As Stoyko’s is head-and-shoulders the most insightful and thorough assessments of the book itself, and the book in context, I’m reposting it here, with thanks. There are also fabulous graphics added, such as these (see more below):

“My search led to Adam Gordon’s Future Savvy. Like Posner, Gordon challenges Taleb’s blanket dismissal of forecasting. Gordon does not deny the existence of Black Swan events. And his book is a giant compendium of all of the things that usually go wrong with predictions. Moreover, Gordon offers a sceptical discussion of the subject that chastises simple-minded futurists, tech enthusiasts, and various other prophets of doom and boom. The difference between Taleb and Gordon is that Gordon doesn’t dismiss out-of-hand the usefulness of structured thinking about the future. Many important decisions require us to speculate about what the future might hold. Gordon wants us to be savvy in the way we anticipate the future instead of flying by the seats of our pants, so to speak.

“To set the stage, Gordon talks about how the forecasting industry is rife with problems. There are no standards, no accepted methods, no standard terminology. There are no penalties for failure given that people tend to forget forecasts by the time they can be proven wrong. And when dealing with the forecasts offered by pundits, stakeholders, and activists, Gordon reminds us, “we are knee deep in predictive wishful thinking, scare-mongering, or blatant self-promotion.” (p. 5) Buyer beware.

“Then there are the data problems. Forecasters use data from the past to project trends into the future. They rely heavily on data gathered for other purposes, not gathered for the task at hand. Availability is patchy. The data comes from multiple sources and is created using different methods. Important statistical caveats get lost. The context of the original studies gets forgotten. Variables are often defined loosely … and change over time … and are measured differently in different places. Data gathering methods often change over time in ways that exaggerate or obscure a trend. Sensationalist “newsy” data often commands the most attention. Some things are inherently difficult or impossible to measure accurately. All sorts of assumptions get embedded in data projected into the future. Furthermore, Gordon talks about the ways in which numbers can be finessed in an underhanded way. He advocates “number scepticism”, warning: “But no matter how scientific the data appears, choices have been exercised at every point about what to observe, what to count, how to measure it, and how to report it. … But numbers are not bedrock. There is no bedrock.” (p. 59)

“As an aside, statisticians have a snide nickname for analysts who mix’n’match statistics from a hodgepodge of sources to create complicated models or story-lines. That nickname is junk-yard dog. Gordon gives the impression that the forecasting business is, by necessity, heavily populated with these collectors.

“The sources of potential error don’t end with data. Our biases cause us to misinterpret and misreport the data.

“Some bias is intentional manipulation. Rascally analysts ignore or downplay countervailing evidence. They give evidence less scrutiny if it confirms the desired result. Emotionally charged language and associations are used. Terms are defined in leading ways. Extreme cases are used to represent the norm. Forecasts that don’t accord with an agenda get ignored, especially if the forecast is sponsored by a powerful interest. Organisational incentives can cause those being scrutinised to fudge the numbers. When forecasts are presented to the media, the most extreme trends get attention and important caveats remain unreported. Gordon is particularly critical of the so-called futurists who use “stretch thinking” and “big-picture thinking” to imagine a world full of only big changes. Many have a technophile bias, or the assumption that technology is the sole motive-force of large-scale societal change. Gordon’s advice is to keep your guard up and be wary of motives.

“Setting aside the thinness of this advice, Gordon has a strange attitude when talking about manipulation. He makes a distinction between forecasts that attempt to be accurate and forecasts that attempt to influence. Employee-prodding managers, partisan policy wonks, and alarmist activists use loaded forecasts to move minds. Humility, qualification, and tentativeness don’t have a place in these circles. There may be a legitimate reason for using leading forecasts, such as communicating the art-of-the-possible or giving someone an ambitious target to strive for. However, leading forecasts without full disclosure are instruments of underhanded manipulation. Gordon is eerily agnostic. His advice and tone of voice suggests that he is oblivious to the ethical problems posed by the manipulative use of forecasts. It’s a strange contrast with Gordon’s advice about being careful and pragmatically sceptical. [Editor's note: Agnostic? Moi? Hardly, but perhaps the chill of my irony was not chilly enough.]

“Back to the sources of error.

“Gordon itemises a number of cognitive biases that are inherent to the way we think. We often miss Black Swan events and abrupt changes in prevailing wisdom (“paradigm shifts”), he argues, because we are always filtering information based on perceived relevance. This “inattentional blindness” causes us to not notice important influences on the future. We also overemphasize recent happenings over older events (the recency effect). We’re susceptible to herd thinking and faddish ideas. A few chance events are often mistakenly interpreted as a trend or other pattern. Gordon places particular emphasis on how our current context frames the way we see and think (situational bias), especially how the prevailing mindset and preoccupations of an era skew the way we think about the future (Zeitgeist bias). For example, nuclear-powered airplanes may have seemed inevitable to someone living in the 1950s, a time preoccupied with thoughts of nuclear technology, suggests Gordon. That notion seems absurd today. To counter this problem, he argues for the need to extract the assumptions underpinning our expectations. Those assumptions need to be questioned and tested. And one good test is to reverse the assumption; that is, consider how the future would be different if the opposite (or very different) assumption were used.

