior systems and technologies, these typically work their way out of people’s lives slowly, often over generations. The transition takes longer than we think it will.

While they are still part of the picture, legacy systems work against change (“This is working fine for me, why should I shift?” or “I’ve invested heavily in this, I can’t afford to shift”). On the other hand, as evidenced by the LP2CD in 2008, opportunities in the legacy system, or in facilitating a transition to the new system, may exist and be significatn long after everyone’s attention has moved on.

There are legacies in all kinds of products and services. A case that is currently pertinent, as discussed in Future Savvy, is the existence of deep legacies in the automobile industry and gasoline-petroleum supply chain. Both petroleum supply constraints and carbon emissions worries are driving hybrid engines, new fuels, and renewable forms of energy (technology is not the obstacle here) but the reality is that we are all deeply invested in a legacy petroleum-automobile system, from the well to the refinery to the factory to the forecourt. Even when new / alternative energies are proven, reliable, and equal in price and performance, the legacy will continue to exist, and it will erode gradually, as companies or consumers slowly renew their investment over time. Of course regulatory or social pressure can accelerate the incremental process, but nothing can make it vanish.

This means, in this example, there’s no possibility of a sudden change in individual land-based transport solutions. Whatever comes along will have to emerge into and live side-by-side with past systems and infrastructure for a very long time.

Legacy as luxury
Here’s another principle of legacy systems surviving into the future. There are many examples where a surpassed technology remains in existence, but moves into a niche or luxury market. The car replaced the bicycle and the horse, but both continue to enjoy massive popularity. In the developed world, more bicycles are sold than ever in history, but these are primarily for exercise or leisure. Horses, once widely distributed through society as instruments of work, are still part of a very active industry, but this industry is about leisure and/or gambling. Similarly, electricity replaced candles as our primary means of illumination, but candles are everywhere – associated with mood and romance rather than functionality. Ball-point pens squeezed the fountain pen off the table, but that merely freed the fountain pen to become an icon of status and refinement.

This was a fairly typical example of the Intro Workshop in Scenarios program that I run, so I’ve decided to post it on SlideShare, see link below. Let me know what you think.

What is different about this course at this venue, particularly, is that the attendees come from a wide spectrum of industries and sectors (from Nestle strategist to the Canadian military planners, to Mauri sustainability experts, and beyond), and have a very wide background/preparation in futures tools and methods. There were relative experts in the room, and some absolute novices. … nothing like a challenge for the facilitator!

Slides from the day are at http://www.slideshare.net/adgo/how-to-build-and-use-scenarios-day-workshop

some pix:

1. First, what do you use scenario planning for?
Initially stated everyone did scenario planning, every time you find yourself doing something unusual it implies you’ve done something wrong with scenario planning. We think about the future all the time, constantly making scenarios in head, if move to London think about what need to earn, where live, family, critical uncertainties. We run forward, think about various challenges, we rehearse the future.

CL wondered about ill thought through scenarios?

AG felt it was inevitable that people will disagree. End of day scenarios boil down to politics. Visionary scenario planning gets everyone talking – firstly need to find common threads (if not grounds), that most buy into – even if it is a low common denominator, thus creating a shared vision, then dystopias, but remember the end point is never total agreement.

RS Acknowledgment of importance of politics? Is that your experience of foresight?
AG believes he is a political animal, but no longer involved in protest. Belief is power is absolutely important when thinking about the future. He then amends it to power and money – as often groups without power have public opinions so they can shape the future. There is a danger that they only talk about market forces and technology and that’s it.
RS Is it a struggle working with other foresight groups – is that view not shared?
AG says they can give a shared opinion, but politics mostly overlooked by the groups

RS describes that we all have a political agenda (social inclusion, voices excluded) within this project – it makes it interesting to deal with those who have an economic/gov background, it’s difficult to pretend futures neutral.

2. Could you talk me through a brief overview of your scenario planning method? (How long does each stage take? What preparation is required for each stage? How many people are involved in each step?)

There is an underlying method – but each stage has some degree of flexibility and can use a variety of methods.