I would add that people habitually rely on lazy assumptions about the future in general. As Howard Segal points out in his book Technological Utopianism in American Culture (2005), late-19th and early-20th-Century intellectuals assumed a technological plateau when describing the future. Even today, we assume our arrival at some destination—a future steady state—instead of a world of on-going change that is unevenly distributed and erratically paced, as exists now.

Gordon invites us to consider the utility people derive from a particular technology before jumping to conclusions about how it will revolutionise everyone’s lives. Tech-happy futurists are too quick to assume broad public acceptance of a new technology while ignoring the trade-offs of adoption. There are costs to be considered. In many cases, the price is too high and existing technologies do a good enough job. Or old technologies have an inertia, such as when users are “locked in” to a particular technology. Or social values change. Or switching creates undue inconvenience and aggravation. Or the technology has uneven appeal across diverse groups in society. Or, or … Gordon reminds us that simple technological domino effects almost never happen. The pace of change is usually slower than anticipated. A variety of factors determine how successful an innovation will be.

That leads us to the dynamics of change. I’m not going to describe each dynamic in detail. Gordon devotes a lot of space to them. Instead, I’ve listed them iconographically in the following diagram. Note that the darker lines signify consequences (and consequences of consequences; a.k.a. second-order and third-order events).

“A trend observed today may not continue onward along a straight-forward path. Trends peter out … change course … hit limits … get caught in reinforcing loops … have side-effects … provoke reactions … et cetera. The same goes for underlying causes. Trends can be particularly difficult to track within the complex systems that govern our lives. Thus, Gordon offers a chapter on system analysis.

“As someone who studies organisations, I’m often seeing policies and strategies change with sadly predictable pendulum swings. Gung-ho leaders push in one direction with gusto only to get a lesson in humility. Their efforts hit limits and opposition. Their assumptions hit reality. Subsequent leaders see wreckage everywhere and push in the opposite direction, looking for balance. Balance alludes them and they go to far. Another pendulum swing begins. Some swings happen from season to season. Others happen over decades. These swings may be predictable, but their exact timing certainly isn’t.

“Gordon rounds out Future Savvy with a utilitarian survival-guide of sorts. His big advice is that “it’s better to be vaguely right than exactly wrong.” Success is being alert to important changes and being prepared to cope, not with having accurate predictions. Narrowing down the things that need to be prepared for is an important practical benefit. In that spirit, Gordon talks about the strengths and weaknesses of using multiple scenarios instead of pat forecasts. He steps the reader through the analysis of some forecasts while looking for weaknesses. A chapter-long battery of questions is offered to guide the analysis. These questions do a good job of summarising the book.

“All told, Future Savvy is an excellent textbook for those who want to discipline the way they think about the future. I disagree with Gordon’s tangents about the inherently subjective nature of truth. I also have a few qualms about his take on scepticism. But these tangents rarely get in the way of his stock-taking exercise. That exercise has led me to be even more suspicious of forecasting, especially forecasts in volatile industries where data is patchy and assumptions are legion. I’d love to know the success rate of high-tech cheer-leaders … er, research firms that peddle forecasting numbers. Gordon dismisses the tracking of forecast failures as “smirk lists”. I’m with Taleb and his tsk tsking. If these numbers are just part of the hype machine and have a dismal track-record, then what good are they? Validation for reckless investment strategies? Fodder for misleading Power­Point slides? Numbers that give a false sense of being in-touch with the market? Tsk tsk.

“That said, Future Savvy has increased my interest in foresight more generally. Gordon’s guide left me wondering how I can better prepare groups of decision-makers to think about the future. How do we get them to see the many changes afoot with greater foresight?”

The harder task in achieving quality foresight is to judge across such lists what is really going to change the world and therefore the operating environment for most firms, and what is just, well, merely of passing interest. The true test is to get trend impact right, not merely to call the trend.

There is no exact science to this of course. But a good heuristic is to judge the strength of the trend (drivers for vs. blockers against) x change to status quo (how new is this really?) x number of people affected. In this regard, a recent FT article reports on a genuinely world-changing trend.

The story is about how Indian television stations, led by Murcdoch’s Star India and Viacom are writing more independent, assertive roles for women in soap operas to reflect new realities in the Indian middle class. They hope to renew viewer ratings, as this clip explains:

New womens’ roles and aspirations have permeated Western society in the past generation and a half, and have profoundly changed everything from dress to daycare. Now the other 5 billion are going there too.

I would not (I stress) expect the Western model to be followed to the letter. Cultures always interpret world trends and technologies their own way. But billions of girls are growing up to be unlike their mothers in key respects, and will demand industries  — not just the media — move with them, and will reward those that do with unprecedented commercial opportunities. That’s a certain future.

“Profiles of the Future” at the Foresight Institute 2010 conference

Says Saffo: “Futurists today are talking to the wrong people, don’t have good methods (for the most part,) and are still doing the kinds of silly things we did (or they did) when futurism got started…We should have an instant prohibition on anyone who writes an article titled: ‘Top 10 Trends to Watch’… We’ve got to get rid of this ‘future entertainment’ stuff and ‘top-10 trends’ stuff, and get serious.”

Part of the raison d’etre of Future Savvy, of course, is to demythologize exactly this kind of self-promoting infotainment foresight, and give real-world managers a way to see through it. Thinking long-term is too important to allow it to be tainted by snake-oil salesmen. Saffo admits he’s ranting on this topic (as I do too.) In a less ranting mode, he would probably admit there are also many high-quality thinkers doing exemplary foresight work. Certainly he’s all in favor of thinking long-term, and doing it better.

Saffo’s solution? “Move foresight to the masses; make policy conversations cool; engage powerful myopics (short-term thinkers on Wall Street and other financial institutions); engage politicians (incl. via the OTA). But he doesn’t say how, and of course therein lies the rub.

.

Witness the uptake of ‘feudal’ protection in a competitive, recessionary marketplace, where Wal-Mart is offering rental space insde a new Chicago store to neighborhood businesses. Apparently tenants already include a dog groomer and a fried chicken outlet, and Wal-Mart is going to be inviting in barbers, manicurists, and other local small businesses.

Regional general manager Rolando Rodriguez told the NY Times: “We want the same resurgence of the community…”.

It’s not all about community of course. Wal-Mart is seeking counter-PR to endemic criticism (and evidence) that their megastores kill mom-and-pop shops on which many local jobs and services depend, and is hoping the gambit will revive its six-year stalled bid for the city’s approval of proposed Chicago stores.

Anyway, as one observer, Marissa Johnson, said of the new arrangement: “It’s like sharecropping.”

Yes, this is the return of a feudal model. The lord owns the land and the small guy works his patch, offering a regular tribute. And small guys will jump at it because — in the absence of fundamental challenge to an iniquitous system — having the protection of a lord is better than not having it.

Another need that’s not going away, merely being reinterpreted (ironically back to pre-feudal organization) is our need to mark the darkest night of the year with ritual. Yule is the pagan winter solstice rite centered on a December 21 dusk-to-dawn vigil. It was absorbed into Christmas and not widely practiced for centuries. But now, as reported in the big UK media Christmas pregame show, there’s been a great surge in Yule festivities and attendance. By how much depends on who is quoted but nobody is denying the trend — which more or less mirrors the decline in formal Christian Christmas (secular, gift-giving, tree decorating Christmas is alive and well.)

The need is a constant. The rituals will change, often mining the past.

Imagine you are a turkey. Every day someone comes to feed you. Every day you get bigger. Your portion sizes get bigger too, brought by a nice man at regular intervals. You extrapolate the trend and you confidently predict a bigger you, with more to eat. Regularly too.

But what happens is … Thanksgiving. Or Christmas

Taleb, N., The Fourth Quadrant: a Map of the Limits of Statistics, Edge Foundation, September 2008

The hard reality for those who predict the future by extrapolating trends (and those gullible enough to believe them) is that even if our turkey had excellent data points (carefully observed and accurately recorded in, for example, a time series analysis) and, moreover, even if our turkey was a mathematically sophisticated — not merely simply projecting trends, but applying all the latest modeling techniques, from moving averages to compound regression — he is still going to be wrong about the future. Dead wrong.

All the data analysis in the world, all the fancy computer software, all the consulting time paid for, and he is still a dead duck.

Ouch. The lesson: there may be (or, vexingly, may not be) something outside the trend, a framing condition, which where it does exist is invisible within the trend projector’s mental model. The only way to get a view of the future that is “robust to Thanksgiving” is (a) to question assumed framing conditions, for example through properly done scenarios, and (b) to hold a view of the future which assumes fundamental ‘game-changing’ surprises can and will occur.

If, as they say, “the trend is your friend” it is assuredly only your fair-weather friend.

Arsène Wenger

But this was different. Wenger (on the eve of the Arsenal vs Celtic Rangers Champions League match) predicted a “European League” in 10 years featuring the continent’s top clubs – that is, he offered foresight into potential structural, industry-wide change in multi-billion-dollar UK and European soccer industry.

Currently clubs play in their national domestic leagues. And all Europe-wide competitions are cup (pool stage + knockout) competitions.

Although not fleshed out, the form is not hard to see: the top four-or-so clubs from each major country (fewer from smaller countries) in one annual league competition. This means that Manchester United, Liverpool, AC Milan, Porto, Juventus, Real Madrid, Barcelona, Roma and so on would all be playing each other on a weekly basis throughout the year (and, presumably, playing in no other league competitions).

Drivers of Change

The point of Future Savvy is that one can judge the validity of predictions like this before time. In this case, part of the way to assess Mr Wenger’s future view would be to gauge the strength of driving vs blocking forces behind his outcome.