A. Handshaking stage
Firstly deciding what you’re doing, basic project stuff – who’s involved, what resources, what deliverables – key is choosing method and who’s involved, may have varying degree of democracy – heads or grass roots, be inter disciplinary or not, have a broad or narrow focus, and what type of input is required. Believes it needs to have mixed agenda. Note that relying totally on academics is bad.
Then you need to decide the dates your final scenarios will relate to – further in the future the more radical but less relevant to other people and harder to action. Most future scenarios are around 10 years ahead – as a rule not less than 5 or more than 20.

To decide the focus you must:
1) Drive management team towards understanding how much influence they have over the future, can they drive future or does external events influence them? Percentage, never exact but idea.
2) Based on the amount of control can decide if creating a visionary scenario or anticipatory (not good word) scenario building. Visionary trying to develop an idea, multiple stakeholders get shared ideal, easier if pressure group, one organisation, as they can develop enrich and jump on to focusing on how we would put this into practice. They create a vision and dystopia. Influence future (money, opinion etc).
In anticipatory, or “Businessy type scenario”, participants don’t mind how things turn out, what they want is to be successful in the world however it turns out. Within organisation goal is to anticipate broad set of possible worlds, particularly critical uncertainties. So they do have research scenarios and different takes on how things emerge, BUT no preferred future. Look at resource and competence so they may have preferences but can adapt. To same extent they look at legacy competencies but this is not a determining factor. Scope alternatives and plan.
The Handshaking stage takes a few meetings to do, small with key meeting, then larger with various stakeholders – perhaps half a day.
In terms of materials one could send out stimulating piece to encourage thought so not stone cold but which doesn’t colour the agenda. Could also have to read a synopsis of what the process is about and the sort of things to think about and expect.

B. Horizon scanning
There are 100s of ways of doing this. Basically need to go into world and do research on what’s going on in key dimensions, technology, markets – broad scan of world relevant to issue area, bring in people outside of own industry. Best tool in this area is “learning journey” – jazzy word for anthropology of own society, structured agenda for talking to people about concerns, what they know, what they’d like, focusing on future – so need to be carefully done to avoid reiteration of now or what they think you want to hear.
Note that you can commission this – but it is not market research.
No answer to how long, dependent on time and resources, but should budget third of total time.

C. Pulling it together
Mulch through the data gathered – preferably in funky creative meetings, collate, output into things like forces, drivers of change, trends, blockers of change, critical uncertainties. What comes out is a picture of world that’s relevant to us.
This activity could be whole group or just the scenario developing team – it’s to pull out what’s important.
The format could be something like workshop, sleep/gap, then another half day or so.

D. Separating critical from predetermined
This could require a Mini Delphi, talk to experts to find out what are the sorts of things in forces of change list that are predetermined – so things we know will happen in 2020, perhaps the number of students, or trends to sustainability.
The various issues will have lifecycles in being a key focus, they’ll always be there but the amount of interest will vary, eg sustainability. In the future sustainability will be less of a concern, but not less important, it will just be integrated into our expectations we won’t focus on it. An example relating to education is how we’ve shifted views on punishment; it was a stick, then detentions, then exclusion…
[Divergent conversation about the failure to correctly predict overpopulation – they just extrapolated – AG argues it was not a failed forecast, just bad forecasting - a failed forecast is interlocked bad assumptions. There was a discussion whether carbon credits will have the same results.]

E. Question all assumptions/Test lists
Note: This stage might highlight the need for more research or stakeholder engagement.
The goal is to end up with two lists that rank for importance. One is about predetermined things – things that we are sure about for our purposes – note that we may, or may not, need to talk about them. Then there are uncertainties (anything we can’t clarify with further research). They are sometimes called “strategic uncertainties”.