There is evidence of strong drivers in favor of a European Super League. These are:

1. The rise of “super-teams.” In the UK and across Europe the same few teams dominate their domestic league year after year. The reason is a simple reinforcing feedback loop where winning teams get more money (from TV rights, from gates, from merchandising, etc.) which means they can buy better players, which means they win more. Over the last decade the English Football Premier League has become, effectively, a competition between Manchester United, Chelsea, Liverpool, and Arsenal. (In the US the “draft–pick” system mitigates against any franchise getting too strong in this way, but no such system-balancer exists in European football.)

2. The growing ease and ubiquity of continental travel. Whether fans will follow their teams across Europe is a key issue, but indicators from cup competitions is that fans can and will travel.

3. The growing role of, and technological sophistication of television, particularly Sky Sports. Despite the many who travel, most people these days follow games at home or in sports bars. Television’s coverage and choices (the remote control options “red button”) have exploded, and screens themselves have got bigger and better. And genuine personalization of camera feed and other forms of interactively is emerging. In this, football, and professional sports as a whole, is becoming more about the screen as the stadium, accelerating a long-term trend. The reality is it makes little difference to most fans if the game is being played 50 miles away or 500.

4. The move to high-level, star-packed, events. There’s a clear trend across sports in general for events featuring the best players playing each other in all-star environments, not as a special “all-star” game but as an everyday occurrence. In cricket, for example, the Indian IPL has ridden this trend, offering franchised matches of, effectively, one mixed team of global superstars versus another. The fans love it.

There is also the financial do-or-die logic that soccer clubs face. The money feedback loop means they must continually drive up their revenues. It’s not possible to stand still. A European Football Super League would compel participation from the top teams for this reason alone.

vs Blockers

Adequately assessing the likelihood of the Wenger view of the future further requires investigation of blockers – factors which will prevent the outcome. In this case these may be overwhelming logistics of moving teams around to this extent week in and week out; limits on fans’ travel energy and budget; extent of fans’ loyalty to the relatively minor (non-super) domestic teams; and domestic league administrators’ determination and ability to keep domestic leagues from loosing their cash cows and following their own downward spiral into television obscurity.

These blockers on the European football league forecast are real. The question is whether they stop the future or how long they delay it. I’d judge the blockers as considerably weaker than the drivers and so I’d go with Wenger in predicting a European Super League (even richer and more “glamorous” than anything soccer has seen before) in about 10 years from now.

The failed “our-future-in-space prediction” cluster is useful because it is the poster child for bad predicting, nothing less than foresight idiocy in its purest form, worth mentioning only because it helps us to see smaller and more subtle future-thinking mistakes we make routinely.

This is what I said in Future Savvy (Chapter 5):

“The forecasts that surrounded the future of space travel and exploration are perhaps the most high-profile and comprehensively poor set of forecasts ever made, and therefore provide a good vantage point to consider what can go wrong in forecasting. From the 1950s, space was a huge topic of interest. All significant earthbound exploration challenges had been overcome, technology was moving rapidly, and what lay ahead, unconquered, was space. The need to explore it was deeply in the zeitgeist.
“At the same time, the Cold War created the specific situation where beating the Soviets in prestige projects was an important priority, important enough to divert massive resources to it. J.F. Kennedy’s rousing (future-influencing) 1961 prediction of putting a man on the moon by the end of the decade motivated and galvanized the United States, and the resulting Gemini and Apollo programs made this not only a human achievement but a successful prediction. As a result, analysts of all stripes were quick to project the trend and predict a moon base, lunar communities by 2000, followed soon by trips to Mars and beyond, and on to the limits of space. . . .
“The last man to set foot on the moon was in 1973. The Space Shuttle tried to maintain forward momentum under the guise of scientific research, not without disaster, and an almost inconsequential international space station has been built. To this day there are many who cry into their soup over the lack of space exploration and conquest. So what happened? The groundswell of prediction was wrong because it failed to see that putting a few U.S. men into orbit did not add enough value to enough peoples’ lives to justify the expense—particularly in the economically uncertain 1970s. In the end, the majority of consumers voted with their wallets to postpone, if not entirely eviscerate, human space exploration.”

One could go into great detail, but simply put, the intertwined elements resulting in this poor view of the future were:

1. Failure to recognize user utility and the choice consumers make in determining the future. That is, for most people the cost of any space venturing is not worth the benefit (i.e. what benefit?) The fact that we “can do it” is hardly relevant. The real futures question is always: do most people want it? In the 1960s space was “worth it” (particularly in that the goal was clear and bounded) because spending billions on a prestige project made sense at a time of (a) absolute US economic prosperity and (b) ideological dispute with the USSR.

2. Projecting trends without considering the strength of underlying drivers. Space exploration was, apparently, on-the-up in the 1950s and 60s. But trends are only as good as the drivers that support them. When the drivers go away (lack of public support due to cost/benefit issues) the trend stops. In fact, there is no real, dependable, trend to space exploration. There was a blip in the 1960s when conditions temporarily favored a national prestige extravaganza. There wasn’t a trend before, and there hasn’t been any since.