F. Create scenarios
These lists form basis of scenarios, they allow us to try out alternate resolutions of the unknowns. AG hates the 2 by 2 matrix approach (from Boston management), but it is sometimes a useful tool. Shell use a fork in the road approach, or possibly a roundabout, where there are clear alternatives. For example, they know sustainability will happen but they have various scenarios predicting the demand for resources. [RS comments that Shell also have a trilemma approach, so deal with three worries.]
More generally scenarios are structured round uncertainties. The van der Heijden approach is to list things and tell stories – NEEDS creative facilitators. He has a pack of cards which people develop stories around. Each group is given part of the puzzle to resolve. Basically you need judgement to choose how.
The groups then write stories and the facilitator ensures the scenarios cover the cone of plausible uncertainties. Note there must be multiple scenarios, a single story is not helpful.
The creation of scenarios will take a minimum of 2 days to talk through, write, draw or tell.

Note: Jump scenarios are conversations over a day. There is no learning journey and it’s hard work for the facilitator. The result is paragraphs rather than stories. These are good for management to emphasis the alternatives and broaden thinking

G. Check and test scenarios
Vital outside people criticise. What could and would work? This stakeholder analysis is meant to be a practical exercise.

H. Put them out
Visioning scenarios (including preferred outcome) get published, they need to get folks on board. Business ones tend to be more internal.
Then test existing strategic agenda within scenarios. They’re a test bed for strategy and choices. How would things work?
Or backcast using the scenarios – so how to reach or avoid scenarios.
AG keen to promote that there needs to be standards, just like testing a pram, you need to exhaust all the things that could happen before it gets its kite mark.
[There was a conversational aside over the time spent on this. AG worried not enough time spent using them and that politics will block change – “if broke don’t fix it”. People need to be desperate to be receptive.]

3. Do you use any tools for scenario planning? If so, can you briefly describe them?

Classic management tools – so getting people to communicate, being experienced based.

4. Are any of those tools online?

Nothing practical is online but there are resources and case studies (see his website). Although then amended that can do Delphi online. Note that learning journey is not market research, not supposed to be investigating people’s mental models – “good foresight is not predictive”.

5. What scenario planning tools would you ideally like to have available?

Something that follows stages explaining steps, timelines, who’s involved, explaining predetermined and uncertainties when listing – basically scaffolding.

6. If time was restricted for a scenario planning exercise, which parts would you keep because they’re the most important?

Would go through all stages (tick all boxes) but go lightly on some. However the less knowledge bought in the more focus on facilitation. The key is to get people to develop interesting motivating diverse stories appropriate to their needs.

7. What tools or approach would you recommend if a non-expert wanted to do scenario planning?

When asked AG said that don’t need a facilitator but would do better if had one. They focus the time and can support the whole process – not just when in workshops. Those involved aren’t experts and have a stake in the present so they’re going to struggle to change their minds.

When asked if it could be done alone AG said yes, BUT that it would be better if one could do at least step G, testing scenarios, with others.

The common view of horizon scanning that it is about seeing what’s “out there” in the world. We look for events and signs and changes in behavior or technology and so on, that suggest the beginning of a larger trend. So far, so good. Many institutions, organizations, and companies practice this, or subcontract this service. But – and this is far less commonly practiced or understood – good scanning should focus equally at what’s going on in people’s heads: their ideas, values, and motivations, because these will determine the choices they make, and these choices aggregated over the population and over time will determine the future. (Internal perceptions and external events are linked of course.)

We can’t look into peoples’ heads. But we can look at what is going into their heads: exposing ourselves to the knowledge and ideas people are getting, or choosing. For some analysts this appears a very “low-brow” experience, too insulting of their intelligence to be worth doing. But there can be no adequate future scanning without it.