3. Forecasting mired in the conditions or spirit of the present, the zeitgeist. Space was important in the golden-era 50s and 60s; and particularly in that it was arena of competition with the Soviets. But it’s always a mistake to assume the framing conditions of the present will exist in the future, and in this case 40 years later, they most certainly don’t.

Don’t hold your breath

What of 40 years time? It is quite likely that “space flip” flights into orbit will be safe and cheap enough to commercialized in the next decade. Unmanned probes (again safe and relatively cheap) will continue, and popular access to their images and experiences will be greatly enhanced. But that’s all that will happen until such time as costs and other conditions of possibility change fundamentally, which implies a completely new form of space travel, of energy, of materials, and of human resilience and longevity. Not in this century.

Wired Science ran a July 20 article “40 Years After Apollo 11, NASA Maps Out the Future,” which puts the best possible spin on  this unmanned-probe future. It is careful to end without crushing the feelings of space junkies, saying: “Any American landing on Mars through the Constellation program would come some time after 2030.” It won’t happen, and here’s another secret: if anyone is going to land anywhere it will be a Chinese person. China still has prestige projects ahead of it, and human space exploration could be one of them.

Christian Blanckaert, Executive Vice President at Hermès International was quoted as saying: “We have absolutely no visibility into 2009!”

On the one hand, fair enough. This economic downturn is steeper than previous down cycles, and the basic viability of the financial sector has been tested. Access to credit is normally easier in a recession, but in this one it is not. All of which makes luxury spending harder to predict.

No doubt the most unlikely prediction of all would have been that Hermès, Burberry, LVMH, Moët Hennessy, Louis Vuitton, and PPR (Gucci , Yves Saint Laurent) have all recently reported better-than-expected results.

Nevertheless luxury industry leaders have declined to provide investors and analysts with any official outlook. What’s curious, from an industry foresight point of view, is how executives such as Blanckaert thought they really had more “visibility” into any previous year, or that they will somehow gain it again when the financial crisis is over. They will not. The world will continue to surprise them and us. What they will gain, certainly, is a greater likelihood that the standard business-as-usual future assumptions they make will not be upset by reality.

Meanwhile, judging by the conference, the luxury goods industry has a very decent grip on current social and moral trends, and clear insight into the bigger picture of change in its industry over the next five to ten years. As they know from before, what happens in a recession is that luxury goes out of fashion. Conspicuous consumption wanes, or retreats further behind secluded walls. This is a basic pendulum swing that tracks the economy (witness how the early 1990s recession stimulated a return to “values” era after the “me, me, me” 1980s.)

Sustainable luxury

So we are again in a swing to modesty. But we also know that each swing of the pendulum also carries with it the specific issues of its time. Current key issues for consumers in this segment are sustainability, global warming, business ethics, and globalization (or fear thereof).

Therefore the luxury brands will be looking for ways of making, transporting, and displaying goods in an energy-efficient and socially conscious way, including a renewed emphasis on local artisans and traditional craftsmanship that speaks sustainability in both natural and human resources. This will be the basis of the “sustainable luxury,” positioning that the famous houses will define and compete in. Fabulous and renewable  – now there’s something you can charge top dollar for.

Guinness' view of the pub of 2259. Image credit: Chris Bainbridge

The Guinness Pub-of-the-Future is a St. Patrick’s day (March 17) promotion. Nothing wrong with a little bit of fantasy foresight. But what they come up is so “20th-century-futurism” it’s hilarious. Among various reports on the project – for example in the Telegraph – the following features are foreseen:

- robotic doorman, greets you by name
- cash obsolete; orders via RFID; payments deducted automatically
- your product tailored to you on the spot
- touch-sensitive tables, send your order straight to the bar
- socializing via virtual / hologram technology
- a running tally of the number of units consumed.

Yawn. Even on it’s own terms (minimal constraints of realism) this is a totally derivative piece of foresight. These “innovations” are the staples of an infotech view of the future, and they have all been thought and spoken of countless times. Also many of the elements and services cited are already here, or not more than a decade away. What we have is the current pub assumptions + digital steriods, while the year 2259 will be, truly, another world.

The limits to growth
But all this leads us to more interesting industry foresight problem. Will there be pubs in even a generation, never mind 250 years? What the Telegraph dryly observes at the bottom of its report is that 39 pubs are closing every week Why? A number of driving forces are coming together:

First is strict drink-driving limits, which makes “the local” literally local or nothing. Second, pubs in the UK have traditionally been a refuge from housing that was poor and/or underheated. Unprecedented waves of affluence (credit-crunch notwithstanding) have led to widespread housing “do-ups.” It’s now a valid option for most people to spend their leisure time at home and entertain at home.

Then there’s the where’s-my-friend trend. You’re likely to go down the pub if your friends are there, but not if they are where most people’s friends are: on Facebook.

The social-legislative clock
Fourth, no matter how you dress it up, pubs are retail outlets. So, like all retail they are under the cosh in a Wal-mart / Tesco world. The price gap between store and pub has become too great for most consumers to cross with good conscience.

Which brings us to the current price-floor legislation bid. Alcohol is a huge social cost in terms of health care and violence. Drink costs the NHS £3bn a year, and the total price of alcohol to the taxpayer is estimated at five times that. Eventually these costs will become unjustifiable so, like smoking before it, the social-legislative clock is ticking for booze. As the 2-martini lunch has become the 2-seltzer lunch, the trend to social stigmatization is clear, and legislators will follow (not with Prohibition, but with a much more subtle community-endorsed squeeze).

Like the good politician he is, Gordon Brown won’t let his party get ahead of the trend. But the trend is clear and it bodes ill for pubs.

My question, as the Dow Jones Industrial Average (DJIA), and the FTSE100, the DAX, the Hang Seng and so on have hit a decade lows is, what is this predicting, if anything? What is the long-term value of this prediction, and could it be used to make better decisions in the real world?
We know that the value of a common stock – a share in a company – is based ultimately on the returns (dividends) it will bring. Buyers and sellers therefore derive a daily market price based on their views of the share’s expected, that is, predicted future payback. The greater the expectation, the greater the price. A high price vis a vis earnings (P/E ratio) suggests confidence in future earnings, and vice versa.
Therefore the current steep fall in share prices is an expectation of (crowd prediction of) lower future payouts. Of course the complexity in human-prediction situations is that this basic level is also overlayed with a meta-level: people are not only trying to figure out what will happen, they are trying to figure out what others think will happen. So falling PE ratios are an expectation of what others will do (predicting they will continue to sell.)

Madness or not?
One of the perplexing things about the markets is they very often seem to react opposite to what is expected; to what would be common sense. They often fall on good news, rise on bad news, close unchanged on big news, and so on. Although there is – famously much irrational behavior and herd instinct in the market – you don’t get hundreds of thousands of decision-makers wagering significant money not using common sense.
What is going on, of course, is that the market has often already risen or fallen in prediction of the news. When a new condition – an interest rate move, for example – is imminent, the market will move to “price in” the expectation. If market participants as a whole have called the future correctly the market will not move much on announcement.

Pricing-in the future
Because of this predictive component to group decision-making in market situations, the stock market as a whole is a classic leading indicator of the real economy. When prices move they may be taken as the crowd “pricing-in” a future prediction. So markets will fall ahead of real economic problems (they may continue to fall, as now, during steep economic declines.) But they will also turn up well before any real, measurable upturn.

By the way, there is little doubt it will overshoot in this time, as it always does. This is because, as in prediction markets, the wisdom of crowds can predict the trend but not the turn. Trend extrapolation will never show you the key shifts, and this is why predicting the bottom or top of a market is so hard.

The point, for market speculators, is that long before the real gloom is over the markets will be zooming upwards. The point for the rest of us is that recession times will be with us even after the markets move up. In the long term the market will go up. Like death and taxes, it’s the surest thing there is.

This punditry is widely read, and sometimes published in respectable places. Some of it is just year-end fun, and nothing wrong with that. But there is also a failed-forecast “nyah-nyah” that is corrosive to the foresight field in general, which demands answers. So at the risk of giving the 20/20 hindsight artists undue oxygen of attention, here are a few thoughts:

Consider Foreign Policy’s “10 Worst Predictions for 2008.” (Dec, 2008). Highlights include:

“If [Hillary Clinton] gets a race against John Edwards and Barack Obama, she’s going to be the nominee. Gore is the only threat to her, then. … Barack Obama is not going to beat Hillary Clinton in a single Democratic primary. I’ll predict that right now.” —William Kristol, Fox News Sunday, Dec. 17, 2006

“Should I be worried about Bear Stearns in terms of liquidity and get my money out of there?’ No! No! No! Bear Stearns is fine! Do not take your money out … —Jim Cramer, responding to a viewer’s e-mail on CNBC’s Mad Money, March 11, 2008 [Bear Stearns was sold to J.P. Morgan Chase at about a 90% discount to it market capitalization at the time of the forecast]

“The possibility of $150-$200 per barrel seems increasingly likely over the next six-24 months.” —Arjun Murti, Goldman Sachs oil analyst, in a May 5, 2008, report [Oil was then around $130 a barrel. By late December it was below $40.]

Or this one from Business Week’s list of 10 (December 24, 2008)

“Existing-Home Sales to Trend Up in 2008″ —Headline of a National Association of Realtors press release, Dec. 9, 2007 [On Dec. 23, 2008, the group said November sales were running at an annual rate of 4.5 million—down 11% from a year earlier—in the worst housing slump since the Depression.]

The Future Savvy question is: how should we think about predictions like this? And how should we think about failed-forecast spotting?

1. Failed-forecast spotting is not remotely “scientific”
This should be obvious, but somehow never is. Purposefully extracting the failed forecasts from the total set of forecasts says nothing about the quality of the set in general. Many did predict Obama; did predict the downturn, etc.