Fred
I’m prompted into this discussion by a post on the Foresight Culture blog which flags the importance of scanning inputs such as Fred YouTube videos. As posted: “Fred is the YouTube character of a Nebraska teenager, Lucas Cruikshank. I came across his videos because they kept turning up under Most Viewed or Most Popular on Youtube. Most viewed doesn’t make the content of a video valid or even viewable, but in my view, it makes it important to know about. His 19 videos have a combined view total of over 4 million, and Fred’s YouTube channel has 290,762 subscribers, the 4th highest total on YouTube…. Good scanning includes knowing what the mass of people are watching and liking. That means tv shows you might not like or even approve of… The Fred videos are interesting because, even though they are silly satire, they may represent a modern teen’s ideas about life, family, and society.”
View Fred’s video channel

Early in the day, as a warmup I think (but for me this was the juice) there was a “Research in the Real World” section. It started with a presentation by Alex McKie reporting on a tour she made across the UK, where she interviewed people asking them what their “three wishes for the future” were. This was followed by Gill Wildman and Nick Durrant of Plot, who presented interviews where consumers were asked where and how they used media, and what they wanted from it.

The research is anthropological, no more or less than a customized field trip: going out, seeing what people do, and how they live, and what’s important and meaningful to them – and then thinking how one’s own area of interest (e.g. product) fits into this, or could fit into it in the future. That gives some clues as to what people will adopt and/or buy – what the market will “pull”.

The futures field lingo for this type of work is “a learning journey,” a process usually omitted in the helter-skelter of tracking new technology capabilities and other apparently more profitable lines of research. There are some good writeups of future learning journeys: one that comes to mind is “The Moen Story” Johnston, R. & Douglas Bate, J., The Power of Strategy Innovation, Amacom Press, 2003, Chapter 5. Another is “Conduct Reconnaissance into the Future,” Sull, D & Wang Y, Made in China, Chapter 3, HBS Press, 2005. I recently saw that Christus CEO Tom Royer said his medical institution had conducted learning journeys (to Canada and India) as part of its Futures Task Force II scenario building process.

Tuning in
No question this type of research is often tedious. You have everyday people umming and aahing inarticulately and often unimaginatively about their preferences and problems, and hopes for the future. In fact the conference audience were impatient about having been presented with the interviews in raw form. But it is precisely in the careful listening that much about the real future is revealed. It is a vital ingredient in thinking about the future, and reigning in poor forecasts.

In the event, the consumers (in Plot’s terms “the people formerly known as … users”) were revealed as media wise, but often their savvy to screen out the information firehose. Although media types were thinking about the cutting edge, real people were articulating the need to be informed in a way they could manage – not too much or too little – and to be able to trust the news source, and be exposed to stories that move or inspire them.

Learning journeys are a very dependable way to think about the future by checking our industry insider preferences against the preferences of real people out there. Any prediction that makes assumptions about the market without this perspective is heading for failure. But there is a wrinkle, and it is this: market research – even this deep market field trip research which is much better than focus groups – is seldom enough to adequately anticipate the next new thing. It tells us what lab fantasies or executive business model fantasies will not fly. But it doesn’t help us make the jump either. Experience is that consumers want what they already have, maybe a bit better, maybe a bit cheaper. Market research did not see the Walkman. And as as Hal Sperling of Chrysler said: “In all the time we spent developing the Minivan, not once did we have a soccer mom come and ask us for one.”

So far so good. Tracking technology change is an important stage in scanning the external environment and anticipating sources or change and/or disruption. But no technology ever changed itself. History is littered with fabulous mind-bending, world-changing technologies that didn’t make it out of the lab. In fact, technologies only change because humans or human institutions want them to change AND (two separate hurdles here) they allow them to change.

Most people, most of the time, want technologies to change because they change for the better, improving products and services and/or making them cheaper. Companies want new technologies because improvements offer new sales options and (sometimes) industry competitive advantage, among other things. Societies express the desire for technology to go forward by stimulating and facilitating change in many ways (for example through government or industry funding of R&D or protecting intellectual property or making capital markets more transparent.)

Technology filtered by human choice
Once a technology breakthrough emerges, that’s hardly the end of the story. In fact it is still very much the beginning. New technologies of any importance are subject to public scrutiny and choices. Individually, or as a society, we ask ourselves, is this technology good for us? Debates happen, and power and politics and regulation takes its course, but one way or another technologies that most people like – mobile phone’s for example – will go forward while technologies such as GMOs will stall. Also, in a market economy, technologies are inescapably subject to consumer economics: those that raise user benefit (pass a buyer;s cost-benefit analysis) will be adopted. Those that don’t sit in the lab.