2. Failed-forecast spotting raises a healthy skepticism, but runs to nihilism
Despite not passing any credible test of knowledge, at least failed-forecast spotting stokes apprehension about forecasts and the wisdom of experts. At base this is healthy. Prediction is hard, and it is mostly done poorly. And experts often transgress the boundaries of their expertise. (Typically, in this instance, they know a lot about their field, but often don’t know more than the next Joe about the future of their field, often because their expertise is wedded to existing practices and assumptions.)
Prediction skepticism is fine. What happens, however, is that tempts a “nobody can predict anything” nihilism. This is its own failing because many predictions are in fact excellent, producing good foresight, which is a key strategic and competitive advantage.

3. Often the short term future is harder to see.
This is the trickiest insight of the lot. As everyone knows, it’s impossible to accurately predict the future (which is not the same as usefully predict the future, see arguments in other posts). The world is just too complex, too chaotic. But there’s a wrinkle. It should be that the further we look into the future the harder it is to see. The world will change more – there is more time for unpredictable things to happen. The short-term future (one year, say) is closer to us, it should be more like today and we should be able to anticipate it better.

In fact, short-term foresight is the most impossible task: a casino game. In the longer term (10-20 years, say) strong trends can be relied on to have had their impact. For example, the move away from fossil fuels, or effective nanotechnology engineering, or simple domestic robotics, can be reliably forecast. But while the sweep of these and other similar evolutions are reliable over time, the short-term picture will suffer lags or reversals that follow no pattern at all. (It’s no accident that is this is just like the stock market. In the long term the market will go up, in the short term it can go anywhere.) Also short-term predictive failure is compounded by the fact that the standard to which it is held is higher – we expect specifics: dates, places, numbers, players, winners – that are not demanded of a long-term view. In other words, near-term predictions are all about “point forecasts,” and there’s nothing more impossible than a point forecast unless you believe in tea leaves and crystal balls.

The take away: short-term point forecasts really are a mugs game and the skeptics are right. Medium-long forecasts, when well done, are worthy of our strategic and competitive attention.

Kelly needs no introduction. He’s the executive editor of Wired and a core who’s-who in the new media technology world. The first lesson he has to share is a key one: the Web is only about 5,000 days old – that’s about 13 years (the Internet, DARPA, etc., is older) – and all the stuff we have and now take for granted, from online investing to social networking to Wikipedia has happened in this short time.

The video is available here:

As Kelly says, and he’s undoubtedly right: “if I had predicted all this would be there (and free) nobody would have believed it. It’s impossible. The lesson is that very big changes do occur in fast-moving industries when considered over a decent-length (e.g. 10-15 year) timeframe. So let’s not kid ourselves: mere extrapolation of current trends doesn’t take us to the future. A leap – a paradigm shift – a willingness to anticipate fundamental shifts in technologies, institutions, and business models, is required.

So, against this, it is interesting that much of what Kelly predicts for the next 5,000 days of the Web is fairly conservative… but he does build in the idea of a new, fundamental shift.

The Web in 2020

What does he see coming in the next 5,000 days?

1. First thing is what Kelly calls “Embodiment” of the Web, by which he means that every device, every screen (laptop, phone, iPod, sat-nav, etc) becomes a “window into the machine” rather than a stand-alone device. There will be one Web, one machine, and everything will go through it. Part of this is that the Web will be embedded into the physical world – inanimate objects from cars to shoes to will have connectivity. Whether through RFID or other technologies, “there will be an Internet of things.”

Hello? We’ve heard this all before. Many times. In fact we were hearing it in the 90s. This doesn’t mean it’s wrong. In fact if we’ve been hearing it for so long, and the trend is still clearly in this direction, the forecast is probably right. What’s interesting is how non-radical it is.

2. Next he talks about “Restructuring” which is his term for the “Semantic Web” or what some call “Web 3.0” The idea is: first we linked computers (the Net), then we linked pages (the Web), and next we will link all the data or information or ideas anywhere on the Web to all relevant data /information/ ideas elsewhere on the Web. (This made possible by technologies such as XML, RSS, OWL, API, RDF)

One of the payoffs of this, says Kelly in an illuminating example, is that we won’t have to “re-friend” in each social networking platform. The technology will know we’re “friends” with Warren Buffet and Tom Peters and Malcolm Gladwell (…lol) as we move from Linked-In to Facebook to Technorati, and so on.

3. Kelly’s final point is that humans will be co-dependent with the Web. It will be always on, always there, ubiquitous, and the single fundamental tool we depend on to do everything.

Again, there’s nothing new in these points. It’s all been said before. In fact, as is often the case in good futures thinking, the value in Kelly’s forecast is that it is a carefully considered “cut” from what is usually forecast, leaving behind the wilder things that are said. Kelly on Web 2020 doesn’t say “expect digital human implants; ‘conscious’ devices; retina-as-screen,” and so on – the beam-me-up-Scotty kind of foresight that unfortunately often gets the headlines.

The next stage
Nevertheless, he is equally not saying the next 5,000 days will be “like the Web, only better.” The capabilities, the embodiment, the dependency, imply a new stage, he says. What that new stage will look like at the business and institutional level – what products/services/delivery will be possible via Web 3.0 – what the Yahoo or Google or Facebook or similar iconic institutions will there be, Kelly does not get into.