In other words, technology possibility is a matter of science and engineering, and the possibility frontier is expanding all the time, but the road from possibility to actuality is the rocky road of human ideas, preferences, and choices. Technology change means technology adoption, that is, it is a form of social change.

Why is this distinction important? Because one of the main reasons forecast fail is they see the technology possibility frontier as the future, underestimating the forces of social triage. There are two sites that I love that illustrate this wonderfully. Check out Paleo-Future (A Look into the Future that Never Was) and Modern Mechanics (Yesterday’s Tomorrow Today). Both are crammed with failed forecasts of this type. This is not to say that we cannot forecast usefully – much more to come on this in this journal – but it does give us pause in viewing many of today’s techno-inspired forecasts which make the same type of error. (Pics credit to the sites mentioned.)

Chapter 1: Recognizing Forecast Intentions, deals with considerations of how forecasts come about, who makes them, and with what intention. Those who research and produce forecasts, those who invest in understanding trends and drivers of change, and those (including the media) who bring the forecasts and their implications to our attention, inevitably have reasons for doing so – to benefit from the knowledge by seizing opportunities or avoiding threats or by affecting outcomes in the world. Understanding a forecast’s “return on investment” gives us an important vantage point in assessing the merits of a forecast.

Chapter 2: The Quality of Information, shows how a forecast communicates information between forecaster and reader subject to the same standards of accuracy, truth-telling, and bias-control by which one would judge any communication. Forecasts can be very different in methods and goals, but all forecasts lay claim to factual truth, particularly truth in the data, and the argument deals with the various ways in which data can be less solid than it looks, even with the best intentions.

Chapter 3: Interpretation and Bias, considers how data – whether good or bad in itself – can be interpreted or misinterpreted in forecasting, that is, the “political” aspects of forecasting. Just as there is no value-free look at history, so too there is no value-free look to the future and asking the right questions allows us be ready to mentally rebalance forecasts that are presented.

Chapter 4: Paradigms and Perception, investigates how predictive statements are exposed to a broader form of interpretive bias that has to do with the forecaster’s mental model or “paradigm,” and the “zeitgeist” (spirit of the times) when the forecast is made. This chapter investigates situations where forecast failure is caused by failure to escape society’s current mental models – which often do not hold through the forecast period.

Chapter 5: The Utility Principle, considers economic and market forces, and the role of consumers, in promoting or resisting the future. Without reigning in creative thinking, some simple economic filters inevitably apply direction or timing realism to futurist flights of fancy.

Chapter 6: Drivers, Blockers, and Trends, consider drivers and blockers of change, and how viewing these dynamics improves forecast assessment. It identifies the roles of Drivers, Enablers, Friction, and Blockers acting on events to cause change or resist it, and problems in dumbly projecting current trends.

Chapter 7: The Limits of Quantitative Analysis, discusses the role of statistical analysis and quantitative modeling in predicting the future – where this is possible and useful and where it is not, and why not.

Chapter 8: The Systems Perspective, investigates “system effects,” which occur whenever different elements or variables that may appear isolated are in fact linked together, such that changes in one element cause changes in others. Anticipating future behavior of any variable hinges on identifying the broader systemic elements influencing it and failing to do this is a big part of what causes forecasts to fail.

Chapter 9: Living with Alternative Futures, investigates non-predictive ways of approaching change – where the tone is more about managing uncertainty than predicting the future. It acknowledges unfathomable complexity of most future questions and provides perspectives that raise chances of success in an inherently unpredictable future.

Chapter 10: Forecast Filtering in Action, illustrates the processes of the book by applying them in case studies to real-world sample forecasts that decision makers in business and policy areas might find themselves interacting with. This demonstrates how real everyday predictive material may be probed and critically evaluated, following the principles developed in previous chapters.

Chapter 11: A Forecast Filtering Checklist, is a cross-cutting checklist which summarizes the principles of the book in one convenient, thematic list.