Fully thinking through the next 5,000 days of the Web involves going from the capabilities to what is built on them. But all in all this is a classy, integrated piece of future thinking (that easily fulfills the Questions to Ask of any Forecast checklist in Chapter 11 of “Future Savvy”) and is a solid foundation on which to consider future business and organizational implications.

I was reminded of this when, as my wife and I were talking happily about the Obama “Yes-We-Can” victory speech, our 3-year-old daughter piped up: “Bob-the-Builder”! [The economy's in crisis, can we fix it?] “Yes we can!” [The war in Iraq, can we fix it?] “Yes we can!”

If this means nothing to you, see http://www.bobthebuilder.com/ca/english/index.asp

This is not subtle stuff, this speechwriting. And politics is nothing if not the art of appealing to the 3-yr-old in all of us. But, as they say, “a win is a win.”

Anyway, it is for the foresight community to to get past the day’s euphoria and ask, what does this mean for the future? I think the win has trend tipping-point implications and allows some future-thinking insights to be accumulated.

The “Hawaiian” Future

One of the things Jim Dator and the Hawaii Research Center for Future Studies have long been saying is, “the future of the world is brown.” The running, long-term trend they are referring to is the movement of power and money from the white West to the brown East, and (eventually) South. And, on similar lines, we have seen rise in number of inter-racial couples (and more acceptance of), and the strong fashion and pop-star chic-ness of being “mixed” race. This aspect of the world’s future has been more obvious, earlier, in Hawaii than other places in the US (and the Obama-Hawaii connection is pertinent here), but now it’s mainstream. This in itself is a lesson that the future is to be seen earlier in some places than others. Anyway, November 4, 2008, is surely the moment where the trend tips and accelerates.

This is not to be naive. Nothing about the result is going to kill racism or ethnic affiliation. The world is a competitive place, and people organize and identify into groups to compete (and restrict access to benefits) more effectively. Whitey halls of privilege will continue to exist. Islamic identification and action will continue to be a huge force, and so on. But now that there is (and in future always will have been) a black person in the world’s top job, nobody can ever look at another person of color and see an intrinsic limitation on what that person can do, be, influence, or own.

Images of the future

For at least half a century the world has known this in theory of course. But theory doesn’t move the world. Pictures move the world. That is, pictures of the future bring the future closer. Obama making the president-elect victory speech, or seeing him and his family move into the White House, will undo more mental models – more educated incapacity – in the area of race than anything that has gone before. For driving the future, the Obama success image is more powerful than a thousand well-meaning affirmative-action programs.

The ratchet effect

The other, simultaneous, foresight principle at work is that change proceeds by ratchet effect. Sticking with politics, the Suffragette movement gathered momentum and finally swept aside millennia of tradition after women were seen to do traditionally “male” jobs during WW1. Here again we have the change-power of images of the future. After women were seen in these new roles there was no way to put the genie back. Yes, social changes can be reversed or stalled (Roe vs Wade is in the mire) but once the image of the future is out there, and minds have absorbed and habituated to it, it may be opposed but never removed. And this is what November 4 promises: visually ratcheting forward the world-wide acceptance of the potential of all people regardless of race as fact not theory – thereby tipping and accelerating the long-term trend to “The Hawaiian Future.”

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.)

Decision success always implies congruence between decisions and the world in which those decisions play out. If we decide today to launch a product, buy a house, study for a degree, build a new light rail system, or take any similar decision of significance, the environment of tomorrow will be a key factor in the success or failure of that decision. What we do will be tested by the future conditions that emerge. Where there is a good “fit” between the initiative and the environment it plays out in — “the right product at the right time” — we can expect success. If not, we should expect to fail. Our decisions are only as good as the view of the future they rest on. All opportunities and successes and profits are realized in the future. All threats, failures, and losses are in the future.

In a fast-moving world, we know that the future environment will be different to that of today in big or small ways. New technologies, market shifts, changes in legislation, or evolving social values damage or destroy the traditional good fit we have between ourselves and the world. To achieve “future fit” we therefore use forecasts to position ourselves and our organizations, creating (or renewing) the fit between our initiatives and environment. In some cases we may be strong enough also to influence future events and outcomes for our own future benefit, and forecasts help us do this too.

All enterprises benefit from narrowing down what they must adapt to and plan for – all effort spent preparing for a future that will not emerge is a waste of personal or organizational resources. Good forecasts are a key ingredient in limiting the vagaries of uncertainty, and therein working smarter not harder, avoiding surprises, exploiting new opportunities and plugging weaknesses in fitting in with the future, and where possible influencing the future to suit the organization. This is true not only of business. People and institutions of all types position themselves for success by anticipating and adapting to events, or shaping them. Whether it is an NGO raising money for developing-world children, an urban planner advocating a light rail system, a homeowner deciding to sell a house, or a student making a career choice, identical principles apply — a higher-quality reading of the future operating environment in which these decisions will play out is what separates winners from losers. We should all be vitally concerned with forecasts as we are all effectively betting significant resources on their validity.

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.